League of Wilderness Defenders/Blue Mountain

Transcription

OBJECTORS’ NOTICE OF OBJECTION, STATEMENT OF ISSUES AND LAWS,
AND REQUESTED REMEDIES
NOTICE OF OBJECTION
March 31, 2014
Regional Forester
Objection Reviewing Officer
Pacific Northwest Region
USDA Forest Service
ATTN: 1570 Appeals and Objections
PO Box 3623
Portland, OR 97208-3623
Email: [email protected]
RE: League of Wilderness Defenders/Blue Mountains Biodiversity Project’s objections
to the Rocket Vegetation Management Project
Dear Objection Reviewing Officer,
League of Wilderness Defenders/Blue Mountains Biodiversity Project (LOWD/BMBP)
hereby formally submits the following objections to the Rocket Vegetation Management
Plan EA. Under 36 CFR 218.5(a), LOWD/BMBP has secured it right to submit
objections and thereby participate in the predecisional administrative review process for
this project. LOWD/BMBP has submitted timely, written comments regarding this
project at all periods in the process where public comments were specifically requested.
Decision Document
Rocket Vegetation Management Project Environmental Assessment and Draft Decision
Notice
Date Decision published
February 14, 2014
Responsible Official
John Allen, Forest Supervisor, Deschutes National Forest (DNF)
Description of the Project
The Rocket Project Draft Decision Notice proposes to thin 9,938 acres, which would
include 1,152 acres of non-commercial thinning and 8,786 acres of commercial thinning.
Notice of Objection – Rocket Vegetation Management Plan
Page 1 The project would also mow 1,118 acres and mow and underburn 6,748 acres. It would
build 5.8 miles of new roads, close 38.6 miles, and decommission 5.4 miles.
Management activities would take place inside the Newberry National Volcanic
Monument, including 1,298 acres of commercial thinning followed by mowing and
underburning; 1,073 acres of ladder fuels reduction and/or mowing followed by
underburning; and 2 clear-cut openings that are 2.5 acres each. The Project proposes to
thin 211 acres inside two Old Growth Management Areas and 741 acres inside Goshawk
Post-Fledgling Areas.
Location
The Rocket Project area includes 22,682 acres on the Bend/Ft. Rock Ranger District,
south of the city of Bend, and east of Hwy. 97. It is in the Deschutes River-Pilot Butte
watershed.
Appellant’s Interests
LOWD/BMBP have a specific interest in this decision, which has been expressed through
participation throughout the NEPA process. LOWD/BMBP members regularly visit many of the
affected area for hiking; camping; backpacking; relaxing; bird, wildlife, and wildflower viewing;
mushroom harvesting; photography; gatherings; hunting; bike riding; leading educational hikes;
and more. The value of the activities engaged in by LOWD/BMBP members and staff will be
damaged by the implementation of this project.
LOWD/BMBP is a non-profit organization that works to protect Eastern Oregon National
Forests. Staff, members, volunteers, supporters, and board members of LOWD/BMBP
live in the communities surrounding the DNF and use and enjoy the Forest extensively
for recreation, drinking water, hunting, fishing, general aesthetic enjoyment, family
gatherings, viewing flora and fauna, gathering forest products, and other purposes.
Request for meeting
LOWD/BMBP requests a meeting to discuss matters in this objection before the DNF
makes a final decision on the Rocket Project.
Specific issues addressed in this objection
Violations of the Newberry National Volcanic Monument Management Plan; using an
out-dated Forest Plan to plan this sale; unlawful Forest Plan amendments; violations of
the Forest Plan, including ineffective protections to viability of wildlife and fish
populations, logging in Old Growth Management Areas, logging in Goshawk PFAs,
effects to soil, reduction of deer thermal cover, commercial thinning in Lava River Cave
recreation site, effects of road construction and reopening old roads; inadequate
cumulative impacts analysis; failure to consider scientific controversy regarding the
density of natural ponderosa pine forests; and more, as specifically mentioned below.
LOWD/BMBP objects to the Rocket Vegetation Management Project for the following
reasons:
Page 2 I.
The Rocket Project violates the standards for the Newberry Volcanic
National Monument Plan
Over half of the Rocket Project occurs in the Newberry Volcanic National
Monument (NNVM). EA at 7. The Rocket Project must comply with the standards in
the Newberry Volcanic National Monument Plan (NNVM Plan). These standards
supersede the standards in the Deschutes Forest Plan. NNVM Plan at 4. The Rocket
Project violates several of the standards in the NNVM Plan, including, but not limited to,
the following standards, as described below.
The Newberry National Volcanic Monument requires that land managers “allow
the natural ecological succession of vegetation to the maximum extent practical.”
(NNVM Plan M-1). The Rocket EA and Draft DN do address this standard, but then
dismiss it, saying that drastic logging is required to be able to introduce fire back into the
landscape. See Rocket EA at 69. However this reasoning goes against both common
sense and other directives of the NNVM Plan.
First of all, allowing “natural ecological succession” means allowing the structure
and composition of animal and plant communities to evolve without human intervention.
See NNVM Plan fn 1 at 19. In other words, the intent of the NNVM Plan is to manage
the land within the Monument with as little human intervention as practical. The Rocket
Project does the opposite. It proposes drastic human intervention, but justifies it by
saying the human intervention (logging) will allow land managers to reintroduce fire
back into the landscape. It is true that fire is a hugely important component of the
“natural ecological succession” of this landscape. However, it is not the only component
of “natural ecological succession” and should not be used as an excuse to create plans for
aggressive logging. The NNVM Plan does allow for fuels reduction. See NNVM Plan
Standard M-46. However, the directive also emphasizes that any fuels reduction projects
“maintain as natural a setting as possible.” NNVM Plan at 38. The plan does not
specifically mention commercial logging, so it is entirely possible that commercial
thinning was not intended under the plan. Fuels reduction and prescribed fire can be
included in management projects, but must not be used to justify aggressive human
intervention.
Secondly, the desire to reintroduce fire into the landscape must also fit within the
mandates of the other directives of the NNVM Plan. For example, the NNVM Plan
requires land managers to protect, enhance, and mitigate damage to the soil within the
NNVM. NNVM Plan Standard M-7. The Rocket Project activities violate this standard,
by allowing disturbance to 48 acres of sensitive soils within the NNVM and moderately
high to high levels of detrimental soil conditions in most of those acres. Rocket EA at
308. The Rocket Project EA admits that effects to sensitive soils may be long-lasting, as
resilience to disturbance is low in these areas. Rocket EA at 308.
Page 3 Further, the NNVM Plan requires land managers to protect habitat diversity, in
general, and specifically, to protect habitat for Northern Goshawks and bat species.
NNVM Plan at 22, 36, and 37 (M-10, M-35, and M-39). The Rocket EA does not specify
how the project will protect habitat diversity or habitat for the Northern Goshawk and bat
species. In fact, the Rocket Project will log within a Northern Goshawk PFA that exists
partially within the NNVM (the South PFA). See Rocket EA at 192. Additionally, the
Rocket EA does not specify compliance with the NNVM Plan Standard M-39, which
requires land managers to protect bat species from disturbance. Clearly, the Rocket
Project will remove habitat for the Northern Goshawk and disturb bat species, both
violations of the NNVM Plan.
Resolution of violations of the NNVM Plan
LOWD/BMBP has commented on the need to adhere to the NNVM Plan. See,
for example, comments in Rocket Project EA at 414.
The easiest way to comply with the NNVM Plan is to avoid management
activities that involve aggressive human intervention. LOWD/BMBP opposes
commercial thinning, ponderosa pine “restoration,” and any activities that damage
sensitive soils or increase detrimental soil conditions within the NNVM. LOWD/BMBP
would like to see the DNF drop all activities within the NNVM, except those that are
designed to allow for “natural ecological succession” through minimum human
intervention.
II.
The Rocket Project violates the National Forest Management Act
The Rocket Project violates the National Forest Management Act (NMFA) in the
following ways: implementation under an outdated Forest Plan; unlawful amendment of
the Forest Plan; failure to maintain population viability; and violation of the Eastside
Screens and DNF Forest Plan standards for Old Growth Management Areas, Goshawk
PFAs, and detrimental soils.
Outdated Forest Plan
NFMA requires that an agency revise its Forest Plan every 15 years. 16 USC
1604(f)(5). The Deschutes Forest Plan was approved in 1990. It is now 2014. The DNF
should have had at least one Forest Plan revision since then. All forest management
activities undertaken by the Forest Service must comply with a Forest Plan, which in turn
must comply with NFMA. Because NFMA itself requires that a Forest Plan be revised
every 15 years, a 24-year-old Forest Plan is invalid under NFMA. A project approved
under an invalid Forest Plan is itself invalid. The DNF must revise its Forest Plan before
it can plan site-specific projects on the DNF. For this reason, the Rocket Project must not
go forward until the DNF has a revised and updated Forest Plan. When the DNF has a
revised Forest Plan, the Rocket Project must then be planned under the directives of that
revised Forest Plan.
Page 4 The legislature has exempted agencies from this Forest Plan revision requirement,
but only when an agency is “acting expeditiously and in good faith” to revise a Forest
Plan. See 123 Stat 746, Sec. 410.
The DNF has not stated, publicly, any intention to undertake a revision of its
Forest Plan. It seems that, instead of focusing resources and planning efforts on its Forest
Plan revision, the DNF is using resources to create behemoth commercial logging
projects, like the West Bend Project and the Rocket Project. Because it’s clear that the
DNF has resources to put towards aggressive timber sale planning but is not using those
resources for an expeditious Forest Plan revision, the delay in revising the Forest Plan is
not in good faith.
Forest Plan Amendments
When an agency makes a “significant amendment” to its resource planning
document, NFMA requires that it follow NEPA procedures and involve the public
through an EIS process. 16 USC 1604(f)(4). Forest Service regulations further govern
the way a Forest Plan can be amended. See 36 CFR 219.13 (2012). The DNF has used
improper procedure to amend its Forest Plan to eliminate standards for burning in Scenic
Corridors, logging in LOS stands that are below HRV, and further reducing deer thermal
cover within MA-7.
First of all, the plans must be amended with proper NEPA procedure. 36 CFR
219.13(b)(3). Proper NEPA procedure requires that an agency take cumulative impacts
into account. 36 CFR 220.4(f). The Rocket EA has not disclosed, and so it is assumed
that the DNF has not considered, the cumulative impacts associated with amending the
DNF Forest Plan on a case-by-case basis. These amendments require consideration of
the cumulative impacts of all site-specific amendments across the DNF. Site-specific
amendments occur in a piece-meal fashion, yet they cumulatively affect the forest. Once
all site-specific amendments to the DNF Forest Plan are disclosed, it becomes apparent
that they are “significant,” as they are occurring across the landscape. In this case, an
EIS must be prepared. 36 CFR 219.13.
Secondly, when an agency makes an amendment to the Forest Plan, it must
document the amendment in a decision notice document, which must include specific
information. 36 CFR 219.14(a)(2012). The DNF has included only very minimal
information regarding the Forest Plan amendments in the Rocket EA and Draft Decision
Notice. This minimal information does not comply with NFMA regulations that govern
plan amendment. The DNF must go back and give the following information: “An
explanation of how the plan components meet the sustainability requirements of §219.8,
the diversity requirements of §219.9, the multiple use requirements of §219.10, and the
timber requirements of §219.11;(a)(2)” and “documentation of how the best available
scientific information was used to inform planning, the plan components, and other plan
content, including the plan monitoring program (§219.3).” 36 CFR 219.14(a)(2) and (4).
Page 5 Forest Plan Amendments should not be done on a project-by-project basis, as
Forest Plans are meant to provide a forest-wide vision for the forest. If each project then
amends the Forest Plan based on that specific project’s needs, there will be no forestwide standards or management.
Population Viability
NFMA also requires an agency to “provide for diversity of plant and animal
communities based on the suitability and capability of the specific land area.” 16
U.S.C.S. § 1604(g)(3)(B). The Forest Service has created regulations to carry out this
mandate at 36 CFR 219.9 (2012). Under those regulations, the agency must ensure the
ecological integrity of the plan area. 36 CFR 219.9(a) Furthermore, the agency “shall
determine whether or not the plan components required by paragraph (a) of this section
provide the ecological conditions necessary to: contribute to the recovery of federally
listed threatened and endangered species, conserve proposed and candidate species, and
maintain a viable population of each species of conservation concern within the plan area.
36 CFFR 219.9(b)(1).
LOWD/BMBP is concerned about the viability of the populations of Lewis’s
Woodpecker, White-headed woodpecker, Townsend’s Big-Eared Bat, Pallid bat, Fringed
Myotis, Johnson’s Hairstreak butterfly, Western Bumblebee, all bat species, all other
Threatened, Endangered, and Sensitive species that use the area, and all other
Management Indicator Species. The Rocket Project would occur in an intensively
managed area with a lot of regular human disturbance. Further impacting this habitat will
compound the stress experienced by these species. The DNF has not shown that it will
comply with the above regulation to “maintain a viable population of each species of
conservation concern.”
For example the DNF has disclosed that the Rocket Project will cause negative
impacts to bats, their habitat, and their prey. Rocket EA at 158. Without showing how or
why, the DNF then goes on to say that “it is assumed that species presence will still be
maintained with any of the alternatives.” Rocket EA at 158. The Townsend’s Big-Eared
Bat avoids clearcuts and regenerating stands. The Rocket Project would create
regenerating stands and harvest 50% of its habitat. Rocket EA at 157. The Fringed
Myotis requires old and mature trees for roosting habitat. Rocket EA at 154.
Management activities that homogenize the forest impact this species. Rocket EA at 154.
The Rocket Project would remove trees up to 20.9” dbh, which are the next old and
mature trees on the forest. Rocket would also remove mistletoe, lodgepole pine, and fir,
further homogenizing the forest. Cumulative impacts throughout the watershed will
impact 32% of total bat and bat prey species habitat. Rocket EA at 157. With impacts on
nearly a third of all bat habitat in the watershed, how can the DNF claim that these
species will not be affected?
Also, the Johnson’s Hairstreak butterfly will lose a full half of its habitat in
Alternative 4 of the Rocket Project. Rocket EA at 160. Still, the DNF, without showing
how, assumes that species presence will still be maintained.” Rocket EA at 161.
Page 6 Similarly, the DNF discloses that the Western Bumblebee population has declined by
70%-100% since the 1990s. Rocket EA at 161. The Rocket Project will remove 57% of
its habitat in the Rocket Project area. Rocket Ea at 162. The DNF does not show how
the population will be maintained under those conditions, but still assumes that it will.
The DNF’s conclusions seem to based on the faulty assumption that it can jump from a
project-wide scale of analysis to a forest-wide scale to make the impacts to species seem
smaller.
Forest Plan Violations
Logging in Old Growth Management Areas
Old Growth Management Areas are intended to provide for naturally evolved oldgrowth forest ecosystems. The OGMAs must provide large trees, abundant standing and
downed dead trees, a multi-storied canopy, and, in general, habitat for plant and animal
species that are dependent on old-growth. DNF Forest Plan MA-15. The Rocket Project
proposes drastic thinning in two OGMAs, including in areas with LOS. Rocket EA at
222. The thinning would occur in over half of one OGMA and in nearly all of another, a
total of 211 acres. Rocket EA at 224. The DNF claims that mowing and underburning in
the OGMAs will “reduce the risk of wildfire.” Rocket EA at 224. However, the thinning
is simply intended to “favor the growth and development of ponderosa pine.” Rocket EA
at 224. And to increase “potential for live, large tree structure to develop in 20-40 years.”
Rocket EA at 105. These are unacceptable purposes. The OGMA must only be logged
under very narrow purposes, as it is a management classification that is intended to
encourage naturally evolved ecosystems. Management for large tree structure is not
synonymous with management for an old-growth ecosystem. An old-growth ecosystem
is more than simply a forest with large trees – it is a complex, diverse, and naturally
evolved system that has developed free from drastic human intervention. The Rocket
Project proposes aggressive thinning within the OGMAs that is not consistent with the
DNF Forest Plan directives.
Logging in Goshawk PFAs
Alternative 4 proposes to “treat” 38% of the total of the three Goshawk PFAs in
the project area and 60% of project level reproductive habitat. Rocket EA at 52.
However, the Rocket EA does not show how these activities will comply with the DNF
Forest Plan mandate to maintain reproductive habitat for 40 goshawk pairs across the
forest. See DNF Forest Plan WL-6. Furthermore, the Eastside Screens requires the DNF
to protect all known active and historically used nest sites, including a 400-acre PFA
around these nest sites. When nest sites are unknown, the Forest Plan provides
physiographic and vegetative characteristics that must be maintained. DNF Forest Plan
WL-9. The Rocket Project proposes aggressive logging activities within the historic
PFAs and in forest that provides reproductive habitat for Goshawk nest-sites. There is no
mention of whether the required reproductive habitat will be maintained across the DNF.
The Rocket EA does not show how the activities in the PFAs or reproductive habitat
comply with planning mandates.
Page 7 A nest is still considered active if activity has been confirmed over the last five
years. The DNF completed Goshawk surveys for 2012/2013, but did not complete
surveys for the five years before that. Rocket EA at 191. Therefore, the DNF has no
proof that the nest sites were not used over the last five years. In order to consider the
nest sites inactive, the DNF has to show that there is no activity within the last five years.
Because the DNF has only completed surveys for one year, and not five, it must assume
that the nest sites are still active.
Soils
The National Forest Management Act requires that an agency’s Forest Plan
“insure that timber will be harvested from National Forest System lands only where:
…soil, slope, or other watershed conditions will not be irreversibly damaged” 16 USC
1604(g)(2)(e)(i). Agency regulations under NFMA require that all site-specific projects
comply with the Forest Plan. The DNF Forest Plan requires that detrimental impacts to
soil remain below 20%.
Table 184 of Appendix C shows that 20 units in the project area are currently
above this 20% standard. This table also shows that all of the units will be quite close to
this 20% standard. The Rocket EA states the 20% Forest Plan standard will not be met in
Alternative 4 without using BMPs, PDCs, and mitigation measures. Yet, there is no
analysis of whether the proposed BMPs, PDCs, and mitigation measures have been
effectively implemented based on monitoring of past projects in which they were used. If
these protective and mitigation measures do play a big part in maintaining soil standards,
then they may not be enough to keep detrimental soil levels within Forest Plan standards.
The protective and mitigation measures include technical procedures that require absolute
vigilance on the part of timber harvesters. If timber harvesters are not adhering to the
measures in every way, there is very little room for error, and detrimental soil conditions
would likely exceed Forest Plan standards. While forest administrators may be
responsible for monitoring these standards, in all reality, it is the commercial harvesters’
responsibility to make sure these measures are followed. The Forest Service has
presented no studies or shown any other evidence to support the idea that these protective
and mitigation measures will actually work or that commercial harvesters, in practice,
actually do implement the design criteria. It is irresponsible for the Forest Service to
push detrimental soil impacts so close to Forest Plan standards and rely on commercial
harvesters to maintain levels below standards. Even though detrimental levels are
“expected” to remain below the Forest Plan standard, relying on timber harvesters to keep
them below standards makes it unlikely that they will actually remain below the standard.
Finally, if maintaining the 20% standard for detrimental soils relies on mitigation
measures that require extra funding, there is no guarantee that such funding will actually
be available.
Deer Thermal Cover
Page 8 LOWD/BMBP opposes the Rocket Project’s proposal to further remove thermal
cover for deer. If the forest is not currently meeting the Forest Plan standard for thermal
cover, then the DNF cannot take management actions that will further degrade thermal
cover across the forest.
LOWD/BMBP also opposes the Rocket Project’s proposal to create 4-12 acre
openings for deer. We request that the openings be limited to 2.5 acres.
Recreation
LOWD/BMBP opposes commercial thinning activities in the Lava River Cave
recreation site.
Resolution of NFMA violations
LOWD/BMBP have commented extensively on the project’s compliance with
NFMA. See, for example, comments in the Rocket Project EA at 405, 406, 407, 411,
416, 417, 418, 421, 422, 423, 424, 426, 429-30, 431, 432, 433, and 444.
In order to remedy the NFMA violations that we have mentioned in our objection,
LOWD/BMBP respectfully requests that the DNF implements the following suggestions
in the final decision of the Rocket Project:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Wait to implement the Rocket Project until the DNF has revised its Forest
Plan, making sure that the project conforms to the new Forest Plan.
Eliminate activities that require a Forest Plan Amendment, or,
alternatively, follow proper NEPA and NFMA requirements for plan
amendments.
Maintain suitable habitat for all Threatened, Endangered, Sensitive, and
MIS species, as required under NFMA’s population viability requirement.
Fully protect all bat habitat areas.
Drop all commercial logging and burning in OGMAs.
Drop all commercial logging in PFAs.
Drop commercial logging in Ponderosa Pine stands with larger tree
structure.
Drop all units, including those with steep slopes, that require mitigation
measures and special design criteria to prevent detrimental soil conditions
that exceed Forest Plan standards. Please see Table 184 for a list of units
that will exceed Forest Plan standards without mitigation measures and
special protective measures.
Drop all activities that will further remove thermal cover for deer.
Reduce proposed deer openings to 2.5 acres in size.
Drop all commercial thinning activities at Lava River Cave recreation site.
Page 9 III.
General concerns
Roads
LOWD/BMBP objects to the construction of any new roads, permanent or
temporary and to extensive road reconstruction, especially if this involves re-opening
closed or overgrown roads. The impacts of open, closed, and temporary roads are all
similar, because all are accessible to off-road vehicles and other human activities, and
encourage the spread of invasive species. Closed roads are often ineffectually closed or
opened at a later date for management activities. Thus, a closed road or a temporary road
really is not “closed” or “temporary.” Just because a road has not been added to the
official road system does not mean that that road has no further impacts. In fact, the road
most certainly will have impacts to wildlife, soil, and quality of recreational opportunities
for decades or longer. More road construction and opening of closed roads means more
disturbance of road sensitive species such as elk, wolverine, lynx, and gray wolves.
Roads also allow easier entry into the forest for fur trappers looking for lynx, wolverines,
and wolves.
Furthermore, constructing or reconstructing new roads creates an even greater
backlog of roads that will require maintenance in the future. Building, rebuilding, and
reopening roads are simply one of the biggest impacts to the forest. Roads break up
habitat connectivity, allow for disturbance and harassment of wildlife, add sediment to
streams, compact soil, impact the function of the watershed, and impair recreation,
among other negative impacts.
LOWD/BMBP respectfully requests that the DNF drop the construction and
reconstruction of all new roads. See the comment in the Rocket Project EA at 440.
Project does not match stated purpose
One stated purpose of the project is to maintain, increase, start, and hasten
trajectory towards the LOS stage. However, the Rocket Project will aggressively thin
trees from 15-20.9” dbh. It is contradictory to say that a project will promote the LOS
stage, but then remove the trees that are almost to that stage.
LOWD/BMBP respectfully requests that the DNF consider a limit of 15” dbh to
fulfill the stated purpose of promoting LOS forest. See comments in the Rocket Project
EA at 416, 417, 421.
Cumulative Impacts
The Rocket Project EA does not adequately analyze cumulative effects of the
project. First of all, the scale that the Rocket Project EA uses for its cumulative effects
analysis is too small. District-wide activities and forest-wide activities should also be
included. Larger scale analysis is important because, forest-wide, land managers are
Page 10 removing protections on a project-by-project basis. However, these actions, when they
happen in project after project across the forest, have a cumulatively significant effect.
Secondly, the West Bend Vegetation Management Plan is an absolutely enormous
14,500-acre commercial thinning project that is in the same watershed as the Rocket
Project. While the Rocket EA often mentions the combined effects of both projects, the
cumulative effects analysis is severely lacking in detail. Because the cumulative impacts
of these large, nearby projects are great, the DNF must provide a much greater level of
detail in its cumulative impacts analysis.
Finally, the DNF does not even mention the nearby Ogden sale, the past Kelsey
sale (units which overlap the Rocket units), or the Newberry Geothermal Consent to
Lease projects. This forest has been aggressively managed by the DNF over many years,
and the cumulative impacts are obvious. The DNF must consider and disclose these
cumulative impacts to the forest in a more thoughtful, forthcoming, and detailed way to
comply with the requirements of NEPA.
LOWD/BMBP respectfully requests that the DNF conduct a cumulative impacts
analysis of all projects district-wide and forest-wide and provide a more detailed
cumulative impacts analysis of the West Bend Project in conjunction with the Rocket
Project. The DNF should also consider and analyze the effects of all of the projects in an
around the Rocket Project, like the Odgen and Kelsey timber sales and the Newberry
Geothermal Consent to Lease projects. We are especially interested in the disclosure of
those projects that make project-specific amendments to district-wide and forest-wide
standards, which end up removing important protections. See comments in Rocket
Project EA at 422, 423, 425, 426, 427, and 429.
Failure to consider scientific controversy
The DNF has based the Rocket Project on science that suggests a very low
stocking density for dry ponderosa pine forests. However, the DNF has failed to consider
the scientific controversy surrounding this issue. We have attached the relevant science
to this objection. LOWD/BMBP requests that the DNF please consider this science and
reevaluate the Rocket Project in light of this science. The DNF plans to thin forests that
are already quite open and do not need to be thinned. Please see comments in Rocket EA
at 418, 420, and 421.
******
Thank you for your consideration of these objections and for the opportunity to
participate in the predecisional administrative review process of the Rocket Vegetation
Management Project. We look forward to meeting with you to work on a resolution to
our concerns.
Page 11 Thank you for your consideration of these objections and for the opportunity to
participate in the predecisional administative review process of the Tollgate Fuels
Reduction Project. We look forward to meeting with you to work on a resolution to our
concerns.
Sincerely,
Sincerely,
+A&--^r-_\
Kristin
Kristin Stankiewicz
Stankiewicz
Authorized
Authorized Representative
Representative
for
LOWD/BMBP
for HCPC and LOWD/BMBP
l.' g.'crrnFc.-
I
V
t^,rL-n .,(tc_
Karen Coulter
Director
VeronicaofWarnock
League
Wilderness Defenders –
Conservation
Director
Blue
Mountains
Biodiversity Project (LEAD OBJECTOR)
Preservation
Hells Canyon
Council (LEAD OBJECTOR)
27803
Williams
Lane
PO
BoxOregon
2768 97830
Fossil,
La
Grande,
OR 97850
(541)
468-2028
office or 385-9167 voice mail
541-963-3950 x.25
veronica@hellscanyon. org
fu&h)n*hq
ADDENDUM
The following
Karen
Coulter documents are attached to this objection:
Director
- Survey
sheets for
the Rocket
League
of Wildemess
Defenders
- Project units, completed by LOWD/BMBP
volunteers
(hard-copy Project
only)
Blue Mountains
Biodiversity
- Williams
The following
scientific
papers (electronic copy only):
27803
Lane
Fossil, Oregon 97830
(541)
Hessburg,
et al.,office
Re-examining
fire voice
severity
relations in pre-management era
468-2028
mail
or 385-9167
mixed conifer forests: inferences from landscape patterns of
Notice
Objection Landscape
- Tollgate Fuels
Reduction
Project
Ecology,
March
2007.
forest ofstructure,
Page 20
Baker and Ehle, Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in
the Western United States, USDA Forest Service Proceedings, 2003.
Baker, Implications of spatially extensive historical data from surveys for restoring dry
forests of Oregon’s eastern Cascades, Ecosphere, March 2012.
Klenner, et al., Dry forests in the Southern Interior of British Columbia: Historic
disturbances and implications for restoration and management, Forest Ecology and
Management, February 2008.
Marlon, et al., Long-term perspective on wildfires in the western USA, PNAS, February
Page 12 2012.
Pierce and Meyer, Long-Term Fire History from Alluvial Fan Sediments: The Role of
Drought and Climate Variability, and Implications for Management of Rocky Mountain
Forests, Boise State University Department of Geosciences and University of New
Mexico Department of Earth and Planetary Sciences.
Drury and Veblen, Spatial and temporal variability in fire occurrence within the Las Bayas
Forestry Reserve, Durango, Mexico, Springer Science and Business Media, October
2007.
Page 13 Landscape Ecol
DOI 10.1007/s10980-007-9098-2
RESEARCH ARTICLE
Re-examining fire severity relations in pre-management era
mixed conifer forests: inferences from landscape patterns of
forest structure
Paul F. Hessburg Æ R. Brion Salter Æ
Kevin M. James
Received: 16 August 2006 / Accepted: 23 March 2007
Springer Science+Business Media B.V. 2007
Abstract For some time, ecologists have known
that spatial patterns of forest structure reflected
disturbance and recovery history, disturbance severity
and underlying influences of environmental gradients.
In spite of this awareness, historical forest structure
has been little used to expand knowledge of historical
fire severity. Here, we used forest structure to predict
pre-management era fire severity across three biogeoclimatic zones in eastern Washington State, USA,
that contained extensive mixed conifer forests. We
randomly selected 10% of the subwatersheds in each
zone, delineated patch boundaries, and photo-interpreted the vegetation attributes of every patch in each
subwatershed using the oldest available stereo-aerial
photography. We statistically reconstructed the vegetation of any patch showing evidence of early
selective harvesting, and then classified them as to
their most recent fire severity. Classification used
published percent canopy mortality definitions and a
dichotomized procedure that considered the overstory
and understory canopy cover and size class attributes
of a patch, and the fire tolerance of its cover type.
Mixed severity fires were most prevalent, regardless
of forest type. The structure of mixed conifer patches,
in particular, was formed by a mix of disturbance
severities. In moist mixed conifer, stand replacement
P. F. Hessburg (&) R. B. Salter K. M. James
Pacific Northwest Research Station, USDA Forest
Service, Wenatchee, WA 98801-1229, USA
e-mail: [email protected]
effects were more widespread in patches than surface
fire effects, while in dry mixed conifer, surface fire
effects were more widespread by nearly 2:1. However, evidence for low severity fires as the primary
influence, or of abundant old park-like patches, was
lacking in both the dry and moist mixed conifer
forests. The relatively low abundance of old, parklike or similar forest patches, high abundance of
young and intermediate-aged patches, and widespread evidence of partial stand and stand-replacing
fire suggested that variable fire severity and nonequilibrium patch dynamics were primarily at work.
Keywords Fire severity Mixed conifer forests Dry forests Non-equilibrium dynamics Mixed
severity fire Ecoregions Inland Northwest USA Historical range of variability
Introduction
The concept of fire severity, the effects of a wildfire
and its mosaic of intensities on the vitality of biota, is
useful to land managers. For example, public land
managers are required to maintain viable populations
(sensu Hunter 1990) of listed or sensitive native
species (Endangered Species Act of 1973). To
accomplish this task, they will imitate the pattern
and effects of historical fires when they distribute
management intensities across a landscape (e.g., see
Hunter 1993; Hunter et al. 1988). This is an intuitive
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approach because the ebb and flow of disturbances
and resultant patterns of forest structure supported a
rich flora and fauna and the native disturbance
regimes. Indeed, recent emphasis on fire history
studies and the historical range of variability is driven
by coarse-filter native species conservation ideas
(Agee 2003; Hunter et al. 1988; Landres et al. 1999;
Thompson and Harestad 2004). Despite knowledge of
linkages between patterns of fire severity and landscape conditions, there has been little use of forest
structural conditions to characterize patterns of
historical fire severity. That is the topic of this paper.
The fire history literature from the Inland Northwest United States couples dry mixed conifer forests
(hereafter, dry forests) of the pre-management era
(ca. 1900) with high frequency (once every 1–
25 years), low severity fire regimes (Agee 1993,
1994, 1998; DeBano et al. 1998; Everett et al. 1997,
2000; Heyerdahl et al. 2001; Weaver 1943, 1959,
1961; Wright and Agee 2004). Prior to management,
dry forest patches and their structural features were
thought to be in a relatively stable equilibrium with
their environment, the regional climate, and primary
disturbance processes. Old, multi-cohort, park-like
ponderosa pine (Pinus ponderosa) stands (referring to
actual vegetation) were thought to be the most stable
structures, and they were maintained by high frequency, low intensity surface fires, whose positive
feedback ensured continued low severity fire and
persistence of the park-like conditions. In the equilibrium model, new cohorts were recruited to the
understory after each disturbance, and the grain of
disturbance and recruitment was relatively fine
(10 3–100 ha), amounting to textural change in the
pre-disturbance structure and arrangement of cohorts
within a patch. Subsequent surface fires (those
lacking significant tree torching or crowning fire)
destroyed much of the recruited understory. The
overstory was multi-cohort, uneven-aged, and few
understory trees were recruited to the overstory in a
given decade. In time, the overstory acquired an
even-aged, single cohort appearance because older
cohorts had slowed in growth and younger cohorts
increased in size. Stand replacement was thought to
be uncommon; relatively slow attrition and recruitment accounted for the persistence of an overstory.
In contrast, historical moist mixed conifer forest
(hereafter, moist forest) patches were associated with
low, mixed, and high severity fires, and mixed
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severity fires were thought to be most influential
(Agee 1990, 1993, 1994, 1998, 2003; Wright and
Agee 2004). The conceptual model of moist forest
patches was one of non-equilibrium dynamics, variable fire severity, and transient structures. In the nonequilibrium model, new cohorts were recruited after
each disturbance; the grain of disturbance and
recruitment could be highly variable, ranging from
fine to relatively coarse within patches (10 3–102 ha),
and representing minor to major changes in the predisturbance structure, composition, and arrangement
of cohorts. Subsequent fires may be low, mixed, or
high severity destroying little to nearly the entire
understory that is recruited, and perhaps any associated overstory. The overstory may be multi-cohort
or single cohort, and even-aged or uneven-aged,
and understory trees may be slowly recruited to
the overstory, or the understory may become the
overstory.
Since the middle of the 20th century, historical dry
forest patches were thought to conform to the stable
equilibrium model (Weaver 1943, 1959, 1961). Here,
we will not suggest that Weaver misinterpreted fire
frequency or severity; rather, we suggest that other
fire frequency and severity storylines were also
probable, and that ordinary spatio-temporal variation
in fire regime and structural features of dry forests
may be larger than could be sampled at one or even
several locations.
Potential bias in point sampling of fire survivors
One reason that low severity fires have been
coupled with dry forests is that estimates of
historical fire severity have been based on point
sampling of recorder trees. In fire history studies,
recall that recorder trees directly record high (kills
the tree) or low severity (scars the tree) fires;
mixed severity is inferred from mortality expressed
across the sample in a given fire year. Recorder
trees, snags, or logs exist because at the point
where they are positioned, fires were generally low
impact. The inference has been that if the impact
on the recorder was low, the severity in the
surrounding area must also have been low. This
type of inference would tend to favor finding low
severity fires and underestimating likelihood of
fires of other severities (e.g., see Baker and Ehle
2001; Swetnam and Baisan 1996).
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Defining mixed conifer forests
Much of the extant western US fire history literature
associates a dominant fire regime with the potential
vegetation type, not the actual vegetation cover type,
because site climate and the fire tolerance of the
vegetation cover are thought to primarily influence
regime (e.g., see Agee 1998; Arno et al. 1985; Hann
et al. 1997). We evaluate this assertion using the
potential vegetation type to group mixed conifer
environments that support similar successional pathways, and absent disturbance, the same shade tolerant
species (Keane et al. 2002; Steele and Geier-Hayes
1989). Mixed conifer forests of the eastern Washington Cascades are typically divided into two broad
potential vegetation types, dry and moist mixed
conifer, due to obvious differences in site climate
and tree productivity, and we do the same here.
Whether dry or moist forest, the actual vegetation
types occurring in either type are roughly the same:
Primary cover types are ponderosa pine, Douglas-fir
(Pseudotsuga menziesii), and grand fir (Abies grandis), or combinations of these. Additional secondary
cover types include western larch (Larix occidentalis),
lodgepole pine (Pinus contorta), aspen, and cottonwood (Populus spp.). For cross-reference, we represent dry forests as the driest Douglas-fir and grand fir
plant associations (Lillybridge et al. 1995). We
exclude ponderosa pine potential vegetation types
from dry forest because they are ecotonal woodland
types, and we suspected they represented unique fire
ecology. We represent moist forests, as types on the
moist end of the Douglas-fir and grand fir series.
Forest structure holds untapped clues
Our methods were based on the premise that the
pattern and abundance of successional or structural
stages of pre-management era landscapes held important clues to the historical distribution of fire severity.
We knew that spatial patterns of forest structure
reflected the broad context of biophysical gradients,
human influence, and ecosystem processes, but we
suspected that patterns would primarily reflect disturbance and recovery history (sensu O’Hara et al.
1996; Spies 1998).
Many have documented effects of fire exclusion
and domestic livestock grazing early in the 20th
century (e.g., Belsky and Blumenthal 1997; Hessburg
and Agee 2003; Hessburg et al. 2000c, 2005;
Langston 1995; Robbins 1999), and these are potentially confounding factors to reconstructing severity
from forest structure. However, considering them did
not help to explain the wide distribution early in the
20th century of stand initiation (1–40 year old) and
young to intermediate-aged (50–150 year old) forest
structures in the dry forests (this dataset). For
example, in eastern Washington, our earliest stereo
aerial photography (1930–1940s) of dry forests
showed that 71% of the area, had understories
dominated by pole-sized and larger trees (12.7–
63.5 cm d.b.h.). Fire exclusion and grazing could not
explain understory trees this large, and over such a
vast area. Similarly, we observed medium- (101–
102 ha) to large-sized (103 ha) patches of stand
initiation structure, which in our experience reflected
prior stand replacement disturbance rather than fire
exclusion or grazing. Moreover, old, park-like or
similar ponderosa pine stand structures did not
dominate the landscapes, and this was particularly
perplexing because this was to be the signature
outcome of frequent low severity fires.
Research objectives
Wildfire effects are known to be spatially heterogeneous (Agee 1993, 1998, 2003; Fulé et al. 2003;
Swetnam and Baisan 1996); patterns of severity vary
with gradients of topography, vegetation, and climate
(Agee 1993; Rollins et al. 2002), and with the
complexity and interactions among disturbances over
space and time. Despite awareness of interrelations
between patterns of severity and landscape conditions, little has been done to characterize spatiotemporal patterns and variation in historical fire
severity (Ehle and Baker 2003; Baker et al. 2007).
Methods too have been lacking to characterize all but
the least and most severe of fires (Fulé et al. 2003;
Johnson and Miyanishi 2001), and this has limited
progress. Here, we take a structural approach to
estimating pre-management era fire severity area and
patch size distribution in mixed conifer forests of
eastern Washington, USA. Objectives were: (1) to
classify for patches of censused landscapes, the most
likely severity of the last fire; and (2) to quantify and
compare abundance and severity of patches for cover
types, structural classes, and dry and moist forest
potential vegetation types. We show trends in
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pre-management era fire severity by potential vegetation type, cover type, and structural class. Potential
vegetation types were used to bin forested patches
and evaluate the premise that dry forests were mostly
visited by low severity fires.
Methods
Assumptions
We used pre-management era (ca.1900) overstory
and understory canopy cover, size class, and cover
type to classify the most likely severity of the last fire
for each patch in the landscape. In method development, we assumed: (1) total canopy cover of a patch
reasonably approximated potential site occupancy;
(2) overstory canopy cover of a patch represented the
area of the oldest cohorts remaining after the last
major disturbance; (3) understory canopy cover
represented the area of the newest cohorts establishing after the last major disturbance; (4) other
disturbances may mix with fire, but fires caused most
stand replacement disturbance and initiated most new
cohorts; and (5) biophysical gradients influenced
canopy cover, size class, and cover type, but fire
effects were most influential.
Uncertainties
We relied on the premise that fire was the principal
disturbance and we could not rule out other disturbances. There were two scales where this was
important: the stand or patch scale (we use these
interchangeably), and that of the landscape mosaic.
Historical forest insect outbreaks have caused significant mortality (e.g., see Weaver 1961 and related
work of Williams and Babcock 1983), were generally
well documented, and where affecting a large area,
salvage logging typically followed. This logging
activity was readily detected and recorded in this
dataset. At a patch scale, where insect mortality was a
consequence of past wildfire, we pooled this mortality with other first order fire effects. Without special
methods, nearly all fire history studies include bark
beetle mortality because bark beetle contributions are
difficult to reliably extract from data. Forest diseases
were also relevant at this patch scale, but disease
progress, even where disease is widespread (e.g.,
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dwarf mistletoes, 40–50% incidence, Bolsinger
1978), is slow and incremental. Forest disease effects
were included in our fire severity estimates, but we
believe the contributed error was small and canceling
because many major mortality-causing forest diseases
tend to be diseases of the site.
Our method assumes that fire exclusion influences
(grazing, roads, fire suppression, urban/rural development) and succession had little effect on our
historical fire severity classification. This assumption
is probably incorrect, but the magnitude of the
uncertainty is difficult to gauge. We used a classification approach that considered the fire tolerance,
size, and percentage of the overstory remaining to
minimize confusion associated with succession or fire
exclusion influences. However, since overstory canopy percent is computed as the ratio of the overstory
canopy cover to the total tree cover as viewed from
above, some influence must occur.
Study area
We used a published ecoregionalization of the
Interior Columbia basin (Hessburg et al. 2000b),
and selected three Ecological Subregions (ESRs)
where dry and moist forests were abundant. The
selected Subregions were ESR5, ESR11, and ESR13
(Fig. 1). ESR5 was the ‘‘Warm’’ (5–98C annual
average temperature), ‘‘Moderate Solar’’ (250–
300 W/m2 annual average daylight incident shortwave solar radiative flux), ‘‘Moist’’ (400–1,100 mm/
year total annual precipitation), Moist and Cold
Forests (predominantly occupied by moist and cold
forest potential vegetation types) Subregion, but
subwatersheds included dry forests. ESR11 was the
‘‘Warm’’, ‘‘Moderate Solar’’, mixed ‘‘Dry’’ (150–
400 mm/year total annual precipitation) and
‘‘Moist’’, Dry and Moist Forests Subregion, and
was composed of extensive mixed conifer forests
occurring between grasslands or shrublands and cold
forests. ESR13 was the mixed ‘‘Warm’’ and ‘‘Cold’’
(0–48C annual average temperature), ‘‘Moderate
Solar’’, ‘‘Moist’’, Moist Forests Subregion, and is
composed of moist mixed and other cool/moist
conifer forest potential vegetation types (e.g., Tsuga
heterophylla, Thuja plicata, and Abies amabilis) with
dry forests in the lowest elevations. In the eastern
Washington, ESR11 is the domain of the archetypal
dry forests.
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Fig. 1 Ecological
subregions and
subwatersheds sampled in
the study area in eastern
Oregon and Washington,
USA, (adapted from
Hessburg et al. 2000b)
Stratification by geoclimatic region
We used an existing vegetation dataset developed for
the Interior Columbia Basin Project (Hessburg, et al.
1999a, 2000c, http://www.fs.fed.us/pnw/pubs/
gtr_458.htm). Vegetation data were spatially continuous across sampled subwatersheds (the 6th level in
the USGS watershed hierarchy, Seaber et al. 1987)
and the sample frame was originally obtained using a
two-stage, stratified, random sample of all subwatersheds in the Interior Columbia basin. Study area
subwatersheds ranged from about 4,000 to 20,000 ha
and were post-stratified by ESRs. The resulting set
included 38 subwatersheds, representing about 10%
of the total subwatersheds and area of each Subregion
(area sampled = 303,156 ha).
Photo-interpreting vegetation attributes
The vegetation attributes of every patch in each study
subwatershed were photo-interpreted from the oldest
available, stereo, aerial photography (1930–1940s;
photo scales: 1:15,840–1:26,000, B + W).
Attributes included the total tree canopy cover
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(overstory + understory 100%); overstory canopy
cover, species composition, and size classes; understory canopy cover, species composition, and size
classes; number of canopy layers; percentage of
canopy cover dead or as snags; and type of prior
logging entry. A new patch was delineated with a
single class difference of one attribute between two
adjacent patches [e.g., 80% vs. 90% overstory cover,
or pole- vs. small-sized understory trees]. To complete the project with available resources, a minimum
patch size of 4 ha was adopted. Preliminary studies
indicated that without a minimum patch size, many
would be <4 ha, similar to what White (1985) found
in the southwestern US. The resulting patch sizes
ranged from 4 to 3, 373 ha in a negative exponential
distribution; average size was 54 ha; there were 5,
741 total patches, and 88% of the patches were
<100 ha.
Detecting early selection cutting
Visual cues used by photo-interpreters to detect logging
included the presence of old forest road or railroad
beds, skid roads connecting to stands, skid trails
connecting to canopy gaps, and ground and vegetation
disturbance. Single tree selection cutting was detected
in many old photos but was generally absent in photos
lacking roads or rails. Because the selection cutting
targeted large trees (>63.5 cm d.b.h.), their removal left
canopy gaps along with ground and vegetation disturbance, and skid trails as heavy logs were yarded to
roads or rails. Also, skid trails were constructed at high
densities because log in-winching distances (usu.
<200 m) were limited by the available technology.
For the 38 study subwatersheds, 14.5% of the area
showed evidence of logging entry, and most was light
selection cutting (10.9% of the total area).
Reconstructing vegetation to pre-harvest
conditions
We reconstructed the vegetation attributes of each
patch showing evidence of harvesting using Moeur
and Stage’s (1995) most similar neighbor inference
procedure. The most similar neighbor algorithm uses
canonical correlation analysis to derive a similarity
function, and then chooses as a stand-in, the most
similar patch from the set of patches that have
detailed design attributes (‘local variables’), and
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lower resolution indicator attributes (‘global variables’). The most similar stand-in patch is selected by
means of the similarity function which maintains the
multivariate relations between the global variables
and the local variables. Global variables (1-km
resolution) assigned to patches were the potential
vegetation type (from Hann et al. 1997); mean annual
temperature, total annual precipitation, averaged
annual daylight incident short-wave radiative flux
(‘‘solar radiation’’, from Thornton et al. 1997); and
slope, aspect northing, aspect easting, and elevation
derived from a 30-m digital elevation model. Climate
data were from the year 1989, which Thornton et al.
(1997) considered to be an average weather year for
the region. Local variables were the photo-interpreted
total and overstory canopy cover, canopy layers, size
class of the overstory and understory, and overstory
and understory species of the patch, which were also
the attributes that were reconstructed for the logged
patches. In analysis, we used the set of all patches in
the sample of subwatersheds (unlogged + logged but
reconstructed), and then compared results with those
obtained using the set of unlogged patches alone to
evaluate effects of vegetation reconstruction on fire
severity area estimation.
Deriving forest structural classes
Forest structural classes were derived for every patch
using classification methods detailed in Hessburg
et al. (1999a, 2000c) and summarized here. Figure 2(A–G) shows the structural classes that are
referenced in the text, defined for Interior Northwest
forests by O’Hara et al (1996), and adapted from
Oliver and Larsen (1996). The classes do not
represent a linear sequence in any strict sense; rather
they partition a continuum of conditions resulting
from stand dynamics, succession, and disturbance
processes into bins representing key mileposts in
stand development. Absent disturbance, the structural
classes are more or less sequential; with disturbance
they can be progressive or retrogressive.
Assigning the potential vegetation type
The potential vegetation type of each patch was
assigned using the methods of Hessburg et al. (1999a,
2000a). We assigned a potential vegetation type to
each patch to directly evaluate the premise that dry
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Fig. 2 Graphic
representation of derived
structural classes of eastern
Cascades forests: (A) stand
initiation, (B) open canopy
stem exclusion, (C) closed
canopy stem exclusion, (D)
understory reinitiation, (E)
young multistory forest, (F)
old multistory forest, and
(G) old single-story forest
(adapted from O’Hara et al.
1996; Oliver and Larsen
1996)
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forest patches were tightly coupled with low severity
fires. The most shade tolerant conifer species was
identified using historical overstory and understory
species composition attributes, and elevation, slope,
and aspect layers generated from 90-m digital
elevation models of the subwatersheds. Potential
vegetation analysis was done separately for subwatersheds of each subbasin (4th level in the USGS
hierarchy, Seaber et al.1987). We separated patches
in the Douglas-fir/grand fir potential vegetation type
into warm-dry (dry forest) and cool-moist (moist
forest) subgroups using the classification rules unique
to each subbasin.
Selecting a severity rating system
There are numerous fire severity rating systems in the
US and worldwide; examples are given in Agee
(1990, 1993, and references therein); we adopted the
definitions of Agee, an authority on Inland Northwest
fire ecology. Thus, low, mixed, and high severity fires
were defined as destroying by fire, 20%, 20.1–
69.9%, 70% of the total canopy cover or basal area
of a patch, respectively.
Classifying fire severity
We classified fire severity of a patch using the
overstory canopy percentage (i.e., percentage of the
total that was overstory), the overstory size class, the
understory size class, and the fire tolerance of the
cover type (Table 1). Overstory canopy percentage
represented the overstory remaining after the last fire.
Overstory canopy percentage classes (= overstory
canopy remaining classes, 80%, 30.1–79.9%,
30%) used to define low, mixed and high severity
fires directly corresponded with published fire severity boundary values (i.e., overstory canopy removed,
20%, 20.1–69.9%, 70%, respectively, Agee
1993).
In 19% of the patches, representing 18% of the
area, the fire tolerance of the cover type was also used
to predict the most likely fire severity (Table 1).
Cover type was used where overstory canopy
percentage exceeded 80%, and where it was impossible to discern from structural attributes alone
whether severity was high (stand replacing fire from
a long time ago) or low (surface fire maintained). For
example, when the cover type was grand fir,
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overstory size was small to medium trees, and
overstory canopy cover was >80%, the assigned fire
severity was ‘‘High’’ rather than ‘‘Low’’. This was
considered the most likely prediction because the
size, canopy cover and fire intolerance of the cover
type suggested that high severity fire had more likely
regenerated the patch some decades ago rather than
high frequency and low severity fire maintaining a
continuous coverage of thin-barked, fire-intolerant
trees. Consistent with the fire ecology literature,
severity classification explicitly assumed that thinbarked, fire intolerant species would be standreplaced, and that thick-barked, fire tolerant species
would be conserved (Table 1). Of the total cases
where the cover type was used, 82% were classified
to low severity fire, 18% to high severity fire.
Statistical analysis
The study entailed a complete census of conditions in
38 subwatersheds. To broaden the scope of inference,
we applied non-parametric rank ordered tests based
on the Chi-square distribution to test for significant
differences in area of a fire severity class by cover
type, potential vegetation type, Subregion, and study
area. We used Society of American Foresters cover
type definitions (Eyre 1980) to represent actual
vegetation cover (http://www.fs.fed.us/pnw/pubs/
gtr_458.htm). We used the Kruskal–Wallis H-test to
compare observed and expected area in fire severity
classes of ponderosa pine or Douglas-fir cover types
in dry or moist forest, within and among Subregions,
and for the study area. Significant difference
(P 0.05) was evaluated using the Mann–Whitney
U pairwise post-hoc comparison procedure. The
Mann-Whitney U-test was also used to compare area
in fire severity classes of ponderosa pine and
Douglas-fir cover types, and area within severity
classes by potential vegetation type within Subregions, and for the study area (Tables 2, 3).
Results
Mixed severity fires were most prevalent across all
forest types of the three Subregions; low, mixed, and
high severity fires occurred on 16, 47, and 37% of
total forest area, respectively.
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Table 1 A dichotomized key to fire severity classification
1a. Patch is not forested
2a. Patch is rangeland
High severity
2b. Patch is non-rangeland
No severity
1b. Patch is forested
3a. Overstory size class small trees and understory size class small treesa
4a. Overstory canopy percent 80%
5a. Cover type is not fire tolerantb
5b. Cover type is fire tolerant
High severity
c
Low severity
4b. Overstory canopy percent < 80%
High severity
6b. Overstory canopy percent >30%
Mixed severity
3b. Overstory size class < small trees or understory size class > small trees
7a. Overstory size class < small trees
High severity
7b. Understory size class > small trees
8b. Overstory canopy percent > 30%
High severity
10a. Cover type is not fire tolerant
High severity
10b. Cover type is fire tolerant
Low severity
9b. Overstory canopy percent <80%
Mixed severity
a
Photo-interpreted tree size classes are: seedlings and saplings (<12.7 cm d.b.h.), poles (12.7–22.6 cm d.b.h.), small trees (22.7–
40.4 cm d.b.h.), medium trees (40.5–63.5 cm d.b.h.), and large trees (>63.5 cm d.b.h.)
b
Fire tolerant cover types of the study area are: ponderosa pine (PIPO), western larch (LAOC), Interior Douglas-fir (PSME),
western white pine (PIMO), and sugar pine (PILA)
c
Fire intolerant cover types of the study area are: lodgepole pine (PICO), grand fir (ABGR), white fir (ABCO), Pacific silver fir
(ABAM), subalpine fir (ABLA2), Engelmann spruce (PIEN), western hemlock (TSHE), western redcedar (THPL), mountain
hemlock (TSME), Whitebark pine (PIAL), subalpine larch (LALY), and all hardwoods
Fire severity by Subregion
In ESR5, mixed severity fires were found on 55% of
the total forest area; the remainder was unevenly split
between low (13%) and high severity (32%) fires.
ESR11 showed the greatest area in high severity fires
with 46%; mixed severity fires comprised 39%, while
low severity fires comprised 15% of the forest area.
Mixed severity fires dominated ESR13 (53%), the
remainder was evenly split between low (21%) and
high severity (26%) fires.
Severity by forest structural class
In general, forest structure pointed to highly variable
mixed severity fire as the prevailing fire process.
Forest structure was dominated by intermediate-aged
patches consisting of young multistory forest
‘‘yfms’’, understory re-initiation ‘‘ur’’, and open
canopy stem exclusion structures ‘‘seoc’’ (O’Hara
et al. 1996, Fig. 3). In ESR11, most area influenced
by low severity fire fell within the open canopy stem
exclusion structure, with the balance falling in the
young multi-story, understory re-initiation, stand
initiation ‘‘si’’, and old single story ‘‘ofss’’ forest
structures (Fig. 3). The dominant fire severity was
mixed, even in old single and multi-story ‘‘ofms’’
structures.
Similarly, in ESR13 old multistory structure was
widespread; forming the 4th most dominant feature,
but mixed rather than low severity fire was associated
(Fig. 3). In ESR5, most low severity fire occurred in
open canopy stem exclusion structures with only a
fraction (1.5% of the area) occurring in old single
story structures. Open canopy stem exclusion structures were comprised of the ponderosa pine cover
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Table 2 Kruskal–Wallis
H-test comparing area in a
fire severity class of
ponderosa pine or Douglasfir cover types, and by
pooled cover type, within
three Ecological
Subregions, and for the
study area
Subregion
Cover type
ESR5
Ponderosa pine
Douglas-fir
Pooled
ESR11
Ponderosa pine
Douglas-fir
Pooled
ESR13
Ponderosa pine
Douglas-fir
Pooled
Study area
Values in bold significantly
differ (P 0.05) in area
(ha) of a fire severity class.
Severity classes followed
by the same letter are not
significantly different
according to Mann–
Whitney U-test pairwise
post-hoc comparisons
Ponderosa pine
Douglas-fir
Pooled
type in all Subregions, accounted for most of the low
severity fires, and came closest to resembling
historical descriptions of park-like pine stands, but
they were not dominated by large trees. Where large
trees were present, they formed a remnant overstory representing less than 30% of total canopy
cover.
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Fire severity class
post-hoc comparison
Area (ha)
Low
5,106
Mixed
9,931
High
5,821
Low
2,904
Mixed
11,372
High
3,606
Low
8,010
Mixed
21,303
High
9,427
Low a
16,203
Mixed a
33,460
High a
5,798
Low
5,685
Mixed
17,656
High
8,498
Low a
21,888
Mixed a
51,116
High a
14,297
Low a
10,071
Mixed a
18,612
High a
1,380
Low
4,720
Mixed
12,686
High
5,391
Low a
14,791
Mixed b
31,298
High a
6,771
Low a
31,380
Mixed a
62,003
High a
13,000
Low a
13,310
Mixed b
41,714
High a
17,495
Low a
Mixed b
44,690
103,717
High a
30,495
v2-value
P-value
1.043
0.594
2.869
0.238
2.686
0.261
9.654
0.008
3.836
0.147
12.096
0.002
15.558
0.0004
2.520
0.284
15.194
0.001
20.852
0.0003
9.467
0.009
28.851
0.0002
Severity by forest cover type
Across the study area, ponderosa pine and Douglasfir cover types provided most of the forested land
cover, pine cover was most prevalent, most low
severity fire occurred in ponderosa pine and Douglasfir cover types, and of these, the greatest share
Landscape Ecol
Table 3 Kruskal–Wallis
H-test comparing area in a
fire severity class of dry,
moist, and pooled potential
vegetation type, within
three Ecological
Subregions, and for the
study area
Subregion
Potential
vegetation type
Fire severity class
post-hoc comparison
Area (ha)
v2-value
P-value
ESR5
Dry forest
Low a
3,712
6.794
0.033
Mixed b
10,414
High a
6,008
0.717
0.699
6.258
0.044
11.777
0.003
38.554
0.0001
42.048
0.0001
24.878
0.0001
6.451
0.04
28.405
0.0001
40.940
0.0001
42.291
0.0001
78.681
0.0001
Moist forest
Pooled
(Dry + Moist)
ESR11
Dry forest
Moist forest
Pooled
ESR13
Dry forest
Moist forest
Pooled
Study area
Values in bold significantly
differ (P 0.05) in area
(ha) of a fire severity class.
Severity classes followed
by the same letter are not
significantly different
according to Mann–
Whitney U-test pairwise
post-hoc comparisons
Dry forest
Moist forest
Pooled
occurred in the pine cover type, but mixed severity
fires dominated both types (Fig. 4). In ESR11,
ponderosa pine cover was dominant over Douglasfir almost 2:1, and most area influenced by low
severity fires occurred in the pine cover type;
suggesting a possible landscape effect of severity
dampening via a relatively more fire tolerant actual
Low
1,278
Mixed
13,827
High
5,482
Low a
4,990
Mixed a
24,242
High a
11,490
Low a
12,019
Mixed b
33,853
High a
12,559
Low a
3,124
Mixed b
15,054
High a
6,742
Low a
15,142
Mixed b
48,906
High c
19,301
Low a
8,470
Mixed b
21,452
High a
3,630
Low a
3,845
Mixed a
9,307
High a
4,392
Low a
12,315
Mixed b
30,758
High a
8,021
Low a
24,200
Mixed b
65,719
High a
22,196
Low a
8,247
Mixed b
38,187
High c
16,616
Low a
Mixed b
32,447
103,906
High c
38,812
vegetation cover. However, Mann–Whitney U-tests
showed that there was no difference in the area of fire
severity class between the ponderosa pine and
Douglas-fir cover types of any Subregion, or for the
study area. In essence, ponderosa pine and Douglasfir functioned as one cover type with respect to fire
severity.
123
Landscape Ecol
35
30
Hectares (thousands)
25
35
ESR5
25
20
20
15
15
10
10
5
5
0
0
si
35
ESR11
30
Low
Mixed
High
seoc
secc
ur
yfms
ofss
ofms
si
70
ESR13
30
60
25
50
20
40
15
30
10
20
5
10
0
seoc
secc
ur
yfms
ofss
secc
ur
yfms
ofss
ofms
Study Area
0
si
New
seoc
secc
ur
yfms
Intermediate
ofss
ofms
Old
si
New
seoc
Intermediate
ofms
Old
Forest structural class
Fig. 3 The proportions of the pre-management era dry forest
area (ha) by forest structural class in low, mixed, and high
severity fire (corresponding with percent canopy mortality
values of 20%, 20.1–69.9%, 70%, respectively) of Ecological Subregions 5, 11, and 13. Structural class abbreviations
are: si = stand initiation, seoc = open canopy stem exclusion,
secc = closed canopy stem exclusion, ur = understory reinitiation, yfms = young multistory forest, ofms = old multistory
forest, ofss = old single-story forest. New, intermediate, and
old designations are used to group structural classes into broad
age groups
We also tested for significant difference in area of
fire severity classes within a cover type of a
Subregion. Kruskal–Wallis tests showed that there
were weak differences in area of fire severity classes
of either the ponderosa pine or the Douglas-fir cover
type of a Subregion (Table 2), however, for the study
area; the test showed a significant difference within
the Douglas-fir cover type and for pooled cover types.
Mann–Whitney U-tests showed that area of mixed
severity fire was greater (P = 0.009 and 0.0002,
respectively) than the areas of either low or high
severity fire.
forests of all Subregions and the study area, with one
exception; area by fire severity class was not different
in moist forests of ESR5 (P = 0.699). When we
pooled the dry and moist forests, we found for all
Subregions and the study area that severity class areas
were also different (Table 3); for the most part, fire
severity was unevenly distributed. In nearly all cases,
the area affected by mixed severity fires was greater
than that of either low or high severity fires. In many
cases, the area of low severity fire did not differ from
that of high severity fire. This was not the case for the
study area, where all severity classes areas of the
moist forest and pooled types were different
(P = 0.0001).
Next, we pairwise compared fire severity class
area of the dry and moist forests of Subregions and
the study area. Potential vegetation types did not
differ by fire severity class area with two exceptions;
in these, area in the high severity class of ESR11 was
two-fold greater (P = 0.001) in the dry than moist
Severity by potential vegetation type
We tested whether fire severity class area was evenly
distributed in the dry and moist forests using the
Kruskal–Wallis H-test (Table 3). We found that class
areas were significantly different in the dry and moist
123
Landscape Ecol
ESR13
15
10
5
ot
r2
tr/
p
pi
co
po
y
ie
n
/la
l
pi
al
gr
la
2/
p
ab
ab
oc
la
e
ps
m
pi
po
0
ot
r2
tr/
p
pi
co
po
pi
al
/la
ly
pi
po
70
60
50
40
30
20
10
0
Study Area
pi
po
ps
m
e
la
oc
pi
m
o
ab
am
ab abg
r
la
2/
pi
en
pi
al
/la
ly
po pico
tr/
po
ts tr2
he
/th
pl
ts
m
e
20
ab
gr
ab
la
2/
pi
en
0
c
5
pi
po
ps
m
e
la
oc
pi
m
o
ab
am
ab abg
r
la
2/
pi
en
pi
c
pi o
al
po /lal
tr/ y
po
ts tr2
he
/th
pl
ts
m
e
Hectares (thousands)
10
ESR11
la
o
Low
Mixed
High
35
30
25
20
15
10
5
0
e
ESR5
ps
m
15
Forest cover type
Fig. 4 The proportions of pre-management era total forest area
(ha) by forest cover type in low, mixed, and high severity fire
(corresponding with percent canopy mortality values of 20%,
20.1–69.9%, 70%, respectively) of Ecological Subregions 5,
11, and 13. Cover type abbreviations are: tshe/thpl = western
hemlock/western redcedar; pimo = western white; potr/
potr2 = Populus and Salix spp.; laoc = western larch;
tsme = mountain hemlock; pial/laly = whitebark pine/subalpine
larch; abam = Pacific silver fir; abgr = grand fir; pico = lodgepole pine; abla2/pien = subalpine fir/Engelmann spruce;
psme = Douglas-fir; pipo = ponderosa pine
forests; likewise for the study area, high severity class
area was 34% greater (P = 0.008) in the dry than
moist forests.
Finally, we compared Subregion fire severity class
area of dry, moist, and pooled types using the
Kruskal–Wallis H-test. In moist forests, ESR11 had
more high severity fire area than either ESRs five or
13 (P = 0.001). Similarly, for the pooled types, the
Kruskal–Wallis test showed that there were differences in the area of high severity fire among the
Subregions (P = 0.018), but Mann–Whitney post-hoc
comparisons were unable to separate them due to
variation. Most of the low severity fire occurred in the
dry forests, but mixed fire severity was most pervasive within each Subregion and across the study area.
For example, in ESR5, 18% of the total area in the
dry forest type was influenced by low severity fire;
52% by mixed severity, and 30% by high severity
fire. In the moist forest type, corresponding values
were 6, 67, and 27%, respectively, and no difference
was significant (Fig. 5). In ESR5, there was three-fold
more area affected by low severity fires in the dry
than in the moist forest type, but the difference was
not significant (P = 0.772). Across the study area,
22% of the area in the dry forest was affected by low
severity, 59% by mixed severity, and 20% by high
severity fire; while in moist forest, values were 13,
61, and 26%, respectively, and no difference was
significant (Fig. 5).
Variability of mixed severity fire
For all dry forest patches of each Subregion, and the
study area that were influenced by mixed severity
fire, we plotted the percentage area in 10% overstory
canopy cover classes (Fig. 6). In ESR11, 43% of the
area displayed an overstory canopy percentage >51%,
indicating that the last fire, even though technically of
mixed severity, looked more like low severity fire in
the aftermath, because most overstory trees survived,
and surface fire effects dominated over stand replacement. Considering together area influenced by low
and mixed severity fires, with the majority of trees
remaining, 63% was affected by surface fire dominated regimes; the balance (37%) was influenced by
123
Landscape Ecol
70
Percentage area
60
70
ESR5
ESR11
ESR13
Study Area
50
40
30
20
10
0
31 - 40% 41 - 50% 51 - 60% 61 - 70% 71 - 79%
Percentage Canopy Cover
Fig. 6 The percentage of the total area of the dry forest
potential vegetation type that was last affected by mixed
severity fire (MSF) in 10% overstory canopy cover classes of
Ecological Subregions 5, 11, and 13, and the study area. The
overstory canopy percentage is the ratio (overstory canopy
cover/total canopy cover) · 100
Influence of the vegetation reconstruction
In all analyses reported thus far, we used the set of
all patches in the censused subwatersheds (unlogged + logged but statistically reconstructed).
We reran all reported analyses using the set of
unlogged patches alone to evaluate effects of
vegetation reconstruction on estimated abundance
of fire severity. We found no significant differences
in relations of fire severity class abundance to cover
types, structural classes, or potential vegetation
70
ESR5
Low
Mixed
High
60
Percentage Area
stand replacement dominated regimes. In ESRs 5 and
13, 41 and 35% of the area in mixed severity fire
displayed an overstory canopy percentage >51%.
Across the study area, 40% of the dry forest area
showing mixed severity fire displayed >51% overstory canopy remaining in the oldest cohorts (Fig. 6).
Considering the area affected by low and mixed
severity fires (with the majority of trees remaining),
62% was affected by surface fire dominated regimes
(those where tree torching and crowning fire are
relatively minor features); the balance (38%) was
affected by stand replacement fire dominated regimes. Hence, our results suggest that pre-management era fires of dry forests were strongly surface fire
dominated but coming from both low and mixed
severity fires. There were no significant differences
among Subregions (P > 0.05) in these relations.
We repeated this analysis for moist forest patches
of the Subregions and the study area. In ESRs 5, 11,
13, and for the study area, 35, 55, 37, and 43% of the
area in mixed severity fire displayed an overstory
canopy percentage >51%, respectively. Considering
together area affected by low and mixed severity
fires, with the majority of trees remaining, 46% were
affected by surface fire dominated regimes; the
balance (54%) were affected by stand replacement
fire dominated regimes. Thus, fires of moist forest
patches tended to be stand replacement fire dominated coming from both mixed and high severity
fires.
70
ESR11
ESR13
Study Area
60
60
60
50
50
50
50
40
40
40
40
30
30
30
30
20
20
20
20
10
10
10
10
0
0
dry
forest
moist
forest
pooled
0
dry
forest
moist
forest
pooled
Fig. 5 The proportions of the pre-management era forest area
(ha) by forest potential vegetation type in low, mixed, and high
severity fire (corresponding with percent canopy mortality
values of 20%, 20.1–69.9%, 70%, respectively) of Eco-
123
0
dry
forest
moist
forest
pooled
dry
forest
moist
forest
pooled
logical Subregions 5, 11, and 13, and the study area.
Comparisons are shown for the dry and moist forest potential
vegetation types, and pooled (sum of dry + moist)
Landscape Ecol
types of any Subregion, or the study area, however,
reconstructions replaced the large trees removed by
selection cutting. This increased total hectares of old
forest, number and area of young and intermediateaged patches with remnant large trees in their
overstory, area of the ponderosa pine cover type,
and amount of low severity fire overall. We use the
reconstructed data in all analysis because it best
represented the natural variation of pre-management
era fire severity and vegetation conditions.
Discussion
Pre-management era fire severity and forest
structure
The observation of abundant intermediate-aged forest
patches is quite revealing. We suspected that the most
similar neighbor reconstructions, by replacing harvested trees, would increase the likelihood of observi n g l o w s e v e ri t y fi r e s . H o w e v e r d e s p i t e
reconstruction, much intermediate-aged forest was
observed. Examining the set of reconstructed patches,
we noted that the algorithm did a good job of
reconstructing old forest patches as well as those with
remnant large trees.
When formulating the study, we hypothesized that
where stable equilibria were operating, those patches
would be dominated by persistent, stable structures
featuring old, fire-tolerant park-like or similar stands,
as the literature suggested. Instead, area was dominated by forest structures that were intermediate
between new and old forests, i.e., by pole to mediumsized, rather than large trees (Table 1 and Fig. 3).
This observation suggested that before any extensive
management had occurred, the influence of fire in the
dry forest was of a frequency and severity that
intermittently regenerated rather than maintained
large areas of old, fire tolerant forest.
We also observed a preponderance of the low
severity fires in open stem exclusion structures
(Fig. 3); this was an important observation. Open
stem exclusion structures could be maintained by
high frequency, low severity fires and become
relatively stable structures, with time, moving
directly into old single story, park-like forest; or they
could be shunted along other structural paths where
fire frequency and severity were otherwise. Perhaps
these were antecedent conditions of park-like stands
described in early fire history studies.
Potential bias in point and area-based estimates
We acknowledged earlier that point sampling of
recorder trees potentially overestimates likelihood of
low severity fires and underestimates mixed and high
fire severity. Similarly, area based methods can
overestimate the likelihood of mixed and high
severity fires, and underestimate low severity fire
(e.g., see discussion in Minnich et al. 2000; Stephens
et al. 2003). For this reason, we suggest coupling of
point and area estimates in future fire history studies;
point observations would register events for which
recorder trees remain, and distribute them spatially;
understory cohorts could be sampled and aged across
the same landscape to determine whether they were
initiated in response to events registered on the set of
surviving recorder trees, or in response to other
events not represented by the recorders. Pairing of
point and area samples would also significantly
improve spatial accuracy of severity mapping.
Non-equilibrium fire dynamics in the premanagement era
Several lines of evidence point to non-equilibrium
rather than equilibrium dynamics in pre-management
era mixed conifer forests. First is the coupled
occurrence of low, mixed, and high severity fires
with young and intermediate-aged forest structures.
Equilibrium dynamics would be represented by the
coupled occurrence of low severity fires and old,
multi-cohort, fire tolerant, park-like or similar stands;
we did not find these conditions in abundance.
Second, highly variable mixed severity fires (Figs. 5
and 6) dominated all Subregions and the study area.
Even when considering old multi-story or single story
forest structures in isolation, most old forest area was
apparently under the influence of mixed rather than
low severity fire. It is noteworthy that nearly twothirds (62%) of study area dry forests were influenced
by surface fire dominated regimes; while fewer than
half (46%) of moist forests were so affected. This
observation is helpful in explaining why fire history
studies in dry forests that employ point sampling tend
to couple such forests with low severity fire.
123
Landscape Ecol
Third, there were few differences in area influenced by a fire severity class between the dry and
moist forests. Mixed fire severity was the primary
influence throughout the mixed conifer forest; surface
firing tended to increase when fires affected drier
topo-edaphic settings and decrease in moist and cool
settings. This stands to reason; dry and moist forests
typically occur in adjacent biophysical settings, often
separated by short distance, and elevation and aspect
differences that can be minimized during the heat and
drought of a summer fire season.
Fire severity in ecotonal ponderosa pine potential
vegetation types
We applied the identical fire severity classification
methods to all patches of the dry ponderosa pine
potential vegetation type throughout the study area.
We found that these patches were tightly coupled
with low severity fire regimes; low, mixed, and high
severity fires affected 66, 21, and 13% of the dry
ponderosa pine patches in the study area, respectively.
Here, we forward an alternative hypothesis concerning equilibrium disturbance dynamics of dry
forests. Low severity fires and equilibrium dynamics
likely occurred in eastern Washington dry forests,
where they fostered fire tolerant, park-like pine
stands, however, these dynamics were perhaps
ephemeral in nature, lasting one or more centuries
at a location, and then switching concordant with
regional climate forcing to non-equilibrium states.
The similarity in fire severity among patches in dry
and moist mixed conifer types may in fact be related
to regional climatic extremes that override a tendency
for moist types to generally experience more severe
fire (Schoennagel et al. 2004).
Potential vegetation types as a proxy for historical
fire severity
In addition to top–down biogeoclimatic controls,
there is likely bottom-up topo-edaphic control of premanagement era and present-day fire severity, but the
potential vegetation type poorly explained this relation in mixed conifer forests in eastern Washington.
There has been a strong tendency to use the potential
vegetation type as a surrogate for the vector of
unknown environmental variables that controls fire
123
severity. This was probably done for at least two
reasons: (1) it is intuitive that the potential vegetation
type might integrate and reflect the biophysical
factors responsible for bottom-up spatial controls;
and (2) foresters and fire scientists interested in
landscape restoration need a method to spatially
distribute historical and present-day fire disturbance
and its effects in order to simulate spatio-temporal
patterns and variation in forest structure and composition (e.g., see Chew 1997; Hann et al. 1997; Keane
et al. 1998, 1999, 2002). These reasons aside, we
suspect that any vector of purely environmental
variables will fall short as a useful surrogate for fire
severity because such patterns are inherently noisy
and influenced by processes with strong stochastic
elements. Schoennagel et al. (2004) used Küchler’s
PNV groups to summarize relations in the Rocky
Mountains (Küchler 1964, 1975). While related to the
potential vegetation type, they are sufficiently different in concept to function well in generalizing
correspondence between fire regime and vegetation
type. Recall that Küchler’s types define what will
occur in an environmental setting considering the
natural disturbance regimes, soils, climate, and
topography.
Pre-management era and present-day fire severity
Many today believe that fire severity in present-day
dry forests throughout the West is unprecedented.
Indeed, the impetus behind the Healthy Forests
Restoration Act (HFRA, U.S. Government 2003) is
the idea that the structures, habitats, and disturbance
regimes of present-day western dry forests are
inconsistent with pre-management era conditions.
There is credible scientific evidence to back up much
of that claim; landscape evaluations conducted in the
western US point to anthropogenic causes along with
climatic signal shifting (e.g., Brown et al. 2004;
Hessburg et al. 2005; SNEP 1996; Whitlock and
Knox 2002). However, the HFRA tacitly incorporates
a notion that dry forests of the western US are
synonymous with frequent low severity fires, and that
conditions supporting such fires should be widely
restored. The evidence for this latter assertion is less
well established. Our results suggest that low, mixed,
and high severity fires each occurred in dry (and
moist) mixed conifer forests of eastern Washington.
The scope of management and restoration activities
Landscape Ecol
could be broadened to not only accept many such
wildfire effects, but to manage for them. This should
be good news for forest managers because it suggests
that some contemporary wildfire effects will meet
management objectives, and a broader suite of forest
structural conditions and a broader range of patch
sizes supported native fire regimes of mixed conifer
forest.
Mounting evidence for variable fire severity
Schoennagel et al. (2004) review an extensive literature concerning pre-management era fire regimes of
Rocky Mountains forests from Montana to New
Mexico, including mixed conifer forests. They show
strong evidence of variable fire severity in those types,
but indicate that mixed conifer systems were probably
dominated by mixed severity fires. Similarly, Baker
and Ehle (2001), Ehle and Baker (2003), and Baker
et al. (2007) show evidence for variable fire severity in
ponderosa pine and Douglas-fir forest types.
Management implications
Spatio-temporal patterns of living and dead trees
influence the likelihood of crowning fire, fire spread
rate, flame length, and fireline intensity at patch to
landscape scales (Agee et al. 2000; Baker 1989, 1992,
1993, 1994; Huff et al. 1995; Shinneman and Baker
1997). Landscape evaluations clearly show that many
Inland Northwest forest landscapes have undergone
extensive change in spatial patterns of living and
dead vegetation (Agee 1998, 2003; Hessburg et al.
1999b, 2000c, 2005; Schoennagel et al. 2004). When
changes to a warmer, drier climate are considered
(Heyerdahl et al. 2002; Whitlock et al. 2003) the
likelihood of large, high-severity fires has increased
over the last century (Agee 1998, 2003; Hessburg and
Agee 2003; Hessburg et al. 2005), and will continue
to increase in the next. In some dry forest systems,
settlement and management have created contagious
vegetation patterns prone to unrestricted fire spread.
In others, development has fragmented landscapes
dissected by roads and housing, where opportunities
for accidentally-caused fires have increased. In contrast, historical dry forest landscapes represented a
relatively complex patchwork of fire regimes and
patch sizes; an imprint that is often difficult to see
today (Hessburg et al. 2005).
Restoring resilient forest ecosystems will necessitate managing for more natural patterns and patch
size distributions of forest structure, composition,
fuels, and fire regime area, not simply a reduction of
fuels and thinning of trees to favor low severity fires.
In shorthand, to enable occurrence of the fire regimes
of interest, spatial and temporal patterns of vegetation
and fuels that will support them are needed. More
natural historical patterns of Inland Northwest structure, composition, and fuels can be distinguished
from empirical estimates of pre-management era
range and variation (e.g., Allen et al. 2002; Hann
et al. 1997; Hessburg et al. 1999b, 1999c, 2000c,
2004), and via projections from succession and
disturbance simulation models (e.g. Chew 1997;
Keane et al., 2002; Kurz et al. 2000). If the
management goal is to produce resilient forest
ecosystems, it will be important to re-establish a
coupling like that which existed between native
landscape patterns of forest vegetation and fuels, and
the native patterns and patch size distributions of fire
regimes. Considering the contemporary climate and
each future shift in climatic regime, it will be
important to forge evolving concordance between
landscape patterns of forest vegetation and fuels, and
the patterns and patch size distributions of fire
regimes that would be expected under each new
climatic regime.
As we state in the Introduction, the mixed severity
fire bin is large, spanning fires that range from surface
to crown fire dominated. Leaving the existing mixed
severity fire class intact probably has limited utility.
Instead, it would be useful to managers if fire and
landscape ecologists explored the mixed severity fire
continuum and erected finer classes reflective of the
comparative roles of surface and stand replacing fires,
thereby giving managers more insight about how they
might vary and distribute management intensities.
Conclusions
We have shown in eastern Washington mixed
conifer forests that the distribution of fire severity
among patches in the dry and moist mixed conifer
forest was more similar than different. We found
that ponderosa pine and Douglas-fir functioned as
similar cover types with respect to fire severity. We
expected to find strong evidence of equilibrium fire
123
Landscape Ecol
dynamics in the pre-management era dry forests and
instead found evidence of variable fire severity, with
mixed severity fires and what we suspect are nonequilibrium dynamics dominating. Four lines of
evidence were important: (1) A persistent and stable
cover of fire-tolerant old forest or similar structures
did not dominate the dry forest landscape; rather it
was dominated by intermediate-aged and young
forest structures composed of fire-tolerant species.
(2) Instead of strong dominance of low severity
fires, we saw variable fire severity—a virtual
continuum of mixed severity fires with lesser
amounts of low and high severity fires. (3) Old
forests were maintained and influenced by mostly
mixed rather than low severity fires. (4) There were
few quantitative differences in the area influenced
by fire severity between the dry and moist mixed
conifer forests. A single and important exception
was that surface firing tended to increase when fires
affected dry forest patches and decrease when fires
affected moist forest patches.
Finally, it is not clear that most present-day fires of
dry or moist mixed forests produce catastrophic
results; rather, each should be evaluated on its own
merits. What is apparent is that the size and intensity
of modern fires may be coarsening the grain of the
future forest landscape, and thereby, altering its
functionality.
Acknowledgments We thank Dave W. Peterson and Richy
Harrod for helpful discussions, and Bruce Rieman, Bill
Romme, Jim Agee, Tom Spies, Monica Turner, Kerry Wood,
Don McKenzie, and four anonymous reviewers for insightful
comments. We are solely responsible for data interpretation
and the conclusions. This research was funded by the National
Fire Plan and USDA Forest Service, PNW Research StationRWU-4577.
References
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forests. In: Walstad J et al. (ed) Natural and prescribed fire
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Uncertainty in Fire History and Restoration of
Ponderosa Pine Forests in the Western United
States
William L. Baker1 and Donna S. Ehle1
Abstract—Fire-history data for ponderosa pine forests in the western U.S. have uncertainties and biases. Targeting multiple-scarred trees and using recorder trees when
sampling for fire history may lead to incomplete records. For most of the western
U.S., research is insufficient to conclude that high-severity fires did or did not occur
in these forests prior to EuroAmerican settlement, because the needed data are not
commonly collected. The composite fire interval is shown here to be misleading,
but this can be remedied in part with interval estimates by fire size class. These
problems mean that an assumption—that high surface-fire frequencies will restore
and maintain the structure of these forests—lacks a foundation in reliable fire-history
research.
Introduction
R
estoration of fire in ponderosa pine forests depends upon fire-history
data that are potentially biased and more uncertain than generally recognized (Minnich et al. 2000, Baker and Ehle 2001). Problems include a lack
of modern calibration, inappropriate measures, targeted sampling, absence of
fire-severity evidence, and insufficient treatment of variability and uncertainty
(table 1). Some of these problems may be resolved quickly, while others will
require longer study or may never be resolved. Here we highlight a few of the
problems, suggest some remedies, and provide some thoughts regarding restoration of fire, given these problems.
No Modern Calibration
A significant problem plaguing fire-history research is a lack of modern
calibration. Pollen studies, fire-history studies, and other paleo-ecological studies require calibration to determine whether evidence is preferentially preserved
or lost and how it can be interpreted. Little is known about how fires leave
evidence in the landscape over time. There is no way of knowing, without
observing actual fires over time, whether it is possible to accurately reconstruct parameters (e.g., mean fire interval) of the fire regime from fire scars,
and, if so, how to sample to best accomplish this. Calibration may allow corrections to be derived that enable reasonably accurate reconstructions.
One calibration approach might be to use fire boundaries reconstructed
using aerial photographs (e.g., Minnich et al. 2000) or use other historical
records, such as atlases of past fires. This would be particularly valuable if
multiple approaches to sampling on the ground were compared to aerial-photo
USDA Forest Service Proceedings RMRS-P-29. 2003.
1
Department of Geography and Recreation,
University of Wyoming, Laramie, WY.
319
Baker and Ehle
Uncertainty in Fire History and Restoration of Ponderosa Pine Forests in the Western United States
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Table 1—Some limitations, potential biases, and uncertainties in fire-history studies in ponderosa
pine forests.
No modern calibration
Only know that some historical fires can be detected
Biases
Targeted sampling
Trees with multiple fire scars
Places with high fire-scar densities
Old trees or forests with long fire records; avoid young trees and forests
Trees with open scars
Fire severity unstudied, but assumed to be low
Necessary age-structure data not collected
Analysis and treatment of fire-scar data
Recorder trees-do they work?
Only scar-to-scar intervals included
Compositing is biased toward smaller fires
Uncertainties
Fire perimeters unknown
Fire record is uncertain due to unrecorded fires and unburned area within fire perimeters
Variability in fire-intervals is large and seldom explicitly treated
Large variability means sample sizes provide insufficient power for comparisons
Bracketing and confidence intervals are warranted
or map estimates. However, photographs and historical sources also have limitations and biases. Small fires may be undetectable in typical aerial photographs,
and dating to single years is usually not possible (Minnich et al. 2000). There
is no research program at the present time to actually undertake this calibration work, but it is surely needed.
In lieu of calibration, all that can be done is to work with sampling designs,
sample sizes, and analysis techniques to see how the sampling estimates vary
relative to a more complete sample. Some of this relative comparison work has
been underway (Baker and Ehle 2001), but even this work is in its infancy.
New sampling designs are being proposed and studied (e.g., Arno et al. 1993,
Heyerdahl et al. 2001). There are promising signs that in a few years we will
know how to sample in the most efficient, unbiased manner.
Potential Biases and Uncertainties
Targeting Multiple-Scarred Trees
Fire-history researchers have seldom sampled randomly or in an unbiased
manner. Instead, they typically and purposely seek trees containing multiple
scars and places that contain high scar densities (table 1). These are assumed
to increase the length of the record and maximize identification of the fires
that burned a stand. However, no study has actually compared the fires identified through targeting with those on non-targeted trees, or examined the
effects of targeting on estimates of fire intervals in ponderosa pine forests.
To compare how targeted and non-targeted trees record fires and fire intervals, we sampled all visible scars on trees in nine plots randomly placed within
the ponderosa pine zone in Rocky Mountain National Park (Ehle and Baker,
in press). A total of 137 scarred trees was sampled. All fire scars were visually
crossdated using a master chronology. Most trees had a single fire scar, but six
trees had four or more scars per tree (figure 1). Trees with four or more scars
are those that typically would have been selected for sampling using a targeting approach, based on a review of ponderosa pine fire histories (Baker and
Ehle 2001). These six trees contained a total of 35 fire scars. We randomly
selected an equal sample of 35 scars from trees that would not have been
320
Baker and Ehle
Figure 1–Percentage of sampled, firescarred trees (n=137) that have one
or more than one scar per tree. The
number of trees is listed above each
bar.
targeted (trees containing �3 scars). A third sample of 35 scars was obtained
from single-scarred trees. Individual trees did not occur in more than one of
these samples.
Then, we separated the fires that were identified by these scars into five
combined size and severity classes (figure 2; see also Ehle and Baker, in press).
Low-severity fires leave numerous surviving trees, while mixed-severity fires
leave only a few survivors in a plot, or are high-severity fires in part of a landscape and low-severity elsewhere (Ehle and Baker, in press). Small fires in this
study scar more than one tree, and are not known to have spread beyond a
50 m X 50 m plot, but could have been as large as 1.2 km2. Large fires burned
>1.2 km2.
The targeted sample identified more fires (n = 29) than did the singlescarred trees (n = 20) or the non-targeted sample (n = 16) even though the
Figure 2–Effects of targeted sampling
on the number of detected fires for
fires of different sizes and severities.
Small fires likely do not exceed the
area of a sampling plot (50 m X 50
m), while large fires burn > 1.2 km2.
321
Baker and Ehle
322
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number of scars was 35 in all cases. The fires identified by the samples can be
compared to the total set of 60 fires identified by the 137 scarred trees in the
nine sampled plots (“Whole Dataset” in figure 2). The targeted sample generally
identified more of the small fires affecting only one tree and the small, lowseverity fires, while the non-targeted sample and single-scarred trees identified
few one-tree fires, but did as well or slightly better at identifying large,
low-severity fires and mixed-severity fires (figure 2). Seventeen one-tree fires
occurred in the nine plots (each of 0.25 ha) over a period of about 300 years,
which is a rate of about one tree/ha scarred by fire every 40 years, an insignificant amount. If one-tree fires are ignored, there is not much difference among
the samples in ability to detect fires of different size and severity.
However, an important difference is that the targeted sample comes from
only six trees, while the single-scarred sample comes from 35 trees. Less effort
is required to obtain the 35 scars from only six trees than from 35 singlescarred trees. However, 35 trees provide a much better spatial sample of where
the fires burned, thus making it possible to more correctly identify fire size
and severity (if age-structure data are also collected). If 35 trees can be sampled
in either case, many more fires will be detected with a targeted sample of trees
containing >4 scars than with a sample of single-scarred trees.
In our review (Baker and Ehle 2001), we expressed concern that fire intervals identified in a targeted sample might be much shorter on average than in
a non-targeted sample. To test this, we used the same sets of samples from
targeted, non-targeted, and single-scarred trees, each sample containing 35
fire scars. Then, we listed all the fires and fire intervals identified by each
sample of 35 fire scars, and used an ANOVA (done using Minitab 12.1; Minitab,
Inc. 1998) to test the null hypothesis that the mean fire interval for small,
low-severity fires is equal regardless of sampling technique. While fire-interval
data can have non-normal distributions, parametric statistical tests remain valid
(Johnson 1995). We repeated the ANOVA for large, low-severity fires. Comparisons for mixed-severity fires are not possible due to small sample sizes
(figure 2). The null hypothesis cannot be rejected for small, low-severity fires
(F = 0.21, p = 0.810) or large, low-severity fires (F = 0.00, p = 0.997).
While the sample from multiple-scarred trees may not be biased in this
regard, multiple-scarred trees alone will not identify all the fires in a stand.
Three of the 60 fires were only found on single-scarred trees, five were only
on double-scarred trees, and three were only on triple-scarred trees, all of
which would be missed if trees containing four or more scars were targeted.
Of these 11 fires (18% of the 60 fires), two were one-tree fires (figure 2), but
eight were small, low-severity fires, while one was a significant high-severity
fire. Three of these 11 fires occurred near or before AD 1700 and documented 30% of the 10 ancient fires found in the study area. Researchers seeking
complete fire histories or long fire histories will miss important fires and ancient fires if only multiple-scarred trees are sampled, at least in this study area.
We conclude that targeting multiple-scarred trees in this case study does
not produce a biased estimate of the fires that occurred in a larger sample or a
biased estimate of the mean fire interval relative to that found with other
samples. However, fire histories derived from targeted sampling may be
incomplete, particularly missing some important fires and ancient fires.
However, this one small study is insufficient to draw strong conclusions
about targeting. Fire intervals in this case study are quite variable, and the
test, as a result, may not have much statistical power. Further testing is needed
before these results are applied elsewhere. The other potentially significant
targeting biases (Baker and Ehle 2001) also need testing. Moreover, until
there is a modern calibration, the possibility remains that these sampling
Baker and Ehle
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approaches simply produce equally biased estimates of fire intervals and other
parameters of fire regimes.
Crown Fires and Mixed-Severity Fires Not Sampled in
Ponderosa Pine Forests
If restoration of fire in ponderosa pine forests is to be successful, historical
variability in fire severity must also be known. The evidence needed to determine fire severity is a combination of fire-scar data and age-structure data near
each scar. Low-severity fires generally lead to low mortality of larger, established trees. High-severity fires can lead to pulses or a cohort of post-fire
regeneration (Ehle and Baker, in press). A mixed-severity fire has a high-severity, crown-fire component and an associated low-severity component.
A fire scar alone, or even multiple fire scars across a landscape, reveal little
about the severity of the fire. Fire scars indicate only that a fire was on the
surface at the scarred tree itself. This tree could be a lone survivor of a fire that
was in the crown of every other tree in the surrounding landscape. Scattered
surviving trees are not uncommon in crown-fire landscapes (e.g., Kipfmueller
and Baker 1998). The fire may have also have been mixed-severity, burning
on the surface over a part of the landscape where the scar was found, and then
crowning out in patches (e.g., Huckaby et al. 2001).
The idea that surface fires predominate in ponderosa pine forests has been
so pervasive that fire-history researchers commonly study fires in these forests
without collecting age-structure data, then erroneously conclude that it is
known that surface fires predominate or that crown fires did not occur. Some
researchers have even implied that, if fire-scars are present and ponderosa pine
is present, this indicates that the fire regime sustained only low-severity surface fires (Heyerdahl et al. 2001). This is false, as crown fires in ponderosa
pine forests can be followed within a few decades by surface fires as the stand
develops (Ehle and Baker, in press).
Thirty-nine studies constitute nearly all the published scar-based fire-history research on pure ponderosa pine forests in the western United States
(Baker and Ehle 2001). Only nine of the 39 collected the age-structure data
needed to determine whether fire severity was low, medium, or high (table 2).
Four other studies collected age structure, but not fire-scar data. These 13
studies with age-structure data reveal three general patterns. First, some studies of small areas or plots reveal an uneven age structure, often with apparent
pulses of regeneration separated by gaps in regeneration, suggesting an absence of crown fires. Regeneration pulses in these plots are sometimes linked
to variations in surface-fire frequency (Arno et al. 1995, 1997; Morrow 1986)
or a combination of fire and climate (Cooper 1960), or they cannot presently
be explained (Mast et al. 1999, White 1985). Second, some plots contain an
even age structure, characterized by large pulses of regeneration commencing
after a date identified on a nearby fire scar, suggesting a crown fire at the level
of the plot (Arno et al. 1995, 1997; Mast et al. 1998). Brown and Sieg (1996)
thought that ages of scarred trees in one plot were roughly synchronous, suggesting a possible crown fire or a climatic event. Age data (apparently collected
but not presented) suggest that infrequent stand-replacing fires occurred in
some parts of two study areas prior to EuroAmerican settlement (Barrett 1988,
Swetnam and Baisan 1996b).
Third, more extensive landscape-scale studies that include multiple plots
across an area of a few thousand hectares have revealed a mixed- or highseverity fire regime in the pre-EuroAmerican era. This was found in pure
ponderosa pine landscapes of Rocky Mountain National Park, Colorado (Ehle
323
Baker and Ehle
Table 2–Evidence of mixed-severity and high-severity (crown) fires in the pre-EuroAmerican period from studies of ponderosa pine
fire history and age structure in the western United States.
Age
dataa
Fire
scar
data
Northwestern U.S.
Bork 1984
Heyerdahl 1997
Morrow 1986
No
No
Yes
No
No
No
Yes
Yes
Yes
Sherman 1969
Soeriaatmadja 1966
Weaver 1943
No
No
No
No
No
Yes
Yes
Yes
No
Northern Rockies
Arno 1976
Arno and Petersen 1983
Arno et al. 1995
Arno et al. 1997
No
No
Yes
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Barrett 1988
Freedman and Habeck 1985
Steele et al. 1986
Yes
No
No
No
Yes
No
Yes
Yes
Yes
Black Hills
Brown and Sieg 1996
Scars
No
Yes
Brown and Sieg 1999
Brown et al. 2000
Shinneman and Baker 1997
No
No
No
No
No
Yes
Yes
Yes
No
Comments on crown fires
No
They did not occur because surface fires did occur.
No, uneven age structure with pulses of regeneration linked to low fire
frequency
No
Yes, they probably occurred on higher elevation, more moist sites
Yes, direct observation of even-aged stands suggesting past crown fires.
No
No
Yes, one stand of six dry-site stands and some wet-site stands
Some dry-site ponderosa pine forests must have experienced
occasional stand replacement fires
Yes, infrequent stand-replacing fires are possible in upper elevations
Yes, early historical observations suggest they occurred
Yes, hypothesizes that they occurred in the past during periods of drought
and high winds.
Yes, they were possible, but not verified; climate an alternative cause of
regeneration events
No
No
Historical records document large stand-replacing fires,
particularly in the moister northern Black Hills
Southern Rockies
Brown et al. 1999;
Kaufmann et al. 2000;
Huckaby et al. 2001
Brown et al. 2000
Ehle 2001; Ehle and Baker,
in press
Goldblum and Veblen 1992
Laven et al. 1980
Mast et al. 1998
Yes
No
No
No
Yes
Yes
Yes, 71% of sampled polygons had stand-replacing fires
No
Yes
No
No
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Rowdabaugh 1978
Skinner and Laven 1982
Veblen and Lorenz 1986, 1991
No
No
Yes
No
No
Yes
Yes
Yes
No
Veblen et al. 2000
No
Review
Yes
Yes, in 6 of 9 plots
Yes, but only in post-settlement
No
Even-aged cohorts and post-fire pulses of establishment, but linked to
gaps or spot fires (crown fires)
No
No
Age structures and early photographs that show crown fires that occurred
near or before EuroAmerican settlement
Yes, early photographs show them, and fire intervals are long enough to
allow them at higher elevations
Southwestern U.S.
Cooper 1960
Yes
Yes
No
Dieterich 1980a
Dieterich 1980b
Dieterich and Hibbert 1990
Fule et al. 1997
Grissino-Mayer 1995
Madany and West 1980
Mast et al. 1999
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
McBride and Jacobs 1980
McBride and Laven 1976
Morino 1996
Savage 1989; Savage and
Swetnam 1990
Stein 1988
Swetnam and Baisan 1996a
Swetnam and Baisan 1996b
No
No
No
No
No
No
Yes
Yes
Yes
No
No
No
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Swetnam and Dieterich 1985
Touchan et al. 1995
Touchan et al. 1996
White 1985
No
No
No
Yes
No
No
No
No
Yes
Yes
Yes
No
a
b
324
Historical
datab
No evidence of crown fires except possibly on a part of the Prescott
National Forest
No
No
No
No
No
No
Same site studied by White (1985); uneven age structure with pulses of
regeneration not clearly linked to either climate or fire.
No
No
No
No
No
No
Yes, some evidence in dates of tree mortality and tree recruitment relative to
fires synchronous over large areas
No
No
No
No, uneven age structure with pulses of regeneration
Sufficient tree age data to be able to identify a crown fire in the pre-EuroAmerican period.
Early photographs or historical observations from near or before settlement by EuroAmericans.
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and Baker, in press) and in mixed-conifer landscapes with considerable ponderosa pine dominance at Cheesman Lake, Colorado (Brown et al. 1999,
Kaufmann et al. 2000, Huckaby et al. 2001). In the Rocky Mountain National
Park study, six of nine plots had stand-replacing fires and another plot had a
stand-replacing event caused by an unidentified agent (Ehle and Baker, in
press). In the Cheesman Lake study, 71% of sampled polygons had standreplacing fires (Huckaby et al. 2001). Fires in both landscapes often were
mixed-severity at the landscape scale, burning as surface fires in some areas
and then crowning over other areas. Both studies reported that smaller parts
of these landscapes contained uneven-aged stands with no evidence of crown
fires for the past few hundred years.
Studies that use historical records or early photographs also found that crown
fires occurred in some ponderosa pine forests, but not others, prior to
EuroAmerican settlement (table 2). Shinneman and Baker (1997) reviewed
historical evidence of extensive crown fires in the moister parts of the Black
Hills, and Freedman and Habeck (1985) also noted historical evidence of
crown fires prior to EuroAmerican settlement in a valley in western Montana.
In early historical photographs Veblen and Lorenz (1991) could see ponderosa pine landscapes that were burned in stand-replacing fires some time before
EuroAmerican settlement. Cooper (1960) reported that a review of early
literature failed to find evidence of crown fires in ponderosa pine forests in
Arizona before 1900, except on part of the Prescott National Forest. There is
no further explanation of the Prescott case. Weaver (1943 p. 9), describing a
broad region in the Pacific Northwest, simply stated that “extensive evenaged stands of ponderosa pine can probably be accounted for by the past
occurrence of severe crown fires, by severe epidemics of tree-killing insects...or
by the occurrence of extensive windthrows...” A more extensive review of
early historical reports and photographs might reveal where stand-replacing
fires had or had not occurred prior to EuroAmerican settlement.
For most of the ponderosa pine forests of the western United States there
are no data at all that would allow a determination of whether crown fires or
mixed-severity fires were present or absent before EuroAmerican settlement,
or have increased or decreased. Where studies have been done or historical
data were examined, crown fires or mixed-severity fires were sometimes found
and sometimes not (table 2). There is a hint in these data that crown- or
mixed-severity fires may occur on moister sites in the ponderosa pine zone.
No one, in any study anywhere in the West, has yet estimated how frequent
crown- or mixed-severity fires were in ponderosa pine forests, how large these
fires may have been, or what the fire rotation for these fires might have been
prior to EuroAmerican settlement. The data are perhaps there to allow this
estimation for study sites at Cheesman Lake, Colorado (Huckaby et al. 2001)
and in Rocky Mountain National Park (Ehle and Baker, in press). These study
areas, however, are small relative to the size of some recent fires (e.g., Hayman
Fire, 2002). Larger areas have been logged or burned, destroying the evidence of past fires. It may be difficult or impossible to determine whether
large, high-severity fires did or did not occur in ponderosa pine landscapes
prior to EuroAmerican settlement.
Given the lack of data, there is little basis for the general perception that
high- or mixed-severity fires, such as the 2000 fire that burned into Los Alamos,
New Mexico, are not natural in ponderosa pine forests (Allen 2002). The
conclusion that a particular fire is unnaturally severe is premature given the
absence of the necessary data. For nearly all the ponderosa pine forests in the
western United States it would also be premature to suggest that treatments
that lower the probability of crown fire or high-severity fire or lower fire risk
325
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are “restoration.” For most of the range of ponderosa pine in the West it is
not yet known whether these kinds of fires were or were not a part of the preEuroAmerican fire regime. Where crown fires occurred, thinning may be an
inappropriate restoration technique, just as it is inappropriate in some pinyonjuniper woodlands (Romme et al., this volume). In some cases, restoration
might even require reintroduction of high-severity fires, if they were unnaturally suppressed.
Analysis and Treatment of Fire-Scar Data
Recorder Trees—Do They Work?
It has long been thought that until a tree receives a fire scar, it is a poor
recorder of fires. Thus, fire historians often do not consider a stand to be
generally capable of recording the fires that occur in a stand until after some
number of trees has received a first scar (e.g., 3; Grissino-Mayer 1995). The
idea of a previously scarred “recorder tree” is that if there is an open scar,
subsequent fires should be more effectively recorded than if fires must produce the first scar. If recorders work, fires should show up more often on
recorder trees than as a first scar.
In our complete sample from 137 scarred trees, we found 60 fires. Nineteen of these fires (31.7%) show up only as first scars, while 17 fires (28.3%)
show up only as scars after the first scar (i.e., on recorder trees). This result
could occur if previously scarred trees are actually no better recorders or if
different fires affected the recorder trees and the trees with first scars. However, 24 fires (40%) show up as a mixture of first scars and scars on recorder
trees. Ninety-six of the 154 total scars (62%) documenting these 24 fires are
first scars while only 58 of the 154 scars (38%) occur on recorder trees. A
chi-square test leads to rejection of the null hypothesis that recorder trees and
trees without scars are equal recorders of fires when fires show up on both
( � 2 = 4.761, p = 0.029). In our study area, previously scarred trees are poorer
recorders of fire than are unscarred trees. Previously scarred trees do not perform as commonly expected, perhaps because multiple factors influence whether
a fire produces a scar. Smaller trees, for example, typically have thinner bark,
which offers less resistance to scarring, perhaps making them better recorders
than are larger trees. Our results suggest that if a complete history is desired,
fire-history data should be collected and used whether a tree is or is not previously scarred. Fire-history studies that only use recorder trees may miss a
significant part of the fire history.
Which Intervals Should Be Used?
Fire historians nearly always have focused on scar-to-scar (SS) intervals recorded on trees, omitting the interval between tree origin and the first scar
(OS interval; Baker and Ehle 2001) as well as the interval between the last scar
and tree death or the present. Yet, the OS interval estimates the real fire-free
interval needed for trees to reach a size sufficient to survive surface fires (Baker
and Ehle 2001). Since the OS interval does not necessarily begin with a fire,
the real fire-free interval may be underestimated by the OS interval.
The OS interval is typically longer than the SS interval (Baker and Ehle
2001). In our sample of 137 fire-scarred trees from Rocky Mountain National
Park’s ponderosa pine zone (figure 3), the pre-EuroAmerican OS interval on
individual trees (n = 71) has a mean of 55.4 years and an estimated median of
51.5 years. The pre-EuroAmerican SS intervals on individual trees (n = 40), in
326
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Figure 3–Distribution of preEuroAmerican fire-scar intervals
for individual trees from a sample
of 137 fire-scarred trees in
ponderosa pine forests of Rocky
Mountain National Park.
contrast, have a mean of 33.3 years and an estimated median of 28.5 years.
The estimated difference in means is 22.1 ±13.2 years (95% confidence interval). The regression equation in Baker and Ehle (2001) for estimating the OS
interval, if only the SS interval is known, suggests that the mean OS interval
would be 53.5 years for a mean SS interval of 33.3 years, reasonably close to
the 55.4 years actually found. The OS interval should be included as a real fire
interval, and including it generally lengthens the estimated mean fire interval
by about 1.6 times (Baker and Ehle 2001).
Compositing Biased Toward Small Fires
The mean “Composite Fire Interval” or CFI (Dieterich 1980a) is the traditional measure of central tendency in fire intervals, but this measure is flawed
as a general measure of the fire regime (Baker and Ehle 2001). One problem
is that the CFI pools fires of different extent and frequency. Regardless of the
real mean fire interval in a landscape, the mean CFI decreases as the number
of sampled fire-scarred trees and sampled area increase (Arno and Petersen
1983). The reason is that the numerous fires that scar only one tree (e.g.,
figure 2) are counted the same as an infrequent fire that scars many trees
(Minnich et al. 2000, Baker and Ehle 2001). By adding sampling area or
sampled trees, one quickly adds these apparently small fires. As a result, a CFI
can be interpreted as mostly reflecting the frequency of small fires that affect
little of the landscape.
A remedy for this shortcoming of a CFI is to analyze and report fire intervals separately for individual classes of fire size. Laven et al. (1980) may have
been the first to use this approach for ponderosa pine forests when they reported separate intervals for small fires and large fires. Bork (1984) showed
means and standard errors for fires varying in size from 1 plot to 5 plots
(figure 4). Morino (1996) calculated separate fire-interval distributions and
descriptive parameters (e.g., mean) for small fires, medium fires, and large
fires. Mean fire intervals for larger fires in ponderosa pine forests are 41.7
years (Laven et al. 1980), 60-150 years (Bork 1985 and figure 4), and 24.4
years (Morino 1996), while the corresponding mean fire intervals for small
fires in these studies are 20.9 years, 5.25 years, and 2.7 years, respectively.
Thus, larger fires in these cases have mean intervals that are 2-10 times as long
as are mean intervals for smaller fires. These estimates are imprecise, but illustrate that the mean fire interval for the fires that do most of the work in
ponderosa pine forests is much longer than suggested by typical CFIs.
327
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Figure 4–Mean return interval for fires of different size from three sites in eastern Oregon,
estimated by proportion of plots having at least two fire-scarred trees. Reproduced from Bork
(1985) Figure I-24 with permission from Joyce L. Bork.
Our review of 11 studies in the western United States show that about 50%
of known fires are documented by a scar on only one tree (Baker and Ehle
2001). Given that these fires affect little land area, but dominate the CFI, we
particularly suggest that the frequency of one-tree fires should be reported
separately.
The idea that there is value in reporting intervals for fires of different sizes
underlies the now-popular reporting of interval data for all fires compared to
those that scar >10% or >25% of recorder trees (Grissino-Mayer 1995). However, it is not progressive restriction (sizes exceeding a certain size) that is
needed, but separate reporting of intervals for each size class. Reporting a CFI
for study areas of increasing size (e.g., Brown et al. 1999) is also not what is
needed, as it is well known that CFIs decrease as study area size increases, even
if the fire regime is the same across scales (Arno and Petersen 1983).
Separating fire-intervals by fire size also allows estimation of the fire rotation, a fundamental measure of the fire regime (Minnich et al. 2000, Baker
and Ehle 2001). Data on the relative frequency and importance of fires of
different sizes are invaluable for fire managers, as this information can be used
directly in prescribed burning plans, regardless of the size of the management
area. This is not the case for the traditional CFI, which is heavily dependent
on the size of the study area in which the CFI was calculated (Arno and Petersen
1983, Baker and Ehle 2001).
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What are appropriate fire size classes to use? Even reporting intervals based
on number of affected plots (figure 4), or linear distances between plots, would
be an improvement over a traditional CFI. Size classes used by the U.S. Forest Service and other agencies would be advantageous, as data from fire-history
studies could then be compared to contemporary data from monitoring programs. Where fire-history data are insufficient to make fine distinctions in fire
size, pooling of adjacent categories would still allow useful comparisons with modern
data, particularly if small fires are segregated from large fires.
Unfortunately, it is difficult to estimate the size of surface fires using fire
scars. Grid-based or random sampling methods are increasingly making it
possible to approximate fire extent (Arno et al. 1993, Heyerdahl et al. 2001,
Morino 1996). However, there is not yet a calibration to guide correction of
size estimates from spatial sampling networks or sufficient study of appropriate spatial sampling designs for detecting fire sizes. Until this calibration and
sampling design work is done, a method to bracket the potential uncertainty
associated with assigning fires to size classes is needed.
Uncertainties
Fire intervals vary, and this variability is often large within a single tree and
among all the intervals within a stand (e.g., figure 3). This variability suggests
that fire intervals are not predictable results of the time for fuel to build up
after a fire; fire intervals are shaped by the timing of weather that promotes
fine-fuel accumulations and the timing of droughts (Veblen et al. 2000). This
variability in fire intervals makes comparison of sets of fire intervals from different periods or different sites difficult, as sample sizes must be large to be
able to detect even 50% or 100% differences in mean with adequate statistical
power (Baker and Ehle 2001). However, few researchers have actually used
statistical inference, instead simply presenting the sample data. Previous evidence that fire intervals have changed over time or differ among sites may not
bear up under statistical analysis, except where the change is obvious, as when
fires appear to virtually stop near or after settlement (e.g., Savage and Swetnam
1990).
Fire-interval data also have uncertainty that comes from at least two sources–
unrecorded fires and unburned area within fire perimeters. There is presently
no method to estimate the magnitude of these sources of uncertainty in a
particular stand or area. Baker and Ehle (2001) thus suggest that all estimates
of mean or median fire intervals should be bracketed using the restricted
(>10% scarred) CFI and individual-tree mean fire intervals. However, if fire
intervals are reported separately by fire size, as we recommend here, then the
appropriate brackets for the estimate of mean fire interval for a stand are the
unrestricted composite and individual-tree fire intervals.
Implications for Restoration
Fire-history research methods are in need of reassessment, as traditional
measures are misleading or in error as sources of information useful for designing a program for restoring fire in ponderosa pine forests. The time that it
took for fire to burn through these forests prior to EuroAmerican settlement
is much longer than is implied by typical composite fire intervals, which have
been reported to be between 2-25 years (Baker and Ehle 2001). The large
fires, that actually account for most burned area, occur at intervals that are
several times longer than reported composite fire intervals. Baker and Ehle
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argued that the population mean fire interval in western ponderosa pine forests is instead more likely to lie between 22-308 years. However, until there is
a modern calibration and further testing of the potential biases and uncertainties we have identified, it would be premature to draw strong conclusions
about what the fire intervals were in pre-EuroAmerican ponderosa pine forests.
Our analysis suggests that repeated prescribed burning of large areas of
ponderosa pine forests at short intervals (e.g., less than 20 years) lacks a sound
basis in science, and should not be done at the present time if the goal is
restoration. In most parts of the western United States there is also insufficient evidence to support the idea that mixed- or high-severity fires were or
were not absent or rare in the pre-EuroAmerican fire regime. Thus, programs
to lower the risk of mixed- or high-severity fires in ponderosa pine forests
(e.g., the National Fire Plan, Laverty and Williams 2000) have insufficient
scientific basis if the goal is restoration.
Fire practitioners interested in restoration can certainly proceed with reintroducing fire into these forests on a limited basis, however. In many areas,
fire has been excluded by livestock grazing or intentional suppression for a
long period. We suggest that prescribed burning a large area once is not likely
to push the ecosystem outside its historical range of variability. Reintroduction of small prescribed fires that burn a single tree or a few trees in a landscape
is also appropriate, at least in our study area. However, prescribed burning of
large land areas after short intervals (e.g., <20 years) has little scientific basis at
the present time, if the goal is to restore the natural variability of the
pre-EuroAmerican fire regime.
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334
Implications of spatially extensive historical data from surveys
for restoring dry forests of Oregon’s eastern Cascades
WILLIAM L. BAKER Program in Ecology and Department of Geography, Department 3371, 1000 E. University Avenue, University of Wyoming,
Laramie, Wyoming 82071 USA
Citation: Baker, W. L. 2012. Implications of spatially extensive historical data from surveys for restoring dry forests of
Oregon’s eastern Cascades. Ecosphere 3(3):23. http://dx.doi.org/10.1890/ES11-00320.1
Abstract.
Dry western forests (e.g., ponderosa pine and mixed conifer) were thought to have been
historically old and park-like, maintained by low-severity fires, and to have become denser and more
prone to high-severity fire. In the Pacific Northwest, early aerial photos (primarily in Washington), showed
that dry forests instead had variable-severity fires and forest structure, but more detail is needed. Here I
used pre-1900 General Land Office Surveys, with new methods that allow accurate reconstruction of
detailed forest structure, to test eight hypotheses about historical structure and fire across about 400,000 ha
of dry forests in Oregon’s eastern Cascades. The reconstructions show that only about 13.5% of these forests
had low tree density. Forests instead were generally dense (mean ¼ 249 trees/ha), but density varied by a
factor of 2–4 across about 25,000-ha areas. Shade-tolerant firs historically were 17% of trees, dominated
about 12% of forest area, and were common in forest understories. Understory trees and shrubs dominated
on 83.5%, and were dense across 44.8% of forest area. Small trees (10–40 cm dbh) were .50% of trees
across 72.3% of forest area. Low-severity fire dominated on only 23.5%, mixed-severity fire on 50.2%, and
high-severity fire on 26.2% of forest area. Historical fire included modest-rotation (29–78 years) lowseverity and long-rotation (435 years) high-severity fire. Given historical variability in fire and forest
structure, an ecological approach to restoration would restore fuels and manage for variable-severity fires,
rather than reduce fuels to lower fire risk. Modest reduction in white fir/grand fir and an increase in large
snags, down wood, and large trees would enhance recovery from past extensive logging and increase
resiliency to future global change. These forests can be maintained by wildland fire use, coupled, near
infrastructure, with prescribed fires that mimic historical low-severity fires.
Key words: Cascade Mountains; dry forests; fire history; historical forest structure; land surveys; mixed-conifer forests;
Oregon; Pinus ponderosa; restoration; variable-severity fire.
Received 10 November 2011; revised 4 January 2012; accepted 6 February 2012; published 7 March 2012. Corresponding
Editor: F. Biondi.
Copyright: Ó 2012 Baker. This is an open-access article distributed under the terms of the Creative Commons Attribution
License, which permits restricted use, distribution, and reproduction in any medium, provided the original author and
sources are credited.
E-mail: [email protected]
INTRODUCTION
In the Pacific Northwest, restoring dry forests is
important in part because they provide habitat
for species, such as the Northern Spotted Owl
(Strix occidentalis caurina), that are declining and
the subject of recovery actions (USFWS 2011).
Uncharacteristic high-severity fires were thought
to be threatening these forests and the owl (e.g.,
Spies et al. 2006). However, recent research
Until recently, dry western forests were
thought to have been historically open, maintained by low-severity fire, to have become
denser from EuroAmerican livestock grazing,
logging, and fire exclusion, and to require
restoration (e.g., Covington and Moore 1994).
v www.esajournals.org
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March 2012 v Volume 3(3) v Article 23
BAKER
Table 1. Tree-ring reconstructions, counts of extant trees, and early scientific observations of tree density in dry
forests in and near the Oregon eastern Cascades province.
Author(s)
Tree-ring reconstructions
Youngblood et al. (2004)
Morrow (1986)
Youngblood et al. (2004)
Perry et al. (2004)
Agee (2003b)
Ponderosa pine variant
Sugar pine variant
Extant trees and stumps
Merschel (2010)
Early scientific observations
Munger (1917)
Location
Reconstructed value
Metolius Research Natural Area, 60 km
northwest of Bend
Pringle Falls Experimental Forest, 40 km
southwest of Bend
Pringle Falls Experimental Forest, 40 km
southwest of Bend
Mount Bachelor volcanic chain, 30 km
southwest of Bend
34–94 trees/ha in ponderosa pine Crater Lake
Crater Lake
348 trees/ha in dry mixed conifer}
170 trees/ha in dry mixed conifer}
North Deschutes National Forest
South Deschutes National Forest
58 trees/ha in dry mixed conifer#
55 trees/ha in dry mixed conifer#
Embody, 50 km SE of Lapine
Near Lapine
Klamath Lake Region
136 trees/ha in ponderosa pinejj
33 trees/ha in ponderosa pinejj
152 trees/ha in dry mixed coniferjj
167 trees/ha in ponderosa pineà
35–79 trees/ha in ponderosa pine 40–80 trees/ha in ponderosa pine in eight
stands, ,30 trees/ha in 5 stands§
These estimates are for only present ‘‘upper-canopy’’ trees in these forests, which likely underestimate the total number of
trees .10 cm present in A.D. 1900.
à This is the mean of the pre-1886 trees, to be compatible with the survey dates, present in two stands sampled by Morrow,
based on his figures (Morrow 1986: Figs. 8–11).
§ This estimate is for ‘‘trees .150 yrs plus large stumps’’ and likely underestimates the A.D. 1900 historical tree density; data
are from Perry et al. (2004: Fig. 2).
} These forests are described by Agee as dry mixed conifer, but the abundance of pre-EuroAmerican white fir could suggest
they are moist mixed conifer. Stands with white fir numerically dominant, as they are in these stands, were generally excluded.
# These estimates, from Merschel’s Table 11, are only for extant trees and extant stumps, so they likely underestimate the
number of trees present before EuroAmerican settlement.
jj These estimates are only for trees, in Munger’s tables, that were .10 cm in diameter.
suggested dry forests and fire in the Northwest
were variable historically (Hessburg et al. 2007),
and the fraction of fire that burned at high
severity lacks a recent upward trend (Hanson et
al. 2009). However, detailed reconstructions of
the variable historical structure and fire are not
yet available to provide a reference framework
for interpreting recent fire or for guiding restoration and management.
In dry forests of Oregon’s eastern Cascades, the
subject of this study, Weaver (1943) first suggested that fire exclusion since about A.D. 1900 was
leading to: (1) dense stands of ponderosa pine
regeneration and shade-tolerant trees, formerly
killed by surface fires, beneath mature pines, (2)
increased mortality of mature trees by beetles,
because of competitive stress from dense regeneration and (3) increased fuels and unnaturally
severe fires, leading to brush fields. He characterized historical forests as ‘‘... like a park, with
clean-boled trees and a grassy forest floor’’ and
with sparse understories: ‘‘a few small bushes of
bitterbrush still persist in the larger openings’’
(Weaver 1961:571). Weaver’s hypotheses have
been supported, elaborated, and modified by
much subsequent research, reviewed in the mid1990s to mid-2000s (Agee 1993, 1994, 2003a,
Youngblood 2001, Hessburg and Agee 2003,
Hessburg et al. 2005).
Although historical structure and fire in
Oregon’s eastern Cascades forests have been
studied, most evidence is from scattered anecdotal early accounts and observations (Appendix
A) and only six scientific studies (Table 1).
Weaver’s ideas about fire exclusion were even
criticized for limited evidence, in appended
comments by A. A. Brown, who said ‘‘overstocked and stagnating stands seem so far from
typical of the region for which he speaks that one
wonders if Mr. Weaver is not generalizing too
much from a single area’’ (Weaver 1943:14). In
contrast to Arizona, where many tree-ring
reconstructions of historical forest structure exist
(e.g., Abella and Denton 2009), tree-ring reconstructions in Oregon’s eastern Cascades are
limited (Table 1).
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BAKER
Oregon with spatially extensive data, and is still
considered an appropriate restoration framework
for the Northwest (Johnson and Franklin 2009).
Also, the Hessburg et al. study could not address
some hypotheses (H2–H5 below). Note that it is I,
not authors, who provided specific quantitative
criteria (e.g., 10%) for qualitative phrases (e.g.,
rare, minor, relatively free, dominated by), so
that hypotheses could be quantitatively tested. I
tried to choose reasonable criteria, but err a little
on the side of generosity toward the hypotheses.
H1 is supported by evidence in Weaver (1943,
1959, 1961), Agee (2003a), Hessburg and Agee
(2003), Wright and Agee (2004), Youngblood et
al. (2004), Hessburg et al. (2005), and by some
early observations (Appendix A: Q4, Q45, Q47,
Q49, Q50, Q52, Q53). Many tree-ring reconstructions support this hypothesis (Table 1), and it is
also supported by the logical inference that lowseverity fires would have kept tree density low
(e.g., Youngblood 2001, Hessburg et al. 2005). H2
is supported by evidence in Hessburg and Agee
(2003), Perry et al. (2004, 2011), Hessburg et al.
(2005), and Spies et al. (2006), and two early
observations (Appendix A: Q65, Q67). Support
was not primarily evidence of the actual historical abundance of shade-tolerant trees, but
instead the logical inference that low-severity
fires would have kept these trees rare, and
observation that they increased after EuroAmerican settlement (e.g., Youngblood 2001, Hessburg
et al. 2005, Johnson et al. 2008). However, early
descriptions from Forest-Reserve reports or
survey data do show shade-tolerant trees were
rare in some dry forests in eastern Washington
(Camp et al. 1997, Wright and Agee 2004), but
were 20% of trees in others (MacCracken et al.
1996). The related H3 is from Morrow (1986) and
However, the Interior Columbia Basin Ecosystem Management Project (ICBMP) included a
spatially expansive analysis in the 1990s, which
documented historical conditions and changes
since EuroAmerican settlement (Hann et al. 1997,
Hessburg et al. 1999). Historical evidence was
from interpretation of early aerial photography
(1930s–1960s). Hessburg et al. (2007) used these
data for about 300,000 ha of dry mixed-conifer
forests, mostly in eastern Washington, and found
that old, park-like forests and low-severity fire
did not dominate. Instead, these forests were
dominated by ponderosa pine and Douglas-fir,
but with a preponderance of intermediate-aged
patches and a diversity of structures, reflecting
fires varying in severity from low to high.
Because this study used early aerial photography,
the details of historical forest structure (e.g., tree
density, diameter distributions) could not be
reconstructed, and remain unknown except for
the half dozen studies (Table 1). Moreover,
Hessburg et al. had to account for the several
decades of EuroAmerican land uses before the
earliest aerial photos. Similarly, a spatially
extensive 1930s survey of old growth (Cowlin
et al. 1942) took place after extensive logging had
begun.
Here I use General Land Office (GLO) survey
data, that are also spatially extensive but from
several decades earlier, before widespread logging and fire exclusion, to reconstruct detailed
forest structure and fire, using new methods that
allow accurate reconstructions (Williams and
Baker 2010, 2011). I used the reconstructions to
test eight hypotheses (Table 2) representing
prevailing evidence prior to the Hessburg et al.
(2007) study. This prevailing evidence has not
been explicitly tested in the eastern Cascades of
Table 2. Hypotheses about historical dry forests in the eastern Cascades, to be tested in this study. See text for
sources.
Hypothesis
H1
H2
H3
H4
H5
H6
H7
H8
Description
Historical forests generally (.90% of area) had low tree density (i.e., ,100 trees/ha)
Douglas-fir and other shade-tolerant trees (grand fir/white fir, incense cedar) were historically a minor
component (i.e., ,10% of trees) in these forests, and the areas where they were most abundant were confined
to moist sites (e.g., north-facing slopes)
Lodgepole pine was historically a minor component (i.e., ,10% of total trees) in pumice-zone dry forests
Historical forests were relatively free (i.e., ,10% of area) of small understory trees
Historical forests were relatively free (i.e., ,30% of area) of understory shrubs
Historical forests generally were dominated by large trees (i.e., .50% of trees were larger than 60 cm)
Historical forests were dominated by low-severity fire (i.e., ,10% of area with other fire severities)
Historical forests had high-severity fires that burned only at long fire rotations (i.e., .400 years)
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BAKER
Perry et al. (2004), who suggested that Sierran
lodgepole pine increased with fire exclusion in
Oregon’s eastern Cascades.
H4 is based on several studies (Weaver 1943,
1961, Hessburg and Agee 2003, Perry et al. 2004,
Youngblood et al. 2004, Hessburg et al. 2005), but
also is mostly based on the idea that low-severity
fires would have kept understory trees rare (e.g.,
Hessburg et al. 2005). This is supported by early
observations that suggest tree regeneration was
poor or sparse (Appendix A: Q2, Q3, Q5, Q57).
Some other observations characterized tree regeneration as scattered or patchy, with the
patches sometimes dense (Appendix A: Q54,
Q58, Q61). Regarding H5, many authors suggested, based on early accounts (Appendix A: Q68–
Q72, Q74–Q76), and the idea of historically
frequent fires, that dry forests of the study area
had few shrubs and small trees (e.g., Johnson et
al. 2008, Busse and Riegel 2009). Agee (1994:17)
said that, in ponderosa pine forests in the eastern
Cascades, ‘‘open, parklike stands had substantial
grass and forb cover ...’’ and ‘‘... herbaceous
vegetation dominated the understory.’’
H6 was reviewed by several authors (e.g., Spies
et al. 2006). Youngblood (2001) and Hessburg
and Agee (2003) suggested large trees dominated
historically and Youngblood et al. (2004) estimated current old growth may be only 3–15% of
historical old growth. Kennedy and Wimberly
(2009) estimated via simulation that dry forests
on the Deschutes National Forest could have
supported about 35% older forest. However,
surveys of Oregon’s eastern Cascades in 1930–
1936 showed (1) ponderosa pine forests were in
the ‘‘large’’ or old-growth stage (dominant trees
averaged .56 cm diameter) on 78.0% of the
Deschutes area and 82.0% of the Klamath
Plateau, and (2) dry mixed-conifer forests were
in the large stage across 80.0% of the Deschutes
area and 99.0% of the Klamath Plateau (Cowlin
et al. 1942: Table 4).
H7 is supported by reviews (Agee 1993, 1994,
2003a, Youngblood 2001), fire-history studies
(e.g., McNeil and Zobel 1980, Bork 1984, Morrow
1986, Wright and Agee 2004), and some early
observations (Appendix A: Q1–Q6). Dry mixedconifer forests in eastern Washington had some
patchy high-severity fire in a low-severity fire
regime (Agee 2003a, Hessburg and Agee 2003,
Wright and Agee 2004). Hessburg et al. (2005)
later suggested dry forests in the Northwest may
have had mixed-severity fire as well, but toward
the low end of 20–70% overstory mortality.
H8 is supported by several studies. Hessburg
et al. (2005:120) said ‘‘... severe fire behavior and
fire effects were uncharacteristic of dry forestdominated landscapes ... Rarely, dry forest landscapes were relatively more synchronized in their
vegetation and fuels conditions and affected by
climate-driven, high-severity fire events ....’’
Wright and Agee (2004:455) said high-severity
fire ‘‘historically occurred at the stand scale (10–
100 ha), not the landscape scale (. 1000 ha).’’
Spies et al. (2006) mentioned patch-scale (e.g., 1
ha) high-severity fire in historical dry forests.
Johnson et al. (2008) thought moister, northfacing slopes had some high-severity fire. One
early observation suggests high-severity fire was
rare in these forests (Appendix A: Q9).
METHODS
Study area
The study area includes dry forests in and near
Oregon’s eastern Cascades province for the
Northwest Forest Plan (http://www.reo.gov/gis/
data/gisdata). Dry forests include ponderosa pine
and dry mixed-conifer forests, which typically
have ponderosa pine (Pinus ponderosa) dominant,
with some Douglas-fir (Pseudotsuga menziesii ),
grand fir (Abies grandis) or white fir (Abies
concolor), western larch (Larix occidentalis), Sierran
lodgepole pine (Pinus contorta var. murrayana),
sugar pine (Pinus lambertiana), incense cedar
(Calocedrus decurrens), or western juniper (Juniperus occidentalis) (Appendix B). Because surveyors
did not distinguish grand and white fir, calling
both ‘‘white fir’’ or just ‘‘fir,’’ I refer to both here
as white fir. I used the GLO survey data
themselves, supplemented by the NW ReGAP
Ecological Systems map of Oregon (http://www.
pdx.edu/pnwlamp/existing-vegetation), to limit
the study to dry forests from the top of the dry
mixed conifer to the lower limit of ponderosa
pine. ReGAP is a national ecosystem mapping
program, based on 30-m Landsat satellite data
(http://gapanalysis.usgs.gov). I used two map
categories for ponderosa pine: 4240 Ponderosa
Pine and 4301 Oregon White Oak-Ponderosa
Pine. Where pine was co-dominant in surveys, I
included some 4204 Western Juniper, 4217 Mixed
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BAKER
California Black Oak-Conifer, and 5304 California Montane Woodland and Chaparral. I used
four map categories for dry mixed-conifer: 4205
East Cascades Mixed Conifer, 4214 Southwest
Oregon Incense Cedar-Douglas-fir Mixed Conifer, 4215 White Fir Mixed Conifer, and 4232
Eastside Douglas-fir-Ponderosa Pine Mixed Conifer. Inclusion of 4237 Lodgepole Pine on
Normal Soil and 4267 Lodgepole Pine on Pumice,
Ash or Barren Soil was unavoidable in the central
region where lodgepole forms a mosaic with
ponderosa pine forests. I included small areas in
other categories if large pines, likely ponderosa
or sugar pine, dominated the GLO data.
These broad ReGAP categories include some
moist mixed-conifer forests, which had to be
omitted or removed. Thus, to further identify dry
mixed-conifer forests, I either did not enter data
or I removed: (1) section lines where the most- or
second-most abundant tree in the surveys was
spruce, hemlock, Shasta red fir, or western white
pine, which characterize moist mixed conifer or
subalpine forests, (2) section corners in the
surveys with 2 of these four species, and (3)
quarter corners with two white fir or section
corners with 3 white fir, which likely are moist
mixed-conifer forests. The resulting sample generally spans the ponderosa pine series and dry
plant-association groups in the Douglas-fir, white
fir-grand fir, and lodgepole pine series (Simpson
2007). However, the sample tends toward the dry
side of ecotones between dry and moist mixed
conifer, which may mean the sample underestimates the abundance of firs. Because they
represent early succession, or possibly natural
non-forested or sparse-forest conditions, I omitted 1,002 ha of burned forest, 9,219 ha of
openings, and 11,707 ha of ‘‘scattered’’ trees from
calculations, but they are shown on maps (e.g.,
Fig. 1). The final sample is 78% pines, 17% firs,
and 5% other trees (Appendix B).
I divided the study area into three regions (Fig.
1), each with 100,000–150,000 ha of sample area
(Table 3, Fig. 1) to facilitate geographical analysis.
The central region is defined by the pumice zone,
based on the Oregon geology map (Walker et al.
2003), which has a different ecology, often with
lodgepole pine on flats and ponderosa pine or
dry mixed-conifer forests on rises (Kerr 1913).
The two other regions extend north and south to
state borders.
The General Land Office surveys and
early historical observations
The study uses historical data from GLO
surveys done in the late-1800s. Surveyors recorded species, diameter, and distance to four (one
per 908 of azimuth) ‘‘bearing trees’’ at section
corners and two (one per 1808 of azimuth) at
quarter corners (0.8 km along a section line). By
revisiting section corners to relocate extant
bearing trees, we found that surveyors nearly
always selected the closest tree in each quadrant;
thus, bearing-tree data represent a valid statistical sample of trees that allows reconstruction of
forest structure (Williams and Baker 2010). Along
each 1.6 km section line, surveyors also recorded
the dominant trees and shrubs (and some
grasses) in order of abundance, and qualitative
descriptions of density. Data from the earliest
valid and complete surveys were input into a
geographical information system, and used to
reconstruct understory composition, as well as
tree density, composition, and diameter distributions using our new methods (Williams and
Baker 2011).
I selected townships included in the sample
based on the quality and dates of surveys. Many
townships could not be used, because surveyors
did not record required trees (e.g., only two
rather than four trees at corners) or understory
trees and shrubs, or inconsistently recorded data.
The sample includes the best GLO data for dry
forests of Oregon’s eastern Cascades. Of the 33
surveyors, 6 recorded excellent data covering
70% of the sample townships (Appendix C).
The sample townships were surveyed before
dry forests of the region were transformed by
industrial logging or fire exclusion. Mining
expanded in the 1860s, and livestock grazing in
the 1870s, but population and agriculture did not
expand widely until the 1880s (Robbins 1997).
Even in 1900, only a few small sawmills were in
operation near Bend and Klamath Falls (Leiberg
1900, Robbins 1997, Bowden 2003). The railroad
and expanded logging reached Klamath Falls in
1909 and Bend in 1911 (Robbins 1997, Bowden
2003). Depopulation of Indians was thought by
Perry et al. (2004) to have significantly reduced
fire by the middle-1800s. However, the idea that
historical burning by Indians was widespread,
rather than local and limited, is not supported by
sound evidence (Whitlock and Knox 2002). Fire
5
BAKER
Fig. 1. Historical tree density, reconstructed from GLO survey tree data at the 6-corner pooling level. Township
boundaries are shown in gray as a backdrop. The tree-density classes represent the quartiles of the distribution of
tree density across the whole study area (Table 3). Openings were defined as areas with no trees, and scattered
trees were defined as areas with 50% of expected trees missing. Small black areas indicate surveyor direct
observations of burned areas. The location of the only available tree-ring reconstruction of full tree density is
shown (Morrow 1986).
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BAKER
Table 3. Historical tree density and composition, based on reconstructions from GLO tree data.
Region
Variable
Tree density, 6 corner
Total area in sample (ha)
n (number of polygons)
Mean (trees/ha)
First quartile
Median
Third quartile
Maximum
Composition, 9 corner
Total area in sample (ha)
n (number of polygons)
Firs
Mean (%)
First quartile
Median
Third quartile
Maximum
Pines
Mean (%)
First quartile
Median
Third quartile
Maximum
Study area
North
Central
South
Ponderosa pine
Mixed conifer
398,346
730
249
143
214
318
1606
146,615
268
246
111
211
328
1055
147,625
272
262
152
215
344
1606
104,106
190
233
156
224
306
732
122,905
568
219
126
195
283
1055
139,768
551
275
170
239
352
1606
398,313
492
146,786
181
147,269
183
104,258
128
123,330
411
140,141
396
17.1
0.0
8.3
27.3
90.9
16.9
0.0
8.3
21.9
90.5
6.6
0.0
0.0
9.1
65.4
33.0
15.2
33.3
47.6
90.9
13.4
0.0
4.5
20.8
80.8
21.1
0.0
13.0
34.7
90.9
77.3
60.0
87.5
100.0
100.0
75.0
56.7
86.4
96.0
100.0
92.7
88.9
100.0
100.0
100.0
58.5
43.7
56.4
73.0
100.0
81.1
66.7
90.9
100.0
100.0
73.5
54.2
80.8
95.8
100.0
Notes: Units for tree density are numbers of trees per hectare. Units for composition are percentages of total trees.
To acquire data for fitting reconstruction
equations (Williams and Baker 2011), I completed modern surveys at 73 corners across the study
area, including ponderosa pine and dry mixedconifer forests with a wide spectrum of stand
ages and densities. For each corner, I measured
attributes of some or all of the four nearest trees,
but usually no more than two per species per
corner, aiming for 20–25 for each main tree in the
surveys (Appendix B). For rare species, trees
were added near corners to increase sample size.
For each tree, I measured diameter at breast
height (dbh) using a caliper, and crown radius
using a laser distance meter (Laser Technology,
Inc.) and canopy densitometer (Geographic
Resource Solutions, Arcata, California). I measured crown radius once for uniform crowns and
the longest and shortest radii for irregular ones.
I also collected data to estimate the Voronoi
area for each tree, which represents the area of
ground controlled by the tree (Delincé 1986).
Tree density equals the land area divided by
mean Voronoi tree area, which Munger recognized (1917: Table 6). I estimated Voronoi area
for each tree by measuring the distance with a
laser distance meter, and bearing with a sighting
compass, to the center of 6 nearest trees
(Delincé 1986), until 1 occurred per 908 of
exclusion is considered insignificant until 1900
(Weaver 1943, Busse et al. 2000, Youngblood et
al. 2004) or even 1915 (Morrow 1986). Of 3,351
lines in the sample townships, 99% were surveyed from A.D. 1856–1900 (median ¼ 1882).
I compiled early observations from publications (e.g., Weaver 1943, 1959, 1961), scientific
studies (Foster 1912, Munger 1917), and ForestReserve reports by government scientists done in
A.D. 1900–1903, which cover 53 of 60 sample
townships (Leiberg 1900, 1903, Dodwell and
Rixon 1903, Langille 1903, Plummer 1903). These
are sorted by topic (Appendix A).
Field research
I field-checked and translated common names
used by surveyors for trees (Appendix B) and
understory species (Appendix D) into Latin
names. I navigated to section corners and
relocated and identified surviving original bearing trees that were unknown (e.g., sassafras
pine). I also navigated to section lines where
unknown understory species (e.g., chaparral,
laurel) were dominant or co-dominant with a
known species. Unknown species were checked
and identified at about 20 section corners and 50
section lines, and almost no uncertainties remain
(Appendix D).
7
BAKER
multiple comparison test to determine which
means differ (Ott 1988). Sample sizes are large
(e.g., 730 reconstruction polygons), so even small
differences may be statistically significant. The
area containing the GLO sample data is also large
(45%) relative to the population, which is dryforest area inside the Oregon Eastern Cascades
province. I thus focus on ecological significance.
Potential missing section-line data must be
addressed. Nearly all surveyors, including the
best, at times did not record information about
understory trees or shrubs. If a surveyor never
recorded understory information about any lines
(Appendix C), that surveyor’s data are excluded
from understory calculations, but otherwise their
lines are included. Some surveyors specifically
said ‘‘no undergrowth’’ or ‘‘no shrubs’’ when the
understory lacked shrubs; in those cases, when
they did not record information about another
line, it could be that this was a lapse in recording
and not an indication that understory shrubs
were lacking, or it could be that these lines also
lacked shrubs. These ‘‘not recorded’’ cases are
thus ambiguous. Since many previous authors
thought understory trees and shrubs were
uncommon, I conservatively interpreted ‘‘not
recorded’’ cases as a lack of trees or shrubs, and
the tables reflect this, but I provide a multiplier in
the table that allows the numbers to be calculated
assuming ‘‘not recorded’’ represents missing
data.
Tree data must be pooled to increase sample
size and accuracy. As in Williams and Baker
(2011), I estimated: (1) tree density for 6-corner
pools (520 ha) to test H1, (2) tree composition for
9-corner pools (780 ha) to test H2 and H3, and (3)
diameter distributions for 12-corner pools (1040
ha) to test H4, H6, H7, and H8. Pools were
generally formed from a 2:1 ratio of contiguous
quarter corners and section corners, to offset the
inequality of two trees at quarter corners and
four trees at corners. In the accuracy trial
(Williams and Baker 2011), relative mean absolute error (RMAE) was about 22% in a modern
calibration and 17% in a cross-validation with
tree-ring reconstructions for six-corner density; 9corner composition was about 90% similar to plot
data and 12-corner diameter distributions were
about 87–88% similar to plot data. I used 10-cm
bins for diameter distributions (Williams and
Baker 2011). Reconstructions include up to 730
azimuth. I used these data in ArcGIS (ESRI, Inc.)
to measure the tree’s Voronoi area. Equations
were fit with regression (Minitab, Inc.) after
logarithmic transformation (Appendix E). For
crown radius, separate equations were fit for
each species and for ‘‘fir’’ and ‘‘pine.’’ Insufficient
data and poor fit prevented Voronoi equations by
species, which were pooled into three groups
(Appendix E), based on similarity of the slope
and intercept of initial Voronoi equations.
Reconstructions and statistical tests
using the survey data
GLO survey notes are online (http://www.blm.
gov/or/landrecords/survey/ySrvy1.php). The necessary data were downloaded, extracted, and
entered into ArcGIS point (tree data) and route
(section-line data) databases, then exported as
spreadsheets. These were used with Minitab
macros to complete calculations for hypothesis
testing. Output tables were joined to the ArcGIS
data for display and analysis. The dataset
includes 11,856 trees and 3,351 section-line
segments for 5,073 km of section lines across
398,346 ha. This is equal to about 43 townships of
data, but includes parts of 60 individual townships. The sample includes about 42% ponderosa
pine and 58% dry mixed-conifer forest. The part
of the study area inside the Oregon Eastern
Cascades province (Fig. 1) contains 45% of the
524,000 ha of dry forests that occur inside this
province.
I used a chi-square goodness-of-fit test for each
hypothesis that the area of the study area with
each attribute (observed) is no different from the
hypothesized fraction of the study area with the
attribute (Ott 1988). Tests for H1, H2, H6, and H7
use GLO tree data, and tests for H2-H5 use
section-line data (Table 2). Public-land survey
lines approximate systematic line-intercept transects that provide unbiased estimates of percent
cover (Butler and McDonald 1983):
Ca ¼
n
X
ai =A
ð1Þ
i¼1
where Ca ¼ percent cover of property a across the
study area, ai is the fraction of line-intercept
transect i with property a of n total transects, and
A is the area of study. I used one-way analysis of
variance to test for differences in means between
groups (e.g., among regions) and the Tukey
8
BAKER
tree-density polygons, 492 composition polygons, and 369 diameter-distribution polygons.
These GLO-based reconstructions approach the
accuracy of tree-ring reconstructions, but are
hundreds of times more spatially extensive
(Williams and Baker 2011).
I reconstructed fire severity, evident in forest
structure, as in nearby studies (e.g., Taylor and
Skinner 1998, Hessburg et al. 2007) to test H7 and
H8. Williams and Baker (in press) calibrated
forest structure with fire severity, based on 64
tree-ring reconstructions in dry forests where
authors reconstructed historical fire severities.
We calibrated the structure associated with lowseverity fire in dry forests to be: (1) mean tree
density , 178 trees/ha, (2) small conifers (,30 cm
diameter) , 46.9% of total trees, and (3) large
conifers (40 cm diameter) . 29.2% of total trees.
High-severity was identified by small conifers .
50% of total trees and large conifers , 20% of
total trees, and mixed severity was between low
and high. For reconstruction of fire severity, I
intersected 6-corner tree density with 12-corner
diameter distributions for conifers, then classified
resulting 6-corner polygons into the three levels
of fire severity. This improves on earlier studies,
as forest structure is directly reconstructed from
surveys done before widespread logging and fire
exclusion, and severities are calibrated with treering studies.
To help address H7, I estimated low-severity
fire rotation for the study area in two ways. First,
although several fire-history studies were done in
the study area, only Bork (1984) estimated area
burned, needed to estimate fire rotation. I
interpolated area-burned estimates for each fire
(Bork 1984: Fig. I-22) from A.D. 1700 (to have a
common starting year for all sites) to 1900, when
fire exclusion is thought to have begun. I then
calculated fire rotation as the period (200 years)
divided by the sum of the fractions of the sample
area burned by each fire, a standard formula
(Baker 2009). Second, I used the section-line data
to approximate the fire rotation. I used snowbrush ceanothus (Ceanothus velutinus) as an
indicator of recent fire within only the lowseverity fire area. Snowbrush ceanothus reappears profusely after fire by resprouting and
reseeding, and within 5–10 years, it often
becomes dense and dominant (Foster 1912,
Zavitkovski and Newton 1968, Conard et al.
1985, Ruha et al. 1996), as also documented by
early observations (Appendix A: Q21, Q22, Q24,
Q25). However, because snowbrush is relatively
shade-intolerant, as regenerating trees overtop it
and it is damaged by snow, it often declines to
low levels by about 15 years after fire (Zavitkovski and Newton 1968, McNeil and Zobel 1980). In
some cases, snowbrush can have an effective
period of dominance lasting 20–40 years (Conard
et al. 1985). To approximate the fire rotation for
low-severity fire, I calculated the fraction of total
section-line length, within only the low-severity
area, on which snowbrush ceanothus was listed
either first or second by surveyors. I then
estimated fire rotation, based on the maximum
period during which snowbrush remains dominant or co-dominant after fire, using 15 and 30
years as the possible estimates, divided by the
fraction of the landscape burned during that
period (fraction of total line length that listed
snowbrush first or second).
To analyze H8, I approximated historical highseverity fire rotation as in Williams and Baker (in
press). The approximation is from the number of
years high-severity fire was detectable using
forest structure evident in the GLO data, divided
by the fraction of the forested landscape in which
those fires occurred. The number of years fire
was detectable is defined by the age of an
average 40-cm tree, the key tree size that
separates the definitions of fire severity (see
above). Munger (1917: Table 10) dated 1,618
ponderosa pines at ten sites nearly spanning my
study area. The average 40-cm tree was about
120 years old in the north, 115 years old in the
central region, and 105 years old in the south,
which I use in each region as the years fire was
detectable using forest structure. Since these are
single approximations for the whole population,
I simply qualitatively interpret the result. Since
no previous study has even approximated
historical high-severity fire rotation, as the
necessary data are difficult to obtain, the approximation has value.
Validation
The ability of crown-radius and Voronoi
reconstruction equations to estimate forest-structure parameters has been validated in an
extensive accuracy trial (Williams and Baker
2011). Here, I supplemented this with a small,
9
BAKER
local trial. At 15 corners, I used modern survey
data I collected, and the derived equations
(Appendix E) to estimate tree density and
compare it to an estimate from a square plot,
centered on the corner and enlarged to contain
30–50 trees. This trial showed RMAE in mean
tree density across five three-corner pools was
25.1%, which is better than the 30.4% RMAE for
three-corner pools in the nearby Blue Mountains
(Williams and Baker 2011). Also, species-specific
crown-radius equations reduced RMAE from
28.0%, for pooled species equations, to 25.1%,
so species-specific equations can increase accuracy. This trial also showed that Mean Harmonic
Voronoi Density (MHVD) was the best density
estimator for the study area, as in the nearby Blue
Mountains (Williams and Baker 2011), and it is
thus used in this study.
For cross-validation (Williams and Baker
2011), only one of the tree-ring reconstructions
(Table 1), at Pringle Falls (Morrow 1986: Fig. 1), is
of tree density, includes all trees .10 cm dbh, and
is inside the study area. Youngblood et al. (2004)
is only for upper-canopy trees, not all trees. Perry
et al. (2004) included only counts of trees pooled
across sites, not density and not at individual
sites. Agee (2003b) was outside the study area.
The estimate of density of pre-1886 trees (compatible with survey dates) was 167 trees/ha
(mean for stands 28 and 29; Morrow 1986: Figs.
8–11). In comparison, reconstructed tree density,
from the mean of four 3-corner pools near these
stands, was 175 trees/ha, which supports that the
reconstructions are valid and accurate.
The methods of fire-severity reconstruction
have been validated (Williams and Baker, in
press), but I added to this by comparing fireseverity reconstructions to information in ForestReserve reports done by government scientists in
A.D. 1900–1903 (Leiberg 1900, 1903, Dodwell
and Rixon 1903, Langille 1903, Plummer 1903).
These describe forest structure, often explain
which part of a township and how much area
burned at high severity, and describe the extent
of fires of all severities (e.g., fire evident
throughout the township). Information is only
at the coarser township scale, but covers 53 of my
60 townships within a few decades of surveys. I
considered the fire-severity reconstruction for a
township to be validated if: (1) the area and
location of high severity in the reconstruction
generally matched the area and location of highseverity fire, or contiguous areas described as
having small trees, in the township description,
(2) if the township description recorded little (i.e.,
,5% of township) high-severity fire, or described
mature or large timber, and the reconstruction
identified the area as having predominantly lowor mixed-severity fire, (3) if the township
description mentioned attributes expected in a
mixed-severity fire regime (e.g., patches of
burned area or brushfields) and the reconstruction identified the area as predominantly mixed
severity, and (4) where the reconstruction
showed multiple fire severities in the township,
they also were evident in the township description.
The fire-severity reconstructions match township descriptions in the Forest-Reserve reports
well. Three of the 53 townships had unusable
descriptions. Of the remaining 50, in 42 townships (84%) the GLO reconstructions generally
matched the township descriptions, although the
township descriptions did not distinguish low
and mixed severity well. In eight townships
(16%), my reconstructions and the township
descriptions did not match. Mis-matches were
usually not large; for example, in T014SR008E,
the reconstruction showed only low and mixedseverity fire, but the township description has
372 ha (4% of the township) of ‘‘burned area,’’
which is high severity. The precision of this test is
not high, as I had to judge what is a match, but
the results do support the validity of reconstructions. The fire-severity reconstructions are further
validated by comparing them to previous findings (Hessburg et al. 2007) in the study area (see
Discussion).
RESULTS
H1 was rejected (X2 (1, N ¼ 730) ¼ 4824.5, p ¼
0.000). Only 13.5% of forest area had open, lowdensity forests, with ,100 trees/ha, and only 25%
of forest area had somewhat low density (i.e.,
,143 trees/ha, the first quartile in Table 3).
Historical tree density across the study area
(Fig. 1) was instead high for dry forests, with a
mean of 249 trees/ha (Table 3). Dry mixed-conifer
forests were quite dense on average, with a mean
of 275 trees/ha, and were significantly denser
than ponderosa pine forests, with a mean of 219
10
BAKER
trees/ha (F (1, 1117) ¼ 42.55, p ¼ 0.000). Lodgepole
pine forests were similar to mixed-conifer forests,
and are pooled with them. There was no
significant difference in mean tree density among
regions (F (2, 727) ¼ 1.92, p ¼ 0.147), likely due to
high within-region variability. Overall, 25% of
forest area had very dense forests, between 318
and 1606 trees/ha (Table 3, Fig. 1) and even 25%
of ponderosa pine forests had 283 trees/ha
(Table 3). This evidence against H1 is also
supported by a few early observations (Appendix
A: Q45, Q46, Q48, Q51).
Instead of widespread low-density forests,
generally dense forests with a mixture of
densities characterized historical forest landscapes at the scale of a few townships. Lowdensity forests were well distributed across
regions, with somewhat more relative area in
the north (Table 3, Fig. 1). Dense forests were also
well distributed, with slightly more in the south.
Some contiguous areas of three to five townships
(e.g., north of Sisters) had more low density and
others (e.g., south of Hood River, southwest of
Bend, southwest of Klamath Falls) had more high
density, but neither low- nor high-density forests
formed large blocks (Fig. 1). At the scale of a few
townships (e.g., 25,000 ha), tree density usually
varied by a factor of two to four or more (Fig. 1).
This large variability was noted by Munger
(1917; Appendix A: Q46).
H2 also was rejected (X2 (1, N ¼ 11,856) ¼ 966.3,
p ¼ 0.000), based on the number of shade-tolerant
trees versus total trees (Appendix B). Section-line
data also show that firs were the most abundant
trees across 12.0% of forest area, were either first
or second in abundance across 56.8% of forest
area, and were present on 64.8% of forest area
(Table 4). Firs were the most abundant tree
across 14.6% of dry mixed-conifer forests, but
only 3.1% of ponderosa pine forests (Table 4).
Firs were present in 80.5% of mixed-conifer
forests and 40.9% of ponderosa pine forests, a
significant difference (F (1, 805) ¼ 29.95, p ¼
0.000). Incense cedar, in contrast, was almost
never the most abundant tree, and was second on
only about 5% of the forest area, but was present
across about 25% of forest area (Table 4). Firs
made up 17.1% of total trees across the study
area, and 21.1% of trees in dry mixed-conifer
forests, but their abundance varied significantly
among regions (F (2, 489) ¼ 75.12, p ¼ 0.000). All
three regions differed, based on Tukey’s MCP),
from only 6.6% of total trees in the central region
to 33.0% in the south (Table 3). Understory
shade-tolerant trees were also historically common, as explained below (H4 ).
Firs, which made up almost all shade-tolerant
trees (Appendix B), were not confined to moist
sites (second part of H2). Firs were somewhat
concentrated, as median composition was only
8.3% firs, yet 25% of forest area had 27.3% firs
(Table 3). Fir concentrations (27.3% firs) were
widely distributed across available environments, indicating a lack of confinement to moist
sites. However, selection was significant for
higher elevations and slopes .5 degrees, but
not for aspect and slope position (Fig. 2).
Lodgepole pine was not historically a minor
component of pumice forests (H3 was rejected),
based on two tests. First, in an 11,000-ha area
enclosing sample sites of Perry et al. (2004), using
surveys from 1880–1883, lodgepole pine was
listed as the first tree on 27.1 km (23%) of 117.0
total km of section-lines in the area, and H3 was
rejected here (X2 (1, N ¼ 117) ¼ 22.5, p ¼ 0.000).
Also, lodgepole was 59% and ponderosa pine
41% of 54 pines identified to species, and the
lodgepole were all ,40 cm dbh. The 11,000 ha
area was reconstructed to have had widespread
evidence of mixed-severity fire in 1880–1883,
with some area of both high severity and low
severity. Second, the surveyor who did the area
of the Morrow (1986) study did not distinguish
pines, but they were in the next township south,
done in 1882 by Henry C. Perkins. In a 3000-ha
area of similar topography, lodgepole is the first
tree (ponderosa second) on 24.1 km (62.6%) of
38.5 km of section lines, with the remaining 14.0
km ‘‘pine-fir,’’ thus H3 is also rejected here (X2 (1,
N ¼ 38) ¼ 120.1, p ¼ 0.000). Early observations also
document that lodgepole pine was historically
abundant and regenerated, and even dominated
to the exclusion of other trees, after high-severity
fires in dry forests in the central zone (Appendix
A: Q28–Q31, Q33–Q36, Q59).
Understory trees were present on 2223 km
(57.4%) of the 3873 km of section lines in the
sample, so H4 was rejected (X2 (1, N ¼ 3,873) ¼
9,667.5, p ¼ 0.000). Also, understory trees were
present and dense on 30.3% of forest area (Table
4). Understory trees were present on 79.4% and
dense on 56.9% of forest area in the north region,
11
BAKER
Table 4. Historical section-line length covered by overstory trees and understory trees and shrubs.
Region
Length covered
Overstory shade-tolerant trees
Percentage with fir first Percentage with fir first or second
Percentage with fir present
Percentage with incense cedar first
Percentage with incense cedar first or second
Percentage with incense cedar present
Total line length in sample (km)à
Understory shade-tolerant trees
Percentage with fir first Percentage with fir first and dense
Percentage with fir present
Percentage with incense cedar first
Percentage with incense cedar first and dense
Percentage with incense cedar present
Multiplier for correcting for missing data§
Understory shade-intolerant trees
Percentage with pine first Percentage with pine first and dense
Percentage with pine present
Understory trees of any species
Percentage with understory trees
Percentage with dense understory trees
Understory shrubs of any species
Percentage with understory shrubs
Percentage with dense understory shrubs
Understory trees or shrubs of any species
Percentage with understory trees or shrubs
Percentage with dense understory trees or shrubs
Study area
North
Central
South
Ponderosa pine
Mixed conifer
12.0
56.8
64.8
0.2
4.7
24.8
4312.7
8.9
55.4
57.6
0.1
7.9
29.8
1601.3
3.0
32.1
37.5
0.0
0.0
0.0
1570.4
14.7
60.2
77.8
0.3
1.7
34.7
1140.9
3.1
36.8
40.9
0.0
6.9
23.2
1363.1
14.6
68.2
80.5
0.1
1.3
24.4
1381.0
10.2
6.6
27.8
0.1
0.0
2.6
3894.4
1.182
22.1
13.8
42.5
0.1
0.0
0.8
1154.3
1.028
2.5
1.1
25.3
0.0
0.0
0.0
1554.3
1.284
8.2
6.3
15.9
0.0
0.0
4.4
1209.5
1.238
4.1
3.1
16.5
0.0
0.0
0.7
945.3
1.195
16.8
11.0
36.4
0.0
0.0
4.6
1424.4
1.126
44.1
21.9
51.0
3894.4
1.182
49.0
38.1
67.7
1154.3
1.028
63.9
19.7
65.8
1554.3
1.284
13.8
8.6
15.3
1209.5
1.238
48.0
28.0
52.7
945.3
1.195
37.6
17.8
46.5
1424.4
1.126
57.4
30.3
3872.6
1.182
79.4
56.9
1154.3
1.028
66.6
20.9
1554.3
1.284
24.9
16.6
1209.5
1.238
58.3
35.2
945.3
1.195
55.7
29.3
1424.4
1.126
71.0
43.6
3992.4
1.178
83.2
54.0
1234.6
1.054
58.1
22.9
1499.2
1.274
77.5
40,0
1258.6
1.209
67.4
40.6
1027.9
1.222
82.2
41.8
1447.7
1.112
83.5
44.8
3863.0
1.165
96.5
67.4
1154.3
1.028
78.0
30.4
1499.2
1.274
77.9
40.1
1209.5
1.238
81.4
51.4
941.7
1.191
89.0
44.9
1422.5
1.101
Surveyors were instructed to record overstory trees and understory shrubs and trees by listing them in order of abundance.
à Line lengths differ between overstory and understory, because some surveyors recorded overstory information but not
understory information. Line lengths also differ between understory trees and understory shrubs for the same reason.
§ Where the surveyor did not record information for a particular section line for understory trees or shrubs, this lack of
information is ambiguous and can be interpreted two ways: (1) the lack of an entry means there were no understory trees or
shrubs, which is how the percentages in this table were calculated, or (2) the surveyor neglected to make an entry and the data
are missing. The former case provides a low estimate of the percentages. In the latter case, the correct percentages would be
higher, and can be calculated by applying the multiplier to the percentages in the table.
but were present on only 24.9% and dense on
only 16.6% of the south region (Table 4). Pines
were the most abundant understory trees, were
present on 51% of forest area, present and most
abundant on 44.1% of forest area, and were dense
and most abundant on 21.9% of forest area (Table
4). Even understory shade-tolerant trees were
common. Understory firs were present on 27.8%
of forest area, were the most abundant understory tree on 10.2% of forest area, and were most
abundant and also dense on 6.6% of forest area
(Table 4). Understory firs were most abundant in
dry mixed-conifer, where 36.4% had understory
firs; understory incense cedars were rare, but
present on 2.6% of forest area (Table 4). Early
observations show that thickets of tree regeneration were common in places, also scattered,
often dense, and may have been favored by fire
interludes (Appendix A: Q60, Q61–Q64).
Overall, 2834 km (71.0%) of 3992 km of forest
area in the sample had understory shrubs, so H5
was rejected (X2 (1, N ¼ 3,992) ¼ 3,194.3, p ¼
0.000), varying from 83.2% in the north to 58.1%
in the central region (Table 4). An observation
12
BAKER
Fig. 2. Area supporting fir concentrations with respect to four topographic variables. A concentration of firs is a
reconstruction polygon with firs 27.3% of total trees, which represents the fourth quartile of fir composition.
Available is simply the fraction of the total forest area with each environmental attribute, and the area used by
firs is the fraction of the total area of concentrations of firs that has each environmental attribute. If the used
fraction exceeds the available fraction, that indicates selection. Chi-square values show that the null hypothesis,
that the two distributions do not differ, can be rejected only for elevation and slope. Note that aspect has a smaller
sample size, because it is only calculated where slopes are 5 degrees.
also suggested shrubs were abundant in the
south region (Appendix A: Q73). Within the
71.0% of area with understory shrubs, about half
had antelope bitterbrush first, one-sixth had
snowbrush, one-eighth had greenleaf manzanita,
and the rest was a mixture. Understory shrubs
were dense across 43.6% of forest area, from
54.0% in the north to 22.9% in the central region
(Table 4). Shrubs were more abundant in dry
mixed-conifer forests than in ponderosa pine
forests (Table 4). Many early observations suggested understory shrubs were sparse (Appendix
A: Q68–Q72, Q74–Q76), perhaps because observations were for the 29% of forest area without
understory shrubs at the time of the surveys
(Table 4).
Hypotheses H4 and H5 together implied an
open understory with few small trees or shrubs,
but this is rejected. Surveyors explicitly recorded
‘‘no shrubs’’ or ‘‘no undergrowth’’ on only 16.5%
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BAKER
of forest area, thus 83.5% of forest area had
understory trees or shrubs, with 96.5% in the
north and about 78% in the other regions, and
they were dense across 44.8% of forest area
(Table 4). Dry mixed-conifer forests had understory trees and shrubs across 89% of the area
(Table 4).
H6 was rejected, as trees .60 cm were only
18.0% of total trees (X2 (1, N ¼ 11856) ¼ 4,856.4, p
¼ 0.000). Trees from 10–40 cm were numerically
dominant (60% of total trees) when pooled across
the 11,856 trees in the study area (Fig. 3). This
pattern had consistency, as 10–40 cm trees were
.50% of trees across 72.3% of forest area. Large
trees would certainly have been prominent
because of their size and canopy position, and
in this sense likely were generally dominant.
Pooled diameter distributions for individual
species show four patterns (Fig. 3). First, all
species had abundant small trees (,40 cm).
Second, most species, including white fir, incense
cedar, western juniper, western larch, and lodgepole pine seldom were .60–70 cm. Only sugar
pine, ponderosa pine, and Douglas-fir commonly
had larger trees. Third, three species (white fir,
western larch, Douglas-fir) had a peaked distribution with fewer trees in the smallest size
class(es). Finally, lodgepole pine’s distribution
stood out, with few trees .40 cm diameter.
H7 was rejected (X2 (1, N ¼ 1132) ¼ 5741.3, p ¼
0.000), as 76.5% of forest area had structural
evidence of mixed- or high-severity fire, and only
23.5% of forest area had evidence solely of lowseverity fire (Table 5), although low-severity fire
likely also occurred in mixed- and high-severity
areas. Fire-severity percentages (Table 5) differed
among regions (X2 (2, N ¼ 1076) ¼ 131.8, p ¼
0.000). Low-severity-fire was highest in the north
(32.5%) and south (29.4%) and least (10.4%) in
the central region (Table 5, Fig. 4). Overall, 26.2%
of forest area had evidence of high-severity fire,
which varied from 41.4% in the central region to
8.9% in the south (Table 5, Fig. 4). Overall,
structural evidence of mixed-severity fire was
dominant (50.2% of study area), but varied from
44.2% in the north to 61.7% in the south (Table 5,
Fig. 4). Fire-severity percentages (Table 5) also
differed among vegetation types (X2 (2, N ¼ 2609)
¼ 50.2, p ¼ 0.000). Dry mixed conifer had less lowseverity and more high-severity fire than did
ponderosa pine forests (Table 5). Lodgepole pine
on pumice had hardly any low-severity, and was
dominated by high-severity fire (Table 5). Early
observations support the occurrence of highseverity fire in lodgepole pine (Appendix A: Q13,
Q32, Q35) and lodgepole pine regeneration after
high-severity fire (Appendix A: Q28–Q31, Q33,
Q34, Q36).
Using Bork’s area-burned data (Bork 1984: Fig.
I-22), I estimated fire rotation for low-severity fire
to be: (1) 78 years at Cabin Lake, southeast of
Lapine in dry ponderosa pine, (2) 29 years at
Pringle Butte, about 40 km southwest of Bend in
ponderosa pine with lodgepole pine nearby, and
(3) 71 years nearby at Lookout Mountain in a dry
mixed-conifer forest. Using snowbrush ceanothus, I approximated low-severity fire rotation as
47–142 years (Table 6).
H8 is supported for the study area and for
north and south regions, as high-severity rotations were estimated at 435, 515, and 1180 years,
respectively, and is supported for ponderosa pine
and dry mixed-conifer forests, with rotations
estimated at 705 years and 496 years, respectively
(Table 5). It is not supported for the central
region, where the rotation was 278 years (Table
5), or for lodgepole pine forests on pumice in that
region, where the rotation was 171 years (Table
5).
DISCUSSION
Historical dry forests in Oregon’s eastern
Cascades were denser than previously estimated,
and denser than that calculated using GLO data
in similar western forests. The historical mean
tree density of 249 trees/ha substantially exceeds
most estimates from tree-ring reconstructions,
extant trees and stumps, and early scientific
observations (Table 1). Causes of this disparity
are discussed later. Historical mean tree density
in the eastern Cascades (249 trees/ha), exceeded
the 217 trees/ha in the Colorado Front Range, 167
trees/ha in Oregon’s Blue Mountains, and 142–
144 trees/ha in northern Arizona from GLO data
(Williams and Baker, in press). Moreover, the
13.5% that was open, low-density forest (,100
trees/ha) in the eastern Cascades was much lower
than the 23% in Oregon’s Blue Mountains, 23–
33% in northern Arizona, and 40% in the
Colorado Front Range (Williams and Baker, in
press). This may reflect more dry mixed-conifer
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BAKER
Fig. 3. Historical tree-diameter distributions for trees recorded by the surveyors, pooled across the study area.
Shown are the distributions for all trees, regardless of species (n ¼ 11,856), and individual species with sufficient
data (n . 100).
ized 25% of historical landscapes in the study
area. Even ponderosa pine forests had .283
trees/ha over 25% of the area (Table 3). There is
some other evidence of historically high tree
density in Northwestern dry mixed-conifer for-
forest and steeper, more complex topography in
the study area than other areas. However, even
ponderosa pine forests, with a mean of 219 trees/
ha (Table 3), were denser than in other areas.
Very dense forests (.300 trees/ha) characterv www.esajournals.org
15
BAKER
Table 5. Percentage of historical forest area meeting the low-severity fire model, percentage of forest area by fire
severity, and approximate high-severity fire rotation.
Region
Mixed
conifer Lodgepole
pine on
pumice
Metric
Study area
North
Central
South
Ponderosa
pine
Total forested area in sample (ha)
Low-severity fire model
Parameter 1: % of forest with ,177.6 trees/ha
Parameter 2: % of forest where ,46.9% of
conifers were ,30 cm
Parameter 3: % of forest where .29.2% of
conifers were 40 cm
Low severity: % of forest that meets all 3
parameters
Reconstructed fire severity
Low (% of total forested area)
Mixed (% of total forested area)
High (% of total forested area)
High-severity fire rotation
Period of observation (years)
High-severity fire rotation (years)§
398,217
146,555
147,502
104,160
123,576
140,422
22,051
36.7
49.0
40.9
62.3
34.8
22.2
33.5
68.3
57.4
60.2
24.7
52.3
28.8
11.1
57.8
69.0
31.8
78.7
69.6
62.5
18.1
23.5
32.5
10.4
29.4
39.8
18.1
4.6
23.5
50.2
26.3
32.5
44.2
23.3
10.4
48.2
41.4
29.4
61.7
8.9
39.8
44.0
16.2
18.1
58.9
23.0
4.6
28.1
67.3
114.1à
435
120
515
115
278
105
1180
114.1à
705
114.1à
496
115
171
Mixed conifer in this case (not in other tables) excludes lodgepole pine on pumice, which is treated in the next column.
à Calculated as mean of periods in the three regions, weighted by forested area in each region.
§ Calculated as period of observation/(% high fire severity/100.0).
ests (Agee 2003b: 348 trees/ha at Crater Lake;
MacCracken et al. 1996: 371 trees/ha at Entiat,
WA).
Open, low-density forests with ,100 trees/ha,
although only 13.5% of total forest area, were
found in some contiguous areas (e.g., north of
Sisters; Fig. 1). These appear to be in areas that
are relatively flat, gently sloping, or undulating.
Also, the open, low-density condition may be
ephemeral, a temporary condition after episodes
of low- to mixed-severity fire (Morrow 1986,
Hessburg et al. 2007). Contiguous areas with
open, low-density forests at the time of the
surveys appear to often correspond with evidence of low- and mixed-severity fire (Figs. 1 and
3). Morrow’s (1986) tree-ring reconstructions of
age structure in ponderosa pine-lodgepole pine
forests in the study area first suggested tree
density and composition fluctuated in this area
as episodes of fire were followed by recovery:
‘‘Historical accounts of open, park-like ponderosa pine forests were made during periods of low
stocking following the increased fire activity
between 1840–1885. These forests were much
more open during periods of increased fire
activity that apparently killed smaller trees and
shrubs than during periods of less fire activity
and high survivorship. It is clear that the density
and structure of the prehistoric stands were not
constant. The historic accounts provide a short
glimpse of the changing primeval forest’’ (Morrow 1986:69).
Morrow’s hypothesis makes sense, as does
Hessburg et al.’s (2007) similar explanation.
Temporal evidence of the fluctuation would
provide added validation. The hypothesis im-
Table 6. Estimated fire rotation (years) for low-severity fire, within the low-severity
fire area, using snowbrush (Ceanothus velutinus) dominance in the section-line data
as the indicator of recent low-severity fire. See text for explanation.
Ceanothus dominance in
section-line data
Either first or second
First
Fire rotation (in years), assuming Ceanothus dominates
For 15 years after fire
For 30 years after fire
15.0/0.3199 ¼ 47
15.0/0.2112 ¼ 71
30.0/0.3199 ¼ 94
30.0/0.2112 ¼ 142
Note: The calculation is the period of Ceanothus dominance (in years) divided by the fraction of
the total section-line length, within the low-severity fire area, that has Ceanothus dominant either
first or second or just first.
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BAKER
plies that open, low-density forests may naturally change to denser forests with abundant small
trees and shrubs as they recover from episodes of
fire. The study area, as explained below, certainly
contained abundant historical evidence of small
trees and shrubs consistent with this hypothesis.
Shade-tolerant trees (grand fir/white fir, Douglas-fir, incense cedar) were usually not the most
abundant trees, but were not historically rare
(H2) in study-area forests. Firs actually dominated on 12% of forest area overall and 14.6% of dry
mixed-conifer forests, and occurred in 65% of
forest area overall and 80.5% of dry mixedconifer forests. With 25.0% of forest area having
.27.3% firs (Table 3), firs were much more
abundant than in northern Arizona, but similar
to the Blue Mountains, where 19.3% of forest area
had .30% fir, and Colorado Front Range, where
26.9% of forest area had .30% firs (Williams and
Baker, in press). Both white fir and Douglas-fir
had pooled size-class structures that suggest
ongoing, if episodic regeneration that allowed
these trees to become canopy dominants or codominants (Fig. 3). Fir concentrations were not
confined to moist sites (Fig. 2), as suggested by
previous studies and in a recent review (Perry et
al. 2011), nor were they forced by fire into
topographic refugia, as in Washington (Camp et
al. 1997). Firs were less abundant in the central
region than the other regions (Table 4), perhaps
partly because of a shorter fire rotation in the
central region. However, firs were found across
all aspects and slope positions, although somewhat favored by higher elevations and steeper
slopes (Fig. 2). It is also possible that fir
concentrations are related to environment at
finer resolutions than can be detected with GLO
data.
Regarding H3, the survey data show that
Sierran lodgepole pine was abundant, and often
small in stature historically, likely because it is
favored by mixed- and high-severity fire. Dominance of high- and mixed-severity fire, relatively
short high-severity fire-rotation (Table 5), and
early observations all suggest the historical
abundance of Sierran lodgepole pine in pumicezone dry forests was promoted by mixed- and
high-severity fire. Historical lodgepole mosaics
are also documented in the central region, from
early photographs and observations (Johnson et
al. 2008). Although this tree is non-serotinous, it
regenerates readily after patchy high-severity fire
or moderate-severity fire with survivors (Agee
1993). It can out-compete ponderosa pine early in
post-fire succession, through superior seeding,
but appears short-lived, based on its sizestructure (Fig. 3) and evidence of susceptibility
to insects and disease (Agee 1993). It is also
favored by soils and frost conditions on flat areas
on pumice (Kerr 1913, Youngberg and Dyrness
1959). Some previous researchers thought abundant young lodgepole and other trees were from
fire exclusion (Morrow 1986, Perry et al. 2004),
but did not reconstruct fire history in their study
areas, and thus mis-interpreted age structures.
Abundant lodgepole pine today represent postfire regeneration after mid-1800s fires, not fire
exclusion, as documented by mixed- and highseverity fire evidence and abundant small lodgepole from 1880–1883 surveys.
Hypothesis H4 was rejected because understory trees, particularly pines but also firs, were
present on 57.4% of historical forest area and
dense on 30.3% of forest area. Dry forests in the
Blue Mountains had understory trees on much
less area, only 33.2% of forest area, and northern
Arizona and Colorado had even lower levels of
understory trees, with presence over only 1.2–
9.9% of forest area (Williams and Baker, in press).
On the Warm Springs Indian Reservation northwest of Bend, West (1969a) reconstructed evidence of historical tree-regeneration thickets,
with tree density from 5,000–10,000 trees/ha,
that he linked to regeneration after insect-killed
patches of trees were blown down and then
burned. Early observations also document scattered dense thickets of tree regeneration. A likely
explanation of common or dense understory
trees is that, where fires burned with moderate
severity or even patchy high severity, as in West’s
example, tree regeneration was stimulated by the
opening of the canopy.
Historical forests generally were not numerically dominated by large trees (H6 ). Instead, trees
from 10–40 cm in diameter made up 60.0% of
total trees, trees 10–40 cm in diameter were
.50% of trees across 72.3% of forest area, and all
tree species had small trees (Fig. 3). Numerical
dominance by small trees is also supported by
directly measured stand structures in the south
region (Munger 1917). The abundance of oldgrowth forests documented by Cowlin et al.
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(1942) suggests large old trees were common
across substantial area, but reconstructions show
that old forests were dense and also had
abundant small trees. Fire-resistant ponderosa
pine and Douglas-fir had more large trees,
suggesting they more commonly survived
mixed- or high-severity fires (Fig. 3), consistent
with Hessburg et al. (2007:14) who found that
‘‘where large trees were present, they formed a
remnant overstory representing less than 30% of
total canopy cover.’’ Size-distributions for white
fir, western larch, and Douglas-fir hint at
episodes of regeneration linked to fires (western
larch) or fire-free periods (white fir, Douglas-fir).
A fire-free period led to canopy white fir in
mixed-conifer forests at Crater Lake (Agee
2003b).
Regarding H5, shrubs also were present on
71.0% of historical forest area and dense over
43.6% of forest area, even more so in dry mixedconifer forests. Dry forests in northern Arizona
and Colorado had much lower historical levels of
understory shrubs, with shrubs present on only
0.3–11.1% of forest area, except 18.3% in the Blue
Mountains, still much lower than in the eastern
Cascades (Williams and Baker, in press). The
main shrubs in Oregon’s eastern Cascade dry
forests historically and today are: (1) greenleaf
manzanita, which resprouts from underground
lignotubers or from seed (Ruha et al. 1996), (2)
snowbrush ceanothus, with fire-stimulated resprouting and seeds (Conard et al. 1985), and (3)
antelope bitterbrush, which regenerates rapidly
after fire from rodent seed caches (Sherman and
Chilcote 1972) or other means (Busse and Riegel
2009). Abundant fire-adapted shrubs capable of
rapid recovery after fire suggest these forests
lacked extended periods or areas without shrubs,
as shown by the reconstructions. Early observations of sparse or shrubless areas may indicate
early postfire conditions or environmental settings unfavorable to shrubs, as found across 29%
of the forest area (Table 4).
Estimated fire rotations for low-severity fire
show they did not occur at intervals short
enough to keep understory trees and shrubs at
low levels. Reports of short intervals for lowseverity fire (e.g., Agee 1993) used mean composite fire intervals, which underestimate fire
rotation and mean fire interval (Baker and Ehle
2001, Baker 2009). Directly estimated fire rotav www.esajournals.org
tions are 29–78 years at the three sites (Bork
1984), a range that includes the 53-year lowseverity fire rotation for dry forests in eastern
Washington (Wright 1996). Indirect estimates
from snowbrush ceanothus (Table 6) are quite
rough, but support the direct estimates. Mean
intervals of 29–78 years between low-severity
fires allow many trees to regenerate over large
areas, reach sufficient size to resist mortality in
low-severity fires (Baker and Ehle 2001) and
allow shrubs to fully recover after fire. A 30-year
fire-free interval allowed white fir to ascend into
the canopy in mixed-conifer forests at Crater
Lake (Agee 2003b). That low-severity fire occurred at modest rotations helps explain widespread understory trees and shrubs, large areas
with dense understory trees and shrubs, and the
common occurrence of dense forests with firs
(Fig. 1, Tables 3–4).
Regarding H7, the reconstructions show that
historical forests were not dominated by lowseverity fire, but instead had all severities,
including substantial high-severity fire (Table 5,
Fig. 4). Simulation shows that the historical mean
tree density of 249 trees/ha across the study area
is congruent with the variety of fire severities
found in the reconstructions (Johnson et al. 2011).
The mixtures (18.1% low severity, 58.9% mixed
severity, and 23.0% high severity) in dry mixed
conifer are also quite similar to those of Hessburg
et al. (2007) for dry mixed conifer, who found
18.5% low, 51.7% mixed, and 29.8% high severity
in their ESR5 vegetation type, which included
some of the Deschutes. This similarity adds
validation to both reconstructions. Hessburg et
al. (2007) found no difference in fractions by
severity, comparing ponderosa pine and Douglas-fir cover types, but in my study area, ponderosa pine forests had more low- and less mixedand high-severity fire (Table 5). A recent review
of mixed-severity fire in Northwestern forests
suggested variable-severity fire did not occur
historically in ponderosa pine forests or dry
mixed-conifer forests, except in Washington
(Perry et al. 2011). However, the reconstructions
show that both ponderosa pine and dry mixedconifer forests in the Oregon eastern Cascades
historically experienced a variety of fire severities, including substantial high severity (Table 5).
The rate of historical high-severity fire was not
high (H8). The overall 435-year high-severity fire
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rotation (Table 5) is shorter than the 522-year
rotation estimated for dry forests in northern
Arizona and 849 years in the Blue Mountains, but
not as short as the 271-year rotation estimated for
the Colorado Front Range (Williams and Baker,
in press). A charcoal-based paleoecological reconstruction (Long et al. 2011) from Tumalo Lake
(T018SR010E, 18 km west of Bend), on the
ecotone between moist and dry mixed-conifer
forests, shows a recent ‘‘fire-episode’’ frequency
of about 3 per 1000 years (333-year mean). This
site is near the border between north and central
regions, which have estimated rotations of 435
and 278 years (mean ¼ 357 years), respectively,
congruent with the paleo-estimate. This adds
validation to the high-severity fire reconstruction, and also suggests the charcoal estimate is
primarily detecting high-severity fires.
The GLO reconstructions show that most past
hypotheses about dry-forest structure and fire
severity were rejected, just as they were by
Hessburg et al. (2007) for eastern Washington
and part of Oregon’s Deschutes National Forest.
Past understanding of historical variability in
these forests was limited by: (1) too much
extrapolation from spatially limited or anecdotal
data, (2) incomplete analysis of historical observations, (3) the inherently limited and often
biased sample from tree-ring-based studies, and
(4) misinterpretation of fire-history parameters.
Weaver (1959, 1961) thought selected observations of park-like historical conditions represented the whole landscape, but the GLO
reconstructions show they did not (Fig. 1, Tables
3–5), as in eastern Washington (Hessburg et al.
2007). Weaver missed that scattered historical
observations actually do include evidence of
low-, mixed- and high-severity fires, young
postfire forests, brushfields, dense understory
shrubs and small trees, and other features of
historically variable fire severity and forest
structure. Tree-ring studies are invaluable, but
use extant evidence, which is inherently limited
because few sites are relatively free of EuroAmerican land-use effects, selection among sites
is often biased by a focus on old-growth forests,
and because they are so labor intensive that it is
difficult to study much land area. Variability in
tree density and fire severity (Figs. 1 and 4)
shows that studies of less than about 25,000 ha in
dry forests are likely to provide only partial
understanding. Most studies in the region covered much less area, did not estimate fire
rotation, and incorrectly assumed that mean
composite fire intervals estimate fire rotation
and mean fire interval (Baker and Ehle 2001).
These limitations led to incomplete understanding of historical dry forests and fire elsewhere in
the West (Hessburg et al. 2007; Williams and
Baker, in press).
Spatially extensive reconstructions from the
GLO surveys and early aerial photography
(Hessburg et al. 2007) overcome many of these
limitations, but have some others. They, like
historical observations and tree-ring reconstructions, ‘‘provide a short glimpse of the changing
primeval forest’’ (Morrow 1986:69). Structurebased reconstruction of fire from the GLO
surveys and early aerial photography cannot
always discriminate effects of fire from insects,
disease, and other disturbances. Spatial extent
and contiguity suggest fire rather than insects or
disease, which rarely are stand-replacing (Youngblood et al. 2004). Also, GLO surveys do not
provide details of forest structure below the area
of reconstruction polygons, about 520 ha for a 6corner pool. Early aerial photography, in contrast, allows reconstruction down to about 4 ha
(Hessburg et al. 2007). However, the GLO
surveys do allow accurate reconstruction of
spatial variability in parameters of forest structure across large landscapes, prior to many
EuroAmerican land uses, not possible with other
methods.
Fuel reduction is not ecological restoration
in dry forests
Today’s fuel-reduction focus in dry forests was
based on the theory that frequent, low-severity
fires maintained widespread low-density historical forests, which are thought today to have a
large surplus of trees and wood that can be
removed, providing both ecological benefits and
wood products (e.g., Johnson and Franklin 2009).
The reconstructions show that this theory of
historical fire and forest structure is incorrect for
dry forests in the eastern Oregon Cascades. This
theory now has also been rejected for dry forests
in eastern Washington (Hessburg et al. 2007), the
Blue Mountains, Oregon (Williams and Baker, in
press), the Rocky Mountains (Baker et al. 2007;
Williams and Baker, in press), and northern
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BAKER
Fig. 4. Fire severity evident in forest structure at the time of the surveys. See text for definitions of the three fireseverity classes.
Arizona (Williams and Baker, in press).
Commonly proposed fuel-reduction actions
would generally alter or degrade, rather than
restore these Oregon forests. First, the idea that
the risk of high-severity fire, or the fraction of fire
burning at high severity, has increased and needs
to be lowered (e.g., Spies et al. 2006, Perry et al.
2011), is not supported. This study shows that
high-severity fire was a substantial component of
historical fire regimes in both dry mixed conifer
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BAKER
and ponderosa pine forests (Table 5, Fig. 4). Also,
the risk of high-severity fire has not increased
relative to historical landscapes, as the 435-year
approximation of historical high-severity fire
rotation is little different from the 469-year recent
high-severity rotation in old forests in the eastern
Oregon Cascades (Hanson et al. 2009). The
fraction of total fire burning at high severity also
has not increased. For example, a recent fire
perceived as unnaturally severe in dry forests of
the eastern Oregon Cascades (2003 B&B Spies et
al. 2006), actually had only 5% high severity
(http://www.mtbs.gov). Much of the high severity was at higher elevations outside dry forests,
and the fraction of high severity in dry forests
was quite low relative to the fraction of historical
forest area with evidence of high-severity fire
(Table 5, Fig. 4). The fraction of total fire burning
at high severity in dry forests of the eastern
Cascades also did not increase from 1984–2005
(Hanson et al. 2009). If the goal is maintaining or
restoring historical fire regimes, treating large
land areas (e.g., about 45% of dry forests in 20
years; Johnson and Franklin 2009) to reduce highseverity fire would, if effective, substantially add
to fire exclusion and alter or degrade, not restore
these forests.
Second, the common practice of burning or
mechanically removing understory trees and
shrubs to reduce fire risk and lower competition
in dry forests will alter or degrade, rather than
restore forest structure, since understory trees
and shrubs were historically abundant (Table 4),
small trees were numerically dominant, and
these forests were generally dense (Table 3).
The notion that trees in these forests today are
unnaturally stressed by competition due to
abnormally high tree density (e.g., Johnson and
Franklin 2009, Perry et al. 2011) is not supported.
Although tree density may be higher today,
relatively dense and even very dense forests,
with a wide diversity of tree sizes, were
historically the norm in the dry forests of the
eastern Cascades, even in ponderosa pine forests
(Table 3).
Even if the focus is on perpetuating dry forests
in the face of impending climatic change, fuel
reduction, as currently practiced, is mis-directed,
as understory trees and shrubs are key sources of
ecosystem resilience in an era of droughts, beetle
outbreaks, and more fire. The dominant conifers,
ponderosa pine and Douglas-fir, have thick bark
and elevated crowns and may resist fire (Baker
2009), but are vulnerable to severe droughts and
beetle outbreaks (Littell et al. 2010). Thinning
might increase the resistance of large, old trees to
droughts and beetle outbreaks up to a point
(Fettig et al. 2007). However, in general it is the
smaller established trees, not the large, old trees,
that often partly survive and may recover after
severe droughts and beetle outbreaks (Cole and
Amman 1969, McCambridge et al. 1982, McDowell et al. 2008). Native shrubs, in contrast, have
fire and drought adaptations (see above), are not
prone to outbreak insects, and provide key nurse
roles in enhancing conifer survival and regeneration (Foster 1912, Zavitkovski and Newton
1968, Conard et al. 1985). It may be more difficult
to maintain resistance than resilience, particularly as climatic change becomes more severe
(Millar et al. 2007). Northwestern pines, in
particular, are expected to decline as their
suitable climate disappears (Littell et al. 2010).
Fuel reduction, as currently practiced, compromises ecosystem resilience by placing too much
emphasis on resistance by old conifers.
Reconfiguring ecological restoration in
dry forests of the Oregon eastern Cascades
If fuel reduction is an inappropriate focus for
restoration, given this study, what management
actions would be compatible with the findings? I
suggest a combination of no action, modest
active restoration with a re-directed focus, and
passive restoration, if the goals are to restore dry
forests, using historical fire and forest structure
as a guide, while considering climatic change.
First, since expansive treatment is infeasible, due
to cost, it is fortunate that a substantial fraction of
dry mixed-conifer forests, that are currently
dense, need no restoration treatment at all, since
dense forests with substantial fir characterized
sizable fractions of the study area (Table 3).
Second, evidence is compelling that a century
of industrial logging of large trees, particularly
pines (Robbins 1997, Bowden 2003), led to an
increase in small firs (West 1969b, Hessburg and
Agee 2003). However, the magnitude of increase
is not yet quantified. This study shows that firs
were more abundant and widespread historically
than previously thought, but may underestimate
the historical abundance of firs overall in dry
21
BAKER
forests, because I focused on the driest forests.
Also, there is some emerging data (e.g., Merschel
2010), but no comparable published spatially
extensive statistical sample of today’s forests for
comparison. Nonetheless, it is likely that some
areas could be restored by reducing white fir/
grand fir to its more modest historical levels, but
not as in common fuel-reduction approaches
today. The approach would instead be to retain
the high diversity of tree sizes that occurred
historically, including small firs in forest understories and mid-size, sub-canopy firs. Also
beneficial would be restoration of elements of
old forests lost to logging, including large live
trees, as well as large snags and down wood
(Youngblood et al. 2004), which would also help
the Northern Spotted Owl (Hanson et al. 2010).
Since Northern Spotted Owls may be favored by
the firs, since the density reduction is likely
modest and unlikely to provide economic gain,
and since ecological threats from firs appear low,
I suggest passive restoration through self-thinning is most sensible. If adaptive-management
thinning trials proposed for spotted owl recovery
(USFWS 2011) show that owls would benefit,
perhaps a short period of active management
makes sense, but there is no ecological reason
ongoing silviculture (e.g., Johnson and Franklin
2009) should be needed.
Third, regional- and landscape-scale variation
is worth maintaining or restoring, including
geographical areas of denser forests with more
firs (e.g., southwest of Klamath Falls) and lowdensity ponderosa pine forests (e.g., north of
Sisters), as well as the high-severity fire and
mosaic of lodgepole and ponderosa pines, that
characterized pumice-zone forests (Fig. 4, Table
5). Although park-like old-growth dry forests
may be ephemeral, ultimately succumbing to
high-severity fire (Hessburg et al. 2007), long
high-severity rotations suggest that restoring
diversity to today’s mosaic of logged, recovering
forests will provide long-term benefits for wildlife and ecosystem functioning. At the landscape
scale of a few townships (e.g., 25,000 ha),
maintaining or restoring the mosaic of tree
densities, which varied by a factor of 2–4 or
more (Fig. 1), is important to enhancing resilience
to climatic change (Millar et al. 2007, Halofsky et
al. 2011). Here, too, retention of the historical
diversity of tree sizes, even in ponderosa pine
forests (Fig. 3) is important. Since pure ponderosa forests are not generally habitat for spotted
owls, concern for adverse effects of active
management is lower and can focus on effects
on other species.
Finally, in all restoration treatments in dry
forests, understory fuels (shrubs and small trees)
would be maintained and restored, rather than
reduced, and then maintained by modest (multidecadal) low-severity fire rotations that allow
high cover of shrubs and small trees. The
diversity of tree sizes and potential for mixedand high-severity fires that occurred historically
can be restored and maintained. Rather than
measuring success by reduction in torching index
and creation of fire-safe forests (e.g., Perry et al.
2004, Johnson et al. 2011), success would be
measured by perpetuation of the historical
diversity of fire severities and forest structures.
This can best be achieved with ongoing wildland
fire use (Zimmerman et al. 2006) or multiobjective wildland fires, supplemented near
infrastructure by prescribed fires, not aimed at
fuel reduction, but instead at mimicking historical low-severity rotations, severities, and spatial
patterns (Baker 2009). These forests are more
likely to persist through the impending period of
climatic change if the ecosystem resilience conferred by the historical density and diversity of
shrubs and small trees is restored, along with the
historical landscape diversity of forest structure
that resulted from variable fire severity.
ACKNOWLEDGMENTS
Thanks to Suzette Savoie for helping collect and
input field data, Deborah Paulson for collecting field
data, and Ryan Anderson and Daniel Waters for input
of GLO data. I appreciate suggestions on the study by
Deborah Paulson, Mark Williams, Dennis Odion, and
Chad Hanson. Thanks to Dave Perry for providing
digital locations for his study sites and for commenting
on the manuscript. I appreciate the comments of two
reviewers. This study is based upon work supported
by Environment Now, Santa Monica, California and
the National Science Foundation under Grant No.
BCS-0715070.
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SUPPLEMENTAL MATERIAL
APPENDIX A
Table A1. Early observations (up to about A.D. 1920) about fire and forest structure in dry forests of Oregon’s
eastern Cascades and nearby areas. Observations are arranged by topic, then from general locations to specific
and from north to south. Phrases in square brackets are this author’s insertions for clarification.
Source
Location
Low-severity fires
Munger (1917:9–10)
Eastern Oregon
ponderosa pine
forests
Munger (1917:11)
Eastern Oregon
ponderosa pine
forests
Von Wernsted
(1906) cited in
Weaver (1959:16)
Warm Springs Indian
Reservation 90 km
northwest of Bend;
north region
Leiberg (1900:248)
Southern part of
Eastern Oregon
Cascades; central
and south regions
Leiberg (1900:288–
289)
Southern part of
Eastern Oregon
Cascades; central
and south regions
Leiberg (1900:290)
Southern part of
Eastern Oregon
Cascades; central
and south regions
Quote
Interpretation
Q1: ‘‘... by far the greatest amount of
damage is done by surface fires which
work in an inconspicuous way. Light,
slowly spreading fires that form a
blaze not more than 2 or 3 feet high
and that burn chiefly the dry grass,
needles, and underbrush start freely in
yellow-pine forests, because for several
months each summer the surface litter
is dry enough to burn readily.
Practically every acre of virgin yellowpine timberland in central and eastern
Oregon has been run over by fire
during the lifetime of the present
forest, and much of it has been
repeatedly scourged.’’
Q2: ‘‘Each fire kills the seedlings and
some of the saplings, so that, if the
fires are of frequent occurrence, no
young growth has a chance to replace
the mature trees that die from natural
causes.’’
Q3: ‘‘The yellow pine reproduction is
uneven and on the whole poor on
account of ground fires which have
been frequent in the past... Fires have
been frequent in the past and there is
hardly any area that does not show
signs of old fires. In the yellow pine
the effect has been mainly to keep
down the reproduction...’’
Q4: ‘‘But the open character of the
yellow-pine type of forest anywhere in
the region examined is due to
frequently repeated forest fires more
than to any other cause...’’
Q5: ‘‘On the eastern side of the Cascades,
especially, fires have run through the
yellow-pine timber many times. The
absence or relative scarcity of young
growth and underbrush is here very
noticeable and striking...’’
Q6: ‘‘A fire in stands of this species
[ponderosa pine] runs rapidly, burns
low, and with no great intensity owing
to the extremely light humus cover.’’
26
Widespread surface
fires burn at low
intensity
Low-severity fires kill
young trees
Widespread lowseverity fires kill
most young trees
Frequent low-severity
fires maintain open
forests
Frequent low-severity
fires maintain open
forests
Low-severity fires are
fast and low in
intensity
BAKER
Table A1. Continued.
Source
Location
Mixed-severity fires
Leiberg (1900:424),
Dodwell and
Rixon (1903:286–
287)
T037S R005E; 40 km
northwest of
Klamath Falls;
south region
Leiberg (1900:446)
T039S R005E; 40 km
west of Klamath
Falls; south region
Munger (1917:9)
Eastern Oregon
ponderosa pine
forests
Weaver (1961:569).
southern part of
Eastern Oregon
Cascades; central
and south regions
High-severity fires in
ponderosa pine
forests
Weaver (1961:569)
High-severity fires:
large fires
Langille (1903:36)
Quote
Interpretation
Q7: ‘‘In many localities the fires have
made a clean sweep of the timber, and
the areas have grown up to brush; in
other places they have been of low
intensity, burning 40 per cent of a
stand here, 5 per cent there, or merely
destroying individual trees, but
consuming the humus and killing the
undergrowth.’’
Q8: ‘‘Fires have run everywhere in the
forest stands, suppressing the young
growth, burning great quantities of the
firs, and filling the forest with a great
many small brushed-over tracts in
place of the consumed timber.’’
Q9: ‘‘Occasionally a fire gets into the tops
of the trees in a pure yellow-pine forest
on a slope and sweeps over the whole
hillside, perhaps a square mile in
extent, killing all the trees in its path.
This spectacular form of fire damage is
uncommon, however; ...’’
Q10: ‘‘The last great fire, or series of fires,
covered over 200,000 acres [80,972 ha]
during the summer of 1918... Little is
known of the 1918 fire, except that it
covered most of the central portion of
the reservation [Klamath Indian
Reservation] and that in general it did
not cause excessive damage, except
where it crowned through lodgepole
pine stands and in the vicinity of
Skelloch Draw and Military Crossing.
There it crowned in patches of
ponderosa pine. Extensive pole stands
of this species there date back to the
1918 fire.’’
Fires are high-severity
in places and lowseverity in other
places
Fires are high-severity
in places and lowseverity in other
places
High severity in parts
of fires
Fire was low severity
over large areas but
high severity in
places
Southern part of
Eastern Oregon
Cascades; central
and south regions
pine stands and in the vicinity of
Skelloch Draw and Military Crossing.
There it crowned in patches of
ponderosa pine. Extensive pole stands
of this species there date back to the
1918 fire.’’
ponderosa pine
forests
North region
Q12: ‘‘... along the eastern slope, toward
the plains ... tamarack has done more
than any other species to restock the
immense burns that have taken place
in this part of the reserve.’’
Very large highseverity fires
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Source
Location
Leiberg (1900:278)
Low-severity fires and
brushfields
Munger (1917:11)
High-severity fires
and brushfields
Langille (1903:68)
Langille (1903:48)
Langille (1903:64)
Foster (1912:213)
Leiberg (1900:286)
Quote
Interpretation
T030S R008E, T031S
R008E, T030S
R009E, T031S
R009E; 30 km east
of Crater Lake;
central region
Q13: ‘‘The largest burns directly
chargeable to the Indian occupancy are
in Ts. 30 and 31 S., Rs. 8 and 9 E. In
addition to being the largest they are
likewise the most ancient. The burns
cover upward of 60,000 acres, all but
1,000 or 1,100 acres being in a solid
block. This tract appears to have been
systematically burned by the Indians
during the past three centuries.
Remains of three forests are distinctly
traceable in the charred fragments of
timber which here and there litter the
ground. Two of these were composed
of lodgepole pine. The most ancient
one appears to have consisted of
yellow pine, which would be the
ultimate forest growth on this area
following a long period of freedom
from fire.’’
High-severity fires
exceeding 24,000 ha
Southern part of
Eastern Oregon
Cascades; central
and south regions
causes. . . If this process is continued
long enough, it will annihilate the
yellow pine by gradually killing off the
old trees and at the same time
preventing the survival and maturity of
any young ones. This very thing has
happened in places in the Siskiyou
Mountains and southern Cascades.
Here areas once covered by fine stands
of yellow-pine timber are now treeless
wastes, covered only by brush or mock
chaparral.’’
How low-severity
fires could
eventually lead to
brushfields
T001N R010E; 25 km
northeast of Mt.
Hood; north region
T001S R010E; 15 km
northeast of Mt.
Hood; north region
Q15: ‘‘Creeping fires have destroyed
much of the timber, and dense brush
has followed’’
Q16: ‘‘The greater part of this township
has been burned over and has grown
up to a dense tangle of willow,
ceanothus, and other shrubs.’’
Q17: ‘‘In the northwestern sections the
brush is very dense where old burns
have taken place’’
Q18: ‘‘Brush occurs very generally
throughout the forest [the old Crater
National Forest], occasionally forming
an exclusive cover, but ... there is
evidence that this condition is
temporary...’’
Q19: ‘‘Growths after fires on the eastern
side of the Cascades in pure yellowpine forest may be either brush or
timber... Brush growths after fire are
due to induced semiarid conditions...
Where, in such places, fire has lessened
the ratio of soil humidity, permanent
brush growths usually take the place of
the forest’’
High-severity fires led
to brushfields
T004S R011E; 35 km
southeast of Mt.
Hood; north region
Southern part of
Eastern Oregon
Cascades; central
and south regions
Southern part of
Eastern Oregon
Cascades; south
region
28
to brushfields
to brushfields
to brushfields
ponderosa pine
forests led to
brushfields
BAKER
Source
Location
Quote
Leiberg (1900:355)
T032S R012E; 70 km
southeast of Crater
Lake; 40 km east of
south region
Dodwell and Rixon
(1903:272),
Leiberg
(1900:382)
T034S R006E; 35 km
south of Crater
Lake; south region
Q20: ‘‘The mill timber is exclusively
yellow pine, fire marked throughout,
easy of access from the Sycan, hence
from the Sprague River Valley; of
medium quality, much intersected by
lodgepole-pine reforestations after fires;
the lodgepole stands extensively
invaded by recent fires which have
utterly destroyed them in many places,
giving rise to fire glades covered with
brush.’’
Q21: ‘‘Where the yellow-pine stands have
been destroyed heavy brush growths of
the vellum-leaved ceanothus have
followed.’’
Dodwell and Rixon
(1903:278)
T035S R006E; 45 km
south of Crater
Lake; south region
Dodwell and Rixon
(1903:286–287)
T037S R005E; 40 km
northwest of
Klamath Falls;
south region
Foster (1912:216)
T037S R005E; 40 km
northwest of
Klamath Falls;
south region
Dodwell and Rixon
(1903:288)
T037S R006E; 30 km
northwest of
Klamath Falls;
south region
Leiberg (1900:428)
T037S R010E; 15 km
northeast of
Klamath Falls;
south region
T039S R005E; 40 km
west of Klamath
Falls; south region
Leiberg (1900:446)
High-severity fires:
the lodgepole
pine and
ponderosa pine
mosaic
Langille (1903:36)
Northern part of
Eastern Oregon
Cascades; north
region
Interpretation
Q22: ‘‘Many of the burned-over tracts are
covered with dense brush growth of
various species of shrubs, the vellumleaved ceanothus being the most
common and prominent species.’’
Q23: ‘‘In many localities the fires have
made a clean sweep of the timber, and
the areas have grown up to brush; in
other places they have been of low
intensity, burning 40 per cent of a
stand here, 5 per cent there, or merely
destroying individual trees, but
consuming the humus and killing the
undergrowth.’’
Q24: ‘‘... a slope east of Lake of the
Woods is typical... It consists of a large
brush-covered area with scattering
trees of yellow pine and white fir–trees
of the lower-slope type ... the brush is
the ubiquitous Ceanothus, with small
clumps of Salix.’’
Q25: ‘‘Fires have run throughout the
entire township, consuming 25 per cent
of the timber and badly damaging the
remainder. Brush growths composed
chiefly of the vellum-leaved ceanothus
(Ceanothus velutinus) have covered the
burned areas in place of
reforestations.’’
Q26: ‘‘Fires have run throughout, and the
forest is in consequence much broken
by brushed-over fire glades.’’
lodgepole pine
forests led to
brushfields
ponderosa pine
forests led to
brushfields
dominated by
Ceanothus velutinus
to brushfields
dominated by
Ceanothus velutinus
to brushfields in
many places
to brushfields
dominated by
Ceanothus velutinus
with scattered tree
regeneration
to brushfields
dominated by
Ceanothus velutinus
to brushfields
to many small
brushfields
Q28: ‘‘Lodgepole pine reclaims large
burned tracts and is valuable in
promoting the growth of more
desirable species.’’
High-severity fires
favor lodgepole
pine
29
BAKER
Source
Location
Munger (1917:18)
Eastern Oregon
Cascades; central
region
Leiberg (1900:250)
Southern part of
Eastern Oregon
Cascades; central
region
Leiberg (1900:355)
T032S R012E; 70 km
southeast of Crater
Lake; 40 km east of
central region
Leiberg (1900:371)
T033S R012E; 75 km
southeast of Crater
Lake; 40 km east of
central region
Leiberg (1900:284)
Southern part of
Eastern Oregon
Cascades; central
and south regions
Leiberg (1900:286)
Southern part of
Eastern Oregon
Cascades; central
and south regions
Weaver (1961:569)
Southern part of
Eastern Oregon
Cascades; central
and south regions
Dodwell and Rixon
(1903:152)
T18S to T029S; central
and south regions
Quote
Interpretation
Q29: ‘‘It [lodgepole pine] is a thrifty and
militant species, and has the ability to
occupy burns to the exclusion of all
others. With the help of periodic
surface fires, which have encouraged
its reproduction and at the same time
discouraged the reproduction of yellow
pine, it has been able to encroach upon
land where yellow pine might be
growing’’
Q30: ‘‘The aspect of the murrayana form,
in its ultimate development, is that of
close or moderately open stands of tall,
straight, slender trees covering welldrained uplands. This form of the
subtype is in every case a reforestation
after fires, in this region after stands of
yellow-pine.’’
Q31: ‘‘The mill timber is exclusively
yellow pine, fire marked throughout,
easy of access from the Sycan, hence
from the Sprague River Valley; of
medium quality, much intersected by
lodgepole-pine reforestations after fires;
the lodgepole stands extensively
invaded by recent fires which have
utterly destroyed them in many places,
giving rise to fire glades covered with
brush.’’
Q32: ‘‘The township contains a small
bunch of yellow-pine stands of poor
quality in the northwest corner. The
balance of the township is covered
with stands of lodgepole pine burned
to the extent of 65 per cent by fires in
recent times, and carrying here and
there small scattered stands of yellow
pine of little or no commercial value.’’
Q33: ‘‘On the levels as well as on the
mountain areas east of the Cascades,
where the normal forest growth is
chiefly yellow pine with small
admixtures of sugar pine and white fir,
reforestations after fires are nearly
always pure growths of lodgepole
pine.’’
Q34: ‘‘Growths after fires on the eastern
side of the Cascades in pure yellowpine forest may be either brush or
timber... When timber, the
reforestations are usually lodgepole
pine.’’
pine stands...’’
Q36: ‘‘The young growth east of the
mountains is generally lodgepole pine
and yellow pine where that timber is
found, and in nearly every case where
burns occur the lodgepole
predominates.’’
High-severity fires
favor lodgepole
pine; low-severity
fires have favored
lodgepole pine over
ponderosa pine
30
ponderosa pine
forests favor
lodgepole pine
ponderosa pine
forests favor
lodgepole pine
lodgepole pine
ponderosa pine
forests favor
lodgepole pine
ponderosa pine
forests favor
lodgepole pine
lodgepole pine
High-severity fires
favor lodgepole
pine
BAKER
Source
Location
High-severity fires
and tree
regeneration:
larch
Langille (1903:36)
High-severity fires
and tree
regeneration:
multiple species
Leiberg (1900:284)
Forest structure: age/
size structure
Munger (1917:11)
Quote
Interpretation
Northern part of
Eastern Oregon
Cascades; north
region
Q37: ‘‘Tamarack has done more than any
other species to restock the immense
burns that have taken place in this part
of the reserve. This is largely due to the
fact that the thick bark of this tree
resists fire better than any other
species, and more seed trees are left to
cast their seed upon the clean, loose
soil and ashes immediately after a fire.
The seeds are small and light, and are
carried to remote places by the winds
and covered deeply by the fall rains. In
the spring a dense mass of seedlings
covers the ground and grows rapidly.
The thickets become so dense that it is
impossible to travel through them. In
time, only the fittest survive, and there
remains a thrifty, vigorous stand of this
valuable timber.’’
Western larch
survives and
reseeds after highseverity fire
T039S R004E; T039S
R005E; T039S
R006E; T040S
R004E; T040S
R005E; T040S
R006E; T041S
R004E; T041S
R005E; T041S
R006E; 20–50 km
southwest of
Klamath Falls;
south region
Q38: ‘‘But in the yellow-pine areas of Ts.
41, 40, and 39 S., Rs. 4 to 6E, inclusive,
reforestations after fires are not
composed of lodgepole pine.
Reforestations here are yellow pine, red
and white fir, sugar pine, and incense
cedar; in short, the same species again
come in which flourished before the
fire.’’
A variety of species
regenerate after
high-severity fires
south of the pumice
zone in the central
region
Eastern Oregon
ponderosa pine
forests
causes.’’
Q40: ‘‘Yellow pine normally occurs in
Oregon in uneven-aged stands in
which trees of all ages are in intimate
mixture; frequent fires prevent the
stand from having the proper number
of young trees.’’
Q41: ‘‘Yellow pine grows commonly in
many-aged stands; i.e., trees of all ages
from seedlings to 500-year-old
veterans, with every age gradation
between, are found in intimate
mixture.’’
Q42: ‘‘In some stands there is a
preponderance of very old trees; in
fact, in many of the virgin stands of
central and eastern Oregon there are
more of the very old trees and less of
the younger than the ideal forest
should contain.’’
Low-severity fires
leave few small
trees
Munger (1917:11)
Eastern Oregon
ponderosa pine
forests
Munger (1917:18)
Eastern Oregon
ponderosa pine
forests
Munger (1917:18)
Eastern Oregon
ponderosa pine
forests
31
Ponderosa pine
forests are typically
uneven-aged, with
few young trees
Ponderosa pine
uneven-aged
Ponderosa pine
dominated by old
trees with a
deficiency of young
trees
BAKER
Source
Location
Munger (1917:19)
Eastern Oregon
ponderosa pine
forests
Langille (1903:33)
T004S R011E; 25 km
southeast of Mt.
Hood; north region
Forest structure: tree
density
Munger (1917:17)
Eastern Oregon
ponderosa pine
forests
Munger (1917:21)
Eastern Oregon
ponderosa pine
forests
Munger (1917:20)
Eastern Oregon
Cascades
Langille (1903:34–
35, Plate IX)
T005S R010E; 30 km
southeast of Mt.
Hood; north region
Plummer (1903:78)
T005S to T017S; north
region
Weaver (1959:16)
Warm Springs Indian
Reservation; 90 km
northwest of Bend;
north region
Munger (1917:21)
Southern part of
Eastern Oregon
Cascades; central
and south regions
Quote
Interpretation
Q43: ‘‘In the virgin stands throughout the
State there seems to be a very large
proportion of trees whose age is about
225 or 275 years, suggesting that after
this age their mortality is greater.’’
Q44: ‘‘The timber in this vicinity is almost
all yellow pine of two classes, viz, old
trees with an average diameter of 30
inches, and a younger growth about 18
inches in diameter.’’
Q45: ‘‘In most of the pure yellow-pine
forests of the State the trees are spread
rather widely, the ground is fairly free
from underbrush and débris, and travel
through them on foot or horseback is
interrupted only by occasional patches
of saplings and fallen trees...On the
north slopes, in draws, or in other
places where mixed with other species,
the yellow-pine forests are usually
denser, more brushy, and therefore
harder to traverse.’’
Q46: ‘‘Yellow-pine forests are so irregular
in density that figures for the average
stand per acre or per quarter section
are apt to be misleading.’’
Q47: ‘‘In pure, fully stocked stands in the
Blue Mountain region there are
commonly from 20 to 30 yellow pines
per acre over 12 inches in diameter, of
which but few are over 30 inches. Over
large areas the average number per
acre is ordinarily less than 20. On the
slopes of the Cascades the number of
trees per acre averages somewhat less
than in the Blue Mountains, but the
trees are larger. In mixed stands, the
number of yellow pines of
merchantable size is naturally less,
though the total number of trees of all
species is as a rule larger...’’
Q48: Plate IX shows the forest being cut.
The forest is obviously dense.
Q49: ‘‘Its forests [ponderosa pine] are
generally open, without much litter or
undergrowth, and for these reasons are
almost immune from fire.’’
Q50: ‘‘Mr James G. Smith, an elderly
member of the Warm Springs Tribe,
recalls that as late as 1914 or 1915 it
was possible to drive a wagon almost
at will throughout most of the
ponderosa pine type.’’
Q51: Table 7 contains diameter-class
distributions and tree-density estimates
for two ponderosa pine stands: (1)
Near Lapine: 32.5 trees/ha .10 cm; 29.3
trees/ha .30 cm; (2) Klamath Lake
region: 151.9 trees/ha .10 cm; 87.0
trees/ha .30 cm.
32
Ponderosa pine
forests often have
trees up to about
225–275 years old
Ponderosa pine
forests with only
two size classes of
trees
Ponderosa pine
low density except
on moister slopes
Ponderosa pine
forests are very
variable in density
In the Eastern
Cascades,
ponderosa pine
forests may have
,50–75 trees/ha
that are .30 cm in
diameter, with few
trees .75 cm, but
mixed-conifer
stands are denser
A dense dry forest
visible in a picture
from near A.D.
1900
Ponderosa pine
forests generally
low density
Early account
suggests ponderosa
pine forests were
low density
Two ponderosa pine
stands had 32.5 and
151.9 trees/ha
BAKER
Source
Location
Weaver (1961:569)
T035S R008E, T035S
R009E, T036S
R008E, T036S
R009E; 30 km north
of Klamath Falls;
south region
20–35 km northeast of
Klamath Falls; 10–
20 km southeast of
south region
Weaver (1961:569)
Forest structure:
spatial pattern of
tree regeneration
Munger (1917:8)
Eastern Oregon
ponderosa pine
forests
Munger (1917:18–
19)
Eastern Oregon
ponderosa pine
forests
Langille (1903:36)
Northern part of
Eastern Oregon
Cascades; north
region
Warm Springs Indian
Reservation 90 km
northwest of Bend;
north region
20 km southeast of
south region
Von Wernsted
(1906) cited in
Weaver (1959:16)
Weaver (1961:569)
Forest structureabundant or
dense tree
regeneration
Munger (1917:11)
Eastern Oregon dry
mixed conifer
forests
Quote
Interpretation
Q52: ‘‘In 1929 Jack Horton, an elderly
cattleman of Hildebrand, Oregon,
stated that in the early days the Ya
Whee Plateau was ‘open and grassy,
like a park.’’’
Early account
suggests dry forests
were low density
Q53: ‘‘Harry Engle, an elderly resident of
Fort Klamath, Oregon, still recalls
vividly the days when he rode the
range in the Sprague River–Swan
Lake–Hildebrand area in the late 1880’s
and the 1890’s... The forest was open
and park-like with considerable
grass...’’
Early account
suggests dry forests
were low density
Q54: ‘‘... yellow-pine reproduction is
extremely patchy in the virgin forest;
here there will be almost a thicket of
young trees, and near by, under
seemingly similar conditions, there will
be little or no reproduction.’’
Q55: ‘‘Usually two or three or more trees
of a certain age are found in a small
group by themselves, the reason being
that a group of many young trees
usually starts in the gap which a large
one makes when it dies.’’
Q56: ‘‘The yellow pine in some instances
does good work in stocking open spots
in the timber, but seldom extends far
beyond the parent tree.’’
been frequent in the past.’’
Fort Klamath, Oregon, still recalls
vividly the days when he rode the
range in the Sprague River–Swan
Lake–Hildebrand area in the late 1880’s
and the 1890’s... The forest was open
and park-like with considerable grass...
To the specific query if there were
young trees Mr. Engle replied that
there were scattered groups of saplings
and trees of pole size. He explained
that fuel seldom accumulated in
sufficient quantity to enable the fires to
become very hot. Therefore, many of
the young trees survived.’’
Ponderosa pine
regeneration was
highly variable
Q59: ‘‘In certain parts of the State
repeated surface fires have the effect of
transforming the forest type from a
stand consisting largely of yellow pine
to one consisting of lodgepole pine,
whose reproduction is extremely
abundant and vigorous after fire.’’
33
Ponderosa pine
regeneration was in
small groups
associated with a
canopy gap
Ponderosa pine
regeneration close
to parent trees
Ponderosa pine
regeneration poor
because of fires
Dry forests had tree
regeneration in
scattered groups
because of fire
patterns
Very dense lodgepole
pine regeneration
after fire
BAKER
Source
Location
Munger (1917:8)
Eastern Oregon
ponderosa pine
forests
Von Wernsted
(1906) cited in
Weaver (1959:16)
Warm Springs Indian
Res. 90 km NW of
Bend; N Region
Leiberg (1900:322)
T030S R010E; 45 km
east of Crater Lake;
22 km east of
central region
Leiberg (1900:339)
T031S R010E; 50 km
east of Crater Lake;
20 km east of
central region
Southern part of
Eastern Oregon
Cascades; central
and south regions
Leiberg (1900:288–
289)
Forest structure:
shade-tolerant
trees
Langille (1903:36)
Plummer (1903:102–
103, Plate XVII)
Leiberg (1900:446)
Munger (1917:17)
Northern part of
Eastern Oregon
Cascades; north
region
Northern part of
Eastern Oregon
Cascades; north
region
T039S R005E; 40 km
west of Klamath
Falls; south region
Eastern Oregon
ponderosa pine
forests
Quote
Interpretation
Q60: ‘‘... yellow-pine reproduction is
extremely patchy in the virgin forest;
here there will be almost a thicket of
young trees, and near by, under
seemingly similar conditions, there will
be little or no reproduction.’’
been frequent in the past. When there
is reproduction in spots, it is, however,
dense.’’
Q62: ‘‘In late years there has been fewer
fires than formerly and the young
growth, formerly mostly suppressed, is
asserting itself everywhere. The young
growth is yellow pine with a few
scattered individuals of white fir.’’
Q63: ‘‘Fires have not run much in later
years and the young growth of yellow
pine is therefore abundant.’’
Ponderosa pine
regeneration was
highly variable,
including some
thickets
Where ponderosa
pine regeneration
occurs in spots, it is
dense
Abundant ponderosa
pine regeneration
Abundant ponderosa
pine regeneration
Q64: ‘‘On the eastern side of the
Cascades, especially, fires have run
through the yellow-pine timber many
times. The absence or relative scarcity
of young growth and underbrush is
here very noticeable and
striking ... where the forest has enjoyed
freedom from fire for a number of
years seedling and sapling trees of the
yellow pine are springing up in the
greatest abundance.’’
Abundant ponderosa
pine regeneration in
places
Q65: ‘‘In the yellow-pine forests most of
the young growth is red [Douglas-fir]
or white fir, which, taking advantage of
the shade and moisture afforded by the
yellow-pine cover, is growing rapidly,
and will, in time, form a larger
percentage of the forest than it has in
the past.’’
Q66: Plate XVII shows a mature stand of
incense cedar
Most regeneration in
dry mixed-conifer
forests is Douglasfir and white fir
Q68: ‘‘In most of the pure yellow-pine
forests of the State the trees are spread
rather widely, the ground is fairly free
from underbrush and débris, and travel
through them on foot or horseback is
interrupted only by occasional patches
of saplings and fallen trees... On the
north slopes, in draws, or in other
places where mixed with other species,
the yellow-pine forests are usually
denser, more brushy, and therefore
harder to traverse.’’
Mixed-severity fires
killed many firs
34
Mature incense cedar
occurred in places
Ponderosa pine
forests were fairly
free of understory
shrubs except on
north slopes or in
moister settings
BAKER
Source
Location
Forest structure:
understory
shrubs
Plummer (1903:78)
Plummer (1903:87)
Northern part of
Eastern Oregon
Cascades; north
region
Northern part of
Eastern Oregon
Cascades; north
region
Von Wernsted
(1906) cited in
Weaver (1959:16)
Warm Springs Indian
Reservation 90 km
northwest of Bend;
north region
Dodwell and Rixon
(1903:152)
T018S to T029S;
central region
Munger (1917:18)
Southern part of
Eastern Oregon
Cascades; central
and south regions
Southern part of
Eastern Oregon
Cascades; central
and south regions
Leiberg (1900:288–
289)
Weaver (1961:569)
Weaver (1961:569)
T035S R008E, T035S
R009E, T036S
R008E, T036S
R009E; 30 km north
of Klamath Falls;
south region
20 km southeast of
south region
Quote
Interpretation
Q69: ‘‘Its forests [ponderosa pine forests]
are generally open, without much litter
or undergrowth, and for these reasons
are almost immune from fire.’’
Q70: ‘‘In the yellow-pine region
bordering the timberless area of eastern
Oregon the forest floor is often as clean
as if it had been cleared, and one may
ride or even drive without hindrance.
As the hills are approached the brush
increases ... on the northern summits
and on all the western slopes the
underbrush is heavy, and together with
the litter makes travel off the trails
impossible with pack animals’’
Q71: ‘‘There is very little underbrush in
the lower country and but very little
grass ... with the foothills there is an
increasing amount of chaparral
undergrowth.’’
Q72: ‘‘Along the eastern slope of the
Cascade Mountains very little
undergrowth is found, as the climate is
much drier...’’
Q73: ‘‘Here [southern Cascades] there is ordinarily a great deal of underbrush and
chaparral, and the more open the woods
the greater the amount of brush.’’
Q74: ‘‘On the eastern side of the
Cascades, especially, fires have run
through the yellow-pine timber many
times. The absence or relative scarcity
of young growth and underbrush is
here very noticeable and striking...’’
Q75: ‘‘In 1929 Jack Horton, an elderly
cattleman of Hildebrand, Oregon,
stated that in the early days the Ya
Whee Plateau was ‘open and grassy,
like a park.’’’
Fort Klamath, Oregon, still recalls vividly the days when he rode the range in
the Sprague River–Swan Lake–Hildebrand area in the late 1880’s and the
1890’s... The forest was open and parklike with considerable grass. There were
clumps of manzanita (Arctostaphylos
spp.), snowbrush (Ceanothus velutinus)
and bitterbrush (Purshia tridentata), but
these shrubs seldom grew very high because of the frequent fires set by cowboys and lightning.’’
35
Ponderosa pine
forests had few
understory shrubs
Ponderosa pine
forests free of
understory shrubs
near lower forest
border, but more
shrubs in foothills
Dry forests had few
understory shrubs
near lower forest
border, but more
shrubs in foothills
Dry forests had few
understory shrubs
Dry forests in south
had abundant
shrubs, especially in
lower-density stands
Ponderosa pine
forests had few
understory shrubs
because of fires
Dry forests had
grassy, not shrubby
understories
Dry forests had
considerable grass,
with only clumps of
shrubs, because of
frequent fires
BAKER
APPENDIX B
Table B1. Trees of the eastern Cascades, common names used by the surveyors, and
abundance in the surveys, by group. Species
Conifers
Calocedrus decurrens
Juniperus occidentalis
Larix occidentalis
Picea engelmannii
Pinus monticola
Tsuga mertensiana
Total
Firs
Abies concolor/grandis
Abies magnifica
Fir sp.
Pseudotsuga menziesii
Total
Hardwoods
Acer circinatum
Alnus sp.
Fraxinus sp.
Populus sp.
Populus tremuloides
Prunus sp.
Quercus sp.
Quercus garryana
Quercus kelloggii
Salix sp.
Total
Pine
Pinus contorta var.
murrayana
Pinus lambertiana
Pine sp.
Pinus ponderosa
Total
Grand total
Common name
Numberà
Percentageà
Cedar
Juniper
Larch, tamarack
Spruce
White pine
Hemlock
178
135
147
26
6
10
502
1.50
1.14
1.24
0.22
0.05
0.08
4.23
W. fir, White fir
Shasta fir
Fir
Douglas-fir, Red fir
110
1
1643
269
2023
0.93
0.01
13.86
2.27
17.06
Vine maple
Alder
Ash
Balm, cottonwood
Aspen, quaking aspen
Cherry
Oak
White oak
Black oak
Willow
2
8
2
3
15
1
22
57
19
7
136
0.02
0.07
0.02
0.03
0.13
0.01
0.19
0.48
0.16
0.06
1.15
B. pine, Black pine, Sassafras
pine, tamarack (in one township)
Sugar pine
Pine
Y. pine, Yellow pine
783
6.60
105
6960
1347
9195
11856
0.88
58.70
11.36
77.56
100.00
These are species groups used in the reconstruction of basal area and diameter distributions.
à These are the number and percentage of trees recorded by the surveyors out of the grand
total of 11,856 trees.
36
BAKER
APPENDIX C
Table C1. Quality and consistency of information recorded by surveyors of the Oregon Eastern Cascades study
area. Analysis of specific parts of section-line descriptions (e.g., understory trees and tree density) used only
surveyors with entries recorded as ‘‘yes’’ in that column.
Used many
density terms to
describe timber
Surveyor
Major
Perkins, Henry C.
Judkins, Thomas C.
Moore, Rufus S.
Lackland, Samuel W.
Meldrum, Henry
Chandler, Henry L.
Minor
Applegate, Daniel W
Applegate, Jesse
Byars, W. H.
Campbell, Frank
Campbell, William B.
Campbell, William S.
Cartee, L. F.
Clark, Newton
Fisher, E. F. F.
Gradon, Herman D.
Handley, T. B.
Howard, James
McQuinn, John A
McClung, John W.
Meldrum, John W.
Mensch, Fred
Mercer, George
Owen, Jason
Pershin, George S.
Ransom, D. W.
Rumsey, James L.
Taylor, Douglas W.
Thompson, David W.
Tolman, James C.
Truax, Sewell
Turner, William M.
Wilkes, Lincoln E.
Yes
Yes
Yes
Yes
Yes
Yes
No;
Yes
No;
Yes
No;
Yes
No;
Yes
Yes
No;
Yes
Yes
Yes
No;
No;
No;
No;
No;
No;
Yes
No;
No;
Yes
No;
No
Yes
Yes
only one term
no use of terms
only one term
no use of terms
only one term
no use of terms
only one term
no use of terms
only one term
only one term
only one term
only one term
only one term
only one term
Recorded
understory trees
and tree density
Recorded
understory shrubs
and shrub density
Approximate
number of
townships surveyed
Yes
Yes; only in
4 townships
Yes
Yes
Yes
Yes
Yes
Yes; only in
4 townships
Yes
Yes
Yes
Yes
6.0
5.0
4.5
2.5
Yes
Yes
No
No
Yes
Yes
No
Yes
No
No; only rarely
No
Yes
Yes
No
No
No
No
No; only rarely
Yes
No
Yes
Yes
No
Yes
Yes
No; only rarely
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
No
No
No
Yes
Yes
No
No
No
No
No
Yes
No
Yes
No
No
Yes
Yes
Yes
Yes
0.5
0.7
0.1
0.8
0.2
0.3
0.1
0.3
0.2
1.0
0.2
1.0
1.0
0.1
0.5
0.1
0.1
1.2
0.4
0.5
1.5
0.1
0.2
1.3
0.1
0.4
0.1
7.5
7.0
Notes: Surveyors are rated as to whether they consistently used density terms (dense or heavily timbered, good, fine, and
scattered) to describe timber and recorded understory trees and shrubs. To be given the rating ‘‘yes,’’ a surveyor had to use all
the terms and had to consistently record information about understory trees or shrubs.
37
BAKER
APPENDIX D
Table D1. Understory species of the Eastern Cascades study area.
Species
Acer circinatum, occasionally A. macrophyllum
Alnus sp.
Amelanchier sp.
Arctostaphylos patula
Artemisia tridentata
Berberis aquifolium, B. repens
Calamagrostis rubescens
Castanopsis chrysophylla
Ceanothus integerrimus
Ceanothus velutinus
Cercocarpus ledifolius
Cornus sericea, or other Cornus spp.
Corylus cornuta
Fragaria sp.
Kraschennikikovia lanata
Populus tremuloides
Prunus emarginata, and other Prunus
Prunus virginiana
Purshia tridentata
Ribes cereum, occasionally other Ribes
Rosa woodsii or other Rosa sp.
Rubus ursinus
Rubus parviflorus
Rubus spectabilis
Salix sp.
Scirpus sp.
Vaccinium sp.
Viburnum edule
Unknown species
Surveyor names
Maple, vine maple
Alder, black alder
Serviceberry
Chamise, manzanita, rhododendron
Sagebrush
Barberry, bearberry, grape, wild grape
Pine grass
Chinkapin
Lilac, heath lilac—based on color and inflorescence; no validation along
section lines
Annis, balm, cinnamon, elk brake, elk brush, greasewood, snowbrush
Mahogany
Dogwood
Hazel, Witch hazel
Strawberry
White sage
Aspen, quaking aspen, quaking ash?
Cherry, plum
Choke cherry
Buck brush, chaparral, laurel, mountain laurel, myrtle, sweet laurel
Currant, gooseberry
Rose, wild rose
Blackberry
Thimbleberry
Salmonberry
Willow
Tules
Huckleberry, whortleberry
Arrowwood; based on wood properties; no validation along section lines
Snowdrop
38
BAKER
APPENDIX E
Table E1. Crown radius and Voronoi equations used in the reconstructions.
Ln crown radius (CR)
Species equations
Group/species
Group 1
Abies concolor
Abies grandis
Calocedrus decurrens
Pseudotsuga menziesii
Quercus kelloggii
‘‘Fir’’
Group 2
Larix occidentalis
Pinus monticola
Quercus garryana
Group 3
Juniperus occidentalis
Pinus contorta
Pinus lambertiana
Pinus ponderosa
‘‘Pine’’
Pooled equations
All species
Ln Voronoi area
Group equations
Equation
n
R 2adj
þ
þ
þ
þ
þ
þ
ln(dbh)
ln(dbh)
ln(dbh)
ln(dbh)
ln(dbh)
ln(dbh)
21
22
24
25
21
88
53.3
34.9
38.8
63.0
20.6
47.5
3.150 þ 1.020 ln(dbh)
1.320 þ 0.714 ln(dbh)
1.270 þ 0.685 ln(dbh)
23
11
22
53.9
80.1
32.8
1.040 þ 0.588 ln(dbh)
1.040 þ 0.572 ln(dbh)
0.946 þ 0.587 ln(dbh)
0.896 þ 0.532 ln(dbh)
1.210 þ 0.625 ln(dbh)
23
24
24
26
97
63.8
52.2
72.8
75.8
74.2
0.786 þ 0.512 ln(dbh)
285
53.3
0.163
0.576
1.000
0.200
0.210
0.573
0.347
0.417
0.529
0.409
0.401
0.484
Equation
n
R 2adj
1.470 þ 0.330 ln(CR/(1/Meandist2))
64
41.3
0.586 þ 0.565 ln(CR/(1/Meandist2))
33
35.2
0.914 þ 0.628 ln(CR/(1/Meandist2))
82
47.1
1.410 þ 0.428 ln(CR/(1/Meandist2))
201
42.8
Notes: The three groups were created based on the similarity of slope and intercept values of Voronoi equations for individual
species, not based on similarity of crown-radius equations. Group equations were fit; individual-species Voronoi equations
could not be used because of insufficient sample size and poor fit; Meandist is a measure of local tree density, based on the mean
distance among the four trees at the section corner. Other abbreviations are: dbh ¼ diameter at breast height (1.37 m); CR ¼
crown radius.
39
Forest Ecology and Management 256 (2008) 1711–1722
Contents lists available at ScienceDirect
Forest Ecology and Management
journal homepage: www.elsevier.com/locate/foreco
Dry forests in the Southern Interior of British Columbia: Historic
disturbances and implications for restoration and management
Walt Klenner a,*, Russ Walton a, André Arsenault a, Laurie Kremsater b
a
b
British Columbia Ministry of Forests and Range, Southern Interior Forest Region, 515 Columbia Street, Kamloops, British Columbia, Canada V2C 2T7
28360 Starr Road, Mt. Lehman, British Columbia, Canada V4X 2C5
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 1 August 2007
Received in revised form 11 February 2008
Accepted 28 February 2008
We critically examine the hypothesis that dry forests in southern British Columbia evolved in the context
of a low-severity fire-dominated disturbance regime, that fire suppression has led to ecological
conditions which are radically different from the past, and that ‘‘restoration’’ initiatives are required to
re-establish former ecological conditions. Four sources of information were used to infer historic
disturbance regimes and forest condition and to quantify the nature of disturbance since the early 1900s:
(1) patterns of annual and seasonal weather and lightning strikes, (2) topographic variability, (3) records
of wildfire, insect attack, and timber harvesting practices, and (4) early systematic forest surveys.
Our analyses consistently indicate that historic natural disturbances were likely diverse and episodic
at multiple spatial and temporal scales. High seasonal and annual variability in weather and the number
of lightning strikes in complex topography suggest that a widespread low-severity fire regime is very
unlikely, with a mixed-severity disturbance regime more consistent with our analyses. Although the
nature of disturbance has changed from one largely dominated by fire and insect attack historically to
harvesting and insect attack since 1950, the area disturbed annually has not diminished. Several
interacting factors including climate, extensive fires coincident with European settlement, harvesting,
fire suppression and insect attack have been key drivers in creating the conditions observed today. A
complex, mixed-severity disturbance regime creates uncertainty about what represents ‘‘natural’’ forest
conditions, or what the target conditions for restoration activities are if the objective is to ‘‘restore natural
conditions’’. We conclude that dry forest ecosystems in British Columbia typically experienced mixedseverity disturbance regimes that included fire, bark beetles and defoliators. Trying to ‘‘restore’’ these
forests with applications of frequent, low-severity fire is not an ecologically sound objective over large
areas. Landscape management should focus on maintaining forest heterogeneity that would have existed
historically under a mixed-severity disturbance regime.
Crown Copyright ß 2008 Published by Elsevier B.V. All rights reserved.
Keywords:
Fire regime
Disturbance regime
Dry forest management
Ecosystem restoration
Douglas-fir
Ponderosa pine
1. Introduction
Since the early work of Leopold (1924), appropriate management of dry forest ecosystems in North America has been the
subject of ongoing debate. Numerous studies from the south and
western United States (Weaver, 1943; Cooper, 1960; Covington
and Moore, 1994; also see reviews in Allen et al., 2002; Baker et al.,
2007) provide evidence supporting the view that prior to
settlement by Europeans, ponderosa pine (Pinus ponderosa Laws)
forests in this area were often composed of stands with a widelyspaced overstory, a vigorous growth of grasses and forbs in the
understory, and experienced frequent low-severity fires. Fire
suppression or exclusion in this area (which began in the early
* Corresponding author. Tel.: +1 250 828 4158; fax: +1 250 828 4154.
E-mail address: [email protected] (W. Klenner).
1900s but was not effective until the mid-1900s; Allen et al., 2002)
is thought to have contributed to several structural changes
including increases in the density of trees (Covington and Moore,
1994), shifts in tree species composition (Weaver, 1943), shifts in
grassland-forest ecotones (Arno and Gruell, 1983), and an increase
in forest fuels and fire severity (Covington, 2000).
At higher elevations and more northern latitudes across the
range of ponderosa pine and in mixed stands that include Douglasfir (Pseudotsuga menziesii (Mirb.) Franco), lodgepole pine (P.
contorta Dougl.) or western larch (Larix occidentalis Nutt.), the
historic range of natural variability for dry forests is far less certain.
Shinneman and Baker (1997), Baker and Ehle (2001), Heyerdahl
et al. (2001), Ehle and Baker (2003) and Sherriff and Veblen (2007)
document spatially and temporally complex fire regimes in
ponderosa pine dominated forest. The occurrence of such mixedor moderate-severity fire regimes (Agee, 1993, 1998; also termed
variable severity fires in Baker et al., 2007) in more productive
0378-1127/$ – see front matter . Crown Copyright ß 2008 Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2008.02.047
1712
W. Klenner et al. / Forest Ecology and Management 256 (2008) 1711–1722
habitats or topographically complex areas creates uncertainty
about natural or historic forest conditions. Forests that historically
experienced mixed- or high-severity fire regimes are less likely to
be in a structural condition that is outside the historic range of
natural variability (Baker et al., 2007), and ponderosa pine forests
in areas like the Colorado Front Range fall into this category
(Sherriff and Veblen, 2007).
In British Columbia, there is little published information on
disturbance history in dry forest ecosystems and no work that has
evaluated disturbance regimes systematically across these forests
in the Southern Interior. Arsenault and Klenner (2005) concluded
the evidence of disturbances they examined suggested a mixedseverity fire regime, while Heyerdahl et al. (2007) reported
evidence of a frequent, low-severity fire regime in their study in
southwestern British Columbia. Despite the lack of information on
frequency and severity of historic disturbance in BC’s dry forests,
calls have been made for widespread and intensive ‘‘restoration’’
efforts to return dry forests to ‘‘natural’’ conditions (Daigle, 1996;
Gayton, 1996; Filmon, 2004). The concern over this perceived
‘‘unnatural change’’ has increased over the last decade primarily
because of recent large fire events and pest outbreaks in the
western United States (Romme et al., 2006), widespread and severe
outbreaks of mountain pine beetle and western pine beetle
(Dendroctonus ponderosae and D. brevicomis) in British Columbia
(Maclauchlan et al., 2006), and numerous large wildfires in British
Columbia in 2003 (Filmon, 2004). Social perceptions of ‘‘unnatural’’
conditions may also be exacerbated by an increasingly populated
wildland urban interface area (Dombeck et al., 2004). Debate has
centred on whether recent large-scale disturbances result from
widespread and abnormal structural changes to dry forest
ecosystems or are simply the result of weather conditions. More
specifically, are the dry forests in southern British Columbia
outside their historic range of natural variability or are wildfire and
insect attacks the consequence of a non-equilibrium disturbance
regime with high spatial and temporal variability (e.g. Botkin,
1990; Sprugel, 1991; Shinneman and Baker, 1997)?
A clear understanding of disturbance regimes is necessary prior
to undertaking restoration treatments (e.g. Schoennagel et al., 2004)
because the inappropriate application of treatments may threaten
site productivity and diminish the abundance of critical habitat
structures (Tiedemann et al., 2000; Feller, 2005). Due to the lack of
information on disturbance regimes in the dry forests and grasslands
of southern British Columbia, an alternative approach is warranted
to establish a technical basis for the management of these
ecosystems prior to implementing costly restoration programs.
We critically examine the hypothesis that, historically, dry forests in
southern British Columbia were shaped largely by a frequent lowseverity fire regime. Because direct information on the historic fire
regime at multiple, unbiased sites is not available, we examine
indirect evidence relating to controls of fire regimes that affect fire
size, frequency and severity, and direct information about disturbances and historic forest condition from early surveys and
annual reports.
2. Study area
The study area is located in southern British Columbia and covers
approximately 7.5 million ha, of which 2,550,170 million ha is dry
forest and grassland in the Bunchgrass, Ponderosa pine and Interior
Douglas-fir biogeoclimatic zones (Fig. 1; Lloyd et al., 1990). These
dry grasslands and forests generally occur at low elevations (under
1200 m a.s.l.) and usually have a lower canopy closure than forests at
higher elevations that receive more precipitation. Frequent lowseverity, ‘‘stand-maintaining’’ fires are thought to have played a key
historic role in shaping these ecosystems. In western North America,
forests of ponderosa pine or Douglas-fir are widespread from Mexico
to southern British Columbia as pure stands or as mixtures with
other species such as larch, grand fir (Abies grandis (Dougl. Lindl.)) or
lodgepole pine. Open grassland and pure stands of ponderosa pine
represent a minor component of the study area (393,824 (15.4%) and
254,554 ha (10%) respectively) with Douglas-fir, Douglas-fir and
ponderosa pine, or Douglas-fir and lodgepole pine or western larch
(primarily in the southern half of our study area) mixtures being the
most common (1,901,792 ha).
3. Methods
We evaluated two regional (‘‘top-down’’) controls (Lertzman
et al., 1998; Heyerdahl et al., 2001) of fire regimes, weather and
lightning, and one local (‘‘bottom-up’’) control, topography, to assess
whether these controls exhibit characteristics likely to create and
maintain a frequent, low-severity fire regime in dry forests across
our study area (Table 1). Several components of local and regional
weather including temperature, precipitation, relative humidity,
and extended periods of drought are widely recognized as key
factors that affect fire regimes (Flannigan and Harrington, 1988;
Bessie and Johnson, 1995; Nash and Johnson, 1996; Flannigan and
Wotton, 2001; Hely et al., 2001; Flannigan et al., 2005). Lightning is
the primary non-anthropogenic ignition source and both the timing
and nature of lightning strikes influence fire regimes (Nash and
Johnson, 1996; Latham and Williams, 2001; Wierzchowski et al.,
2002). Topography, a local or ‘‘bottom-up’’ control, was examined
since numerous studies in western North America indicate that the
frequency and severity of fires can be strongly affected by slope,
aspect and elevation (Agee, 1993; Larsen, 1997; Taylor and Skinner,
1998; Heyerdahl et al., 2001, 2007).
3.1. Patterns of annual and seasonal weather and lightning strikes
The BC Ministry of Forests and Range (Protection Branch)
maintains a system of weather stations and lightning strike sensors
throughout the study area to facilitate early detection of wildfires, to
develop fire hazard ratings and to predict fire behavior. Forty-nine
weather stations were active across the grassland and dry forest
areas between 1982 and 2006. We chose a centrally located weather
station (Merritt: 50850 N; 1208450 W) to illustrate the annual and
seasonal variability in temperature, relative humidity, fine fuel
moisture (FFMC, Fine Fuel Moisture Code; Van Wagner, 1987) and a
composite index, the Fire Weather Index (FWI, a measure of frontline
fire intensity; Van Wagner, 1987), that incorporates several weather
variables and fuel moisture indices. Lightning strike data for 1982–
1997 were acquired from the BC Ministry of Forests and Range
(Protection Branch) provincial lightning detection network, and for
1998–2006, from the Canadian Lightning Detection Network
maintained by Environment Canada.
3.2. Topography
To evaluate topography, we created a digital elevation model
(DEM) of the study area from 1:250,000 scale 25 m cells with slope
and aspect information. These were then classified into four
categories (gentle 0–20% slope, moderate 21–50%, steep 51–100%,
very steep > 100%) and two aspect classes (warm = southeast to
west [120–2708], cool = west to southeast [271–1198]) that were
then derived from the overall DEM using ArcMap spatial analyst
functions.
3.3. Observations on disturbances and forest conditions
We reviewed historic documents and more recent forest
inventory records for information on the timing, nature and
1713
Fig. 1. Overview of the study area illustrating boundaries of Provincial Forest survey areas. Star on the inset diagram indicates the location of the city of Kamloops (518450 N;
1208200 W) and grey shading delineates the extent of dry forest and grasslands. Provincial Forest survey areas: (1) Martin Mt.; (Hodgins, 1932a), (2) Monte Hills (Hodgins,
1932b), (3) Arrowstone (Hodgins, 1932c), (4) Okanagan (Anonymous, 1930), (5) Grizzly Hill (McGee, 1926), (6) Inkaneep (Stevens and Mulholland, 1925), (7) Fly Hill
(Hodgins, 1932d), (8) Niskonlith (Andrews, 1932), (9) Aberdeen Mt. (McKee, 1926), (10) Tranquille (Andrews, 1931), (11) Nicola (Hodgins, 1932e), (12) Hat Creek (Hodgins,
1932f), (13) Little White Mt. (Stevens et al., 1925), (14) Long Lake (Hodgins, 1932h), (15) Mt. Ida and Larch Hills (Hodgins, 1932g) and (16) Pennask (Schultz, 1931).
Table 1
Ecological features and expected conditions required to support the frequent lowseverity fire regime hypothesis in dry forests of southern British Columbia
extent of fire and other disturbances. Historic survey documents
were consulted for descriptions and photographs of forest
conditions that would help interpret or reconcile statements
made in these reports.
Feature
Conditions that would support the frequent,
low-severity fire regime hypothesis
3.4. Fires
Weather
A low level of between year variability in
temperature and moisture regimes
during the snow-free period
Periods of extreme droughts are unlikely
Lightning
Low to moderate spatial and temporal
variability
Lightning unlikely when weather conditions
would promote catastrophic fires
Topography
Flat or gentle topography that allows fires to
spread
Fires
Low variability in fire frequency (intervals
10–30 years)
Low-severity fires predominant
Due to the lack of direct empirical information on fire regimes
across the dry forests in our study area, we reviewed Provincial
Forest Survey reports (e.g. Hodgins, 1932a) for information on fires
in dry forests, with a focus on return intervals during the 10 years
preceding the survey, and observations or anecdotal observations
of fire regimes in general. For 1919 to present, we assessed the area
burned using historical fire records (1950–2006) maintained by
the BC Ministry of Forests and Range, Protection Branch, and recent
updates to this database (Taylor and Thandi, 2002; see http://
cfs.nrcan.gc.ca/subsite/disturbance/sources). This information was
supplemented with summaries from BC Forest Service Provincial
Annual Reports for 1915–1950.
Other disturbances
Little evidence of other large-scale
natural disturbance
3.5. Insect disturbances and timber harvesting
Forest structure
Little evidence of dense even-aged stands
Open canopy condition predominant,
primarily large trees with occasional
patches of regeneration that escaped fires
To place fire in the context of other disturbances and to assess
the likely role of these disturbances in creating current conditions,
we reviewed historic and recent information on insect attack and
harvesting from two sources: (1) Provincial Forest survey reports
1714
for descriptions of the extent and nature of insect and harvesting
disturbance from 1920 to 1930. Also, BC forest resources reports
from 1918 (Whitford and Craig, 1918) and 1937 (Mulholland,
1937) provided information on forest conditions and management
outside the Provincial Forest areas. (2) Insect disturbances were
mapped and severity evaluated using data from the Canadian
Forest Service Forest Insect and Disease Surveys (see http://
cfs.nrcan.gc.ca/subsite/disturbance/sources). Harvesting data from
1950–1996 were obtained from forest inventory planning data.
We recognized that these reports and databases may be flawed
by imprecise mapping, accidentally omitted records, inconsistencies in data collection or the presentation of results, but they
represent the only systematic information that documents the
timing and amount of insect attack and harvesting. To minimize
errors, we attempted to cross-reference data from different sources
to identify duplicate information or omissions.
3.6. Historic forest structure
We reviewed reports published between 1914 and 1935 for
information on the condition of dry forest habitats at that time, to
assess the nature of forest and grassland management prior to 1930,
and to gather further information on insect and fire disturbances for
the 1900–1930 period when many forests had not been harvested
and when the effects of fire suppression on forest conditions were
minimal. Most information came from surveys undertaken by the BC
Forest Service, Forest Surveys Division, to assess the economic
potential of Provincial Forests existing at the time (see Mulholland,
1937, p. 137). The reports were based on systematic timber surveys
and hence should represent a more accurate depiction of historic
conditions than anecdotal accounts which seldom give insight into
the frequency or extent of a particular forest or grassland condition.
The reports represent a relatively extensive and dispersed sample
across the study area (Fig. 1), however forest and range conditions
and management may have been different on areas outside the
surveys, especially on private lands or crown lands adjacent to
settlements.
We reviewed 16 reports that covered an overall area of
1,589,822 ha, and we focused primarily on the mature ‘‘selection’’
or ‘‘uneven’’ aged forests in the reports (320,328 ha) as these relate
directly to dry forests. The methods and measures used during the
surveys were somewhat difficult to reconcile with our objective of
describing forest structure since only trees that were >11 (28 cm)
and 17 (43 cm) in. d.b.h. for Douglas-fir and ponderosa pine,
respectively, were tallied. Trees with defects and that were
unsuitable for lumber were not included, and trees less than the
minimum diameters for timber were inconsistently recorded as
‘‘fuelwood’’ or ‘‘cordwood’’. Each Provincial Forest was divided into
‘‘compartments’’ that represented species–age combinations (from 4
to 40 dry forest compartments in each of the 16 reports examined).
To evaluate the relative abundance of different stand conditions, we
reviewed the 238 individual compartments (from 120 to 4800 ha
each) in the 16 survey reports for which timber information was
available and recorded estimates of timber volume (foot board
measure, f.b.m.) as a surrogate for stand density. Compartments that
did not contain estimates of volume were excluded from the analysis.
The dry forest area was classified as <1000, 1000–3000, 3000–5000
and >5000 f.b.m. per acre, and we present photographs from the
survey reports to illustrate structural conditions in each category.
To complement the information on forest and range conditions
found in the Provincial Forest survey reports, we examined the
British Columbia Forest Service (BCFS) Annual Reports for the
1912–1955 period (see BCFS Annual Reports, 1911–1992).
Although these reports did not provide quantitative estimates of
conditions, they qualitatively summarized the type and general
magnitude of key issues relating to forest and range management
at that time. Two provincial forest resources reports by Whitford
and Craig (1918) and Mulholland (1937) focused on a broader
provincial overview than the Provincial Forest survey reports but
addressed several issues pertinent to dry forests in the study area.
4. Results
4.1. Annual and seasonal weather patterns
Temperature and relative humidity showed consistent patterns
during the 25-year monitoring period at the Merritt weather station.
Average monthly temperatures peak in July and August, while
relative humidity in general shows the opposite trend (Fig. 2a and b).
High temperatures and low relative humidity are correlated with
area burned (Flannigan and Harrington, 1988), likely due to the
effects of these variables on the rate at which fuels dry. July and
August are also the period when the FFMC can be above 92 for a large
proportion of the month (Fig. 2c), but there is considerable
variability from year to year. FFMC values need to exceed 87 if
lightning strikes are to become ignitions (Nash and Johnson, 1996),
and at FFMC values above 92, lightning strikes have approximately a
1% chance of becoming ignitions. In addition to the FFMC values
which influence the likelihood of an ignition occurring, the number
of consecutive days with less than 1.5 mm precipitation is correlated
with area burned (Flannigan and Harrington, 1988). We observed
high variability in the pattern of extended droughts at the Merritt
weather station from 1982 to 2006 (Fig. 2d), and the two most
prolonged periods (1998 and 2003) coincided with large areas
burned and intense fires that were largely stand-replacing in nature
(Filmon, 2004). High FFMC values are correlated with high FWI
values (Fig. 3), but considerable variability exists. For example, at an
FFMC value of 92, the FWI ranges from approximately 15 to 110, and
this will likely translate into a wide range of fire severity should an
ignition occur (Harvey et al., 1986). The weather, fuel moisture
(FFMC) and fire intensity (FWI) conditions presented relate to the
Merritt weather station. We examined data from three other
weather stations (representing locations approximately 100 km to
the north, northwest and southeast) and found similar patterns,
although not entirely synchronous, suggesting the Merritt station
was indicative of conditions in dry forest areas within our study area.
4.2. Annual and seasonal lightning strikes
Lightning is the primary non-anthropogenic ignition source of
forest fires (Latham and Williams, 2001; Wierzchowski et al.,
2002). We examined seasonal and annual patterns of lightning
strikes to evaluate the period when strikes are most common and
the likely weather and fuel conditions during these periods. From
1982 to 2006, July and August were the peak periods for lightning
strikes, but there is high variability among years (Fig. 4a). Positive
polarity lightning strikes are more likely to initiate a wildfire
(Latham and Williams, 2001) and these follow essentially the same
pattern, except the density of positive polarity strikes is
approximately 10% of the overall total (Fig. 4b). An examination
of the 1950 to 2006 fire history data is consistent with this result,
with both the number of fires of lightning origin and the area
burned by these fires greatest in July and August (Fig. 4c and d). The
greater area burned and higher proportion of fires from anthropogenic ignitions is not representative of all forest types in BC (e.g.
high elevation Engelmann spruce forests have far fewer humancaused fires), and likely relates to the accessibility and proximity of
dry forest ecosystems to settlements and human activity.
Lightning strikes are common under a wide range of FFMC
conditions but it is unlikely that strikes occurring when FFMC
1715
Fig. 2. Patterns in (a) temperature, (b) relative humidity and (c) the number of days in a month in which Fine Fuel Moisture Code values are I92. Data recorded during 1
March–31 October with mean monthly values presented. Each line represents data for 1 year. (d) Maximum number of consecutive days from 1 May to 31 August with
precipitation <1.5 mm (bold line, open circles) and the annual dry forest area burned within 25 km of the Merritt weather station (solid line). All weather data collected at the
Merritt weather station from May 1982 to October 2006.
values are less than 87 will become ignitions (Nash and Johnson,
1996). Lightning strike density is highly variable at FFMC values
>87, and on a subset of days when the FFMC is 92 (representing a
higher probability of ignition), the corresponding FWI is also highly
variable (Fig. 5a and b). FWI values of around 20 represent the
transition to very high hazard conditions (Harvey et al., 1986), with
FWI conditions above 50 representing conditions when fire
intensity and behavior can become extreme and result in large
stand-replacing fires. Many factors, including precipitation associated with weather systems that generate lightning activity, affect
the likelihood of a strike becoming an ignition. However, given the
wide range of fuel and weather conditions associated with
lightning activity, we believe lightning ignitions are likely to
generate fires of variable severity.
4.3. Topography
Fig. 3. The relationship between the Fine Fuel Moisture Code and Fire Weather
Index values at the Merritt weather station from May 1982 to October 2006. Only
data for FFMC values I87 are presented. Two extreme FWI values (131, 188) were
omitted.
Topography, a ‘‘bottom-up’’ control of fire regimes, can affect fire
severity directly by affecting fire spread rates and fuel conditions
directly ahead of the fire (Agee, 1993), influencing the length of the
fire season and the moisture content of fuels (Taylor and Skinner,
1998; Heyerdahl et al., 2001), creating barriers in fuel continuity
(Larsen, 1997; Heyerdahl et al., 2001) and by affecting vegetation
characteristics (Taylor and Skinner, 1998; Odion et al., 2004). About
half (48.8%, Table 2) of the dry forests and grassland in the study area
are on flat or gentle slopes (0–20%), 36.5% are on moderate (21–50%)
and 14.7% are on steep ground (50 to >100%). Large, flat areas are
usually associated with valley bottom grasslands that have sparse
1716
Fig. 4. Patterns in (a) the overall density of lightning strikes and (b) the density of positive polarity lightning strikes in dry forest and grasslands in the study area during March
to October over a 25-year period from 1982 to 2006. Each line represents data for 1 year. (c) Number of fires and (d) area burned each month by human (open) and lightning
origin fires (grey) in dry forests and grasslands in the study area between 1950 and 2006.
tree cover. Approximately, 40% of the dry forests are on ‘‘warm’’,
southern exposures that typically are more open and have a less
dense understory than ‘‘cool’’ northern exposures. Together, slope
and aspect create a complex mosaic of different structural
conditions including relatively dense forest, open forest, and
riparian vegetation along watercourses and wetlands.
4.4. Observations on disturbances and forest conditions: fires
The Provincial Forest surveys reported few fires in ponderosa
pine or Douglas-fir forests in the 10 years prior to the survey
(Table 3), with most fires in lodgepole pine types. An exception to
this pattern was the Inkaneep Forest (Stevens and Mulholland,
Fig. 5. Lightning strike density in dry forest and grassland habitats (169 368 ha out of a potential 196 350 ha) within a 25 km radius area around the Merritt weather station
between 1 May 1982 and 31 October 2006 in relation to (a) Fine Fuel Moisture Code (n = 487), and (b) the Fire Weather Index on the subset of days when the FFMC I92
(n = 102). Only days with lightning strikes are presented.
1717
Table 2
Area and percent of study area (in parentheses) of dry forest and grassland summarized by slope class and aspect (warm and cool)
Aspect class
None (flat)
Warm (120–2708)
Cool (271–1198)
Total
0–20% Slope ha (% of area)
108,776 (4.3)
503,242 (19.7)
633,424 (24.8)
1,245,441(48.8)
>100% Slope ha (% of area)
0 (0)
378,260 (14.8)
552,656 (21.7)
0 (0)
152,123 (6.0)
202,809 (8.0)
0 (0)
7,709 (0.3)
10,706 (0.4)
930,922 (36.5)
354,933 (14.0)
18,415 (0.7)
1925, p. 14) where 18,895 ha were ‘‘destroyed’’ by wildfire in 1925.
No data on fire severity are presented other than the observation of
large areas ‘‘swept by fires and the original Fir, Larch and yellow pine
timber destroyed’’, suggesting these fires were high-severity, standreplacing events that killed most mature trees in the stand. Where
fires were noted in the decade preceding the report, 1920–1930 is
repeatedly identified as a period of high fire years and this is
consistent with our compilation of the area burned within the study
area (Fig. 6). In addition to the early 1920s, almost all Provincial
Forest survey reports directly cite episodes of extensive and
repeated fires relating to the period of settlement by Europeans
(1860–1890). These reports, along with the results presented in
Fig. 6, indicate periods of extensive fires associated with prolonged
drought as occurred in 2003 are not without historic precedence (BC
Forest Service Annual Report; Filmon, 2004).
In addition to the moderate- and high-severity fires that
affected mature timber, frequent reference was made to areas that
had been previously affected by ‘‘low-severity ground fires’’. These
non-stand-replacing fires were inferred from the many large, firescarred trees surveyors recorded (Andrews, 1931; Hodgins,
1932e). These fire legacies were noteworthy in the surveys
because they diminished timber quality by contributing to pitch
seams and general decadence. The spatial extent of these lowseverity fires, the site conditions they occurred on (e.g. soil type
and moisture regime), their impact on the stand and their
frequency are not quantified in the reports so it is difficult to
determine the extent of low-severity fires relative to moderate- or
high-severity wildfires.
4.5. Insect disturbances and timber harvesting
Timber losses stemming from insect attack were widely
documented in the Provincial Forest survey reports (Table 3),
and relate primarily to bark beetles in mature timber. In the
Provincial Forest surveys, 68 of 151 compartment descriptions
made note of insect attack, with 24, 27 and 17 compartments
described as low, moderate and high severity attack, respectively.
Infestations by the Douglas-fir bark beetle (D. pseudotsuge) and
Douglas-fir tussock moth, (Orgyia pseudotsugata) were the most
common insect disturbances documented in the reports in
Douglas-fir stands and were also identified by Whitford and Craig
(1918) and Mulholland (1937). Douglas-fir bark beetle infestations
Table 3
Summary of harvest, insect outbreaks and wildfire from Provincial Forest survey reports (1925–1933) (total area and dry forest area within the survey area (in parenthesis) in
hectares)
Forest # and total
area (dry forest area)a
Harvest by 1930 b
Insect outbreaksc
Wildfire
Large fires during settlement. Negligible fire
during last 10 years
No fires in past 5 years. Earlier fires coincident
with settlement
2835 ha of LP burned in last 11 years
PP
DF
(#1) 22,804 (7408)
H
L
Extensive BB and TM in DF; BB in PP
(#2) 90,212 (22,553)
M
L
(#3) 75,595 (15,710)
N
N
(#4) 260,104 (45,562)
H
L
(#5) 153,631 (13,776)
(#6) 83,068 (14,616)
M
L
L
N
BB killed most mature LP; serious
outbreak of TM in DF
BB in mature and immature LP.
Some DF damaged by TM
LP not expected to reach maturity
due to BB
BB mentioned on LP
None recorded
(#7) 65,919 (8608)
L
L
(#8) 116,550 (21,011)
M
M
Some DF BB killing 5–50% of stand;
TM locally abundant
None recorded
(#9) 30,675 (2582)
#(10) 76,405 (19,769)
N
N
L
N
None recorded
75% of mature LP killed by BB
(#11) 151,814 (49,938)
N
N
(#12) 198,826 (56,106)
N
N
High BB attack on LP, some BB on PP
and DF. TM present in DF
Sporadic BB on PP and TM on DF
(#13) 75,272 (4249)
(#14) 69,339 (24,346)
N
N
N
N
(#15) 26,677 (2272)
N
N
(#16) 92,932 (8822)
N
N
None recorded
Severe LP BB outbreak in last 10
years; some TM
None recorded
Large fires during settlement. In last 10 years,
20,250 ha burned (mostly LP)
Lightning caused 71% of fires over last 3 years
18,895 ha of immature and mature DF, PP,
LP and WL burned in 1925
6480 ha burned in last 10 years, large
areas destroyed by wildfire
6694 ha burned in last 7 years. High levels
of burning between 1871 and 1890
1738 ha burned in last 8 years
1154 ha burned in last 7 years. Earlier extensive
fires by miners and railway
6318 ha burned in last 7 years (mostly LP)
1863 ha burned in last 11 years (mostly LP).
Majority burned in 1925–1926
None recorded
891 ha burned in last 10 years (most
in immature stands and in 1925–1926)
11,417 ha of LP attacked by BB;
some BB in DF
a
Refer to Fig. 1 for reference key to areas covered by historic surveys and sources.
b
H, M, L and N refer to High, Moderate, Low and Negligible in relation to the calculated sustainable yield (SY) in 1930. H exceeds SY, M = approximately 50% of SY,
L = approximately 25% of SY, N = very minor use.
c
PP = Ponderosa pine, LP = lodgepole pine, DF = Douglas-fir, WL = western larch, BB = bark beetle, TM = tussock moth.
1718
Fig. 6. Area burned in the overall study area (all forest types) between 1915 and
2006.
do not appear to have been perceived as a serious threat to the
timber resource due to the lower level of within stand mortality
and the relatively extensive but scattered nature of the infestations. The western pine beetle and the mountain pine beetle were
important pests that attacked ponderosa pine, the tree most valued
for timber at the time. Whitford and Craig (1918, p. 221) noted the
extensive nature of bark beetle attacks and losses of mature timber
on productive forest lands, indicating that attacks by bark beetles
‘‘have killed an immense quantity of timber’’. The extent and
percent mortality were not quantified, but the southern half of the
dry forest habitats in our study area (Okanagan Lake, Princeton,
Merritt) was identified as having ‘‘large areas upon which the pine
has been already almost entirely killed off by the beetles, and
others upon which 50% or more of the pine is now dead or freshly
infested this season’’. Mulholland (1937, p. 62) also comments on
the effects of bark beetles in stands of ponderosa pine, noting that
bark beetles ‘‘have destroyed most of the yellow pine [ponderosa]
occurring in pure stands in the Province’’. How accurate these
reports are is difficult to determine, but extensive attacks by the
mountain pine beetle in lodgepole pine stands, a forest type with
relatively low commercial value at the time, were also noted,
suggesting that insect attacks leading to losses of current or future
timber were noted and consistently reported.
Harvesting of low elevation ponderosa pine forests began
concurrent with European settlement around 1860. By the early
1900s, harvesting of ponderosa pine was so extensive that
sustainability of harvest levels was a widespread concern (BC
Forest Service Annual Report, 1923, p. 9). Whitford and Craig
(1918, p. 65) reported that heavy harvesting had already occurred
in ponderosa pine forests in most of the interior, noting ‘‘greater
inroads on this type than any other’’. By 1950, little ponderosa pine
remained and Douglas-fir became the primary species harvested in
dry forest areas (BC Forest Service Annual Reports, 1912–1950).
Some Provincial Forests that contained a significant proportion of
ponderosa pine and dry Douglas-fir forest had experienced little or
no commercial harvesting prior to publication of the reports, while
others had been affected by harvesting. Of the 13 Provincial Forest
areas with pure stands of ponderosa pine, 6 areas reported
negligible harvest of ponderosa pine and the remainder reported
low (2), moderate (3) and high (2) use. In Douglas-fir dominated
stands, 8 documented no use, 7 low and 1 reported moderate
utilization (Table 3). Over the next 30 years, harvesting removed
large trees greater than 43 cm d.b.h. for ponderosa pine and greater
than 28 cm for Douglas-fir, leaving small stems and openings.
The overall perspective that these reports present indicates
extensive and intensive utilization of ponderosa pine forests
beginning shortly after settlement by Europeans in the mid-1800s
and continuing until approximately 1950 when supply was
exhausted. Douglas-fir represented a less desirable resource
largely because of wood characteristics, hence extensive and
intensive harvesting of this forest type began somewhat later (e.g.
1920) than for ponderosa pine and continued into the 1980s
(Fig. 7). Since harvesting focused primarily on the removal of the
largest stems in the stand, the structural condition of dry forests
was heavily modified by harvest.
When viewed comprehensively, it is clear that dry forests in the
Southern Interior of BC have been affected on an ongoing basis by a
wide range of disturbances including wildfire, insect attack and,
more recently, harvesting, that began at the time of European
settlement (1860). To protect the timber resource, increased fire
suppression effort, a more extensive system of roads and aerial fire
suppression technology implemented in the 1970s kept the area of
dry forest affected by fire below 1% per decade (Fig. 7). Not shown
in Fig. 7 is the current outbreak of mountain pine beetle and
western pine beetle that has affected large areas of ponderosa pine
dominated stands in the study area since 1999. In 2006, over
40,000 ha of ponderosa pine in the northern half of the study area
experienced within stand mortality greater than 11% and, of this, at
least half the area had mortality levels greater than 50% in that 1
year alone (Maclauchlan et al., 2006). Although the area affected by
fire may have diminished since 1950, the overall area affected by
disturbance has not declined. Harvesting, especially during the
1960–1990 period, and insect attack have affected extensive areas
of dry forests.
4.6. Historic forest conditions
The 16 Provincial Forest Survey reports provide information on
structural conditions in dry forest and grasslands prior to extensive
management (Fig. 1 and Table 3). Prior to these surveys, Whitford
and Craig (1918, p. 65) noted that forests dominated by ponderosa
pine were characterized primarily by grass in the understory and
that the area ‘‘as a whole is fairly open’’. Douglas-fir and ponderosa
pine mixtures, and relatively pure stands of Douglas-fir that were
more common than pure stands of ponderosa pine, are described in
all the reports as ‘‘uneven aged’’ or ‘‘open, park-like’’ (e.g. Hodgins,
1932a, p. 7). However, it was difficult to reconcile this generic
description with photographs in the survey reports that depicted
diverse structural conditions. Using the 238 individual compart-
Fig. 7. The percent of dry forest area (2 156 346 ha) each decade affected by
harvesting, fire, insect defoliators (Douglas-fir tussock moth western spruce
budworm) and bark beetles (mountain pine beetle, Douglas-fir beetle) in the study
area between 1950 and 1999. Insect disturbances with associated mortality of <10%
are not included in these estimates. Harvesting records for the 1990s are not
complete for the entire study area beyond 1996.
1719
Fig. 8. Examples of forest conditions in relation to timber yield estimates from Provincial Forest surveys. (a) 1000 f.b.m., (b) 2500 f.b.m., (c) 4000 f.b.m. and (d) 6500 f.b.m. per
ha. Original figure captions: (a) Hodgins, 1932e, p. 10; ‘‘Uneven-aged fir—yellow pine [Ponderosa pine] type. Portion of Compartment 5, averaging 1000 f.b.m. and 4 cords per
acre. Note the open grazing.’’ (b) Hodgins, 1932c, p. 9; ‘‘Illustrative of the fir—yellow pine type. Portion of Compartment 4 averaging 2500 f.b.m. per acre (80% yellow pine) and
2 cords per acre (75% fir). Note the open Grazing’’, (c) Hodgins, 1932f, p. 9 ‘‘Illustrative of the better stands of yellow pine-fir. Average volume 4000 f.b.m. per acre, Oregon Jack
Creek. Bark beetles have infested the yellow pine on this area. Note the open grazing.’’ (d) Hodgins, 1932d, p. 7; ‘‘Illustrative of fir stands growing on better sites’’.
ment descriptions (representing 315,232 ha) with information on
f.b.m. per acre, we found that 27.4% of the area was <1000 f.b.m.
per acre, 32.2% was 1000–3000, 27.1% was 3000–5000 and 13.4%
was >5000 (Fig. 8). These results clearly indicate that the dry forest
areas in the Provincial Forests were structurally diverse and the
term ‘‘open, park-like condition’’ likely reflects a description
relative to the often very dense forest types encountered in the
surveys.
5. Discussion
Our analysis of weather patterns, lightning strikes, topography, historic fire, insect attack and harvesting, and historic forest
structure questions the likelihood of a region-wide, low-severity
fire regime in dry forests. High variability in seasonal and annual
weather patterns and lightning strikes that occur across a wide
range of fuel moisture and fire hazard conditions suggests that
fire intensity will likely vary in space and time, especially when
placed in the context of complex topography found in our study
area.
High temperatures, low relative humidity and extended periods
of drought (Flannigan and Harrington, 1988; Flannigan and
Wotton, 2001) are correlated with area burned, and extreme
weather conditions are often associated with very large fires across
a wide range of forest conditions and types (Harvey et al., 1986;
Flannigan and Harrington, 1988; Bessie and Johnson, 1995; Larsen,
1997; Filmon, 2004). Although fuel accumulation arising from
reduced fire frequency has been identified as a leading cause of
high-severity fires in some areas (Covington, 2000), other studies
do not support this perspective (Odion et al., 2004). Extended
droughts in our study often coincided with large fire years, and this
is consistent with the increased likelihood of lightning strikes
becoming ignitions as fuel moisture decreases (Nash and Johnson,
1996). It does not appear that seasonal or annual patterns in the
weather, fuel moisture or lightning strike patterns we examined
are likely to singly or in concert provide a mechanism that
promotes frequent low-severity fires across extensive areas.
In British Columbia lightning is the primary non-human cause
of fire, often causing ignitions in areas of poor access where
suppression efforts may be slow to respond (Wierzchowski et al.,
2002). A peak in lightning activity is common in July and August,
the period when temperature and drought indices are typically
highest. In years of periodic drought combined with lightning or
human-caused ignitions, extensive and severe wildfires will likely
occur (Harvey et al., 1986; Filmon, 2004).
Topography in our study area also does not appear to be
conducive to extensive low-severity (ground) fires. Steep or upper
slopes and south aspects are more likely to experience highseverity fires than north aspects and lower slopes (Taylor and
Skinner, 1998), and warm aspects (south and southwest) are likely
to have more frequent fires (Heyerdahl et al., 2001, 2007).
Increased solar radiation on south aspects facilitates the drying
of fuels, and a longer snow free period extends the season over
which fires are likely to occur. We believe the complex topography
in our study area would prevent the spread of low-severity fires
across extensive areas, and would promote some moderate- and
high-severity fires.
The few direct observations of fire severity in our study area
also found a mix of fire regimes. In a 300 ha portion of the Stein
Valley (western part of our study area), Heyerdahl et al. (2007)
found evidence of a low-severity fire regime with a return
frequency of 14–24 years, with differences in fire frequency and
season related to aspect and vegetation composition. They used the
presence of fire-scarred trees to identify low-severity fires and
noted fires in their study were most common in July and August,
indicating that low-severity fires are not necessarily inconsistent
with mid-summer fires. However, they suggested some of the
stand structures they observed (plots with only young trees) may
indicate moderate- or high-severity fires also occurred in their
area, and Wong (1999), working in the same valley, documented
these conditions. Lightning is not the only source of ignitions, and
First Nations likely contributed to fires in the Stein Valley because
of the cultural significance of the area to the Nlaka’pamux First
Nation (Heyerdahl et al., 2007). Also, there is considerable evidence
to suggest that fire was routinely used by First Nations peoples to
create desired vegetation conditions (Turner, 1991; Agee, 1993;
Krech, 1999). There is no consensus on the effects of First Nations
burning on historic fire regimes, as the extent was likely strongly
affected by the culture of the group, weather, and topography. We
believe the complex incised topography in much of our study area
would likely have frustrated attempts to apply low-severity fire
over extensive areas.
1720
Our observation of variable weather patterns, lightning activity
and topography does not provide support for a mechanism that
would promote frequent, low-severity fires across the regional
scale that our study addresses, and suggests a mixed-severity fire
regime is more likely. This perspective is consistent with a number
of recent studies from the western United States and British
Columbia (Shinneman and Baker, 1997; Arsenault and Klenner,
2005; Daniels, 2005; Hessburg et al., 2005; Baker et al., 2007;
Sherriff and Veblen, 2007) that indicate mixed-severity fire
regimes are common in some regions. Mixed-severity fire regimes
encompass a broad range of fire severity (Agee, 1998; Baker et al.,
2007) which are likely to be non-equilibrium (Botkin, 1990;
Sprugel, 1991) and spatially and temporally dynamic if fire
controls exhibit high variability.
Fire is an obvious disturbance that affects forest composition
and density, and we present evidence that bark beetles and
defoliators also played a strong role in determining historic and
present forest structure in our study area. Mountain pine beetle,
western pine beetle and Douglas-fir beetle (Dendroctonus pseudotsugae) are common forest insects that attack ponderosa pine
and Douglas-fir across much of the range of these forest types
(Weaver, 1943; Romme et al., 2006). As they have in the past
(Table 3), recent outbreaks of mountain pine and western pine
beetle in our study area are profoundly changing forest structure in
ponderosa pine stands, with large areas affected by high-severity
attacks of greater than 50% mortality annually within stands
(Maclauchlan et al., 2006). Pine- and Douglas-fir beetles kill the
larger stems in a stand and mortality from these agents works
largely in opposition to the ‘‘from below’’ thinning effect of lowseverity fire. Severe attacks of defoliators (Hadley and Veblen,
1993) such as Douglas-fir tussock moth and western spruce
budworm (Choristoneura occidentalis) also change forest structure
by thinning stands, creating gaps or favoring non-host species. The
extensive and severe attacks by bark beetles in ponderosa pine
stands (Whitford and Craig, 1918, p. 221; Mulholland, 1937, p. 62),
and by bark beetles and defoliators in Douglas-fir forests in our study
area (Fig. 7), suggests that disturbance studies should focus on a
broader suite of agents. In addition to bark beetles and defoliators,
harvesting has played a key role in affecting forest structure in dry
forest habitats in our study area over the last century. The harvest of
large diameter overstory trees in ponderosa pine and Douglas-fir
forests (e.g. >43 and 28 cm d.b.h., respectively) was standard
practice in logging operations (e.g. Hodgins, 1932a,b) and likely
increased the density of small diameter stems in the stand while
reducing the density of large overstory trees. Canopy gaps would
likely promote dense regeneration (Kaufmann et al., 2000) that
either resists fire during periods of high relative humidity or is
vulnerable to stand-replacing fires during hot, dry periods.
In an assessment of historic forest conditions in the Black Hills
of South Dakota, Shinneman and Baker (1997) found evidence of
episodic stand-replacing disturbances, and demonstrated the
value of systematic surveys to infer historic conditions and
disturbance regimes. Our review of Provincial Forest surveys in
BC led to a similar conclusion: forest conditions prior to extensive
management were likely diverse, and that high-severity fires and
insect attack occurred episodically and played a strong role in
shaping forest conditions. Although these reports consistently
referred to dry forests as ‘‘open and park-like’’, a closer
examination of photographs and survey results revealed greater
complexity than was inferred from the term.
Initiatives to modify existing forest structure and pattern in dry
forests are well established in some regions (e.g. Friederici, 2003)
and are based largely on the perspective that frequent, lowseverity fires are the key process that will restore productivity and
desired structural characteristics (Weaver, 1943; Covington, 2000;
Allen et al., 2002). In BC, extensive fires during the initial period of
settlement by Europeans were followed by a long period (1860–
1970) of largely unregulated harvesting and extensive insect
attack. These disturbances likely have had long-term implications
for forest structure (Hadley and Veblen, 1993; Smith and Arno,
1999; Kaufmann et al., 2000). Although mechanical thinning
followed by frequent low-severity prescribed fire (Covington et al.,
1997; Allen et al., 2002) is the most common approach to restoring
ecological integrity in dry forests, we believe a more comprehensive and site specific understanding of forest disturbances and
historic conditions should be developed prior to the widespread
application of this approach in BC. Fire has and continues to be an
important disturbance that shapes forest structure and pattern. It
is, however, only part of a historic suite of disturbances ranging
from single tree windthrow events to large stand-replacing
wildfires. Timber harvesting is a relatively recent disturbance
that has and continues to affect a large proportion of the dry forests
in southern British Columbia. The structural consequences of
historic and recent harvesting, along with insects and fire, need to
be considered when assessing the need for restoration, and when
developing a coordinated approach to implementing silvicultural
(Agee and Skinner, 2005) and prescribed fire treatments to achieve
desired conditions.
6. Management implications
Our analyses indicate that a mixed-severity disturbance regime
(including fire, insects and other disturbances) likely maintained
diverse stand and landscape conditions in our study area. Hence,
choosing a reference condition for ‘‘ecological restoration’’ is
problematic as conditions likely changed in space and time. Future
management in dry forest ecosystems in British Columbia should
include the development of a better understanding of the spatial
and temporal variability of historic disturbances, and the historic
role of low-, moderate- and high-severity fires and other
disturbances at the regional level versus specific locations. Forest
managers should: (1) focus on clearly defining desired stand
conditions and the mosaic of habitats necessary to maintain
multiple values across landscapes (e.g. Fischer et al., 2006), (2)
identify the commodity, social and ecological objectives that will
be met or compromised with these conditions, (3) identify the
most effective interventions for achieving these objectives, and (4)
implement a program to monitor, assess and revise activities to
ensure objectives are met.
Acknowledgements
Much of this project has evolved from work done with the NDT4
dry forest management committee of the former Kamloops Forest
Region. In particular, we would like to acknowledge contributions
by R. Beck, P. Belliveau, D. Lloyd, S. Schell, and R. Tucker. E. Meyer
from the BC Ministry of Forests and Range, Protection Branch,
provided data on fire history, weather and lightning strikes, G.
McGregor performed data queries and ArcMap analyses for the
topography analyses, M. Swan provided information on study area
fire history for 2000–2006, and S. Cadieux assisted with map
preparation. We also thank two anonymous reviewers for their
insightful and constructive reviews.
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PNAS PLUS
Long-term perspective on wildfires in the western USA
Jennifer R. Marlona,1, Patrick J. Bartleinb, Daniel G. Gavinb, Colin J. Longc, R. Scott Andersond, Christy E. Brilese, Kendrick J.
Brownf, Daniele Colombarolig, Douglas J. Halletth, Mitchell J. Poweri, Elizabeth A. Scharfj, and Megan K. Walshk
a
Department of Geography, University of Wisconsin, Madison, WI 53706; bDepartment of Geography, University of Oregon, Eugene, OR 97403;
Department of Geography and Urban Planning, University of Wisconsin, Oshkosh, WI 54901; dSchool of Earth Sciences and Environmental Sustainability,
Northern Arizona University, Flagstaff, AZ 86011; eSchool of Geography and Environmental Science, Monash University, Victoria 3800, Australia;
f
Canadian Forest Service, Victoria, BC, Canada V8Z 1M5; gOeschger Centre for Climate Change Research and Institute of Plant Sciences, University of
Bern, Altenbergrain 21, CH-3013 Bern, Switzerland; hBiogeoscience Institute, University of Calgary, Alberta, Canada T2N 1N4; iNatural History Museum of
Utah, Department of Geography, University of Utah, Salt Lake City, UT 84112; jDepartment of Anthropology, University of North Dakota, Grand Forks,
ND 58202; and kDepartment of Geography, Central Washington University, Ellensburg, WA 98926
c
Understanding the causes and consequences of wildfires in forests
of the western United States requires integrated information
about fire, climate changes, and human activity on multiple temporal scales. We use sedimentary charcoal accumulation rates to
construct long-term variations in fire during the past 3,000 y in the
American West and compare this record to independent firehistory data from historical records and fire scars. There has been
a slight decline in burning over the past 3,000 y, with the lowest
levels attained during the 20th century and during the Little Ice
Age (LIA, ca. 1400–1700 CE [Common Era]). Prominent peaks in
forest fires occurred during the Medieval Climate Anomaly (ca.
950–1250 CE) and during the 1800s. Analysis of climate reconstructions beginning from 500 CE and population data show that temperature and drought predict changes in biomass burning up to
the late 1800s CE. Since the late 1800s , human activities and the
ecological effects of recent high fire activity caused a large, abrupt
decline in burning similar to the LIA fire decline. Consequently,
there is now a forest “fire deficit” in the western United States
attributable to the combined effects of human activities, ecological, and climate changes. Large fires in the late 20th and 21st century fires have begun to address the fire deficit, but it is continuing
to grow.
F
orest fires in the western United States have been increasing in
size (1) and possibly severity (2) for several decades. The increase in fire has prompted multiple investigations into both the
causes (3, 4) and consequences of this shift for communities, ecosystems, and climate (5). Climate changes and human activities
have both contributed to the observed changes in fire, but understanding the nature and magnitude of these impacts has been
challenging first because there is substantial ecological heterogeneity and variability in terms of vegetation, soils, hydrology,
topography, and other factors that affect fire regimes across the
western United States, and second because most fire-history data
come from recent decades and centuries when climate and human activities have both undergone rapid and unique transformations. As a result, studies tend to focus either on local ecological
and anthropogenic factors that drive fire at fine scales (6, 7), or on
climatic influences at broad scales (3, 4). Furthermore, the limited
temporal scope of many fire-history studies does not provide
adequate context for examining the joint impacts of climate and
human activities on broad-scale, long-term fire regime changes.
In addition, projections of future climate change and its ecosystem
impacts place the expected changes well outside the range of variations in the past few centuries. Thus, coupling multi-decadal-to
millennial-scale data on fire, climate changes, and human activities
can reveal linkages among these components that are often missed
in studies restricted to finer scales or fewer factors.
Here we use sedimentary charcoal accumulation rates to construct variations in levels of burning for the past 3,000 y in the
western United States (i.e., the West) and compare this record to
independent fire-history data from historical records and fire
scars. The long charcoal records enable identification of baseline
www.pnas.org/cgi/doi/10.1073/pnas.1112839109
shifts in fire regimes that cannot be detected with shorter records
and allow us to view the nature and extent of human impacts on
fire in a long-term context; this approach helps to distill the dominant patterns in fire activity across the West, but it does not reveal
the important differences in fire controls and effects among
vegetation types, ecoregions, or elevation gradients that exist at
finer spatial scales (e.g., ref. 8).
Our focus here is specifically on multi-decadal-to-centennialscale variations in fire over the past few millennia and on the
West as a whole. Climatic variations on this time scale are characterized by extended periods of persistent anomalies, such as the
Medieval Climate Anomaly (MCA) and Little Ice Age (LIA)
(9, 10), which feature broad-scale (i.e., across the whole of the
western United States) anomalies of both surface climates and
atmospheric circulation (10). We use temperature (10), drought
(9), and population (11) data to compare with the fire-history
reconstructions. We also construct a simple statistical model for
predicting biomass burning from the temperature and drought
data. Our analysis builds on the rich historical narratives of fire
in the western United States (12) as well as on many more
detailed but shorter broad-scale studies (4, 13, 14). The results
illustrate the importance of climate in explaining the variations
in fire over time, and show the development of a 20th century
“fire deficit” related to the combined effects of fire exclusion,
land-use change, and ongoing climate change.
Broad-Scale Controls on Fire
Fire regimes are primarily a product of climate, vegetation, topography, and human activities—factors that interact in a variety of
ways and on a range of spatial and temporal scales. Climate influences fire at the broadest scales via the annual cycle, weather,
and the distribution of vegetation (fuels). Humans have a broad
influence on fire through intentional or accidental ignitions,
exclusion (e.g., suppression and fuel alteration from grazing),
and indirectly through climate change. Topography, winds, and
the type, distribution, and structure of vegetation become more
important controls on fire at regional-to-local scales. Feedbacks
from fire to vegetation and climate add additional complexity
to ecosystem dynamics. Increases in human-caused fires, for exAuthor contributions: J.R.M. and P.J.B. designed research; J.R.M., P.J.B., D.G.G., C.J.L.,
R.S.A., C.E.B., K.J.B., D.C., D.J.H., M.J.P., E.A.S., and M.K.W. performed research; J.R.M.
and P.J.B. analyzed data; and J.R.M., P.J.B., D.G.G., C.J.L., R.S.A., C.E.B., K.J.B., D.C., D.J.H.,
M.J.P., E.A.S., and M.K.W. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
Data deposition: The charcoal records were collected from the Global Charcoal Database/
International Multiproxy Paleofire Database.
1
To whom correspondence should be addressed. E-mail: [email protected].
See Author Summary on page 3203 (volume 109, number 9).
This article contains supporting information online at www.pnas.org/lookup/suppl/
doi:10.1073/pnas.1112839109/-/DCSupplemental.
PNAS ∣ Published online February 14, 2012 ∣
E535–E543
ENVIRONMENTAL
SCIENCES
Edited by B. L. Turner, Arizona State University, Tempe, AZ, and approved January 10, 2012 (received for review August 13, 2011)
ample, can trigger changes in the structure and composition of
vegetation, which may in turn alter carbon storage and land
surface characteristics that are known to affect climate (15).
Furthermore, interactions among fire and its primary controls
—particularly climate and vegetation—often involve lag times that
span years, decades, and even centuries (16), making long-term
data on fire-regime changes a vital component of fire research.
Despite major human influences on western U.S. wildfires since
Euro-American settlement (17, 18), climate is generally considered
to be the primary control on fire in the region (1, 3, 4, 14, 19). The
processes by which concurrent climate and vegetation conditions
support or suppress fire vary by scale. On seasonal-to-interannual
time scales, field observations and satellite data have demonstrated
the importance of temperature, the variability of precipitation, and
drought in controlling patterns of burning (1, 3, 20). Given sufficient vegetation productivity (21), high temperatures and drought
are consistently linked with greater area burned and with large
fire years in the West (22, 23). Fire activity in dry shrublands
and grasslands is also strongly linked with antecedent precipitation
that drives the development of fine fuels necessary for the spread
of large fires in these ecosystems (24, 25). High temperatures during the fire season promote fire-conducive weather and lightning
ignitions, but temperature is also important in the spring and fall
because it extends the fire season (1, 4). In winter, high temperatures reduce snowpack, which affects soil (and fuel) moisture (1).
The effects of temperature on fire apply on centennial (4) and
longer time scales (26) as well. In any given year, the spatial distributions of areas burned are highly irregular, although organized
temporally by weather variations (27).
Field observations and longer dendrochronological fire-scar
records demonstrate the importance of El Niño Southern Oscillation (ENSO) on interannual climate variability (28), particularly in the southwestern United States (22). ENSO creates a
dipole pattern in the western United States characterized by
opposing climate conditions in the northwest and the southwest.
During La Niña events, ocean surface temperatures are cold in
the eastern equatorial Pacific and the southwest tends to receive
reduced precipitation and have abundant fires (29); the northwest tends to be wetter-than-normal and to have few fires (30).
During El Niño events climate and fire conditions are reversed
from La Niña conditions (22). La Niña conditions were a contributing factor to the large fires in Texas and Arizona this year
(2011) in June, for example. Despite the prominence of this
dipole pattern in discussions in the fire science literature, the most
important mode of interannual variability of climate is a regionwide pattern of anomalies of similar (rather than opposing) sign
for temperature and precipitation (31), snowpack (32), and the
timing of snowmelt runoff (33), as reflected by the first principal
component of each dataset.
On decadal-to-centennial scales, fire patterns have been linked
to slow changes in ocean/atmosphere patterns associated with
low-frequency variations in sea surface temperatures (14, 23).
Most work has focused specifically on linking fire patterns to
ocean/atmosphere dynamics associated with the Pacific Decadal
Oscillation (34) and/or the Atlantic Multidecadal Oscillation
(14). Fire patterns during the 20th century for example show that
large fire years are associated with a strong, persistent trough
over the northeastern Pacific Ocean and an associated ridge over
the West Coast, which leads to subsidence and thus dry conditions
in all western U.S. forests (23). Years with few fires are associated
with a weakened Aleutian Low, high sea surface temperatures in
the central North Pacific, a stronger-than-normal jet stream, and
low geopotential heights that combine to produce wet conditions
in the West (4).
Our knowledge of millennial-scale changes in fire activity
comes primarily from sedimentary charcoal data, which shows the
strong influence of annual temperature and summer drought (35,
36). Vegetation productivity (37) and changes in forest composiE536 ∣
tion and structure [e.g., related to succession (38, 39)] are also an
important control on fire regimes in many parts of the United
States at centennial and millennial time scales. The magnitude
of variation in climate and fire analyzed here are beyond the range
of the instrumental and historical records of the 20th and 21st centuries, but they are still smaller in amplitude than those projected
to occur over the next century.
Human impacts on forest fires in the western United States
since Euro-American settlement are well documented and primarily resulted from altered ignition patterns associated with
land and debris clearance, agriculture, fire suppression, and fire
exclusion more broadly. Grazing and the introduction of nonnative species had major impacts on a host of ecological processes
that affect fire, including forest composition and structure, nutrient cycling, soils, and hydrology. Many studies document such
human impacts on fire at local scales (40–42), but the scale of
earlier impacts from indigenous burning are still debated [e.g.,
(43)]. Temporal variability in indigenous fire impacts likely
occurred across two spatial scales: locally within individual populations (i.e., within territories of indigenous cultural groups), and
across larger areas related to longer-term cultural changes. There
is good evidence for local effects on vegetation and fire history
from fossil charcoal, pollen, and archaeological data (44–46), but
little evidence for widespread impacts, which we focus on here
and index by regional population levels for lack of more nuanced
synthetic or continuous data.
Our analyses provide convergent evidence from charcoal, historical, and tree-ring data for trends in fire activity during recent
centuries; they also show that the variations in charcoal over the
interval between 500 and 1800 CE (Common Era) are explained
by variations in temperature and drought. We then use the charcoal data to characterize fire history for the past three millennia
across the western United States The spatial scale of our study
matches that of climatic and human impacts on fire today, and
the long-term perspective allows us to study the response of fire
regimes to a wide range of climate and human influences.
Sources of Fire-History Data and Their Treatment
Each type of fire-history data has unique strengths and weaknesses
in terms of spatial and temporal coverage. Detailed estimates of
recent fires, area and/or biomass burned are available from remote
sensing and historical records (24, 47), but these data span a few
decades at most. Longer historical reconstructions inferred from
documents, photographs, ethnographic records, or other archives
tend to focus on the most destructive fires and rarely provide evidence of broad changes in fire regimes; an exception to this is the
unique historical record created by the United States Department
of Agriculture (USDA) Forest Service in order to estimate the extent, use, and destruction of original saw timber stand (i.e., trees
older than 50 y in 1630 CE) across the United States through the
period of historic settlement (48, 49). The data include regional
estimates of the original stand, amount cut, destruction and regrowth, and remainder. The data are provided by decade from
1630–1940 CE (Fig. 1, 48). We scale these estimates of widespread
disturbance by the percent destruction in the western regions to
obtain estimates of western U.S. fires over time. While the report's
estimates are inevitably coarse, the level of detail available for selected years and areas suggests that substantial effort and care went
into compiling the data. These early data can be supplemented and
indirectly validated by examining stand-establishment data derived
from forest inventories from the western United States (50). Such
data document the course of establishment and reforestation
following the widespread disturbances associated with historic
settlement.
Cross-dated fire-scar records provide a consistent long-term
history of fire frequency over centuries, and in rare cases millennia (51–54). Fire-scar data however are only available in forests
that do not typically experience stand-replacing fires (52). StandMarlon et al.
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age data can be used to reconstruct fire history in such forests
[e.g., (55)], but stand-age data are temporally more limited
because only the most recent fire can be dated at each site.
The International Multiproxy Paleofire Database (IMPD*) contains annually-resolved fire scars from over 350 sites (Fig. 1). The
number of sites recording fires varies from year to year in this
dataset, so we calculated the proportion of recording sites with
≥1 and ≥2 scars for each year (Fig. 2B). Changes in the proportion of sites with fire scars in a given year were summarized (see
Methods, SI Text) to illustrate widespread trends in fire incidence
(56) regardless of size or synchrony throughout the western United States.
Charcoal data are the most widespread proxy for fire occurrence and biomass burned on decadal-to-millennial time scales.
Composites of multiple charcoal accumulation rate (influx)
records have been shown to reflect coherent regional trends in biomass and area burned (37, 39). We obtained 48 charcoal records
from the Global Charcoal Database version 1 (57) plus 21 recently
published records (Table S1). The 69 charcoal records (Fig. 1) were
converted to influx data and standardized using a protocol designed
to facilitate intersite comparisons and synthesis (58) (Fig. 2C). A
subset of 41 high-resolution records were further analyzed by
decomposing the charcoal data into “background” and “peak” or
“fire-episode” time series (59). Peak time series were then composited into a region-wide summary of peak densities, reflecting broadscale changes in fire frequencies (Fig. 2D).
The differences in historical, fire scar, and charcoal datasets
make direct comparisons challenging (see SI Text), particularly
at the local scale, where past analyses have produced mixed results [e.g., (35, 39, 54, 60)]. At broad scales, however, the differences in fire-scar and charcoal data are an asset, allowing more
*http://www.ncdc.noaa.gov/paleo/impd/.
Marlon et al.
spatially and temporally comprehensive reconstructions of fire
history than is possible with either type of data alone.
Results and Discussion
Historical Evidence of Fire in the West. The western United States is
comprised of four regions in the USDA historical dataset: the
North and South Pacific and the North and South Rocky Mountains. The original (ca. 1700 CE) stand volume for these four
regions together was estimated to be 2.24 × 109 board feet (bf,
1;000 bf ¼ 2.36 m3 ), with about 73% in the north and 27% in
the south. The “original” forest in the western United States
accounted for about 18% (58.7 million hectares) of the total
original U.S. forest area, whereas the remaining stand in 1940
accounted for about 66% of the total forested area in the United
States.
Historical records of national timber resources document the
increasing impacts on forests from Euro-American fuelwood use,
lumbering, and land clearing across the country (Fig. S1). Fire use
also increased (Fig. 2A). Information on regional differences in
timber use and damage are not available, but the primary spatial
pattern of Euro-American impacts on fire likely followed the
westward expansion of the frontier from the Missouri River
ca. 1830 to its final close by the early 1900s. The recovery or
reforestation following the widespread disturbance of the 1800s
can be seen in stand-age data from forest inventories from the
western United States (Fig. S1). These data show a modal year
of stand origin in the first decade of the 20th century, with half the
stands originating between 1870 and 1950 CE.
Fire-Scar and Charcoal-Based Evidence of Fire in the West. All the firescar data and most of the charcoal data come from forested ecosystems (Fig. 1A; Fig. S2). Fire-scar records (n ¼ 369 sites,
>50;000 individual scars) are more evenly distributed between
north and south than the charcoal data, but there are more
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Fig. 1. (A) The geographic distribution of fire-scar (green triangles) records and charcoal-based fire-history records (influx and peak frequency records are
blue, influx-only records are purple) in the western United States on a base map of tree cover (84); (B) The latitudinal distribution of dendrochronological sites
recording fire scars for the past 1,000 y. A site is gray when it is recording fire, and a red tick mark indicates a fire scar (URL: http://www.ncdc.noaa.gov/paleo/
impd/paleofire.html); isolated gray tick marks at the beginning of each record indicate the beginnings of individual tree records. (C) Anomalies of charcoal
influx over the past 1,000 y from 69 sites in the western United States arranged latitudinally from north (top) to south (bottom). Each row represents a study
site. Blue dots indicate less burning than average; red dots indicate more burning than average. Spacing of the dots reflects the sampling resolution and
sedimentation rate of the record.
Year CE
600
800
1,000
1,200
1,400
1,600
1,800
2,000
Western U.S.
16
12
Historical Fires
[Reynolds and Pierson (1941)]
8
A
4
0
Fire Scars
[IMDP accessed Mar 2011]
C
0.05
Biomass Burning
0
LIA
Late-20th Century
Fire Deficit
0.25
Amplitude of
Climate-driven
Variability
0
-0.25
Fire Frequency
-0.5
0.0005
D
0.0004
0.0003
0.4
0.0002
0.2
0.0001
0
0
-0.2
E
Temperature
0.45
[Mann et al. (2009)]
-0.4
F
0.4
Drought
-0.6
0.35
[Cook et al. (2004), v2a (2008)]
(millions)
Poplulation
100
0.3
10
1
G
Population
0.25
Drought-Area Index (JJA)
Mean Annual
Temperature Anomalies (oC)
MCA
0.5
0.1
Peak Density
(high-res. charcoal records)
Z-Scores of Transformed Charcoal Influx
B
Proportion of Recording
Sites with Scars
Burned Saw Timber
(billions of board feet)
20
[HYDE 3.1, Klein Goldwijk et al. (2010)]
0.1
1,400
1,200
1,000
800
600
400
200
0
Years BP
Fig. 2. (A) Estimated historical saw timber affected by fires (48). (B) Smoothed proportions of dendrochronological sites recording fire scars (the green curve is
based on locally fitting nearest-neighbor parameter of 0.25, while the gray curve is based on a parameter value of 0.10. (C) Smoothed and standardized 25-year
(gray) and 100-year (red) trend line through standardized biomass burning records (n ¼ 69) along with predicted biomass burning based on a GAM (black
dashed line) fit to the 100-year biomass burning records. (D) Smoothed peak density (inferred fire frequency) from charcoal values (n ¼ 41). (E) Smoothed
gridded temperature anomalies for the western United States (10). (F) Smoothed Palmer Drought Severity Index for the western United States (9) . (G) Population estimates for the western United States (11). All smoothed curves are plotted with 95% bootstrap confidence intervals.
fire-scar data from low-and midelevation xeric interior forests of
the Rocky Mountains than in the higher elevations or more mesic
forests, although some data do come from less xeric/midelevation
forests; e.g., in Colorado (61) (Fig. 1A). In addition, fires do not
always leave scars, especially in forests with high fire frequencies
and on young trees, so the fire-scar data likely underestimates
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true fire frequency (62). General patterns in the fire-scar data
however, should be robust. For example, it is clear that northern
sites tend to burn less frequently than southern sites (Fig. 1C,
Fig. S2), and fires were more frequent from ca. 1600 to 1900
CE than after that interval. Specific years when widespread fires
occurred are evident when the fire-scar records are not overlapMarlon et al.
Climatic and Human Influences on Fire in the West. Mean annual temperature (MAT) and summer drought (drought-area index, DAI)
were summarized in a similar fashion to the charcoal data (see
Methods) and also show a general downward trend, at least until
the early 1800s (Fig. 2 A–D). The long-term decline in fire is also
evident for the 1,500 y prior to the beginning of the joint record
at 500 CE (Fig. 3). Superimposed on this trend are several large
and generally parallel variations in biomass burning and fire frequency (i.e., “fire activity”) during the past 2,000 y (Fig. 2 C and D).
Fire activity was high at 1000, 1400, and 1800 CE, and low at 900,
1600, and 1900 CE. The rise in fire at 1000 CE occurred at the
beginning of the MCA, when temperatures (MAT) and drought
area (DAI) were both high. Biomass burning remained high for at
least two centuries during the MCA (from 750 to 1000 cal y CE),
whereas fire frequency declined at 1100 CE. Another increase in
fire activity occurred at the beginning of the LIA around 1400 CE,
when drought increased rapidly. Biomass burning reached its late
Holocene minimum during the LIA, and fire-episode frequency
was also low at this time, although it is presently lower. The decline
in fire activity during the LIA occurred as drought declined and
temperatures reached their 1,500-y minimum (Fig. 2 E and F).
Similar trends and centennial-scale variability in climate and fire
until the 1800s suggests that baseline levels of fire activity in the
West were predominantly controlled by climate.
High fire activity during the MCA has been documented by
individual local studies based on both fire-scar and charcoal records [e.g., (54, 66)], and our results indicate that such activity was
widespread. Biomass burning was high throughout the MCA and
peaked at 1200 CE during a period of severe drought; the level of
Year CE
-1,000 -800 -600 -400 -200
0
200
400
600
800
1,000 1,200 1,400 1,600 1,800 2,000
1
Z-scores of transformed charcoal influx
Western U.S. Biomass Burning
0.5
0
-0.5
-1
3,000 2,800 2,600 2,400 2,200 2,000 1,800 1,600 1,400 1,200 1,000 800
600
400
200
0
Cal yr BP
Fig. 3. Relative changes in biomass burning in the western United States for the past 3,000 y based on 69 standardized sedimentary charcoal records. The red
line is a lowess curve based on a 200-year window width and the dark gray line is a lowess curve based on a 100-year window. 95% bootstrap confidence
intervals are shown as a gray band.
Marlon et al.
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is comparable in magnitude to the decline in fire that occurred
during the transition into the LIA.
Charcoal peak density shows distinct maxima similar to the
composite charcoal influx record over the past 1,500 y (Fig. 2 C
and D). The sharpest maxima in peak frequency occur at the
beginning of the MCA and LIA, and, as was the case for the other
records, during the early 1800s; smaller maxima occur at the ends
of the MCA and LIA. The association of peak density maxima
with rapid or large warming or cooling events is consistent with
that observed during deglaciation (64), and during the last glacial
interval (65); increased fire at these times would be supported by
vegetation changes that increase fuels available for combustion
(e.g., due to increased mortality).
ENVIRONMENTAL
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ping (Fig. S2). Widespread fires are easier to identify in the northwest in part because there are fewer fires in general, but there
also appears to be greater fire synchrony in the north than in
the south in general. Widespread fires occur fairly regularly during the high fire period from 1600–1900 CE, but an increase in
small fires is also evident from ca. 1850 through the early 1900s
(most visible in central and northern records; Fig. S2). The most
salient feature of the fire-scar data is the widespread, abrupt
reduction in fires around 1900 CE.
Charcoal data from the West are more prevalent in the north
(where lakes are more common) than in the south (Fig. 1C).
Charcoal influx rates (CHAR) vary continuously during the past
1,000 y at most sites, although the nature of within-record variability differs from site to site. Some records show low CHAR for
the past millennium followed by high CHAR during historic
settlement, for example, whereas other records show high variability from decade to decade. In many records there is a tendency
toward high CHAR between 1100–1200 CE, between 1800–1900
CE, and in the most recent samples. Low CHAR are common ca.
500 y ago, particularly in the north.
A temporal summary of the fire-scar data (Fig. 2B; Figs. S3 and
S4) shows that the proportion of sites recording scars increased
from about 1400–1800 CE, with a broad maximum between 1800
and 1850. The earliest part of this trend (i.e., prior to ca. 1500
CE) is more uncertain than latter parts, however, because (i) fewer sites were recording fire activity early in the millennium, so the
data reflect changes in burning at fewer than 40 locations, and (ii)
the early increase in the proportion of sites recording scars may
partly reflect an increasing number of trees susceptible to fire
scarring at each site (63). Comparison of analyses using either
one or more or two or more scars to indicate fire years across
the West as a whole, as well as for the north and south show that
trends in fire activity summarized with this method are robust to
alternative minimum scarring criteria (Fig. S4).
Combining the 69 charcoal influx records (Fig. 2C; Fig. 3)
provides an indication of the trends and variability in biomass
burning across the western United States during the past 3,000 y.
Burning declined slightly over the past 3,000 y, with the lowest
levels attained during the LIA, (ca. 1400–1700 CE/550-250 cal
y BP) and in the 20th century. Peaks in burning occurred during
the MCA ( ca. 950–1250 CE/1000-700 cal y BP) and during
the 1800s CE (Fig. 3). There is a large and rapid shift from high
burning in the 19th century to low burning in the 20th century that
burning then was similar to that reached about a century ago
(during historic settlement). Fire frequency also reached a peak
during the MCA at ca. 1000 CE, when both drought and temperatures were particularly high. Fire frequency in the West was higher at this time than at any other time in the past 1,000 y. Warm,
dry conditions in the western United States during the MCA
resulted from prevailing La Niña-like conditions in the tropical
Pacific, which is consistent with both increased drought and high
temperatures (Fig. 2 E and F) (10, 67).
Biomass burning and fire frequency were also high during the
transition into the LIA, during a prolonged period of severe
drought (Fig. 2E); fire then declines to minimum levels at
1500 CE and ca. 1575 CE for fire frequency and biomass burning,
respectively (Fig. 2 C and D). The fire-scar record becomes dense
enough to analyze during the LIA and indicates very low levels of
fire activity then. Evidence from glacial advances in the Sierra
Nevada range of California and the Cascade Range of the Pacific
Northwest (68) suggest decreases in summer temperature during
the LIA of ∼2 °C in Sierra Nevada (69). Native American populations also collapse after approximately 1;500 CE, which would
have significantly reduced the impact from human-caused fires
where they were important previously (Fig. 2G). The combination of low values for drought, temperature, population, biomass
burning, and fire frequency during the LIA suggest that multiple
factors, including reduced vegetation productivity from lower
temperatures, reduced fire-conducive weather (wetter conditions), and fewer human-caused fires to some extent, combined
to reduce fire activity generally during the LIA.
The charcoal influx record over the past 3,000 y (Fig. 3) indicates that variations in biomass burning have been particularly
large over the past 1,000 y. The negative excursions in biomass
burning during the LIA and in the past century for example, are
remarkable in the context of the past 3,000 y. In general however,
large shifts in the magnitude and rate of burning have occurred
throughout the past. For example, there is an abrupt decrease of
charcoal influx around 2,000 y ago comparable to the first step in
the decrease between the MCA and LIA, and there is a gradual
increase commencing around 1300 CE that is analogous to that
leading into the MCA. There are several features of the charcoal
records that are not well explained by climate, for example the
maximum in peak density around 800 CE, but overall, until
the 1800s, increases in temperature and drought are coeval with
increases in charcoal influx and peak density.
To further quantify the relationship between biomass burning
and climate, we developed a statistical regression model (Generalized Additive Model or GAM; Fig. S3). The regression was fit
using centennial changes in biomass burning from temperature
and DAI from 500 to 1800 AD (i.e., from the beginning of the
joint temperature and drought records to settlement). Climate
explains most of the multidecadal to century-scale variations
of biomass burning (R2 ¼ 0.85; F ¼ 47.0; p < 0.001). Temperature alone can account for half of the total variance of biomass
burning (R2 ¼ 0.53; F ¼ 51.2; p < 0.001), while drought area can
explain about one-third of the overall variance (R2 ¼ 0.34;
F ¼ 24.4; p < 0.001). The dashed black curve on Fig. 2C shows
the fitted (to 1800 CE) and predicted (1800–2000 CE) values
from the model (see also Fig. S5). The general features of the
influx record are captured, including the upturn in influx at the
end of the LIA, and a subsequent peak in biomass burning around
1800 CE. The observed and predicted influx curves diverge after
1800 CE, when the combined effects of landscape fragmentation
and fire exclusion reduced biomass burning in the face of postLIA and 20th century temperature increases. Because the model
was fit only to data prior to 1800 CE, we checked whether the
predictions over the past 200 y are extrapolations beyond the
range of the calibration data (Fig. S6). The values of the predictor
(climate) variables fall outside the general envelope of climate
values only after 1980 CE, so the divergence between observed
E540 ∣
and predicted charcoal influx beginning in the 1800s CE is most
likely due to nonclimatic controls.
Prior to the 1800s and within the temporal and spatial scales of
this study, human activity, expressed as population from the
HYDE 3.1 database (Fig. 2G), does not appear to influence
either the charcoal influx or peak density variations. Population
gradually increased (in contrast to biomass burning, which decreased) until after 1500 CE, when European contact resulted
in an abrupt population decline owing to disruptions such as disease and warfare (70). Although the low levels of biomass burning
attained throughout the Americas during the LIA are often ascribed to contact (71, 72), the general decline in biomass burning
was underway before contact [e.g., (26)], and seems largely accounted for by climate. The divergence between the observed and
predicted (by climate) charcoal influx curves after 1800 CE is thus
the main expression of human impacts on fire.
During the transition out of the LIA and into the Settlement
Era, historical records, fire-scar, and charcoal data (both observed and predicted) track increasing temperatures and drought,
showing a multicentury increase in forest fire activity from very
low levels during the LIA to very high levels of burning between
1700 and 1900 CE (Fig. 2 A–D).
The close association between observed and predicted biomass
burning prior to the late 1800 s suggests that climate changes
alone can explain the increase in fire activity between 1600 and
1800 CE. The more variable (25-year smoothed) biomass burning
curve (Fig. 2C, thin gray line) however, shows that fire activity
increased to very high levels in the 1800s despite an apparently
earlier decline in observed and predicted biomass burning
(Fig. 2C). The peak in fire activity in the mid to late 1800s is undoubtedly due in part to increased human-caused burning, which
reaches its maximum from 1850–1870 CE (Fig. 2A). Settlers arriving in the western United States at this time ignited many fires
for clearing forest and brush, lumbering, railroad construction,
agriculture, arson, etc. Road building and technological advances
were also linked to increased anthropogenic burning (and erosion), such as with the development of steam power and railroads
that created sparks leading to large numbers of wildfires until the
early 1900s (when the railroads were required to start clearing
woodlands within 100 feet of tracks to prevent fires). The introduction of the band saw in 1880 CE, and powerful logging machinery in 1890 CE, for example, also led to changes in harvesting
that further altered forests and fuels as well as the locations of
intentional and accidental fires. Increased anthropogenic burning
in the west from 1850–1900 CE is widely recognized in dendrochronological studies (61), but increased variability in moisture
availability associated with ENSO also contributed to increased
burning then (74).
Prior to the arrival of large numbers of Euro-Americans in the
western United States, the fire-history records show a short-lived
decline in fire in the 1810s CE. The annual fire-scar data indicate
that this decline in burning was driven by very low fire activity in
the years 1816 and 1818 CE; only 13 sites record scars in 1816 and
15 sites in 1818 compared with a century-long average of 36 sites.
These results are consistent with the hypothesized effects of widespread cooling following the eruption of Mount Tambora in 1815
CE (75).
Observed and predicted changes in biomass burning begin to
diverge in the late 1800s creating a fire deficit that has been growing throughout the 20th century (Fig. 2C). Predicted biomass burning generally rises from the late 1800s CE to present, consistent
with increased temperature and drought trends. In contrast,
observed biomass burning, as well as fire scars, charcoal-based fire
frequencies, and human-caused fires decline rapidly. The minimum in burning during the 20th century is similar to the low fire
activity levels that occurred during the LIA. Less than 10% of the
original sawtimber stand remained at that point, mostly on the
Pacific Coast (48), so while it is plausible that a reduction in forest
Marlon et al.
Marlon et al.
Conclusions
Biomass burning in the western United States has remained in
dynamic equilibrium with climate at least since 500 CE to the
1800s CE. Burning generally increased when temperatures and
drought area increased, and decreased when temperatures and
drought declined. The onset of persistent century-scale climate
anomalies like the MCA and LIA are marked by peaks in fireepisode frequency and gradually increasing biomass burning levels during warm intervals and generally decreasing levels during
cool intervals; this is consistent with observations on longer time
scales that abrupt climate changes, toward either warmer or cooler conditions are marked by peaks in biomass burning (although
peaks are larger when the shift is toward warmer conditions).
Against the backdrop of climatic and ecological processes, human activities had a marked impact on biomass burning after the
late 1800s. Our synthesis distills the dominant patterns in human
impacts, but it does not reveal the large spatial differences in fire
controls and effects, such as those that vary with vegetation type
and elevation gradients, that are necessary to inform management and restoration efforts (8), which, if applied uncritically,
can result in collateral damage (83). The data do suggest however
that even modest increases in temperature and drought (relative
to those being projected for the 21st century) are able to perturb
the level of biomass burning as much as large-scale industrialized
human impacts on fire.
More dramatic increases in temperature or drought are likely
to produce a response in fire regimes that are beyond those observed during the past 3,000 y. Since the mid 1800s, the trend
in fire activity has strongly diverged from the trend predicted
by climate alone and current levels of fire activity are clearly
out of equilibrium with contemporary climate conditions. The divergence in fire and climate since the mid 1800s CE has created a
fire deficit in the West that is jointly attributable to human activities and climate change and unsustainable given the current trajectory of climate change.
Based on the fire data alone, the levels of burning during the
19th and 20th centuries are not anomalous; there were times
(i.e., the LIA) when fire was as low as it has been over much of the
20th century, and times when it was as high as during the 1800s, as
around 50 to 1 BCE. When climate is considered however, the past
approximately 150 y (i.e., back to 1850) are remarkably anomalous. Although the current rate of biomass burning is not unusual
(even allowing for post-1980 CE increases in burning such as in
ref. 3), it is clearly out of equilibrium with the current climate. Our
long-term perspective shows that the magnitude of the 20th century fire decline, while large, was matched by “natural” fire reduction during cold, moist intervals in the past (e.g., LIA). Current fire
exclusion and suppression however, is taking place under conditions that are warmer and drier than those that occurred during
the MCA, which calls into question their long-term efficacy.
Finally, the historical, dendrochronological and charcoal records are in accordance when examined from similar temporal
and spatial perspectives. The different records each provide unique information on particular scales of variation and their causal
mechanisms. Given the size of the current fire deficit and its
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has expanded consistently during the past 100 y as a result of increasing population growth and drought (81, 82), however this
pattern is not reflected in the composite curve due to a lack
of paleofire data from that state. Second, differences in interannual to decadal-scale variations in burning such as those due to
ENSO are also not reflected in our data. Third, the recent increases in large wildfires across western states also do not appear
in the composite tree-ring or charcoal summaries, most likely
because their occurrence is too recent to be incorporated into
most sediments or fire-scar records. However, increases in fire
are evident in individual charcoal records, particularly from the
northern forests (Fig. 1).
ENVIRONMENTAL
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cover contributed to reduced burning, this seems unlikely because
timber extraction and destruction does not necessarily lessen wildfire risk, and in some cases increases it (76).
Multiple factors combined to cause the 20th century fire decline (77), largely due to human activities but also due to ecological processes following the intensive fire activity in the 1800s.
Grazing was perhaps the earliest primary cause of fire exclusion
in the West. Hundreds of thousands of livestock were introduced
to pine forests and grasslands in western states (40, 42) in the late
1800s. The widespread herds reduced grassy fuel loads, compacted soils, and sharply reduced fire frequencies. Road and trail
building also created fire breaks that limited the natural spread of
fires. Cultural changes were also taking place that may have reduced fire ignitions well before effective fire suppression in the
1940s. By 1900 CE, the western frontier had largely closed and
several large catastrophic fires, such as the Peshtigo Fire in Wisconsin in 1871 that killed over one thousand people (78) were
helping to change attitudes towards fire and fire policies. In
1891, the Forest Reserve Act was introduced that allowed the
President to reserve forests from the public domain (79), and
in 1905 the U.S. Forest Service was established with a primary
mission of suppressing all fires that occurred on reserved lands.
Responsibility for fire management was transferred from the
Army to the National Park Service when it was created in 1916,
and full suppression remained the policy for the next five decades
(with greatly increased efficiency in the 1940s) (79). Natural ecosystem changes also likely contributed to decreased fire in the
1900s, however. Increased fire in the late 19th century, for example, resulted in young stands in subalpine forest that were less
susceptible to fire in the early 20th century. A major increase
in fire-resistant aspen stands due to 19th century fires also likely
reduced biomass burning and fire frequencies.
In general, western U.S. forests were fundamentally changed
in the 1800 and 1900s from previous centuries. The increased
burning of the 1800s and the subsequent widespread exclusion
of fire altered stand structure and composition, understory vegetation and fuel loads, and facilitated entry of nonnative species
(76). Coupled with timber extraction and land clearance, the consequences for western forests were dramatic.
The fire deficit identified here might appear to contrast with
observations of recent increases in western U.S. fire activity (1)
and also to the well established fire-climate interactions documented across the region (14). These apparent differences can
be reconciled by explicit consideration of the time scale of the
variations. We show that mean or baseline levels of biomass
burned and fire frequency decreased substantially during the past
century compared with previous centuries; the recent increase in
“fire activity” (i.e., large-wildfire occurrence) is therefore occurring during a period of unusually low levels of biomass burning.
Furthermore, the increase observed since 1980 has a short duration compared with the longer decline in burning from the 19th to
20th centuries, or increases at the beginning of the MCA or following the LIA. Similarly, the associations between large fire occurrence, fire frequency, and climate that are well documented in
literature on western U.S. fire regimes (61, 80) are also dependent on scale and fire-regime dimension; interannual and even
multidecadal fire synchrony for example, may have been as strong
in the past as they are today with no “decoupling” of fire and
climate on these time scales. During the past two centuries, however, centennial-scale changes in biomass burned and fire frequency however, are decoupled from climate due to the strong
human influences on forests and fires.
Although the changes in fire described here were undoubtedly
widespread, our results do not address several important aspects
of fire history of the western United States. First, the trends do
not reflect subregional patterns of burning or changes in burning
in grasslands and shrublands. A good example is the increase in
area burned in California during the 20th century. Area burned
potential to grow in the future, the unique perspectives provided
by each data source will be necessary for projecting the response
of fire in the western United States to both ongoing and future
climate changes.
Methods
We used historical, fire-scar, and charcoal data to construct three independent records of millennial-and centennial-scale trends in fire occurrence
across the entire west. Historical data were obtained from Reynolds and Pierson (48), and from Littell, et al. (3). All fire-scar data available in the IMPD†
were used but only injuries to the trees defined by the data contributor as a
fire scar were used in the analysis. We calculated the proportion of recording
sites with scars each year, and summarized them using a locally fitted binomial logit model with bootstrap confidence intervals.
Charcoal data were obtained from the Global Charcoal Database [GCD
version 1 (57)] and authors (Table S1). Age estimates for data in the GCD were
taken as-is and were not modified or improved. For charcoal analyses, concentration data (particles cm−3 ) were converted to influx values (particles
cm−2 y−1 ) and were then standardized using methods described in detail
in Power, et al. (58). The transformed and standardized influx data were
summarized (Fig. 2C) using locally weighted regression (lowess). Bootstrap
confidence intervals were calculated by resampling (with replacement,
1,000 replications) the charcoal data by site (as opposed to by sample) in
order to illustrate the uncertainty of the smoothed values to the particular
distribution of sites in the dataset.
A subset of high-resolution records were analyzed using CharAnalysis (59),
which separates peaks from background charcoal (SI Text). The binary peak
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ACKNOWLEDGMENTS. We thank Jeremy Littell and two anonymous reviewers
for manuscript comments. We greatly appreciate the contributors to the
International Multiproxy Paleofire Database and the Global Charcoal
Database. We also thank Brad Smith from the USDA Forest Service for the
historical fire data. Support for this study was provided by a National Science
Foundation Grants ATM-0714146 to P.J.B., SBR-9700544 to E.A.S., and Postdoctoral Fellowship EAR-0948288 to J.R.M.
†
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Long-Term Fire History from Alluvial Fan Sediments: The Role of Drought and Climate
Variability, and Implications for Management of Rocky Mountain Forests
Jennifer PierceA C and Grant MeyerB
A
Boise State University Department of Geosciences 1910 University Drive, Boise, ID 83725-1535 USA
Telephone +1 208 426 5380 Fax +1 208-426-4061.
B
University of New Mexico Department of Earth and Planetary Sciences Albuquerque, NM 87131 USA
[email protected]
C
Corresponding author [email protected]
Suggested running head: Fire, climate, and alluvial fan sediments
Brief summary: Alluvial fan deposits preserve millennial-length records of fire. We used these
records to examine changes in fire over the last 2,000 years in Yellowstone National Park mixedconifer forests and drier central Idaho ponderosa pine forests. Severe fires occur in both areas
during past intervals of drought and increased climate variability.
1
Abstract. Alluvial fan deposits are widespread and preserve millennial-length records of fire.
We used these records to examine changes in fire regimes over the last 2,000 years in
Yellowstone National Park mixed-conifer forests and drier central Idaho ponderosa pine forests.
In Idaho, frequent small fire-related erosional events occurred within the Little Ice Age ~14501800 AD, when greater effective moisture likely promoted grass growth and low-severity fires.
This regime is consistent with tree-ring records showing generally wetter conditions and frequent
fires before European settlement. At higher elevations in Yellowstone, cool conditions limited
overall fire activity. Conversely, both Idaho and Yellowstone experienced a peak in fire-related
debris flows ~950-1150 AD. During this generally warmer time, severe multidecadal droughts
were interspersed with unusually wet intervals that likely increased forest densities, producing
stand-replacing fires. Thus, severe fires are clearly within the natural range of variability in
Idaho ponderosa pine forests over longer timescales. Historical records indicate that large burn
areas in Idaho correspond with drought intervals within the past 100 yr, and that burn area has
increased markedly since ~1985. Recent stand-replacing fires in ponderosa pine forests are
likely related to both changes in management and increasing temperatures and drought severity
during the 20th century.
Additional keywords: Ponderosa pine, Yellowstone, Idaho, debris flows
Introduction
The 20th century increase in global temperature (e.g. Jones and Moberg, 2003; Brohan, et al.,
2006) has been accompanied by a decrease in precipitation over the western United States (Karl and
Knight, 1998) and recent (~1999-2005) severe drought (Cook et al., 2006; www.drought.unl.edu).
Drought conditions correspond with an increase in the size and severity of large fires, and studies
demonstrate that 20th century fire occurrence in the in the western U.S. is strongly linked to changes in
climate (Westerling et al., 2006). For example, in 2002 record precipitation deficits in the western U.S.
2
led to fires that burned over 2.8 million hectares, including the largest fires of the past century in
Colorado, Oregon and Arizona (www.nifc.gov, NASA, 2004). In 2006, wildland fires in the western
states of Washington, Idaho, Montana, Alaska and Utah burned over 1 million hectares, or 30% of the
total wildland fire acres burned across the entire U.S (www.nifc.gov). The economic costs associated
with droughts and fires are significant: droughts are the most costly natural disasters in the U.S. (Cook et
al., 2006), and fire-fighting expenditures by federal land-management agencies now regularly exceed $1
billion dollars per year (Whitlock, 2004).
In order to understand how forests may respond to fire in a potentially warmer and drier future, it
becomes increasingly important to examine longer records of fire and the relationships between fire and
drought on different timescales. Analysis of trends in the regional Palmer Drought Severity Index (PDSI)
and percent land area in the western U.S.A. experiencing drought indicate that the duration of the current
drought is unusual when compared with conditions over the past century (Cook et al., 2004). When
compared with drought reconstructions between ~900-1250 AD, however, 20th century droughts are not
extreme (Cook et al., 2004). This indicates that over centuries to millennia, the western U.S.A.
experiences more severe droughts—and likely more severe fires—than have been typical over the
instrumental period of record. Proxy records used in fire reconstructions include charcoal records from
lake sediments, fire-scar records from trees, stand-age reconstructions, and alluvial fan records of firerelated sedimentation. These records indicate fires correspond with drought conditions over decadal (e.g.
Swetnam and Betancourt, 1990; Kipfmueller and Swetnam, 2000), centennial (e.g. Meyer et al., 1995;
Pierce et al., 2004), and millennial time-scales (e.g. Thompson et al., 1993; Whitlock et al., 2003).
To assess the effects of climate change on fire regimes in northern ponderosa pine and mixed
conifer forests, we have described and interpreted fire-induced deposits preserved in alluvial fans in the
South Fork Payette River area of central Idaho and Yellowstone National Park over the last 2000 years,
and compared these records with regional drought reconstructions (Cook et al., 2004). This study
provides 1) a summary of historic (last ~100 yr) fires and drought in the Boise National Forest of central
Idaho, 2) an examination of alluvial fan records of fire over the last 2000 years in central Idaho and
3
Yellowstone National Park within the context of millennial-scale reconstructions of drought, and 3) a
comparison between records of fire recorded in alluvial fan sediments and other proxy records of fire.
Study area
The South Fork Payette study area is located in the mountainous terrain north of the Snake River
Plain in south-central Idaho (Fig. 1). Annual precipitation, which falls mostly as snow derived from
Pacific moisture, varies from about 1000 mm at high elevation sites to about 600 mm in the lowest
valleys. Variations in climate and vegetation within the Idaho study area are determined largely by
elevation and aspect. On south-facing slopes in the lower basin (below ~900 m), shrubs, grasses, forbs,
and sparse ponderosa pines characterize hillslope vegetation. At elevations between 900-1400 m, open
ponderosa pine forests cover south-facing slopes and mixed pine and Douglas-fir (Pseudotsuga menziesii)
forests are found on north-facing and more mesic sites. Higher elevations above about 2200 m are
typified by ponderosa pine and Douglas-fir forests on south-facing slopes, and spruce (Picea
engelmannii), Douglas-fir and pine forests on north-facing slopes.
Northeastern Yellowstone National Park is located ~400 km to the east of the Idaho study area on
the borders between Idaho, Montana and Wyoming (Fig. 1). The northern Yellowstone National Park
study area lies at a higher elevation (>2000 m) and is covered by dense mesic conifer forests dominated
by lodgepole pine (Pinus contorta). Douglas-fir and Engelmann spruce (Picea engelmannii) are also
common, with a transition to subalpine fir (Abies lasiocarpa) and whitebark pine (Pinus albicaulis) at
higher elevations (ca. 2750-3050 m). Within the focus of this study in northeastern Yellowstone, annual
precipitation varies from 360 mm at lower elevations (2000 m; Lamar Ranger Station) to as much as 1300
mm at 3050 m along the eastern park boundary (Dirks and Martner, 1982). While Yellowstone also
receives most precipitation as winter snow, summer convective storms provide a source of intense, but
localized, moisture.
Background
4
Tree-ring records of fire in ponderosa pine forests, climate change, and management
Since ~1900, documented increases in tree density and changes in forest structure in some
western USA ponderosa forests (Cooper, 1960; Covington and Moore, 1994; Arno et al., 1995; Swetnam
and Baisan, 1996; Fule et al., 1997) have been accompanied by a shift from frequent surface fires during
the pre-settlement era to large stand-replacing fires during recent decades (e.g. Westerling et al., 2006).
This shift has often been attributed to 20th century fire suppression, grazing, and other land uses that limit
surface fires and promote increased stand densities and ladder fuels (Steele et al., 1986; Baisan and
Swetnam, 1990; Covington and Moore, 1994; Brown and Sieg 1996; Fulé et al. 1997; Covington, 2000).
Management in ponderosa forests has sought to re-establish or mimic the high-frequency, low-severity
fire regime and low tree densities that are believed to be characteristic of the pre-settlement era (White
House, 2002; U.S. Department of Agriculture, 2002). The pre-settlement ‘reference period’ for fire
regimes in ponderosa pine forests, however, is mostly from tree-ring records developed during the last
500 years, a time characterized by cooler climates than today. Cooler conditions during the “Little Ice
Age” (LIA) ~1400-1900 AD have been well documented in the western US (Carrara, 1989; Luckman,
2000) and throughout the northern hemisphere (Grove, 1988; Pollack et al., 1998; Esper et al., 2002).
Generally cooler temperatures during the pre-European settlement era contrast with instrumental records
showing temperature increases between ~0.5-1.0 °C since the late 1800’s (Jones et al., 1999; Jones and
Lister, 2002; Briffa and Osborn, 2002; Jones and Moberg, 2003; Brohan et al., 2006).
Most of the studies that demonstrate a pattern of frequent non-lethal fires in ponderosa forests
during the pre-settlement era are from the American Southwest. Fire-scar studies from ponderosadominated forests in other regions often do not support this model of frequent, low-severity fires, even
during the relatively cooler and wetter conditions of the Little Ice Age (see review in Baker et al., 2006).
For example, fire-scar records demonstrate a history of mixed-severity fires in pure ponderosa pine and
mixed ponderosa-Douglas fir forests in the Rocky Mountains of Colorado (Brown et al., 1999; Huckaby
et al., 2001; Ehle and Baker, 2003; Romme et al., 2003). Similarly, tree-ring data from ponderosa pineDouglas-fir forests in Montana (Barrett, 1988; Arno et al., 1995) and ponderosa forests of the Black Hills
5
of South Dakota (Shinneman and Baker, 1997) indicate pre-settlement fire regimes characterized by a
mix of frequent low-severity and infrequent high-severity fires.
Tree-ring records of fire in Idaho and Yellowstone
With the exception of Steele et al. (1986), few detailed fire history studies exist for mid-elevation
ponderosa pine-Douglas fir forests of central Idaho. Existing fire-scar reconstructions of fire history in
ponderosa pine-Douglas-fir forests in Boise National Forest indicate that between 1700-1895 AD, mean
fire return intervals ranged from 10 years at drier sites to 22 years at moister sites (Steele et al., 1986). In
the 1900’s, fire return intervals lengthened considerably; 3 of 7 sites do not show any record of fire
between 1900-1983, while the other 4 sites only record 1 or 2 fires during this interval (Steele et al.,
1986). Fires were severe during the 1900’s, however, with extensive (>160 km2 and 90 km2) burns in the
Boise National Forest during the 1931 drought.
The Selway-Bitterroot Wilderness Area, ~300 km to the northeast of the South Fork Payette
study area, includes a range of forest types from low-elevation ponderosa pine forests to high-elevation
mixed conifer forests. Fire-scar records extending back to 1709 AD from the Selway-Bitterroot
(Kipfmueller and Swetnam, 2000) were compared with fire years from historical fire atlas data and treering reconstructions of PDSI (Cook et al., 1999). Results of superposed epoch analysis (used to establish
associations between surface fire and antecedent climate conditions) show that drier than average
conditions during the summer of the fire were significantly (p < 0.001) related to the largest fire years
(Kipfmueller and Swetnam, 2000). A significant (p < 0.05) relationship was also found between wet
conditions four years prior to the year of a fire event in the Selway-Bitterroot forests (Kipfmueller and
Swetnam, 2000), and likely reflects the influence of antecedent moisture on the growth of young trees and
other fine fuels.
In Yellowstone National Park, dense, high-elevation lodgepole pine-dominated forests burn
primarily in large, severe fires with recurrence intervals of ~200 to >350 years (Meyer et al., 1992;
Barrett, 1994; Meyer et al., 1995), and 150-350 year-old even-aged forest stands are common in high-
6
elevation forests (Romme, 1982; Romme and Despain, 1989). Fire-scar records and stand ages from
Yellowstone mixed conifer forests show large burn areas in the early to mid-1700’s and mid-1800’s
(Romme and Despain, 1989; Barrett, 1994).
Records of fire preserved in alluvial fans compared with tree-ring and lake charcoal records of fire
Alluvial-fan records add to data from other charcoal-based proxy records of fire that provide
evidence of relationships between fire, vegetation, and climate over centennial to millennial timescales
(Fig. 2). Alluvial fan records provide a longer fire record than tree-rings, are more ubiquitous in
mountain environments than lakes, and record stand-replacing fires. The typical time-scale of alluvial fan
records is intermediate between lake records and tree-ring records, thereby allowing documentation of fire
response to multi-decadal to millennial-scale climate change.
Pollen and charcoal records from lake sediments can be used to reconstruct relationships among
fire, climate, vegetation and geomorphic response on millennial to multi-millennial timescales. On multimillennial timescales, fire frequency inferred from lake charcoal records in the northwestern US increased
during warmer, drier intervals coincident with the mid-Holocene solar insolation maximum ~10-6 ka
(Long et al., 1998; Millspaugh et al., 2000; Long and Whitlock, 2002; Brunelle and Whitlock, 2003;
Whitlock, 2003). Increased fire frequency is inferred to be associated with decreased fire severity, based
on contemporary associations that show an inverse relationship between fire severity and fire frequency in
forested ecosystems (McKenzie et al., 2000; McKenzie et al., 2004).
Fire-scar proxy records preserved in tree-rings provide annual to seasonal resolution of fires, and
can be used in conjunction with records of climate preserved in tree-rings to resolve relationships between
fire, temperature, and precipitation over annual to centennial timescales. These records can also be used
to reconstruct fire return intervals, burn areas, and fire seasonality, which provides valuable information
to managers and scientists who seek to understand fire regimes and how fire regimes change among
different regions and forest types. Fire scars do not, however, record stand-replacing fire. Stand-age
reconstructions can be used in conjunction with fire-scar records or can be used independently to establish
7
the time of the last stand-replacing disturbance (including fire) within a forest. These records, however,
are limited by the ages of stands or fire-scarred trees, which is typically <500 years in ponderosa pine
forests.
Alluvial fan records of fire do record stand-replacing fires (indeed, severe widespread fires are a
major cause of datable sedimentation events), and alluvial fan records of fire extend back >10,000 years.
Although alluvial-fan deposition is discontinuous in both space and time, the episodic nature of
deposition on alluvial fans can be offset by compiling the records from individual stratigraphic sections,
yielding a detailed history for the region (e.g. Meyer et al., 1995; Pierce et al., 2004). The method of
reconstructing an area-wide composite chronology using partial records from many different alluvial fans
is analogous to fire history reconstructions that use area-wide composites of fire-scar records from tree
rings (e.g., Swetnam and Betancourt, 1990; Brown et al., 1999). Alluvial fan stratigraphy is complex and
variable, and analysis of fire related deposits requires intensive field study and interpretation of
stratigraphic relationships.
Historic records of drought and fire in central Idaho
Across the west, the mid-1980’s are marked by a distinct increase in large (>400 ha) wildfires
corresponding with higher summer temperatures and inferred earlier snowmelt (Westerling et al., 2006).
Historic (1908-2006) records of fires in the ponderosa pine and Douglas-fir-dominated Boise National
Forest mirror regional trends (Fig. 3). Annual area burned (km2) in historic fires in the Boise National
Forest was calculated from spatial coverages of burn areas (spatial data courtesy of the Boise National
Forest). Burn area data were compared with monthly PDSI values and with mean summer (June, July,
August) temperature for Idaho Climate Division Four (http://lwf.ncdc.noaa.gov). Between 1908 and
2006, historic burn area data from the Boise National Forest show that fires burned at least 4097 km2,
although the total burn area is likely higher since small fires in remote areas were less likely to be
recorded during the early part of the 20th century. Palmer Drought Severity Index (PDSI) values for
central Idaho show that drought severity has significantly (p < 0.01) increased over the period of
8
instrumental record (1895-2006). Mean summer temperature (June-August) has also increased by ~0.3
○
C. In Yellowstone, PDSI values show a very significant (p < 0.001) increase in drought conditions since
instrumental records began in 1895, accompanied by an increase in summer (June-August) temperatures
of over 2oC (p < 0.01) between 1985 and 2002.
The majority of the fires in the Boise National Forest burned during two intervals of severe
drought: 1015 km2 (25% of the total burned area) between 1926-1935, and 2363 km2 (58% of the total
burned area) from 1985 to 2006, including a few severe fires totaling >800 km2 in 1994. Interestingly,
the large fire year of 1926 does not correspond with anomalously low regional PDSI values. This
discrepancy could be due to a number of factors, including a difference between regional PDSI and local
soil moisture values, antecedent moisture, fuel conditions, or high winds that could have contributed to
large burn areas during this year.
The earlier part of the ~1936-1984 interval of limited fire activity corresponds with moister
conditions (~1940-1965) and a decrease in summer temperatures (~1942-1958). The dramatic decrease in
burn area ~1950-1985, however, likely reflects at least in part the influence of fire suppression. Only 228
km2 burned between 1950-1984 (6% of the total burned area; 35% of the total time interval). These
decades are marked by increased effectiveness in fire suppression due to increases in road access in
forested areas, the use of aircraft and motorized equipment in fire-fighting efforts, and increased monetary
support for fire-fighting efforts.
Records of fire preserved in alluvial fan sediments
To investigate changes in fire activity over millennial-timescales, we identified individual firerelated sedimentation events in alluvial fans in central Idaho (Pierce et al., 2004) and Yellowstone (Meyer
et al., 1995), described deposit characteristics in the field, and radiocarbon dated charcoal fragments to
create composite chronologies for the two study areas. In central Idaho, we radiocarbon-dated 91
charcoal samples from 35 alluvial fan sections associated with 34 different tributary basins ranging in size
9
from 0.01-6 km2. In northern Yellowstone National Park, 50 charcoal samples from 34 fan sections were
dated (Meyer et al., 1995).
Fires dramatically increase rates of erosion on recently burned slopes. Evidence of fires and firerelated erosion and deposition is recorded in alluvial fans as fire-related deposits and buried burned soil
surfaces (‘burn surfaces’). The thickness and character of fire-related deposits provide information about
the severity of the associated burn. Deposit characteristics (sedimentary structures, sorting, clast size and
content, proportions of sand, silt, and clay in the fine [< 2 mm] fraction of the deposit, and color) were
described in the field and used to characterize deposits within a fan section. Boundaries between deposits
were determined by the presence of burn surfaces, erosional surfaces, and variations in deposit
characteristics (Fig. 2).
Abundant angular charcoal fragments and (or) dark mottles of charcoal or charred material in
deposits are characteristic of fire-related deposits. Burn surfaces within fan sediments are also indicative
of past fire activity, and are characterized by discrete, laterally extensive layers of charred organic
material of the litter layer (e.g., conifer needles, twigs, and grasses) approximately 0.5->2 cm thick (Fig.
2). In severe burns, the litter layer is almost completely ashed. Since these severely burned ashy surfaces
are not usually preserved, presence of an underlying burn surface is not required for recognition of firerelated units. In many cases, burn surfaces are directly overlain by a fire-related deposit. An undisturbed
and continuous surface implies rapid burial by postfire sediments prior to bioturbation and (or) erosion. If
the overlying deposit contains coarse, abundant charcoal fragments, this further indicates that the
depositional event is likely a response to the fire represented by the underlying burned surface.
Dating methods
Individual charcoal fragments were 14C-dated by accelerator mass spectrometry (AMS) at the
NSF Arizona AMS Facility. To avoid dating samples of inner heartwood and bark from older trees that
have ‘inbuilt’ ages significantly older than the fires that burned them (Gavin, 2001), small twigs, cone
fragments, needles, and seeds were selected for dating where available. These materials are also less
10
likely to survive multiple cycles of erosion and deposition. Individual charcoal fragments were selected
for dating to avoid mixing of charcoal ages. Rootlets were removed manually, and acid and base washes
were used to remove soluble carbonate and organic contaminants. Identification of charcoal macrofossils
helped determine the type of vegetation burned. Macrofossil identification is especially important
because it helps establish whether major vegetation changes (and associated changes in fire regimes) have
occurred over the dated interval. ‘Inverted’ dates (those with dates significantly older than underlying
dates in a sequence) can be caused by bioturbation, deep burning of roots, reworking of older charcoal
from existing soils or deposits, or large inbuilt ages. Analysis of radiocarbon dates within their
stratigraphic context and careful selection of samples limits error from these sources. For multiple ages
obtained within the same deposit, the youngest age was assumed to have the least inbuilt age and to be the
most accurate. After removal of inverted and multiple ages (Pierce et al., 2004), probability distributions
for 97 radiocarbon ages (14C yr BP) were calculated using their associated one sigma analytical
uncertainty and calibrated to calendar years before present using the program CALIB 4.3 (Stuiver and
Reimer, 1993). Individual probability distributions from the calibrated ages of radiocarbon samples were
summed to produce an overall probability spectrum for fire-related sedimentation events over the
Holocene for the Idaho study area. Materials in deposits known to be less than ~200 yr BP were not
collected for dating in order to avoid the large analytical error, thus ambiguous age, associated with these
samples.
Classification of large and small fire-related events
In central Idaho, large fire-related events were differentiated from small events based on
stratigraphic characteristics (Pierce et al., 2004). Burn severity is reflected in the volume and to some
extent the transport processes of postfire alluvial-fan deposits. Severely burned basins tend to produce
thick debris-flow and sheetflood deposits (Cannon et al., 2003; Meyer et al., 2001; Meyer and Pierce,
2003) that can be preserved in alluvial fans. We define ‘large fire-related events’ as events represented by
debris-flow units with abundant coarse angular charcoal that are generally coarser grained than other units
11
in a stratigraphic section and comprise at least 20% of the thickness of the section (Pierce et al., 2004).
These deposits are often underlain by burn surfaces and most likely represent high-severity burns.
Divergence and thinning of debris flows tend to occur down the length of alluvial fans, and distal fan
units are usually thinner than proximal ones (Blair and McPherson 1994; Meyer and Wells, 1997; Meyer,
1993). Because even large debris flows often produce thin deposits locally, the relative thickness of
deposits at any fan position provides a usable measure of relative event size. We therefore define ‘large
events’ as having a large thickness relative to the rest of the stratigraphic section. Deposits that are
clearly fire-related (contain abundant coarse charcoal), but that do not fit the criteria stated above are
classified as small fire-related events. In the Idaho study area, these are commonly pebble and finer
sheetflood deposits of cm-scale thickness that likely issued from low- to moderate-severity burns. Most
alluvial fan sites at middle to low elevations in the study area are characterized by a mix of large and
small event deposits.
Records of drought and fire in central Idaho and Yellowstone over the last 2000 years
In Yellowstone National Park mixed-conifer forests and in central Idaho ponderosa pine forests,
charcoal fragments and fire-related deposits in alluvial fan sediments record changes in fire regimes and
geomorphic response over the last 8,000 years (Meyer et al., 1995; Pierce et al., 2004). In order to
compare our results with other regional studies of drought (Cook et al., 2004), and because the majority of
our dates (54 of 97) fall within the last few millennia, this paper focuses on fire-related sedimentation
over the last ~2000 years.
In Idaho, the highest frequency of fire-related erosional events occurred as small events during
cool episodes such as the Little Ice Age (~1400-1900 AD), when greater effective moisture likely
promoted grass growth and low-severity fires (Fig. 4a). The peak in frequent, low-severity fires in Idaho
~1400-1700 AD corresponds with tree-ring records of frequent fires in ponderosa forests during the preEuropean settlement era in central Idaho (Steele et al., 1986) and in ponderosa forests throughout the
western US. At the same time, fire-related sedimentation was minimal in the high-elevation mixed
12
conifer sites of Yellowstone – evidence that a cooler, effectively wetter climate prevented most fires from
spreading in this moister environment. A similar lull in fire-related sedimentation is centered ca. 400-600
AD.
The Little Ice Age interval characterized by frequent fires in Idaho and limited fire activity in
Yellowstone corresponds with records of wetter-than-normal conditions throughout much of the western
U.S. (Cook et al., 2004). Between ~1500-1850 AD, tree-ring reconstructions indicate the percent drought
area in the west dropped below the long-term (~1200 yr) average, and regional Drought Area Index (DAI)
for the western USA is lower during this interval (Cook et al., 2004). Local PDSI reconstructions from
tree-ring records for central Idaho and northern Yellowstone (http://www.ncdc.noaa.gov; reconstructions
centered on 115.0○W 45.0○N and 110.0○W 45.0○N, respectively) show a lower range of variability in
drought conditions ~1400-1900 than the prior interval ~200-1300 AD, and records from both regions
exhibit a series of 8-10 decadal to multi-decadal wet episodes (PDSI > 0; Fig. 4a) during the Little Ice
Age.
Conversely, both Idaho and Yellowstone fan records show a peak in fire-related debris flows
between ~950-1150 AD corresponding with “Medieval Climatic Anomaly” (MCA) drought conditions
~900-1300 AD. Drought indices for the western US indicate that 1140-1175 AD is the most extreme
period of multidecadal drought in the last 1200 years (Fig. 5; Cook et al., 2004). In Idaho, despite the fact
that large fire-induced debris flows account for only a small proportion of the total number of fire-related
events, 24-27% of the total dated fan thickness was emplaced by only 9 major debris flows between
~950-1150 AD. During this time, apparently stand-replacing fires occurred throughout the study area,
including low-elevation rangeland sites, mid-elevation ponderosa pine-dominated sites, and high
elevation mixed conifer forests (Pierce et al., 2004). Evidence of large debris flows ~950-1150 AD
corresponds with recent fire-related debris flows in Idaho study area that have produced significant
(~43,000 Mg/km2) amounts of sediment (Meyer et al., 2001).
Prior to the onset of regional drought conditions during medieval times, PDSI reconstructions
indicate several dry intervals between ~600-750 AD. PDSI records from the Yellowstone show four
13
decadal to multi-decadal intervals of drought between ~600-750 AD; drought reconstructions from central
Idaho show intervals of drought ~600-630, and two drought intervals between ~700-750 AD (Fig. 4b).
These intervals of drought correspond with peaks in large fire-related debris flows in Yellowstone and
Idaho ~650-775 AD (Fig. 4b). While sample depth during this interval is low and regional DAI has not
been extended back prior to 824 AD, regional PDSI reconstructions indicate drier than average conditions
for Wyoming, Colorado, and the American Southwest during the interval between 600-750 AD (Fig. 5).
Multidecadal climate variability and fire
The peak in fire-related sedimentation in Idaho and Yellowstone ~900-1250 AD corresponds with
PDSI reconstructions of multidecadal drought conditions in central Idaho and northern Yellowstone 900950 AD, 1000-1020 AD, 1120-1170 AD, and 1220-1270 AD (Fig. 4b). Interestingly, this interval also
contains prolonged wet episodes ~1080-1120 AD and ~1175-1220 AD. Vegetation growth during these
wet intervals likely provided fuel for large fires during the subsequent drought (1230-1280 AD). The
pronounced alternations between wet and dry intervals during the MCA highlight the fact that climate
during this interval may have been quite variable (Fig. 5). Lake-level reconstructions from the Great
Basin (Adams, 2003), western regional tree-ring records of drought area (Cook et al., 2004), records of
drought in now-submerged tree stumps in the Sierra Nevada (Stine, 1994), lake salinity changes in South
Dakota (Laird et al., 1998), and intervals of dune stability and soil formation vs. dune mobility in
Wyoming (Mayer and Mahan, 2003) all indicate that the Medieval Climatic Anomaly was characterized
by both droughts and wet intervals of multidecadal length. Prior to the MCA, relatively wetter conditions
in the northern Rocky Mountain region ~540-560 AD may have enhanced fire activity during subsequent
dry intervals ~600-675 AD (Fig. 5). Both during the MCA and ~540-675 AD, prolonged wet intervals
could enhance tree germination and understory growth of young trees, brush, and grasses at moisturelimited sites, creating denser stands and abundant ladder fuels for fires during subsequent droughts.
Regional DAI shows lower variability during the Little Ice Age (DAI values range between ~2535 %) than during the Medieval Climatic Anomaly (DAI ranges between ~25-50%). Peaks in ‘small-
14
event’ fire activity in Idaho during the Little Ice Age, however, appear to correspond with intervals of
relative drought within this overall cooler and effectively moister time (Fig. 4b). For example, the ~1600
cal yr BP peak in fire-related sedimentation in Idaho may partly reflect the well-documented “late 16th
century megadrought” (Woodhouse and Overpeck, 1998; Cook et al., 2003). Other drought episodes in
the western US during the LIA, including the 1660-1675 AD “17th century pueblo drought”, and 18651875 AD “mid-19th century megadrought” (Woodhouse and Overpeck, 1998; Cook et al., 2003) are
associated with peaks small fire-related events in Idaho between 1400 and 1850 AD. Widespread fire in
the mid-1800’s follows a wet interval from ~1825 to 1840 (Cook et al., 2003) that may have promoted
seedling generation and understory growth. Fire-scar records and stand ages from Yellowstone mixed
conifer forests also show large burn areas in the mid 1700’s and mid 1800’s (Romme and Despain, 1989;
Barrett, 1994).
High climate variability on annual timescales (alternating wet and dry intervals) has been shown
to promote surface fires (e.g. Swetnam and Betancourt, 1990, Swetnam and Betancourt, 1998,
Kipfmueller and Swetnam, 2000). The growth of grasses and fine fuels is enhanced by several wet years,
followed by drying of fuels and ensuing fires during a subsequent drought year. Wet and dry intervals on
multidecadal timescales may enhance fire activity through an analogous mechanism. Long intervals of
wetter-than-average conditions could suppress surface fires and significantly increase stand densities, in
addition to increasing fine fuel production in moisture-limited forests. Multi-decadal drought could then
act to desiccate both understory fuels and the forest canopy, including increased ladder fuels that
developed during the preceding moist decades. Severe and prolonged droughts result in large canopy
fires even in forests normally too wet to burn, as in the higher elevations of Yellowstone, synchronizing
severe fires across disparate forests of the western United States (as in 2002). In this way, prolonged wetdry intervals could enhance fire activity in both fuel-limited forests and in forests where normal high
moisture levels usually preclude stand-replacing fire. This hypothesis is supported by evidence of severe,
likely stand-replacing fire in Yellowstone, and at a range of elevations and forest types in Idaho during
past wet-dry intervals ~~950-1250 AD.
15
Conclusions and Implications for Management
Over both the last century and the last two thousand years, drought is a primary driver of fire
activity in central Idaho and Yellowstone. These results support other studies that conclude that climate
is a major control over fire occurrence during both the pre-settlement era (e.g. Whitlock et al.,. 2003;
Swetnam and Betancourt, 1990) and in recent decades, when climate, not land management, is likely the
predominant factor in our study areas and over much of the northern Rocky Mountain region (e.g. Balling
et al., 1992; Westerling et al., 2006). Historic fire records from the ponderosa pine-dominated Boise
National Forest show that large burn areas correspond with past intervals of drought. PDSI and
temperature records from central Idaho indicate that the 1985-2006 fires and fires during the ‘dust bowl’
era drought of the 1930’s correspond with intervals of drought and high summer temperatures. Over 3375
km2 or >80% of the total burn area occurred during these two intervals of drought, and over 50% of the
area burned after 1985. This pattern mirrors national trends; across the west, the mid-1980s are marked
by a distinct increase in large (>400 ha) wildfires corresponding with higher summer temperatures and
inferred earlier snowmelt (Westerling et al., 2006). In addition, since 1970, 60% of the increase in large
wildfires has occurred in mid-elevation (1680-2590 m) forests of the Northern Rockies where fire
suppression has had little effect (Westerling et al., 2006). Therefore, while fire suppression and other
land use changes in the Boise National Forest may have played a role in reducing fire activity in the
1950’s-1970’s, recent drought is likely the primary driver of recent stand-replacing fires.
In Idaho ponderosa forests, the highest frequency of fire-related erosional events occurred as
small events during inferred multi-centennial cool episodes, in particular during the “Little Ice Age”
~1400-1900 AD. Large fire-related debris flows are not unprecedented, however, and widespread, likely
severe fires occurred during past intervals of multidecadal drought ~900-1300 AD. These fires burned
throughout a range of forest types including Idaho ponderosa forests, lower elevation rangeland sites, and
high elevation mixed conifer and lodgepole pine-dominated sites in Idaho and in Yellowstone. These
results indicate that large stand-replacing fires were part of the natural range of variability in fire regimes
16
in ponderosa pine forests during past intervals of drought. Fire-related sediments and burn surfaces
provide records of fire and geomorphic response over millennial timescales. In addition, soil erosion and
sediment loading of streams following severe crown fires is of major concern in forest ecology, fisheries,
and overall land management. Alluvial fan records provide a way of assessing whether recent post-fire
erosion is unusual or unprecedented over longer time periods.
In addition to drought, high multidecadal climate variability may promote widespread fires. A
strongly variable climate during Medieval time ~900-1300 AD is associated with large fire-related debris
flows throughout a range of forest types in central Idaho and Yellowstone. Other proxy records from the
western U.S. provide evidence of an at times extremely dry, but also highly variable Medieval climate
(e.g. Stine, 1994; Laird et al., 1998; Adams, 2003; Cook et al., 2004). More recently, generally wet
conditions ~1960-1980 AD may have contributed to large burn areas during droughts in the 1980’s to
present. Multidecadal wet intervals likely increase stand densities and ladder fuels. If followed by
prolonged severe drought, desiccation of the forest canopy may result in large canopy fires, even in
typically low-density ponderosa pine stands, as well as in high-elevation forests normally too wet to burn.
We propose that through theses processes, high-amplitude multidecadal wet-dry cycles enhance canopy
fire activity in a range of forest types.
Evidence for geomorphically effective stand-replacing fires in Idaho ponderosa forests supports
other studies that demonstrate a diverse pre-settlement fire regime in ponderosa pine-dominated forests in
the Colorado Front Range, Montana, and the Black Hills of South Dakota, one that includes high-severity
fires (e.g. Brown et al., 1999; Huckaby et al., 2001 Ehle and Baker, 2003; Romme et al., 2003; Barrett,
1988; Arno et al., 1995; Shinneman and Baker, 1997; Baker et al., in press). Recent research
demonstrates that a model of low-severity fire alone is not suitable as a basis for restoration efforts in all
ponderosa-dominated forests (e.g. Baker et al., in press). In addition, reference conditions for ponderosa
forests that are defined based on fire regimes during the cooler, effectively wetter conditions of the Little
Ice Age cannot apply to warmer climates of the present and probable future. Attempts to ‘restore’ a forest
to either (1) a fire regime that is less diverse than those of the past, or (2) fire regimes characteristic of a
17
climate that no longer exists, may therefore be both costly and ineffective. Given that our results support
a natural regime of mixed-severity fire in ponderosa-dominated forests in Idaho, a fire model that only
includes frequent, low-severity fire is not applicable to this region. With predicted future warming, a high
probability of severe fires in ponderosa forests will likely persist. Management should therefore consider
how to maintain ecosystem resiliency within the context of a warmer and more fiery future.
Acknowledgements
Many thanks to Tim Jull, Spencer Wood, and Steve Wells for collaboration, and Katharine
North, Lydia Rockwell, Sarah Caldwell, Tim Lite, Wallace Pierce-Andersen, and Molly Watt for
aid in Idaho field work. Kari Grover-Wier and Paula Dillon of the US Forest Service provided
valuable logistical support and help with burn area data. Thanks to Ed Cook and his colleagues,
and the NOAA NCDC program for making drought data accessible and available online. This
work was supported by National Science Foundation grants EAR 9005058 and EAR 0000905 to
Meyer, and EAR 9730699 in support of dating at the NSF–Arizona AMS Laboratory.
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25
Figures
49○ N
N. Yellowstone
study area
Idaho study area
111○ W
117○ W
42○ N
Fig. 1. Location of Idaho and Yellowstone National Park study areas with state boundaries in
white and elevations coded as green (low) to light gray (high) shades. The majority of the Idaho
study area is dominated by ponderosa pine forests, although south and southwest facing-slopes at
elevations below 1300 m and elevations below 1000 m are predominantly rangeland, and
elevations over 2130 m are dominated by higher elevation mixed-conifer forests. Most of the
glaciated Yellowstone study area lies at over 2000 m elevation and is covered by dense conifer
forests dominated by lodgepole pine.
26
Fig. 2. Example of an alluvial fan site in Idaho showing the continuity of burned buried soil
surfaces (thin dark bands), the radiocarbon ages and analytical error of charcoal from these
surfaces, and overlying charcoal-rich debris-flow deposits. The burn surfaces and overlying
deposits can also be seen in smaller trenches oriented parallel to the main axis of the fan, ~15
meters above, and ~10 meters below this trench. Close-up shows stratigraphy, the continuity of
units, and lack of bioturbation within the burn surfaces.
27
1924
(a)
1926
1929
1986
1940
1935
1961
1917
1989
1949
1966
1939
1989
th
Sou
iver
eR
etttte
Pa y
k
r
Fo
1939
1931
1979
1934
1994
iver
eR
Bois
ork
F
h
rth
Nort
1990
1945
1930
1931
1920
1960
900
800
1949
0
1994
0
5 10 Km10 K m
5
1994
(b)
km 2 burned
700
600
1926
500
400
1989
1931
300
2006
1987
200
100
0
1890
1910
1930
1950
year
1970
PDSI
extremely dry
-7
1990
2010
y = -0.0095x + 18.355
R2 = 0.0199
(c)
-5
-3
-1
extremely wet
1
3
5
7
1890
1910
1930
1950
1970
1990
2010
Fig. 3. Burn areas and records of drought over the last century in the ~10,570 km2 Boise
National Forest. (a). Burn area associated with 20th century fires within the South Fork Payette
study area. The year of the fire is shown in year AD, where colors grade from yellow (early 20th
century) to red (1991-2000). Note the generally larger burn area for fires in recent times (1980’s
through present) and fires in the 1930’s era drought. (b) Approximate area burned annually in
the Boise National Forest between 1908-2006 (Data courtesy of the Boise National Forest).
Intervals of major fires occurred 1926-1935, and after 1985. (c) Monthly Palmer Drought
Severity Index (PDSI) for Idaho division 4 (south-central to north-central Idaho) 1895-2006
28
(http://cdo.ncdc.noaa.gov/). Negative PDSI values (note inverted scale) represent below average
soil moisture conditions and there is a significant (p<0.01) decrease in PDSI over the period of
record. Trendline shows the yearly moving average PDSI value.
29
30
Fig. 4. Comparison of Drought Area Index (DAI) for the western USA (top), PDSI
reconstruction for central Idaho and the Yellowstone area (middle) and fire related sedimentation
events in Yellowstone and Idaho (bottom). DAI and PDSI data are from Cook et al. (2004) and
are available online at http://www.ncdc.noaa.gov/paleo/newpdsi.html. A 50-year running mean
has been applied to the DAI data to highlight multidecadal trends. PDSI reconstructions (note
inverted scale) are from tree-ring records for central Idaho (gridpoint 69, 115.0○W 45.0○N) and
the Yellowstone area on the northwestern border of Wyoming (gridpoint 100, 110.0○W 45.0○N).
The number of tree-ring records in Idaho and Yellowstone used for the PDSI reconstructions
varies from 1-9 (Yellowstone) and 2-9 (Idaho) where sample depth increases with decreasing
age. Plots show the 20 year low-pass filter of the PDSI data. (a) Probability distributions of
individual radiocarbon ages on fire-related sedimentation events based on their analytical
uncertainty, calibrated into calibrated year BP (Stuiver and Reimer, 1993), where the ‘zero’ year
is AD 1950. Individual probabilities are summed to show the overall spectrum of relative
probability for the last 2000 years of fire-related sedimentation in the Idaho area (Pierce et al.,
2004; blue and black lines) and in Yellowstone (Meyer et al., 1995; gray-filled curves). In order
to reduce the influence of short-period variations in atmospheric radiocarbon (peaks unrelated to
fire-related sedimentation peaks), calibrated probability distributions were smoothed using a
100-year running mean. Idaho ‘small events’ (blue line) are thin deposits likely related to lowor moderate-severity burns. ‘Small events’ dominate the record of all fire-related events (black
line). Maxima in the record of Idaho small events corresponds with minima in fire-related
sedimentation in Yellowstone, most notably during the ‘Little Ice Age’ (LIA) ~~1400-1900 AD.
The lower probability of events in recent times (last ~300 years) results from the selection of
fewer near-surface deposits for dating because of bioturbation and large uncertainties in
31
radiocarbon calibration during this time. Blue vertical shading shows intervals of relative
drought from DAI and PDSI data and corresponding peaks in fire related sedimentation in Idaho
~1430-1490, 1550-1585, 1630-1660, and 1770-1800 AD.
(b) Red line shows Idaho ‘large events’ (major debris flows) likely related to severe fires. Large
fire-related events in Idaho ponderosa forests coincide with fire-related debris flow events from
severe fires in Yellowstone lodgepole-dominated forests (orange shading). Peaks in fire activity
in both areas correspond with multidecadal drought shown in the DAI and PDSI records (Cook
et al., 2004), and the prominent peak in large-event probability corresponds with regional
drought during the ‘Medieval Climatic Anomaly’ ~900-1300 AD. Red shaded bars show
intervals characterized by drought and large fire-related debris flows in both areas.
32
540-560
1070-1090
600-675
1130-1160
Fig. 5. Examples of the spatial distribution of relatively wet intervals and subsequent dry
intervals inferred from tree-ring reconstructions of PDSI (Cook et al., 2004). Maps were created
online (http://www.ncdc.noaa.gov/cgi-bin/paleo/pd04plot.pl) using summer(June-August) PDSI
values across North America for specified years, where warmer colors indicate more pronounced
drought conditions. Two wet-dry intervals are shown: the pre-Medieval wet interval (540-560
AD) and subsequent drought (600-675 AD) in Idaho and the Northern Rockies, and the medieval
wet interval (1070-1090 AD) and subsequent drought (1130-1160 AD). The ~1140-1160
drought is one of the most severe intervals of multidecadal drought in the last millennia (Cook et
al., 2004). In both intervals (600-675 AD and 1130-1160 AD), drought corresponds with peaks
in fire-related debris flows in Yellowstone and Idaho. Exact comparison is difficult, however,
given the error in radiocarbon dating (± 30 years) and potential inbuilt age in charcoal samples.
33
Plant Ecol
DOI 10.1007/s11258-007-9379-5
Spatial and temporal variability in fire occurrence
within the Las Bayas Forestry Reserve, Durango, Mexico
S. A. Drury Æ T. T. Veblen
Received: 24 January 2007 / Accepted: 26 October 2007
Ó Springer Science+Business Media B.V. 2007
Abstract Patterns of fire occurrence within the Las
Bayas Forestry Reserve, Mexico are analyzed in
relation to variability in climate, topography, and
human land-use. Significantly more fires with shorter
fire return intervals occurred from 1900 to 1950 than
from 1950 to 2001. However, the frequency of
widespread fire years (25% filter) was unchanged
over time, as widespread fires were synchronized by
climatic extremes. Widespread fire years occurred
during dry years that lagged wet years. Widespread
fire years lagged the negative El Niño phase (wet
winters) of the Southern Oscillation by 1 year, but
were not synchronized by the positive, La Niña phase
(dry winters) of the Southern Oscillation. The
smaller, localized fires that occurred more frequently
during the first half of the 20th century were
attributed to changes in land tenure with the introduction of the ejido system in the early 1950s. Ejido
management strategies lowered fire frequencies by
suppressing fires and reducing anthropogenic fires.
S. A. Drury (&) T. T. Veblen
Department of Geography, University of Colorado,
UCB 260, Boulder, CO 80309, USA
e-mail: [email protected]
Present Address:
S. A. Drury
Missoula Fire Lab, USDA Forest Service,
Rocky Mountain Research Station, 5777 W Hwy 10,
Missoula, MT 59808, USA
There were likely more ignitions prior to the arrival
of the ejido system as fires were ignited by lightning
and indigenous people. As the movement of indigenous peoples across the landscape has been
restricted by changes in land tenure, numbers of
human-ignited fires subsequently decreased post
1950. After 1950, fires occurred less frequently, were
more synchronized, and more restricted to years of
extreme climate.
Keywords Mexico Fire Climate variability Land-use changes Forest ecology Disturbance
Introduction
Fire is a common disturbance regulating species
composition, forest structure, and regeneration potential in many xeric conifer forest types such as the
long-needled pine ecosystems of western North
America (Weaver 1951; Cooper 1960; Agee 1998).
Fire occurrence and severity have been shown to be
highly variable throughout these xeric conifer ecosystems as they are controlled by environmental
processes that vary over space and time (Kaufmann
et al. 2000; Ehle and Baker 2003; Sherriff and
Veblen 2006). Three recent fire history studies in
the Mexican state of Durango describe temporal and
spatial variability in fire regimes within the mixed
pine–oak region of Mexico’s Sierra Madre
123
Plant Ecol
Occidental (Fulé and Covington 1997, 1999; Heyerdahl and Alvarado 2003), yet a clear understanding of
the local and regional influences on fire regimes in
these forests remains elusive. Moreover, the influence
of topography and habitat type on fire history and
fire–climate relationships has not been systematically
investigated. Thus the main objective of the current
study is to elucidate the primary drivers of fire
occurrence in Mexican pine–oak forests in the Las
Bayas Forestry Reserve, Durango, Mexico (Fig. 1).
Our objectives are: (1) to describe the fire regime
in Sierra Madrean pine–oak ecosystems within the
Las Bayas Forestry Reserve and (2) to assess how
climate variation, changes in land-use practices,
Fig. 1 Locations of sample
sites in the Las Bayas
Forestry Reserve (Predio de
Las Bayas), Mexico
123
habitat type, and topographic position influence the
fire regime. A thorough understanding of how fires
occurred over time in these landscapes is necessary
for land managers to make more informed land
management decisions (Landres et al. 1999). Specifically, we address the following questions for the Las
Bayas region: How does variation in climate influence fire occurrence and fire severity? Is the
occurrence and severity of fires influenced more by
the top down influence of regional climate or by the
bottom up influence of topography and vegetation on
microclimate? And, is there a link between changes
in land-use practices and temporal and spatial
patterns of fire occurrence?
Plant Ecol
Background
Fire and fire regime variability are thought to play
important roles in maintaining the high diversity
characteristic of Madrean pine–oak forest ecosystems
in Durango (Bye 1995; Felger and Johnson 1995;
Fulé and Covington 1997, 1999; Heyerdahl and
Alvarado 2003). Fire in many xeric conifer ecosystems has been shown to be related to inter-annual to
multi-decadal variation in climate (Swetnam and
Baisan 1996; Swetnam and Betancourt 2000; Veblen
et al. 2000; Heyerdahl et al. 2002; Heyerdahl and
Alvarado 2003). A common pattern is that fires occur
in dry years that follow wet years in association with
El Niño-La Niña events (Grissino-Mayer and Swetnam 2000; Swetnam and Betancourt 2000; Heyerdahl
and Alvarado 2003). Regionally widespread fires tend
to occur during drier La Niña events (years) that
follow wet El Niño events (years). Presumably the
wetter El Niño years promote the growth of fine,
herbaceous fuels, but are unfavorable for the ignition
and spread of fires. During the drier La Niña years,
fine fuels dry and the occurrence and spread of fires is
favored by low moisture conditions.
Climate variability in the Sierra Madre Occidental
and the Las Bayas Forestry Reserve
Climate in the Sierra Madre Occidental is seasonal
with mild, dry winters and wet, warm summers
(Douglas et al. 1993; Metcalfe et al. 2000). Much of
the annual precipitation occurs during the summer
months starting in late May to early June and ending
in September or October depending on the year
(Douglas et al. 1993; Metcalfe et al. 2000; Fig. 2).
Annual climate variability tends to be associated with
the ENSO phenomenon. El Niño years tend to be
wetter than normal while La Niña years tend to be
drier than normal (Ropeleweski and Halpert 1986;
Kiladis and Diaz 1989; Cavazos and Hastenrath
1990).
Multi-year droughts and multi-year periods of
above average precipitation also appear to be part of
the historical range of variability for northern Mexico
(Diaz et al. 2002; González-Elizondo et al. 2005;
Fig. 3). Multi-year droughts have been identified
from tree-ring climate reconstructions for Baja California (1939–1958; Diaz et al. 2001), for Durango
(1540–1579, 1857–1872, 1950–1965; Stahle et al.
1999; Cleaveland et al. 2003), and for Chihuahua
(1664–1677, 1751–1765, 1798–1810, 1948–1964;
Diaz et al. 2002). A long period of drought from
the late 1940s into the 1960s is reflected in many
instrumental records for the state of Durango (Fig. 2).
In the state of Chihuahua, Diaz et al. (2002) noted
several multi-year periods of above average precipitation during the 18th and 19th centuries, and from
1905–1932. Extended wet periods for the state of
Durango have been identified by Cleaveland et al.
(2003) and Stahle et al. (1999) between the years of
1477–1486 and from 1831 until 1857. These multiannual to decadal fluctuations in precipitation may be
related to the Pacific Decadal Oscillation (PDO) and
the Atlantic Multidecadal Oscillation (AMO) as has
been suggested for the southern Rocky Mountain
region (Gray et al. 2003, 2004).
Previous fire history studies in Durango, Mexico
Heyerdahl and Alvarado (2003) related widespread
fire occurrence prior to 1900 to climate in the
northern Sierra Madre Occidental. Specifically,
regionally widespread fire years tended to occur
during dry La Niña events that followed wetter El
Niño events. In contrast, Fulé and Covington (1999)
suggested that fires in southern Durango were weakly
related to the Southern Oscillation (SO), but widespread fires were not synchronized by climate as only
one widespread fire year occurred during a positive
SO (La Niña) in their study area.
Fire occurrence was also shown to vary spatially in
some areas (Fulé and Covington 1999). In southern
Durango, Fulé and Covington (1999) concluded that
fire varied spatially in relation to elevation, slope
gradients, and proximity to human habitation. On
their higher elevation, mesic north-facing slopes, they
speculated that fire occurrence was limited by humid
weather conditions and/or infrequent fire ignitions.
On their contrasting xeric sites they concluded that
these areas were climatically dry enough to support a
fire every year. However, fires were limited on these
xeric sites by elevation, low slope gradients, and
proximity to human settlement. In contrast, Heyerdahl and Alvarado (2003) found no relationship
between fire regime and physical site differences.
They speculated that due to the low latitudes of their
123
Plant Ecol
Fig. 2 Average monthly
and annual precipitation
records for El Salto and
Ciudad Durango, Durango,
Mexico. The heavy black
lines in (c) and (d) are mean
annual precipitation
A) Average Monthly Precipitation (mm) at
B) Average Monthly Precipitation (mm) at
El Salto, DGO
(1940-1993)
Durango, DGO
(1933-1999)
200
200
150
150
100
100
50
50
0
0
Nov
Dec
Sep
Oct
Jul
Aug
Jun
Apr
May
Feb
DGO
(1933-1999)
DGO
(1940-1993)
800
700
600
500
400
300
200
1600
1400
1200
1000
800
600
400
1993
1983
1973
1963
1953
1943
1933
1990
1980
1970
(a)
1960
1950
1940
150
140
130
120
number of trees
110
100
90
80
70
60
50
40
30
20
10
0
2000
1990
1980
1970
1960
1950
1940
1930
1920
1910
1900
1890
1880
1870
1860
1850
1840
1830
1820
1810
1800
1790
1780
1770
1760
1750
1740
1730
1720
1710
1700
1690
1680
1670
1660
1650
Fig. 3 (a) Numbers of
conifers that established in
5-year periods from 1650 to
2000 within the Las Bayas
Forestry Reserve. Vertical
dashed lines indicate years
classified as severe fire
years (1871, 1890, 1928,
1938 and 1945—see
Table 5 for list of all years
identified as potentially
severe fires on a study site
by study site basis). (b) A
climatically sensitive treering chronology of
Pseudostuga menziessii
from the Las Bayas Forestry
Reserve (BAY, GonzálezElizondo et al. 2005) The yaxis data is percent
deviation from the mean
tree-ring index; positive
deviations indicate greater
moisture availability
Mar
Jan
Nov
Dec
Sep
Oct
Jul
Aug
Jun
Apr
May
Feb
Mar
Jan
D) Annual precipitation (mm) at Durango,
C) Annual precipitation (mm) at El Salto,
123
1
0
-1
2000
1990
1980
1970
1960
1950
1940
1930
1920
1910
1900
1890
1880
1870
1860
1850
1840
1830
1820
1810
1800
1790
1780
1770
1760
1750
1740
1730
1720
1710
1700
1690
1680
1670
1660
1650
deviation from the mean
(b)
Plant Ecol
sites, there was less difference in solar energy input
among different slope aspects, and consequently the
fuel moisture and microclimate conditions were
similar on slopes with different aspects (Heyerdahl
and Alvarado 2003). They also argued that topographic position may not be as important in the Sierra
Madre Occidental as in more northerly areas due to
the lack of fire breaks (roads, etc.). Consequently,
when fires occur in areas where ignitions are
common, the lack of firebreaks, the continuity of
fuels, and fuel moisture similarities on slopes with
different aspects enables the fire to spread from the
ignition area throughout the surrounding landscape
(Heyerdahl and Alvarado 2003).
In addition to climate, Fulé and Covington (1997,
1999) and Heyerdahl and Alvarado (2003) attributed
some fire regime variability to changes in human
land-use patterns. They attributed the abrupt cessation in fires in the 20th century that occurs in many
areas throughout the Sierra Madre Occidental to
changes in land access or land tenure patterns such as
the establishment of community cooperative landholdings after the Mexican Revolution (Fulé and
Covington 1997, 1999; Heyerdahl and Alvarado
2003). Although the possibilities that humans could
have significantly contributed to fire ignitions were
addressed, these authors felt that due to the generally
small indigenous population prior to ejido establishment, most fires during the pre-fire exclusion period
were ignited by lightning.
Methods
Study area
Site selection
Las Bayas Forestry Reserve
Twelve sample sites were located within the Las
Bayas Forestry Reserve (Fig. 1). Six sample sites
were located in areas that had burned within the last
10 years (Table 1): La Fortuna (LFA), El Solitario #1
(ESO), Arroyo El Pescador (AEP), Frenton Colorado
(FRC), Los Alisos (ALI), and La Fortuna #2 (LFA 2).
These six sample sites areas were further differentiated by topographic position: LFA, FRC, and LFA2
are located on a large broad mesa that lies within the
southeast section of the Reserve (Fig. 1). ESO is a
steep, exposed, south-facing slope in the mid-section
of the Reserve, AEP is a steep, northwest-facing
slope in the northwest section of the Reserve, and
ALI is a very steep, exposed, southwest-facing slope
in the midsection of the Reserve (Fig. 1).
This study was conducted within the 5,000 ha Las
Bayas Forestry Reserve in the Mexican state of
Durango (Fig. 1) which has been owned and managed as a forestry Reserve by the University of Juarez
Durango (UJED) since 1987. Lying within the
Madrean pine–oak biogeographic province (Brown
et al. 1995), the Reserve sustains a diverse forest
vegetation that consists of multiple combinations of 6
species of Pinus, 8 Quercus species, 4 Arbutus
species, Pseudotsuga menziesii, and 2 Juniperus
species. Current forest structure is heavily influenced
by harvesting and management activities under the
direction of the UJED forest managers.
Individual fires were dated based on the tree rings of
fire scars on live trees and dead trees (both snags and
cut tree stumps; Arno and Sneck 1977; Dieterich
1980). Indices of fire history for each of 12 sample
sites and for the Reserve were constructed from these
fire dates. Since we wanted to address questions of
how topographic variation, differences in vegetation
type, land-use changes, and climatic variability influence fire occurrence, we selected sample sites based
on topographic position and the presence or absence of
fire. Areas that showed signs of human influences such
as logging were not excluded from study. We did not
restrict our study sites to mature forests but investigated fire occurrence within a range of stand ages and
habitat types as most Mexican pine–oak forests are
young due to extensive timber harvesting. All of our
sites contained evidence of past harvesting events,
although the extent of past logging was unknown due
to the rapid decomposition rates in this region.
Sample sites were subjectively located to represent
the full range of forest types and time since last fire
(see below). There was no need to target areas of
unusually abundant fire-scar evidence as fire-scarred
trees were common regardless of topography, species
composition, or stand structure. The ease of scarring
of Madrean pine–oak species, and the large number
of trees that survive scarring, reduced the problem of
fires not being recorded.
123
Plant Ecol
The remaining six sample sites were located in
areas that appeared to have not been influenced by
fire within the past 20 years (Table 1): El Solitario #2
(ESO 2), Cordon de Burro (CDB), El Solitario #3
(ESO 3), El Cerro Fuera (ECF), La Grulla (LGA),
and El Cerro Alto (ECA). These six sample sites
occupied the following topographic positions: ESO
#2 and ESO #3 are located on the northeast facing
and the west-facing slopes respectively of the same
hill in the mid-section of the Reserve (Fig. 1). ECA
occupies a steep, north-facing slope near the ESO
study sites while CDB is located on a steep, exposed
south–southwest-facing slope on the western boundary of the Reserve (Fig. 1). The LGA and ECF
sample sites occupy steep hillslopes in the northern
section of the Reserve (Fig. 1). ECF is a cove-like
southern exposure, while LGA is a steep, exposed,
northeast-facing slope.
Fire-scar collection
Fire-scars were collected as evenly as possible within
each 5 ha sample site (search area of uniform slope
and cover type) by sampling on an 18 point-center
plot grid set up to sample vegetation in a companion
study (Drury 2006). A maximum of two live tree firescar samples and/or two dead tree fire-scar samples
were collected at each point to avoid issues of data
clumping. A minimum of 15 fire-scar samples were
collected within each sample site.
Fire-scar dating
Once fire-scarred trees or stumps were located in the
field, a cross section was removed from each sampled
tree using a chain saw as close to the tree base as
possible. In addition, each fire-scar sample included
the pith (innermost ring) when feasible. Some trees
were sampled at higher positions based on scar
location and the number of observable scars at
different positions along the tree bole. Sample collection height was recorded for each fire-scar sample.
Fire-scar samples were later transported to the lab,
sanded with progressively finer grits of sandpaper until
the individual cells could be seen using a dissecting
microscope, and the individual annual ring growth
increments (rings) were counted. Each fire-scarred
section was visually cross-dated using the marker ring
method (Stokes and Smiley 1968; Yamaguchi 1991).
All fire-scarred sections that were dead when collected
were cross-dated with a master tree ring chronology
from the Las Bayas Forestry Reserve provided by
Martha González-Elizondo (BAY; González-Elizondo et al. 2005) using the computer program
COFECHA (Holmes 1986). Additionally, COFECHA
was used to compare and test a subset of the live
Table 1 Characteristics of the 12 sample sites located within the Las Bayas Forestry Reserve
Canopy
covera (%)
Forest floor organic
material depth (mm)
1
52
20
1
57
46
14
1
51
58
16
3
0.45
25
46
30
3
Los Alisos (ALI)
0.19
48
82
11
3
La Fortuna 2 (LFA2)
0.14
18
50
33
6
El Solitario 3 (ESO3)
0.26
52
41
47
22
Study area
Transformed
aspecta
La Fortuna (LFA)
0.29
El Solitario 1 (ESO)
0.15
Arroyo El Pescador (AEP)
0.73
Frenton Colorado (FRC)
Slope
percent (%)
Time since
last fire (year)
El Cerro Alta (ECA)
1.56
53
34
54
23
Cordon de Burro (CDB)
0.08
58
59
33
27
El Cerro Fuera (ECF)
0.44
44
61
65
33
La Grulla (LGA)
1.2
43
43
35
34
El Solitario 2 (ESO2)
1.96
43
57
43
35
a
Cosine transformed aspect (Beers et al. 1966). Canopy cover is expressed as percent open sky. Canopy cover methodology is
described in Drury (2006)
123
Plant Ecol
fire-scarred samples for accuracy with the master tree
ring chronology. Since other disturbance events can
also result in tree scars, fire years were identified and
labeled as a year in which a fire occurred only if at least
one of the scars on individual trees was clearly
identifiable as a fire injury (Dieterich and Swetnam
1984). In addition to fire year, the season of burning
was assigned to each dated fire scar whenever possible
(Dieterich and Swetnam 1984; Baisan and Swetnam
1990). Fires were classified as spring fires (fire-scar tip
located in the early wood section of the annual ring),
summer fires (fire-scar tip located later in the early
wood), late summer or fall fires (fire-scar tip located in
the late wood). Fire-scar tips that were found in the
boundary between annual rings were conservatively
assigned to early spring of the following year.
Tree cohorts
Stand ages for conifer species (Pinus spp. and
Pseudostuga menziesii) within the study area were
determined using a combination of increment cores,
tree ages from fire-scarred sections that included the
pith, and complete bole cross sections from dead trees
and stumps within each five hectare sample site.
Increment cores were not collected from angiosperms
within the area due to indistinct growth ring boundaries that prevented reliable age determination.
Increment cores were also collected from conifer
saplings (C2 cm at the base) to capture the range of
conifer tree sizes and conifer tree ages within a sample
site. All samples tree age samples were processed
following the procedures described earlier for fire-scar
sections. For samples that missed the pith, Duncan’s
geometric method of conifer tree growth was used to
estimate the number of rings (years) missed (Duncan
1989). Samples that missed the innermost ring by
more than 20 years were excluded from analysis. Each
tree was cored at the lowest possible position on the
tree to collect the maximum number of rings and the
coring height was measured and recorded. Linear
regressions were developed to calculate estimates of
tree age at coring height by destructively sampling
conifer seedlings within the Reserve (Drury 2006).
The calculated tree ages at coring height were used to
adjust the tree establishment age for each conifer
tree back in time to provide a closer estimate of the
actual date of tree establishment. Individual tree
establishment dates were later compiled into 5-year
age classes and displayed graphically to identify
successful seedling establishment.
Fire and climate relationships
The computer program FHX2 (Grissino-Mayer 1995)
was used to produce composite fire history charts for
each area. We used the Superposed Epoch Analysis
(SEA; Baisan and Swetnam 1990) module within
FHX2 and the BAY tree-ring chronology compiled
by González-Elizondo et al. (2005) to test the null
hypothesis that there were no significant differences in
climate between fire-event years and non-fire years
(Grissino-Mayer and Swetnam 2000). This chronology was significantly correlated with regional climate
using instrumental meteorological records showing
that tree ring growth patterns indicate climate variability, particularly moisture availability (GonzálezElizondo et al. 2005). Average climate conditions
during widespread fire years (fires that scarred trees in
at least 25% of the sample sites) were compared with
the average climate for 5 years before fire and 4 years
after the fire year (-5, +4). SEA was also used to test
for relationships between years of widespread fire and
the Southern Oscillation Index (SOI). We used Stahle
et al.’s (1998) reconstruction of winter (December–
January) SOI, which is based on a regional tree-ring
dataset from Mexico and Oklahoma. Variation in the
tree-ring chronology accounts for 41% of the variability in winter SOI from 1900 to 1977.
Temporal differences in fire occurrence
We used FHX2 to calculate composite mean fire
return intervals (MFI) and the Weibull Median
Probability Intervals (WMPI) and to test for changes
in these time intervals over time for each sample site
and the entire Reserve using the student’s t-test
(Grissino-Mayer 1995). MFIs tend to be positively
skewed due to a lower limit for the minimum fire
return interval of 1 year and no upper limit for the
maximum fire return interval (Grissino-Mayer 1995).
The WMPI is viewed as an unbiased measure of the
central tendency as it is associated with the 50%
exceedance probability: half the fire intervals will be
shorter than the WMPI and half will be longer
(Johnson and Van Wagner 1985).
123
Plant Ecol
Two time periods were analyzed for potential
differences in fire occurrence over the entire 1750–
2001 time period using FHX2. The three time periods
were subjectively determined based on the length of
the fire record, the sample depth between time
periods, and temporal changes in land use for the
area (introduction of the ejido system of land
management). Initially, the study period was divided
into two halves (1750–1874, 1875–2001) to identify
potential differences in fire occurrence over the
length of the study. Later, the time span from 1900
to 2001 was divided into two halves which corresponded with the introduction of the ejido system in
the early 1950s. Although the Las Bayas Forestry
Reserve proper was never under ejido land ownership, the surrounding ejidos and community land
tenure organizations were formed at this time. In
addition, this time period was chosen because the
number of sample sites recording fires and number of
trees recording fires were relatively low prior to 1900.
Fire variation by habitat type
Fire occurrence from 1750 to 2001 was also
compared by habitat type using the same procedures
described above for the entire dataset. Habitat types
were identified by Drury (2006). Mean fire-return
intervals were compared among habitat types using
the Students t-test (Grissino-Mayer 1995).
Severe fires
Indicators of fire extent, damage to individual trees,
tree mortality, and post-fire tree establishment were
used to assess the potential severity of past fires
(Kaufmann et al. 2000; Ehle and Baker 2003; Sherriff
and Veblen 2006). In the current study, ‘‘severe fires’’
are defined as fires that killed large numbers of canopy
trees in contrast to low-severity fires that kill only
juvenile trees. Assessment of past fire severity cannot
be based on fire-scars alone, and instead requires a
congruence of multiple lines of evidence of past fire
effects on individual trees and stand structure (Baker
et al. 2007). Due to disappearance of evidence of the
ecological effects of past fire, no single criterion
suffices to identify past fire severity and instead we
used a combination of criteria. One indication of a
123
widespread and potentially severe fire was when a
majority of recorder trees recorded a fire event. Alone,
this criterion does not identify all high-severity fires
but it eliminates events that did not spread to a large
area. Presence of dead trees that died at the time of the
fire was a strong indicator of fire severity, but due to
decay could only be applied to more recent fires. If a
high percentage of a tree bole was injured by the fire,
it was assumed that the fire was more intense than fires
that resulted in less damage to the tree. If many trees
showed a high degree of bole damage in a particular
event, it was interpreted as an indicator of a more
severe fire. Identification of post-fire cohorts within a
20-year period after the fire-scar date (i.e., allowing
for uncertainties in the determination of germination
dates and lags in establishment following a fire) was a
critical criterion in identifying severe fire events.
Thus, we compared the percentage of tree establishments in the 20 years following a fire to the
percentage in the 20 years pre-dating the fire. This
procedure clearly identified major pulses of post-fire
establishment (e.g., 80 to over 90% of trees in the 40year window established in the 20 years following the
fire). However, if two fires occurred in an interval of
\40 years, the overlap of post-fire cohorts resulted in
smaller percentages of tree establishment linked to the
second fire event.
In our study as well as others in similar pinedominated ecosystems (Kaufmann et al. 2000; Sherriff and Veblen 2006), the most useful indicator that
past fires were relatively severe was age structure—
e.g., evidence that many shade-intolerant trees established soon after the opening of the canopy caused by
the fire. When these multiple lines of evidence
converged, a fire was defined as potentially severe.
However, the designation of a fire event as severe is
made cautiously because individually each line of
evidence can be the result of causes unrelated to the
overall intensity of fire.
Results
Composite fire histories
Fires were common within the Las Bayas Forestry
Reserve over the 250-year time span of this study
(Fig. 4). Most fires occurred during the early growing
season as spring fires. However, fire occurrence was
Plant Ecol
(a)
Exposed oak-pine
Pinus leiophylla
Mesa-top pine-oak
Xeric hillslope pine-oak
Mesic hillslope pine-oak
100
12
90
80
9
70
60
50
6
40
30
3
20
10
highly variable in both time and space among sample
sites with fires burning at least part of the Reserve in
every decade (Fig. 4). Fires were both asynchronous
and synchronous: synchronous and more extensive
fires were identified with a 25% fire filter (i.e., years
when fires burned at least 25% of the sample sites;
Fig. 4). Using the 25% filter, we classified 23 years
as widespread fire years (Fig. 4). Note that the
percentage filter strength increases moving backwards in time as the number of sample sites recording
fires decreases over time (Fig. 4b). For example, the
La Fortuna and Los Alisos sample sites did not have
large sample sizes of recorder trees prior to 1960 and
1930 respectively (Fig. 4b).
2000
1980
1990
1970
1960
1940
1950
1920
1930
1910
1900
1890
1870
1880
1850
1860
1840
1820
1830
1810
1800
1790
1780
1770
1760
0
1750
0
number of sample sites recording fires
(b)
% of sample sites scarred
Fig. 4 Composite fire
records for the 12 fire
history sample sites (a) and
percentage of sample sites
recording fires in individual
years from 1750 to 2001 (b)
Vertical lines in (a) are
years in which a minimum
of two trees recorded fire in
the site. In (b) the solid
horizontal line is the sample
depth (i.e., number of sites
recording fire prior to that
date); the dotted line
indicates the 25% filter used
to identify widespread fire
(minimum 3 of 12 sites
recording fire). A total of 23
fire years are identified as
widespread fire years on the
horizontal line: 1848, 1854,
1857, 1866, 1871, 1906,
1909, 1915, 1916, 1923,
1928, 1932, 1943, 1945,
1960, 1965, 1967, 1972,
1982, 1988, 1994 and 1998
availability (Fig. 5a). Widespread fire years followed
the negative phase of the SO (typically El Niño years
when winters are wet) by 1 year, but there is no
statistically significant association with the positive
phase of the SO (La Niña when winters are cool and
dry) during the fire year (Fig. 5b). Graphically, there
were no observed relationships between widespread
fire occurrence and the Pacific Decadal Oscillation
(PDO) or the Atlantic Multidecadal Oscillation.
Similarly, superposed epoch analysis did not yield
any significant statistical results for these indices
(results not presented).
Fire variation according to habitat type
Fire occurrence and climate
Widespread fire years tended to be dry years that
were preceded by a year of above average moisture
There was some synchronization of fire occurrence
among habitat types during the widespread fire
years, but the overall number of fires, and the
frequency of fire as measured by mean fire return
123
Plant Ecol
tree ring indices
(a)
1.2
+
1
+
0.8
-5
-4
-3
-2
-1
0
2
1
3
4
fire year
(b)
1
southern oscillation index
year, relative to fire year
0
-1
-2
-3
-4
-5
+
-5
-4
-3
-2
-1
0
1
2
3
4
fire year
(c)
1.15
tree ring indices
Fig. 5 (a) Tree ring
departures from the mean
prior to, during, and
following widespread fire
years (25% filter, minimum
of three sample sites
recording fires) from 1750
to 2001 (N = 23). Fire event
years and non-fire years
were compared to long term
climate variability using the
González-Elizondo et al.
(2005) tree ring chronology
for the Las Bayas Forestry
Reserve as a climate proxy
and Superposed Epoch
Analysis (SEA; Baisan and
Swetnam 1990). Crosses for
all figures note significant
departures from chance
determined by
bootstrapping (1000 runs,
95% confidence interval).
(b) Average departure of
reconstructed winter (Dec–
Feb) Southern Oscillation
Indices (SOI: Stahle et al.
1998) for widespread fire
years (C25% trees scarred:
N = 23) from 1750 to 1977.
(c) Tree ring departures
(González-Elizondo et al.
2005) from the mean prior
to, during, and following
years of potentially severe
fires from 1750 to 2001
(N = 5)
0.95
+
0.75
-5
-4
-3
+
-2
-1
0
1
2
3
4
fire year
interval (MFRI), differed between habitat types
(Table 2; Fig. 4). The xeric and mesic hillslope
communities did not differ significantly with regard
to the number of fires or the mean fire return
interval. However, there were significantly more
fires, and these fires occurred more frequently, in the
123
xeric and mesic hillslope communities than in the
exposed oak–pine communities, the Pinus leiophylla
community, or the mesa-top pine–oak communities
(Fig. 4). The number of fires and the interval (MFI)
between fires did not differ significantly among
the Exposed oak–pine communities, the Pinus
Plant Ecol
leiophylla communities and the Mesa-top pine–oak
community (Table 2).
Temporal changes in fire regimes
Fire regimes as measured by mean fire return interval
varied significantly over the time spans covered in
this study (Fig. 4). Fire was encountered much more
frequently with significantly shorter mean fire return
intervals from 1876 to 2001 than from 1750 to 1875
(Table 3; Fig. 4). However, this result is presented
cautiously as there may be a problem with missing
evidence as far fewer fire-scarred trees with establishment dates prior to 1875 were encountered
(Fig. 4). In addition, evidence of some early fires
on fire-scarred trees that date from the 1750–1875
time period may have been removed by subsequent
fires. These problems could lead to fewer fires, and
longer mean fire return intervals as identified in the
1750–1875 time period. However, the frequency of
widespread fire years was not significantly different
between 1750–1875 and 1876–2001 (Table 3) even
though fewer widespread fire years were identified
during the earlier time period (5 vs. 18 fire years).
More substantive conclusions can be made comparing the first and second half of the 20th century due to
the much larger sample sizes (Fig. 4).
Mean fire return intervals also differed significantly between the 1900–1950 and the 1951–2001
time periods (Table 3). A total of 36 fire years
(MFRI = 1 ± 1) were identified within the Reserve
from 1900–1950 and 25 fire years (MFRI = 2 ± 1)
were identified from 1951–2001 (Table 3). Although
fire frequency within the Reserve was lower post1950, fires were still common within the Reserve
(Fig. 4). Interestingly, the mean fire interval of
widespread fire years did not differ between 1900–
1950 and 1951–2001 (Table 3) providing additional
evidence that regional climate is influencing widespread fire occurrence. Ten widespread fires occurred
within the Reserve from 1900 to 1950 (MFRI = 4 ±
1), while eight widespread fires (MFRI = 5 ± 1)
occurred from 1951–2001 (Table 3).
Temporal changes in fire regimes by habitat type
The temporal trends noted Reserve-wide tended to be
maintained within the different habitat types with
some exceptions (Table 4). When there was enough
information for statistical analysis, there was significantly more frequent fire on the landscape from 1900
to 1950 than in the later half of the 20th century for
all communities (Table 4; Fig. 4). In addition, the
temporal trends for the 1750 to 2001 time periods in
exposed oak–pine communities were consistent with
the Reserve wide trends: significantly longer fire
return intervals occurred from 1750 to 1875 than
from 1876 to 2001 (Table 4) which may be an artifact
of missing information as the sample size pre-1876
for this community type is considerably smaller
(Fig. 4).
However, temporal trends in the xeric and mesic
hillslope communities diverged from the observed
Reserve wide patterns (Fig. 4) The longer fire records
Table 2 Mean fire return interval (MFRI) for the 1750–2001 by community type
Community type
Number
of fire
events
Weibull median
probability
interval
(years)
Median
fire return
interval
(years)
Mean
fire return
interval (±SE)
(years)
Number of fires
and MFI differs
significantly with
(95% confidence level)
Exposed oak–pine
21
7.0
5.5
10.6 ± 2.7
Xeric hillslope pine–oak, Mesic hillslope
pine–oak
Pinus leiophylla
8
Mesa-top pine–oak 13
6.7
5.5
4.0
6.2
8.6 ± 2.8
7.4 ± 1.8
Xeric hillslope pine–oak, Mesic hillslope pine–oak
Xeric hillslope pine–oak, Mesic hillslope pine–oak
Xeric hillslope
57
2.7
2.0
3.6 ± 0.6
Exposed oak–pine, Pinus leiophylla, Mesa-top
pine–oak
Mesic hillslope
65
3.2
3.0
3.6 ± 0.3
Exposed oak–pine, Pinus leiophylla, Mesa-top
pine–oak
Data are for fire years with a minimum of two scarred trees per fire at each site). Far right hand column designates the habitat type(s)
that differ significantly in terms of fire numbers and mean fire return intervals with the habitat type in the far left column
123
Plant Ecol
Table 3 Mean fire return intervals (MFRI) from 1750 to 2001 organized by time periods for all 12 sample sites combined
All fire years
Widespread fire years
All fire years
all fire years
Time periods compared
Number of
fire events
Mean fire return
interval (±SE) (years)
Significantly different
(95% confidence level)
1750–1874
41
2.7 ± 0.3
Yes
1875–2001
77
1.6 ± 0.1
1750–1874
5
5.8 ± 1.3
1875–2001
18
5.4 ± 0.8
1900–1950
37
1.4 ± 0.1
1951–2001
25
2.0 ± 0.2
1900–1950
10
4.3 ± 0.7
1951–2001
8
5.4 ± 0.9
1950–1975
16
1.7 ± 0.2
1976–2001
11
2.2 ± 0.3
1950–1975
4
4.0 ± 1.0
1976–2001
4
5.3 ± 0.7
No
Yes
No
No
No
Data are for all fire years in a minimum of two trees were scarred per fire per sample site. Widespread fire years were determined
using a 25% filter (fires occurred in at least 3 of the 12 sample sites)
and larger sample sizes for identified fire scars
(Fig. 4) allow for a more complete comparison
between the 1750–1875 and the 1876–2001 time
periods. There were no significant differences in fire
occurrence between the earlier and later halves of the
study time frame in these community types (Table 4).
Numerous fires were encountered in both time
periods and the mean fire return intervals between
these time frames did not differ significantly
(Table 4).
Table 4 Mean fire return intervals organized by habitat type and time periods for 1750 to 2001
Community type
Sample sites
Exposed oak–pine
(all fire years)
ALI, CDB
Exposed oak–pine
(all fire years)
Pinus leiophylla
FRC
Mesa-top pine–oak
(all fire years)a
LFA, LFA2
Xeric hillslope pine–oak ECA, ESO, ESO3
(all fire years)
Significantly different
Time periods compared Number of Mean fire
fire events return interval (95% confidence level)
(±SE) (years)
1750–1874
4
26.7 ± 10.8
1875–2001
17
6.5 ± 1.7
1900–1950
10
4.4 ± 1.9
1951–2001
6
9.4 ± 3.6
Yes
Yes
Not enough information
1900–1950
4
10 ± 3
1951–2001
9
5±1
1750–1874
12
6.9 ± 2.8
Yes
No
1875–2001
45
2.8 ± 0.3
1900–1950
22
2.3 ± 0.3
Yes
1950–2001
1750–1874
13
31
4.0 ± 0.9
3.5 ± 0.4
No
1875–2001
34
3.7 ± 0.5
Mesic hillslope pine–oak AEP, ECF, ESO2, LGA 1900–1950
(all fire years)
1951–2001
22
2.4 ± 0.3
7
6.3 ± 1.9
Xeric hillslope pine–oak
(all fire years)
Mesic hillslope pine–oak
(all fire years)
Yes
a
Not enough information due to small sample size to analyze the entire 1750–2001 study interval. Data are for all fire years with a
minimum of two scarred trees per fire per sample site
123
Plant Ecol
Severe fires
A minimum of one fire year was identified as a
potentially severe fire year in all sample sites from
the late 1920s to early 1940s based on multiple,
intersecting lines of evidence discussed earlier
(Table 5). Potentially severe fires also occurred at
earlier dates within the Reserve, with a common
period of severe fire occurrence from 1860 to 1890
(Table 5). Although cohort establishment dates were
used in conjunction with number of trees recording an
individual fire, the death dates of possibly fire killed
trees, and the amount of tree bole killed by the fire, no
fires identified as potentially severe fires occurred
independently of post-fire tree cohort establishment
(Table 5). About 5 years of widespread, potentially
severe fires were identified: 1871, 1890, 1928, 1832,
1938, and 1945. Four of these severe fire years
were previously identified as widespread fire years
associated with years of below average moisture
availability (Fig. 5a, b). Moreover, potentially severe
fire years tend to be associated with multi-year
periods of extremely low moisture availability or
drought (Figs. 3 and 5c).
Discussion
How does variation in climate influence fire
occurrence and fire severity in the Las Bayas
region?
Regional variations in annual climate appear to be
influencing fire occurrence, particularly widespread
fire occurrence in the Las Bayas Reserve (Fig. 5).
This is in agreement with earlier studies in the Sierra
Madre Occidental where years with high incidence of
fire occurred during years with below average
moisture availability that followed years when moisture was abundant (Fulé and Covington 1997, 1999;
Table 5 Percentage of conifers that established in the 20 years following each severe fire year, based on the total number of
establishment dates in 40-year windows centered on the fire year
Exposed oak–pine
communities
Pinus leiophylla
Coummunity
Mesa-top pine–oak
communities
Xeric hillslope pine–oak Mesic hillslope pine–oak
communities
communities
Los Alisos (ALI)
Frenton Colorado (FRC) La Fortuna (LFA)
El Solitario (ESO)
Arroyo El Pescador (AEP)
1875 (75%)
1938 (83%)
1866 (86%)
1871 (88%)
1885 (50%)
1945 (93%)
1940 (93%)
1951 (67%)
1906 (60%)
1950 (69%)
Cordon de Burro (CDB)
La Fortuna #2 (LFA2) El Cerro Alto (ECA)
El Cerro Fuera (ECF)
1874 (75%)
1890 (70%)
1802 (67%)
1938 (92%)
1932 (86%)
1938 (95%)
1890 (57%)
1932 (82%)
1945 (33%)
1977 (44%)
El Solitario #3 (ESO3)
La Grulla (LGA)
1840 (75%)
1928 (89%)
1855 (50%)
1871 (69%)
1928 (87%)
1960 (74%)
El Solitario #2 (ESO2)
1879 (71%)
1928 (89%)
Other criteria (number of fire scars, extent of damage to tree boles, and presence of dead trees) were also used in designating a year as
a severe fire event
123
Plant Ecol
Heyerdahl and Alvarado 2003). An important result
from this study was the lack of significance between
fire occurrence and the positive phase (La Niña) of
the SO (Fig. 5). Heyerdahl and Alvarado (2003) in
their regional study on the drivers of fire regime
variability found that widespread fire years were
significantly related to fluctuations of the SOI. In
their study, widespread fires were synchronized
during drier La Niña years (positive SOI) that
followed wetter El Niño years (negative SOI). In
the Las Bayas Forestry Reserve, widespread fire years
tended to occur more frequently one year following
the negative phase of the SO (Fig. 5). The El Niño
years tend to be wetter and cooler especially in winter
in northern Mexico (Ropeleweski and Halpert 1986;
Kiladis and Diaz 1989; Cavazos and Hastenrath
1990). The higher moisture availability associated
with El Niño events enhances the growth of forbs and
grasses. In the following drier years, these herbaceous
plants remain on the landscape as fine fuels. The
increased quantities and continuity of fine fuels in the
landscape increase the probability that fire will spread
throughout the area.
It is unclear at this time why the positive phase of
the SO did not significantly synchronize fire within
the Las Bayas Forestry Reserve (Fig. 5) as has been
shown for other areas in Durango (Heyerdahl and
Alvarado 2003). However, our results suggest that in
the southern Sierra Madre Occidental, widespread
fire years may be driven by other climatic events such
as fluctuations in the Mexican Monsoon (Douglas
et al. 1993). Based on the intra-ring scar position
identified in this study, most fires in the Reserve
occurred in early spring. These early spring ignitions
appear to be strongly influenced by the strength and
onset of spring and summer monsoon precipitation.
The timing of the arrival of the Mexican Monsoon
varies year to year depending on the latitudinal
movements of the intertropical convergence zone
(Douglas et al. 1993). It is possible that fluctuations
in the Mexican Monsoon may lessen the influence of
SOI on fire occurrence. A later arriving, or weak
monsoon season would tend to decrease fuel moistures and increase fire ignition probabilities, even
during negative SOI event years. Further study on the
relationship between the SO and the onset of
monsoon precipitation needs to be done to clarify
the climatic drivers influencing this region of the
Sierra Madre Occidental.
123
Is the occurrence and severity of fires more
influenced by the top down influence of regional
climate or more a consequence of the bottom up
influence of topography on microclimate?
The topographic differences in fire regimes noted in
the Las Bayas Forestry Reserve (Table 2) contrasted
with Heyerdahl and Alvarado’s (2003) more regionally oriented study. In more northerly latitudes, it has
been shown that topographic differences in solar
insolation may influence microclimate and fuel
moisture conditions and may influence the probability
of fire occurrence (Taylor and Skinner 1998; Heyerdahl et al. 2002). Moreover, Fulé and Covington
(1999) noted spatial differences in their La Michilia
study which they attributed to locational differences
in fire ignition and fire spread. However, Heyerdahl
and Alvarado (2003) found no evidence that topography was a major driver of fire regimes in the Sierra
Madre Occidental. Our results were more in agreement with the arguments put forth in Fulé and
Covington (1999). For example, fires were more
common in hill slope habitat types than in the flatter
or more exposed communities (Table 2). Part of this
difference may be a reflection of methodological
problems; more evidence of historical fire occurrence
was present in the hill slope habitat types than in the
other habitat types which allowed the creation of
longer, more complete fire records (Fig. 4). Nevertheless, fires tended to occur more frequently in
hillslope communities than in the other habitat types
(Table 2), which may be a result of more variable
microclimate conditions in the hillslope communities.
The more exposed oak–pine communities and the
flatter, mesa-top communities are more xeric and may
have microclimate conditions conducive to burning
every year—however, fine fuel production may be
limited by these dry conditions. Lower fuel production would limit the amount of fuel available for fire
ignition and fire spread. The more variable conditions
on hillslopes may allow for substantial biomass
production during wetter years (when fuel moistures
are high), which would dry and cure during low
precipitation years. These dried and cured fuels
would then carry the fire throughout the area when
ignition sources were present.
Fire ignition potentials may also vary spatially.
Although there was little elevational difference
between sample sites, the hillslope communities
Plant Ecol
may have a greater chance of lighting strikes and
subsequent fire ignition than the flatter mesa-top
communities. Also many of the hillslope communities were located near roads, enhancing the potential
for human caused fires.
Species compositions and prior fire severity may
also result in variable fire regimes. For example, the
longest intervals between successive fires and the
largest quantities of evidence to suggest that these
sample sites burned more severely over time were
found in the exposed oak–pine communities (Fig. 3).
Many historic fires within this habitat type resulted in
tree death, especially in the pines. These exposed
oak–pine communities were comprised predominately of an evergreen oak, Quercus arizonica. The
tough sclerophyllous litter from these trees may
require hotter, drier conditions characteristic of multiyear droughts to reach the fuel moisture conditions
necessary for fuel ignition. Also, this oak species
vigorously resprouts after fire, particularly severe
fires. The dense cohort of resulting Quercus arizonica
sprouts would shade the forest floor leading to higher
fuel moistures and longer intervals between successive fires. Fuel quantities would increase during the
long time intervals between successive droughts and
associated fires. Once ignited, the fires that occur
would potentially be more intense favoring continued
Quercus arizonica dominance and a more severe fire
regime.
Is there a link between changes in land-use
practices and temporal and spatial patterns of fire
occurrence?
Temporal variation in fire occurrence has been linked
to land use change in many xeric conifer ecosystems.
For example, the sharp decline in fire frequency in the
late 1800s throughout the southwestern United States
has been attributed to the introduction of grazing
animals in the 19th century (Swetnam and Baisan
1996). In northern Mexico, Fulé and Covington
(1997, 1999) and Heyerdahl and Alvarado (2003)
also concluded that the temporal changes in fire
frequency they observed were related to changes in
human land use. In their studies, fires abruptly ceased
in some areas and fire frequency decreased in many
other areas during the early to mid-20th century (Fulé
and Covington 1997, 1999; Heyerdahl and Alvarado
2003). These authors concluded that the temporal
changes in fire frequency coincided with increased
human manipulation of the landscape due to the postMexican revolution establishment of the ejido system
of cooperative land ownership. They argued that the
ejido system effectively granted more people greater
access to the land. In their view, fuels would have
been more contiguous prior to the arrival of ejidos.
The ejidos would have created greater fuel discontinuity due to the construction of roads, tree
harvesting, increased but limited fire suppression,
and other land management activities. Most fires
would have continued to be ignited by lightning, but
these fires would not have spread into adjacent areas.
Subsequently, the number of fires within an area
would have decreased while the interval between
subsequent fires would greatly increase.
We observed similar, but less dramatic, changes in
the temporal distribution of fire occurrence for the
Las Bayas Forestry Reserve (Fig. 4). Fires have not
occurred in many areas within the Reserve since the
mid-1960s to late 1970s, but this 25–35 years break
in fire occurrence is not outside the historic range of
fire free periods for individual sample sites within the
Reserve (Fig. 4). However at the Reserve scale, the
frequency of all fires (i.e., including small fires) was
lower pre-1950 than post-1950, whereas the incidence of years of widespread fires was the same preand post-1950 (Tables 3 and 4; Fig. 4).
The lack of change in occurrence of years of
widespread fires during the 20th century in conjunction with a reduction in the number and/or spread of
small fires after 1950 (Table 3) implies that indigenous people may have been a more significant cause
of fires prior to 1950. It is likely that fuels became
more discontinuous due to more intensive land use
since 1950 as also noted by Fulé and Covington
(1999) and Heyerdahl and Alvarado (2003) which
would reduce fire spread potentials. We suggest that
if fuel discontinuity alone was the limiting factor to
the number of fire scars encountered in the post-1950
fire record, then the number of years of widespread
fire post-1950 should have declined also. An alternative explanation to the fuel discontinuity argument is
an explanation based on a change in the number of
fires set by the indigenous people. Many of the small
patchy fires we see in the pre-1950 fire record (Fig. 4)
were possibly ignited by indigenous peoples, but in
years that were not climatically extreme, these fires
123
Plant Ecol
did not spread to large areas due to topographically
controlled differences in fuel conditions. These
anthropogenic ignitions, both intentional and accidental, probably declined 1950 as the free movement
of people across the landscape became more limited
when land tenure changed. Thus, we suggest that in
addition to lightning-ignitions there was a small but
significant contribution of human-set fires to fire
frequency in the pre-1950 period which subsequently
declined following ejido establishment in the Las
Bayas vicinity.
The interpretation that indigenous people contributed significantly to the number of fires recorded in
the tree-ring fire record at Las Bayas is consistent
with ethnographic and historical knowledge of this
region. Prior to the introduction of Spanish rule in the
mid 1500s the dominant cultural group in the Las
Bayas area (Municipio de Pueblo Nuevo) of the
Sierra Madre Occidental were the Tepehuanes who
sporadically located homes across the landscape in
single to multi-family homesteads Pennington (1969).
While the Tepehuan population may have never
reached great numbers, Pennington (1969) discusses
how the Tepehuan regularly used fire for cooking,
heating homes, and as a tool for clearing and
maintaining agricultural fields. The dispersed, seminomadic lifestyle of the Tepehuanes and their everyday use of fire suggests that even a small population
could have significantly influenced the number of fire
ignitions (by accident or with intent) across a broad
landscape and may have left long-term legacies
reflected in vegetation patterns.
Conclusions
Years of widespread fire in the Las Bayas Forestry
Reserve coincide with dry years that follow wet
years. The predominance of spring fires appears to be
influenced by the onset of the Mexican monsoon. In
the Las Bayas Forestry Reserve, widespread fire years
were not strongly synchronized by fluctuations in the
strength of the SO as found in nearby fire history
studies (Heyerdahl and Alvarado 2003). In agreement
with earlier studies, widespread fire years tended to
occur more frequently following El Niño years.
However, our results contrast with earlier conclusions
that widespread fires occurred during La Niña years.
123
Although widespread fire years were drier, they were
not significantly synchronized with La Niña events.
In the Las Bayas Forestry Reserve, stand structural
evidence is cautiously interpreted as indicating that
severe fires (i.e., fires that kill large percentages of the
canopy trees) occurred at long time intervals but
played a significant role in all community types.
Severe fires, as identified in this study, are significantly associated with multi-year episodes of below
average moisture availability. Moreover, severe fires
are followed by episodes of abundant tree establishment and appear to play an important role in the
dynamics of Madrean pine–oak forests (Drury 2006).
The idea that severe fires are not outside the historic
range of variability for fire regimes in the region is
consistent with historical photographs of forests taken
at the end of the 19th century (Lumholtz 1902). A
total of 12 forest photos in Lumholtz (1902) can be
interpreted as representing young, even-aged pine
cohorts that presumably regenerated after a severe
disturbance, most likely fire, in the latter part of the
19th century.
In addition to climatic variation, human activities
also appear to have influenced fire regimes in the
Madrean pine–oak of the Las Bayas Forestry
Reserve. Fire occurrence has decreased within the
Reserve since the 1950s, a decrease that corresponds
with the establishment of the ejido system of land
management in the region. The decreased presence of
fire on the landscape since the 1950s may be a result
of more attention to fire suppression as the timber
resource is better protected. However, while there are
fewer total years recording fires (i.e., including small
fires) in the Reserve, the frequency of widespread fire
years has not been significantly altered since the
1950s. From this we infer that the observed decrease
in the number of fires and the subsequent increase in
the time between successive fires may be an artifact
of the removal of the indigenous groups from this
area as the land changed hands. Before the 1950s,
indigenous groups may have moved throughout the
area igniting fires, accidentally or intentionally, and
most of these fires would have been local in nature.
During most years only the more exposed sites with
drier microclimate conditions would be conducive to
fire ignition and spread. A much more heterogeneous
and patchy fire regime such as we see within the
Reserve prior to 1950 would result.
Plant Ecol
In conclusion, both climate and humans have
influenced the fire regime within the Las Bayas
Forestry Reserve over the time frame of this study.
Regional climate and topographical climate differences influence the potential for fires to ignite and
spread. Human activities and lightning strikes served,
and continue to serve, as ignition sources within the
Reserve. As humans continue to manage these forest
ecosystems it is likely that small fires will continue to
be suppressed whenever possible. The suppression of
small fires may lead to increased intensity and
severity of fires on some sites that historically burned
more frequently. However, intense, biologically
severe fires do not appear to have been outside the
historic range of variability within the Reserve.
Moreover, these biologically severe fires appear to
have been ecologically important drivers of tree
regeneration and community composition within the
Las Bayas Forestry Reserve.
Acknowledgments This research was funded by the National
Science Foundation (Award BCS 0201807) and the Beverly
Sears Student Grants Program of the University of Colorado.
For granting permission to conduct this research we thank the
Universidad Juarez del Estado de Durango, the Instituto de
Silvicultura e Industria de la Madera (ISIMA), and the Facultad
de Ciencias Forestales. For information, logistical assistance,
and/or research assistance, we thank Jorge Luis Bretado
Velázquez, Esteban Pérez Canales, Raúl Solı́s Moreno, Efrén
Unzueta Ávila, Luis Jorge Aviña Berúmen, Jeffrey R. Bacon,
Socorro Mora Cabrales, Don José Gallegos, Eduardo Gallegos,
Leon Gallegos, Guadalupe Ivonne Benicio, Bibiana Rivas
Arzola, Anna Milan, Dave Stahle, Art Douglas, and Martha
González-Elizondo. Emily Heyerdahl furnished some of the
data used in the La Grulla study site.
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