understanding the relationships between habitat suitability

Transcription

BOBCATS IN NEW HAMPSHIRE: UNDERSTANDING THE RELATIONSHIPS
BETWEEN HABITAT SUITABILITY, CONNECTIVITY AND ABUNDANCE IN A
CHANGING LANDSCAPE
By
GREGORY CABELL REED
B.S., Montana State University 2009
Thesis
Submitted to the University of New Hampshire
in Partial Fulfillment of
the Requirements for the Degree of
Master of Science
In
Natural Resources: Wildlife Ecology
December 2013
This thesis has been examined and approved.
________________________________________________
Thesis Director, Dr. John A. Litvaitis,
Professor of Wildlife Ecology,
University of New Hampshire
________________________________________________
Dr. Rebecca J. Rowe,
Assistant Professor,
University of New Hampshire
________________________________________________
Catherine Callahan,
GIS Specialist,
New Hampshire Fish and Game Department
_______________________________
Date
ACKNOWLEDGMENTS
Funding for this project was provided through NH Wildlife Restoration program
grant W-97-R-1 in cooperation with the U.S. Fish and Wildlife Service, Wildlife and
Sport Fish Restoration Program.
iii
TABLE OF CONTENTS
ACKNOWLEDGMENTS ................................................................................................. iii
LIST OF TABLES ............................................................................................................. vi
LIST OF FIGURES ......................................................................................................... viii
ABSTRACT ....................................................................................................................... xi
CHAPTER 1 – INTRODUCTION ......................................................................................1
Objectives ................................................................................................................5
CHAPTER 2 – MODELING BOBCAT HABITAT SUITABILITY ACROSS NEW
HAMPSHIRE: A HIERARCHICAL APPROACH ............................................................6
Methods..................................................................................................................10
Study Areas ................................................................................................10
Factors Affecting Statewide Distribution ..................................................11
Habitat Features that Influence Selection within Home Range .................14
Modeling Habitat Selection .......................................................................15
Environmental Variables ...............................................................15
Habitat Suitability Modeling..........................................................16
Model Extrapolation and Validation ..............................................18
Results ....................................................................................................................20
Factors Affecting Statewide Distribution ..................................................20
Habitat Features that Influence Selection within Home Range .................23
Habitat Suitability Maps ............................................................................25
Discussion ..............................................................................................................28
Incidental Sightings to Explore Distribution .............................................28
Fine-Scale Habitat Selection ......................................................................31
Implications of Multi-Scale Habitat Selection ...........................................33
CHAPTER 3 – MODELING CONNECTIVITY FOR CARNIVORES IN A
DEVELOPED LANDSCAPE: WHAT IS THE BEST APPROACH? .............................36
Modeling Connectivity ..............................................................................37
Evaluating Connectivity Models................................................................39
Focal Species in Connectivity Models .......................................................41
Objectives ..................................................................................................42
Methods..................................................................................................................42
iv
Study Areas ................................................................................................42
Resistance Layers.......................................................................................43
Expert-Opinion ..............................................................................45
Resource Selection Probability Functions .....................................47
Connectivity Modeling and Assessment ....................................................47
Circuit Theory ................................................................................47
Brownian Bridge Movement Models.............................................49
Extrapolation of Connectivity Models .......................................................51
Bobcats as Surrogates ................................................................................52
Results ....................................................................................................................54
Resistance Layers.......................................................................................54
Connectivity Modeling and Assessment ....................................................56
Circuit Theory ................................................................................56
Brownian Bridge Movement Models.............................................57
Extrapolation of Connectivity Models .......................................................57
Bobcats as Surrogates ................................................................................61
Discussion ..............................................................................................................64
Assessment of Connectivity Methods ........................................................64
Statewide Connectivity Models .................................................................68
Surrogate Species .......................................................................................70
Conclusions ................................................................................................71
CHAPTER 4 – POPULATION ESTIMATE FOR BOBCATS IN NEW HAMPSHIRE
BASED ON HOME-RANGE SIZE AND COMPOSITION ............................................74
Methods......................................................................................................77
Home-range Estimation .................................................................77
Population Estimation ....................................................................77
Results ........................................................................................................79
Home-range Estimation .................................................................79
Population Estimate .......................................................................81
Discussion ..................................................................................................83
Potential Population Estimate ........................................................84
Implications of Home-range Size ..................................................86
Comparison to New England States ..............................................88
LITERATURE CITED ......................................................................................................92
APPENDICIES ................................................................................................................107
APPENDIX A ..................................................................................................................108
APPENDIX B ..................................................................................................................110
APPENDIX C ..................................................................................................................111
APPENDIX D ..................................................................................................................113
APPENDIX E ..................................................................................................................114
v
LIST OF TABLES
TABLE
PAGE
2-1
Habitat variables, justification for inclusion, data source, and resolution for GIS
layers used to model bobcat habitat selection at second and third order ...............17
2-2
Models for Second Order-Home Range used three variables, average monthly
(Nov-March) maximum snow depth (Snow_Max), average monthly (Nov-March)
mean snow depth (Snow_Mean), and elevation (Elevation). Sightings reported
between 2007 and 2013 were used to model the relationship between probability
of use and the habitat variables ..............................................................................21
2-3
Parameter estimates from the best third-order habitat suitability model. Models
were made utilizing GPS-location data from 18 collared bobcats in New
Hampshire from 2009-2011 ...................................................................................24
2-4
Rank correlations (Spearman) and expected vs. observed regression for the best
RSPF model. The model was validated with k-fold cross-validation utilizing 5
folds........................................................................................................................25
3-1
Variables used to model probability of detection and occupancy at camera sites
along Highway 101 from January-April 2013. Temporal variables measured
differences in deployment order and duration, and were used to model differences
in the probability of detection. Probability of occupancy was modeled using
habitat and modeling variables. Habitat variables were measured in the field
during camera deployment. Modeling variables utilized connectivity scores, with
low scores indicating poor connectivity and high scores indicating good
connectivity ............................................................................................................54
3-2
Spearmen correlations between BBMM and circuit theory models for all bobcats
and separate individuals, and all roads and major roads. Significant correlations
are highlighted in bold, and indicate agreement between BBMM models and
circuit theory models..............................................................................................58
3-3
Gray fox occupancy model set. Model selection was based on AIC weight
(AICw). The number of parameters (K), Akaike’s Information Criterion adjusted
for small sample sizes (AICc), and the difference in AICc score (∆AICc) are also
reported. Ψ = the probability of occupancy, p = detection probability .................62
3-4
Fisher occupancy model set. Model selection was based on AIC weight (AICw).
The number of parameters (K), Akaike’s Information Criterion adjusted for small
sample sizes (AICc), and the difference in AICc score (∆AICc) are also reported. Ψ
= the probability of occupancy, p = detection probability .....................................63
vi
TABLE
PAGE
3-5
Coyote occupancy model set. Model selection was based on AIC weight (AICw).
3-6
Raccoon occupancy model set. Model selection was based on AIC weight (AICw).
4-1
Sex, age, weight, study area, number of usable locations, home range (95%
utilization distribution) and core area (50% utilization distribution) of bobcats in
the study. Bobcats from the southwest study area were captured and collared in
2009-10, and bobcats in the southeast were captured and collared in 2011 ..........81
4-2
Population estimates of bobcats in New Hampshire generated using home range
requirements and habitat suitability. Total population estimate is the sum of
resident adults and kittens. A survival rate from May/June to October/November
of 0.85 (Knick 1990) was assumed for adults, and a survival rate of 0.36 (Rolley
1985) for kittens .....................................................................................................82
4-3
The current status of bobcat populations in the six New England states. Table was
adapted from Broman (2012) and Roberts and Crimmins (2010). Information was
obtained from state wildlife agencies. Whether there is currently a harvest,
monitoring techniques, estimates of potential abundance, suitable habitat area
(km2), and overall population status are included. Monitoring methods included
harvest analysis (HA), incidental harvest (IH), monitored individuals (MI), public
sightings (PS), and vehicle collisions ....................................................................90
vii
LIST OF FIGURES
FIGURE
PAGE
2-1
Geographic range of bobcats (from Hansen 2007; top). Study areas where bobcats
were monitored in southwest (2009-2010) and southeast New Hampshire (20102011; bottom left) along with names of geographic regions in New Hampshire.
Road density in the state generated with a 1-km2 moving window (bottom
middle). Mean monthly snow depth (SNODAS, Callahan) from November-March
2007-2012 (bottom right) .......................................................................................12
2-2
Probability of habitat use given the average monthly mean snow depth between
November-March 2007-2012.................................................................................21
2-3
Bobcat habitat suitability in New Hampshire modeled at second-order selection
using incidental observations collected from 2007-2013 and mean monthly snow
depth measured from 2007-2012 ...........................................................................22
2-4
Parameters estimated for each of the habitat variables used to fit the best model of
third-order habitat selection. Estimates are bounded by two standard deviations.
Dummy variables were used for all land cover and aspect variables, and therefore
comparison between these categorical variables and the numerical variables (road
density, forest edge, slope, ruggedness) should not be used to determine the
relative effect of each variable ...............................................................................24
2-5
Graph of bin number vs. area-adjusted frequency of withheld locations from kfold cross-validation...............................................................................................25
2-6
Bobcat habitat suitability in New Hampshire modeled at third-order selection
using GPS-telemetry data collected from 2009-2011 from 18 collared bobcats ...26
2-7
Bobcat habitat-suitability in New Hampshire based on two spatial scales.
Incidental observations were used to model second-order selection. GPStelemetry locations from 18 collared bobcats were used to model third-order
habitat selection. These analyses were then combined to generate the scaleintegrated map of habitat selection. Major freeways are shown, and the White
Mountain region of northern New Hampshire is labeled for reference .................27
3-1
GPS locations from marked bobcats in the southwest and southeastern portions of
New Hampshire. Bobcats were monitored from 2009-2011. Major highways and
names of regions are shown for reference .............................................................44
viii
FIGURE
PAGE
3-2
Resistance scores for riparian areas are a function of distance from stream (top
left). Resistance scores for slopes are resistance a function of percent slope (top
right). Resistance scores for roads are a function of traffic volume, and separated
into three classes-low, medium, and high (lower left). Land cover variables were
assigned resistance scores from 1-10 with 1 being lowest resistance and 10 being
the highest (lower right) Figures were recreated from NH Audubon and NHFG
(2010) .....................................................................................................................46
3-3
Comparison of circuit theory and least cost pathways. (A) Cost surface layer used
for modeling. The white patches in the lower left and upper right corners are the
source patch patches. Resistance is shown using a gray scale. Areas with low
resistance are light grey, areas of high resistance are dark grey, and black areas
are barriers to movement. (B) Least cost modeled corridor is shown in white. (C)
Output from circuit theory. Areas of blue are have a lower probability of
utilization, while areas of red have a higher chance. Areas in yellow show
extremely high utilization points and thus identify ‘pinch-points’ of movement.
These areas are constrictions in the landscape where organisms are forced to
travel, highlighting the area of highest conservation value. A real world example
of such points may be an undeveloped underpass on a major freeway system
(from McRae et al. 2008) .......................................................................................48
3-4
Modeling scheme used to test connectivity predictions for bobcats. RSF generated
using all location except from Bobcat #47, with his locations overlaid. This served
as the ‘conductivity’ layer (top left). Sampling protocol adopted from Anderson et
al. (2012) to map connectivity in bobcat home range with minimal source/ground
bias (top right). Output from circuit theory using top as source/bottom as ground,
bottom as source/top as ground, left as source/right as ground, and right as
source/left as ground (middle panels – L to R). Cumulative circuit theory map
with actual path of bobcat #47 (bottom left). Actual path overlaid with 100
random paths, rotated and shifted (bottom right) ..................................................50
3-5
Resistance models used for mapping bobcat connectivity. The resource selection
probability function (RSPF) model was generated using GPS-telemetry data from
collared bobcats (right). The expert opinion model was generated independently
by agency biologists (NHFG and NH Audubon 2010) ..........................................55
3-6
Plot depicting the difference between actual and random path mean scores for
resource selection probability function and expert opinion connectivity models.
The connectivity score for the actual path was standardized to 0 for each
individual bobcat. The difference between the mean connectivity score of random
paths and actual paths was then plotted. If connectivity was successfully
predicted, the mean score of the random paths will be less (below zero) than the
actual path ..............................................................................................................56
ix
FIGURE
PAGE
3-7
Connectivity models for New Hampshire generated using RSPF habitat suitability
models and Program Circuitscape (left). Areas with highest ‘conductance’ or
movement are in yellow and lowest are in dark blue. Connectivity scores for
major roads in New Hampshire (right) ..................................................................59
3-8
Connectivity models for New Hampshire generated using expert-opinion
resistance models and Program Circuitscape (left). Areas with highest
‘conductance’ or movement are in yellow, and lowest are in dark blue.
Connectivity scores for major roads in New Hampshire (right) ............................60
4-1
Habitat suitability map generated from incidental observations and marked
bobcats. Pixels in the map have been re-scaled to the size of the average marked
females home range (23.8 km2) and habitat suitability scores (range 0-1) of the
aggregated cells were averaged (left). Home-ranged size cells were classified as
either occupied (≥0.5) or unoccupied (<0.5) based on average habitat suitability
scores. Occupied cells were summed to obtain an estimate of resident female
bobcat territories ....................................................................................................82
x
ABSTRACT
BOBCATS IN NEW HAMPSHIRE: UNDERSTANDING THE RELATIONSHIPS
BETWEEN HABITAT SUITABILITY, CONNECTIVITY AND ABUNDANCE IN A
CHANGING LANDSCAPE
By
Gregory C. Reed
University of New Hampshire, September 2013
I examined bobcat habitat suitability in New Hampshire at the landscape and
local-scale using incidental sightings and telemetry locations, respectively. By modeling
habitat selection at two scales I was able to make inferences about bobcat distribution,
while still getting detailed information about home range and habitat requirements.
Bobcats appeared to be limited by greater snow depths at large scales, and human
development at finer scales. Because bobcats may be limited by development, mainly
roads, I modeled connectivity using empirical and expert-opinion based methods.
Empirical methods performed better than expert-opinion methods when assessed at the
home-range scale. When models were extrapolated statewide, major differences were
apparent and this could be assessed using genetic methods. Habitat suitability and home
range requirements were used to estimate a potential resident carrying capacity and
associated reproduction for bobcats at the birth pulse (n=2237) and six months later
(n=1386).
xi
xii
CHAPTER I
INTRODUCTION
Habitat fragmentation and loss are worldwide concerns (e. g., Noss 1987, Theobald et al.
2011) because isolation of populations of plants and animals makes them susceptible to
stochastic extinctions (e. g., Andren 1994, Keller and Waller 2002). These effects may be
most severe among large carnivores that have extensive home ranges, exist at low
densities, and have low reproductive rates (Crooks 2002, Singleton et al. 2002).
Increasing habitat connectivity, so that individuals may move between separated patches
of suitable habitat, may mitigate these effects (Taylor et al. 1993, Tischendorf and Fahrig
2000). There are two leading solutions to the problem of conserving connectivity: set
aside large tracts of suitable land for conservation, or maintain corridors connecting
protected land or highly suitable habitat.
Most current efforts to increase or maintain connectivity focus on corridor design.
Corridors have become a widely cited approach for assuring connectivity in humanaltered landscapes (e. g., Beier and Noss 1998, Hilty and Merenlender 2004, GilbertNorton et al. 2010). Essentially, a corridor is a linear strip of land that is surrounded by
lower quality habitat (the matrix) to facilitate movement between suitable patches of
habitat to another, thus enhancing population viability (Beier and Noss 1998). However,
1
despite the widespread endorsement of corridors as a conservation tool, limited evidence
exists of their success (e.g., Haddad et al. 2003).
Much of the criticism of corridors has focused on three arguments: i.) ecological
disadvantages of corridors outweigh the benefits; ii.) the amount of money required to
design, implement, and protect corridors; and iii.) lack of empirical evidence of their
success (Simberloff and Cox 1987, Hobbs 1992, Simberloff et al. 1992, Bennett 1999).
Among the disadvantages of corridors, Simberloff and Cox (1987) suggested that
corridors can act as transmission zones for diseases, provide pathways for invasive
species, and subject animals to increased risks of edge effects. Given these costs it has
been argued that without demonstrated success of corridors, monies could be better spent
on enhancing existing habitats.
Increasingly, benefits of corridors have been observed, including: areas for
dispersal (Beier 1995), assuring gene flow (Keller and Waller 2002), re-colonization of
uninhabited habitat patches (Hanski and Gilpin 1991), paths for migration (Berger 2004),
movement paths for climate change induced displacement (Heller and Zavaleta 2009),
and avoidance of direct mortality from roads (Clevenger and Waltho 2000). Empirical
evidence on the value of corridors has been shown within experimental mesocosms and
field studies (Haddad and Baum 1999, Haddad and Tewksbury 2005, Levey et al. 2005,
Damschen et al. 2006). Additionally, Gilbert-Norton et al. (2010) used a meta-analysis of
78 studies, and determined that corridors increased movement by 50%.
Despite the focus on corridors, this approach to increasing connectivity may not
be a panacea for all organisms, especially those that do not fit within a core patch/ matrix
2
framework. Many species exist across landscapes because of generalist tendencies, with
habitat preferences and selection being displayed across a gradient. These species are still
susceptible to barriers and isolation, and thus connectivity must be considered, but
modeling corridors through the landscape may not be ideal.
Focal species, particularly large carnivores, are often used to plan for connectivity
(Beier 1993, Noss and Daly 2006, Servheen et al. 2001, Singelton et al. 2002), but
caution is warranted given diverse habitat requirements and movement capabilities of
animals that should benefit from corridors (Beier et al. 2008, Chetkiewicz and Boyce
2009). However, given the time, money, and data requirements needed to design
empirically defensible connectivity schemes, we may not be able to abandon the use of
focal species (Haddad and Tewksbury 2006, Cushman et al. 2010a).
Bobcats may be an appropriate animal to evaluate the focal species approach
because they are a wide-ranging carnivore (Beier 1993, Cushman et al. 2006, Servheen et
al. 2001, Singleton et al. 2002) and rely on specific habitat features (Litvaitis 1986,
Broman 2012), yet are considered a generalist species (Anderson and Lovallo 2003) that
are adapted to many different biomes across their geographic range. In New England,
they select early-successional forests, scrub-shrub, and wetlands, and avoid agricultural
lands, developments, and high road densities (Litvaitis 2001, Litvaitis et al. 2006,
Litvaitis and Tash 2008, Donovan et al. 2011, Broman 2012). Despite their sensitivity to
development and roads (Crooks 2002, Riley 2006), bobcats can adapt to living in
fragmented environments (Tigas et al. 2002). Therefore, they should show some
behavioral response to roads and development resulting in obvious selection of pathways
that can be used to map connectivity.
3
Before understanding and conserving connectivity for a species it is imperative to
understand its distribution and habitat preferences. This information can be attained via
sightings, identifying sign (tracks, scat, etc.), telemetry or GPS collars, and trapping.
While this information can provide the researcher or manager with knowledge of where
an organism has been; to understand which resources it is selecting for, a comparison
must be made between what is being used and what is being unused or avoided.
Currently, this is most often done with resource selection (probability) functions (RSF or
RSPF; Manly et al. 2002, Boyce et al. 2002, Johnson et al. 2004, Johnson et al. 2006),
which use the logistic model to compare sites of ‘used’ or ‘unused’, or ‘used’ or
‘available’ to determine which resources an animal utilizes compared to what is available
on the landscape. This resource use informs how the landscape affects the animals’
processes and movements, and to do what degree we can facilitate conservation of those
resources.
Bobcats in New Hampshire have experienced dramatic fluctuations in abundance
since European settlement (Litvaitis et al. 2006) due to anthropogenic land alterations
(Litvaitis 1993, Litvaitis 2001). Harvest records, paired with knowledge of land use,
show a dramatic increase in bobcat numbers through the mid-20th century followed by an
abrupt decline in the 1970s (Litvaitis et al. 2006), which led to a closure on harvest in
1988. Since that time, bobcat abundance seems to be increasing based on incidental
captures and sightings (Litvaitis et al. 2006, Roberts and Crimmins 2010, Broman 2012),
yet more formal and detailed information on current habitat selection and potential
connectivity is lacking.
4
OBJECTIVES
The goals of this research are: (1) to create a multi-scale habitat-suitability model for
bobcats in New Hampshire using incidental sightings and telemetry locations, (2)
compare predictive connectivity models designed with circuit theory using two types of
resistance layers; expert opinion and RSFs, (3) validate corridor modeling techniques and
use of focal species with field data, and (4) estimate the potential carrying capacity of the
state given bobcat habitat and home range requirements
5
CHAPTER II
MODELING BOBCAT HABITAT SUITABILITY ACROSS NEW HAMPSHIRE:
A HIERARCHICAL APPROACH
Where an organism is found in space and time is a function of its life-history strategies
(Southwood 1977), and by studying an animal’s habitat selection we can gain insight into
the ecological processes that influence its life. These habitat choices are made
hierarchically (Johnson 1980, Weins 1987, Orains and Wittenberg 1991). Johnson (1980)
described four orders of habitat selection, all of which must allow an animal to meet its
most basic life-history requirements. First-order selection confers the animal’s physical
or geographic range; second-order selection is the choosing of a home range within that
geographic range; third-order is the selection of various habitat components within the
given home range; finally, fourth-order is the procurement of food or shelter from that
habitat component (Johnson 1980). Because of the intrinsic linkages across spatial scales;
it is important to study multiple orders of selection when studying the habitat suitability
of an animal (Bergin 1992, Luck 2002, Johnson et al. 2004, DeCesare et al. 2012).
Bobcats (Lynx rufus) are the most widespread and abundant felid in North
America due to their ability to adapt to a wide variety of habitats (Anderson and Lovallo
2003). They inhabit every state in the continental United States except Delaware, and
have a range that spans from central Mexico to Florida in the south and from British
Columbia to Nova Scotia in the north (Sunquist and Sunquist 2002). In southern regions,
6
they are limited by competitive interactions with other felid species (Sanchez-Cordero
2008), and to the north, by limited adaptations to low temperatures (Gustafson 1984,
Mautz and Pekins 1989) and deeper snow (McCord 1974, Hamilton 1982). This
avoidance of greater snow depths has generally separated them from lynx (Lynx
canadensis; Buskirk 2000); however, there are instances of sympatric populations and
hybridization has been reported (Schwartz et al. 2004, Homyack et al. 2008).
Additionally, bobcats are limited by large-scale agriculture in the Midwest and intense
urban development in portions of the eastern United States, at the landscape level. Absent
these limiting factors, they select home ranges across a broad array of habitats in areas
that contain broken and rugged topography, with a thick and dense understory.
Additionally, unfragmented or natural habitat is needed to support core areas (Riley
2006).
Within home ranges, habitat selection is largely influenced by high prey
abundance (Litvaitis et al. 1986) and breeding opportunities (Lovallo and Anderson
2003). Females are more often associated with better quality habitats; specifically areas
that support high prey densities, rocky and cliffy areas for escape cover and denning, and
avoidance of roads (Bailey 1981, Hamilton 1982, Lovallo 1999). These areas generally
have a dense understory that allows for stalking and hunting of prey. Males seek to
maximize breeding opportunities, establishing home ranges that overlap 2-3 female home
ranges (Lovallo and Anderson 2003). Within their home range, males may also choose
areas where they can hunt successfully, such as near deer yards in winter (Fox 1990) or
adjacent to agricultural areas in spring (Broman 2012).
7
New Hampshire is completely contained within the current geographic range of
bobcats (Fig. 2-1). The southern boundary of lynx distribution also falls within New
Hampshire, and the two distributions overlap in the northern portion of the state.
Historically, the northern portion of the state was dominated by lynx, whereas bobcats
were most common in the southwestern portion of the state (Seton 1925). After European
colonization, land was cleared for agriculture and timber harvests allowing bobcats to
expand their range northward into lynx habitat (Seton 1925, Litvaitis 1993). As farms
were abandoned, there was an abundance of early-successional forests, and the prey
densities associated with them, resulting in a large increase in the bobcat population
(Litvaitis 2006). Bobcats were abundant across the state, with particularly large harvests
in the north. However, forests are maturing into later successional classes (Morin and
Woodall 2012), potentially changing habitat suitability for bobcats and lynx in the
northern portion of the state.
In addition to changes in land-use practices, New Hampshire is experiencing rapid
growth, especially in the southeastern portion of the state and along major transportation
corridors (Sunquist and Hewes 2010). Crooks (2002) found that bobcats are moderately
sensitive to habitat fragmentation. Similarly, Riley (2006) noted that bobcats situated
their home ranges in predominantly natural areas opposed to urban areas. Finally, Lee et
al. (2012) found that highways and urban areas could genetically isolate populations of
bobcats.
Our ability to understand bobcat habitat selection in New Hampshire is further
complicated by three things. First, the state of New Hampshire has dramatically different
climates because of the range of elevations; from sea-level to the alpine tundra. This
8
range in elevation results in drastic differences in snow pack across the state (Fig. 2-1).
Second, population density and the associated development, also vary across the state. In
the south, the Merrimack Valley and Seacoast region have major urban areas, whereas
Coos County, in the north, is predominantly rural and sparsely populated. Finally, we
need an appropriate way to integrate models of habitat suitability that occur at different
scales. Currently, most methods used to analyze wildlife habitat focus on broad scale
distributions (i.e. species distribution modeling) or fine scale telemetry studies. Recently,
however, efforts have been made to integrate analyses of selection at multiple scales into
one cohesive map of habitat suitability (Johnson et al. 2004, DeCesare et al. 2012).
Integrating maps of habitat suitability across scales requires appropriate data to fit those
scales, a potential constraint when resources are limited.
While GPS technology and analytical techniques are advancing, relevant
information on habitat use and distribution can still be obtained from less technical
approaches and can then be used to inform our knowledge of habitat selection at a
broader scale. Use of incidental observations, hunter surveys, and citizen surveys to
define habitat selection have been increasing among studies of carnivores (e.g., Cooper et
al. 2012, Linde et al. 2010), and have been a vital part of tracking population and
distribution of birds for many decades (e.g., breeding bird surveys). Although application
of occurrence data presents challenges (e.g., inaccurate reports leading to errors) they can
still be useful for relatively common species if a priori guidelines are followed
(McKelvey et al. 2008).
In New Hampshire, bobcats have experienced dramatic population declines
(Litvaitis et al. 2006) precipitated by major changes in land use practices (Litvaitis 1993,
9
Litvaitis 2001). However, recent observations suggest bobcats are rebounding in
abundance and expanding into previously sparsely populated areas (Litvaitis et al. 2006,
Broman 2012), warranting an investigation into habitat use across scales. Objectives for
this chapter were: i.) identify how environmental factors limit the distribution of bobcats
in New Hampshire, ii.) identify local features that influence habitat selection within home
ranges, and iii.) combine habitat suitability maps to generate one hierarchical model of
habitat selection for the state that can be used in abundance estimates and connectivity
modeling.
METHODS
Study Areas
To understand bobcat habitat suitability in New Hampshire habitat analyses were
conducted at two scales. Incidental sightings were solicited statewide for landscape scale
analysis, while fine scale habitat selection was assessed using bobcats outfitted with GPS
collars in two portions of the state (Fig. 2-1, see Chapter III for detailed description).
New Hampshire has experienced many changes in land use throughout its history.
After European settlement, the state was dominated by agriculture, with only 47% of the
state forested in 1880 (Litvaitis 1993). Farm abandonment in the 18th and 19th century
resulted in forest regeneration, with an abundance of early-successional forests in the
mid-20th century. By 1960 87% of New Hampshire was forested (Sunquist and Hewes
2010), however those forests matured into later-successional, closed-canopy forests,
resulting in dramatic effects on wildlife species (Litvaitis 1993, Litviatis 2001). While
10
forest stands continue to mature (Morin and Woodall 2012), overall forest cover has
decreased to 82%. This decline is continuing and forest cover is expected to be 78.5% by
2030 due to rapid human development and increases in population (Sunquist and Hewes
2010). Development is most pronounced in the southeastern portion of the state and along
transportation corridors (Sunquist and Hewes 2010) where road density is dramatically
higher than in the northern portion of the state (Fig. 2-1).
In addition to these anthropogenic effects on habitat, the state spans a range of
climatic conditions resulting in different levels of environmental suitability for bobcats.
New Hampshire is positioned at the northern extent of bobcat’s geographic range (Fig. 21). Elevation ranges from sea level, along the coast in the southeastern portion of that
state, up to 1917 m. on the summit of Mt. Washington. These differences result in a large
gradient of snow levels throughout the state, with areas in the White Mountains and
northern portion of the state receiving significantly more snow than coastal areas, as well
as the Connecticut and Merrimack River valleys (Fig. 2-1).
Factors Affecting Statewide Distribution
To investigate environmental factors influencing bobcat distribution throughout
the state (second-order selection), I relied on bobcat locations that were opportunistically
collected statewide since 2007 via reports to New Hampshire Fish and Games employees,
and a project website (http://mlitvaitis.unh.edu/Research/BobcatWeb/bobcats.html).
Broman (2012) used a subset of this data to model habitat selection at the third-order, but
found the data to be biased when compared to telemetry based methods and thus
11
inappropriate for use at this scale. Therefore, these locations were used to model
landscape level effects on bobcat habitat selection at second-order selection.
Fig. 2-1. Geographic range of bobcats (from Hansen 2007; top). Study areas where
bobcats were monitored in southwest (2009-2010) and southeast New Hampshire (20102011; bottom left) along with names of geographic regions in New Hampshire. Road
density in the state generated with a 1-km2 moving window (bottom middle). Mean
monthly snow depth (SNODAS; Callahan 2013) from November-March 2007-2012
(bottom right).
12
The accuracy of incidental observation is a cause for concern when attempting to
model habitat use or geographic range of a species (e.g. McKelvey et al. 2008, Broman
2012). Species can either be misidentified or located imprecisely, compromising their use
in habitat analysis. To address this, reported bobcat sightings were verified for accuracy
either by a submitted picture or a detailed and accurate description. Reported sightings
were then scored on a scale of 1-3. Sightings that received a score of 1 contained either
an address verifiable on Google Earth or geographic coordinates. Sightings received a
score of 2 if the description was less precise, but an estimate of location could still be
made. Sightings with a score of 3 contained a very general location (i.e., the town sighted
or location based on major landmarks). Only locations with a score of 1 or 2 and from
January 2007- January 2013 were utilized for analysis.
Additionally, location data solicited from the public are often spatially biased to
developed areas (e.g. Snall et al. 2011). To address this, each sighting was buffered by
the radius of the maximum home-range size of marked bobcats (9.6 km, see ResultsChapter IV). The maximum home-range was used to insure that all possible areas suitable
for home ranges were sampled. Next, 10,000 random points were then generated
throughout the state. Points contained within home range buffers were considered ‘used’,
and ones that were not within the range were considered ‘unused’. This sampling scheme
eliminates the clusters of incidental observations and ensures an even distribution of
points across the study area. Resulting locations were used in Type I design, as described
by Manly et al. (2002), in which used and available resources are sampled for the entire
study area and individual animals are not identified. Given the coarse-scale habitat
analysis, the screening of reports, the commonality of bobcats, and the sampling scheme
13
utilized, I believed using this approach is useful to describe statewide distribution of
bobcats (second-order habitat selection).
Habitat Features that Influence Selection within Home Ranges
Bobcats were trapped and collared during the winters of 2008-09 and 2009-2010
in the southwestern and southeastern portions of the state, respectively (Fig. 2-1).
Bobcats were fitted with either Siritrak drop-off collars (Internal Release, 220g, Siritrack
Limited, Havelock North, New Zealand) or Lotek Wildcell collars (Wildlcell, 270g,
Lotek Wireless, Newmarket, Ontario). All study animals were handled in accordance
with the University of New Hampshire Institutional Animal Care and Use Committee
(Protocol #081201, Appendix A). Siritrack collars were retrieved after collar release, or
the bobcat was re-captured if the release mechanism failed. Lotek collars sent locations
via short message services (SMS) to a ground station located at the University of New
Hampshire.
Location error, habitat-induced GPS fix bias, and issues of independence are
important considerations when using GPS technology (Frair et al. 2004, Lewis et al.
2007, Hebblewhite et al. 2007, Frair et al. 2010). Mallet (2013) measured location error
for both types of collars in Maine and found that location error averaged 17.8 m during
the leaf-on season, and 12.9 m during the leaf-off season. Both are within the highest
resolution (30m) used for habitat layers. Additionally, locations were screened to ensure
accurate data while still utilizing the greatest amount of data possible, following Lewis et
al. (2007). All locations generated from 3D fixes were kept, and locations generated from
2D fixes were kept if the dilution of precision was less than or equal to 5.0 (Lewis et al
14
2007). Mallet (2013) found overall fix success was 87% and 95% for Lotek collars and
80% and 100% for Sirtrack track collars during leaf-on and leaf-off seasons, respectively.
Hebblewhite et al. (2007) recommend weighting locations when GPS-bias is >10%.
Given the different study areas and types of collars, GPS locations were weighted after
model building to assess potentials effects of habitat-induced GPS fix bias. Linear
logistic model coefficients for forest (closed conifer = 1.83; deciduous = 1.1; mixed
forest = 0.27) and slope (percent slope = 0.03; percent slope * conifer = 0.046; percent
slope * deciduous = 0.056; percent slope * mixed=0.014) obtained from Frair et al.
(2004) were used to weight GPS locations. Temporal independence should also be
considered. However, correlation in GPS studies has been found up to 30 days (Cushman
2010) and withholding this amount of data would have resulted in destructive sampling
(Swihart and Slade 1985, Broman et al. 2012), so I elected to utilize all locations despite
autocorrelation. Resulting locations served as ‘used’ points in model generation. An
equal number of ‘available’ points were randomly generated within individual home
ranges. These points were used to compare habitat selection at the third-order selection,
where individuals are defined, following Design III by Manley et al. (2002).
Modeling Habitat Selection
Environmental Variables. Three GIS layers were used to model second-order
habitat selection (Table 2-1). Due to the potential bias associated with using sightings
data, only large scale topographic and climatic layers were used. Two snow depth layers
were compiled from the NOAA National Weather Service's National Operational
Hydrologic Remote Sensing Center’s (NOHRSC) Snow Data Assimilation System
(SNODAS; NOHRSC 2004). These layers consisted of the average of the maximum
15
monthly snow depth and the average of the mean monthly snow depth from November
through March in the years 2008-2012. Elevation was also included as a potential
variable because bobcats show preference for lower elevations (Lovallo and Anderson
1996) and less productive forests may limit bobcats at higher ecosystems.
Given the large number of potential explanatory variables available to describe
habitat selection, exploratory data analysis was used to test an initial suite of different
approaches (Appendix B); including the use of the National Land Cover Database 2006
(Fry et al. 2011), an Ecological Land Unit dataset (The Nature Conservancy 2008), and
an unfragmented habitat layer (NHFG) combined with variables measuring proximity to
important habitat features. From these analyses ten variables were selected for model
development of third-order habitat selection (Table 2-1), including; National Land Cover
Database 2006 (Fry et al. 2011), distance to forest edge, distance to stream, elevation,
slope, aspect, a vector ruggedness measurement (VRM, Sappington et al. 2007), distance
to road (km), road density (km/km2), and traffic density (unit/km2).
Fitting of Habitat Suitability Model. Resource selection probability functions
(RSPF), as defined by Manley et al. (2002), were used to model habitats at both orders.
Following Manly et al. (2002), a RSPF was fitted using generalized linear models and the
R (R Core Team 2012) package ResourceSelection for second and third order,
respectively (Lele et al. 2013). Typically, the RSPF is approximated with the proportional
RSF by exponentiating the parameters fitted with the logistic function (Johnson et al.
2006). Lele (2009; see also Lele and Keim 2006) demonstrated that the stable estimations
of parameters in an RSPF can be obtained by using weighted distributions. The
16
advantages of this approach are a potentially better fit model, compared to the
exponential RSF, and output values represent the true probability of selection.
Table 2-1. Habitat variables, justification for inclusion, data source, and resolution for
GIS layers used to model bobcat habitat selection at second and third order.
Habitat Variable (units)
Justification
Source
Resolution
Second Order Selection
Snow - max (mm)
Avoidance of deep snow depth
SNODAS1
1km
Snow - mean (mm)
SNODAS1
1km
Elevation (m)
2
Lower productivity at high elev.
DEM
30m
Land Cover*
Represents habitat
NLCD 20063 30m
Elevation (m)
DEM2
30m
2
30m
2
30m
Third Order Selection
Aspect (flat, north, south)
Slope (degrees)
Sun exposure influences snow
Terrain for dens, escape cover
VRM**
Terrain for dens, escape cover
Distance to edge (km)
Prey densities, travel corridors
Distance to stream (km)
Prey densities, travel corridors
Distance to road (km)
Avoidance of roads
2
Road density (km/km )
Avoidance of roads
2
Traffic Density (unit/km )
Avoidance of roads
DEM
DEM
2
DEM
30m
NLCD 2006
3
30m
GRANIT
4
30m
GRANIT
4
30m
GRANIT
4
30m
GRANIT
4
30m
*Collapsed into development, deciduous forests, coniferous forest, mixed-woods,
shrub/scrub, agriculture, wetlands, and open water
**Vector Ruggedness measurement (Sappington et al. 2007)
1
Snow Data Assimilation System
2
USGS Digital Elevation Model
3
2006 National Land Cover Dataset
4
New Hampshire Geographically Referenced Analysis and Information Transfer System
Second-order habitat models were fit using the three explanatory variables
(snowmean, snowmax, and elevation) into single variable models because of collinearity.
Models were then re-fit using quadratic terms in case the relationship between selection
and the variable was non-linear. The best model was selected using Akaike’s Information
17
Criterion (Akaike 1973) adjusted for small sample sizes (AICc, Burnham and Anderson
2002).
Recommendations by Hosmer and Lemeshow (2000) were used to fit third-order
habitat suitability models with logistic regression. First, to avoid collinearity, all
continuous variables were compared using Pearson’s Correlation coefficient. If variables
were highly correlated (r>0.7), and biologically redundant they were not included in the
same model. Next, univariate models were fit for all variables and only statistically
important (P>0.25) variables were kept (Hosmer and Lemeshow 2000). This conservative
approach to significance was used to insure that all biologically relevant variables were
included. I then fit a multivariate logistic model using a manual forward-stepping
procedure (Squires et al. 2012, Hosmer and Lemeshow 2000) starting will a null model
that only contained land cover variables. Each variable was added to the null model
sequentially according to its univariate strength, measured by Wald statistics. To
determine if a variable was retained, likelihood-ratio tests between sequential models
were utilized (P<0.05) because each subsequent model was nested in the previous one.
Variables excluded after univariate tests were then added to the model to determine if
they improved model fit. During model fitting procedures, attention was paid to any large
changes or reversals of sign in the coefficient estimates to insure a stable model was built.
Model Extrapolation and Validation. The top second-order model was generated
using a used-versus-unused design, so standard logistic regression validation techniques
were used, including; confusion matrices, Kappa statistic, and Receive Operating
Characteristic (ROC) curves, as recommended by Boyce et al. (2002).
18
The top third-order model was tested using k-fold cross-validation following
Boyce et al. (2002), with refinements by Johnson et al. (2006). This entailed partitioning
the data into five folds and using four folds as ‘training’ sets and the fifth fold as a
‘validation’ set. The ‘training’ sets were used to re-fit the model, and then the estimated
parameters were spatially applied in ArcMap 10.0. The resulting raster grid was divided
into 10 ordinal ‘bins’ using quantile breakpoints. The number of ‘validation’ points in
each bin was divided by the area of the bin to get area-adjusted frequencies. The
Spearman correlation between this area-adjusted score and bin number was then
computed (Boyce et al. 2002). Next the proportion of observed ‘validation’ points in each
bin was compared to the expected proportions for each bin using linear regression.
Expected proportions for each bin were computed by multiplying the midpoint RSPF
score of each bin by the area of the bin and then dividing by the sum of these products for
each bin.
The two final models were then mapped in ArcMap 10.0 using ‘Raster
Calculator’. All habitat variables present in the RSPF were represented by raster layers in
ArcMap 10.0. The model equation was then used to extrapolate the RSPF for New
Hampshire at both levels of selection. Continuous variables were capped at the minimum
and maximum values measured, so that models were not extrapolated outside the
observed data (DeCesare et al. 2012).
19
RESULTS
Factors affecting statewide distribution of bobcats
Bobcat habitat suitability was modeled at second-order selection using incidental
observations (Fig. 2-3). A total of 729 sightings were reported between December 2007
and January 2013 to the project website or NHFG. After screening, 665 were deemed
sufficiently accurate to assign GPS coordinates. These locations were buffered by the
largest home-range radius of male bobcats (9.6 km).
The model with average monthly (Nov-March) mean snow depth with a quadratic
term was considered the best model as judged by AICc (Table 2-2). Probability of bobcat
use declined with increased mean monthly snow depth (Fig. 2-2). The model predicted
well for used sites (sensitivity = 0.96 ± 0.002), but poorly for unused sites (specificity =
0.50 ± 0.013). A receiver operating characteristic (ROC) curve showed Area Under the
Curve (AUC) score of 0.90 suggests a good predictive model. However, the model
received a moderate kappa statistic of 0.51 (± 0.013) because of the high false positive.
20
Table 2-2. Models for Second Order-Home Range used three variables, average monthly
(Nov-March) maximum snow depth (Snow_Max), average monthly (Nov-March) mean
snow depth (Snow_Mean), and elevation (Elevation). Sightings reported between 2007
and 2013 were used to model the relationship between probability of use and the habitat
variables.
Model
K
AICc
∆AICc
AICw
log(L)
Snow_Mean2
3
5409.80
0.00
0.80
-2701.90
Snow_Mean
2
5412.59
2.79
0.20
-2704.29
Snow_Max2
3
5645.24
235.44
0.00
-2819.62
Snow_Max
2
5688.49
278.69
0.00
-2842.24
2
Elevation
3
6058.92
649.12
0.00
-3026.46
Elevation
2
6515.31
1105.51
0.00
-3255.65
Fig. 2-2. Probability of habitat use given the average monthly mean snow depth between
November-March 2007-2012.
21
Fig. 2-3. Bobcat habitat suitability in New Hampshire modeled at second-order selection
using incidental observations collected from 2007-2013 and mean monthly snow depth
measured from 2007-2012.
22
Habitat Features that Influence Selection within Home Ranges
Bobcat habitat suitability was modeled at third-order selection using GPStelemetry locations (Fig. 2-6). Variables used included NLCD 2006 (Fry et al. 2011;
layers collapsed to open water, light development, heavy development, barren, evergreen
forests, deciduous forests, mixed forests, shrub/scrub, agriculture, and wetlands), distance
to forest edge (excluding open water/forest edge), elevation, slope, aspect, and a vector
ruggedness measurement (VRM, Sappington et al. 2007). Bobcats selected for forests,
shrub/scrub, and wetlands, and selected against developed areas, agricultural areas, and
open water (Table 2-3, Fig. 2-4). They showed avoidance of high road density, and
selected areas closer to forest edge. They preferred more rugged and steeper sloped areas,
with southern facing slopes. Finally, there was selection for areas closer to streams, but
no significant relationship to elevation at this order of selection.
The third-order model validated well. K-fold cross validations with 5 folds were
all highly correlated (Table 2-4, Fig. 2-5). A mean spearman correlation of rs = 0.968 (σ =
0.014) indicated that bin number and area-adjusted frequency of used points were highly
correlated. All slopes (b1) were significantly different from 0, indicating the model
performed better than neutral model where use would be equal to availability. All slopes
(b1) were not significantly different from 1, indicating the model was proportional to use.
Additionally, none of the intercepts (b0) were significantly different from zero (Table 23) which is expected for a model that was proportional to use. Model fit for expected vs.
observed regression was moderately strong (range = 0.634-0.774) indicating a good
fitting model.
23
Table 2-3. Parameter estimates from the best third-order habitat suitability model.
Models were made utilizing GPS-location data from 18 collared bobcats in New
Hampshire from 2009-2011.
Habitat Variable
β estimate
SE
P value
(Intercept)
0.32
0.16
0.0453
agricultural
-0.02
0.13
0.8881
deciduous
0.06
0.06
0.3205
developed
-0.50
0.10
< 0.0001
evergreen
0.11
0.08
0.1619
open water
-0.76
0.24
0.0017
shrub/scrub
0.67
0.32
0.0349
wetlands
2.29
0.68
0.0007
aspect-flat
-1.50
0.40
0.0002
aspect-south
0.19
0.05
0.0006
distance to forest edge
-0.23
0.03
< 0.0001
VRM
0.15
0.06
0.0127
slope
0.10
0.03
0.0044
road density
-0.27
0.03
< 0.0001
elevation
0.00
0.06
0.9868
distance to stream
-0.07
0.03
0.0256
Fig. 2-4. Parameters estimated for each of the habitat variables used to fit the best model
of third-order habitat selection. Estimates are bounded by two standard deviations.
Dummy variables were used for all land cover and aspect variables, and therefore
comparison between these categorical variables and the numerical variables (road
density, forest edge, slope, ruggedness) should not be used to determine the relative effect
of each variable.
24
Table 2-4. Rank correlations (Spearman) and expected vs. observed regression for the
best RSPF model. The model was validated with k-fold cross-validation utilizing 5 folds.
Rank Correlation
Expected vs. Observed Regression
Fold
rs
p
b0
b1
R2
1
2
3
4
5
0.988
0.976
0.964
0.964
0.952
<0.001
<0.001
<0.001
<0.001
<0.001
0.020
0.038
0.016
0.031
0.020
0.796
0.624
0.836
0.689
0.795
0.774
0.634
0.735
0.670
0.730
Fi
g. 2-5. Graph of bin number vs. area-adjusted frequency of withheld locations from k-fold
cross-validation.
Habitat Suitability Maps. The resulting models of second and third order
selection were combined by multiplying them together using Raster Calculator in
ArcMap 10 (Fig. 2-7; Johnson et al. 2004), and includes the two relevant scales of
selection for this area. It can be seen that there is a strong selection for more southern
areas of the state or lower elevations, and an avoidance of areas of deeper snow in the
White Mountains and northern portion of the state. Additionally, highly developed areas
are avoided, especially along the I-93 corridor and throughout the Merrimack Valley.
Outside of these areas, it seems that most of the state is moderate to good habitat for
bobcats.
25
Fig. 2-6. Bobcat habitat suitability in New Hampshire modeled at third-order selection
using GPS-telemetry data collected from 2009-2011 from 18 collared bobcats.
26
Fig. 2-7. Bobcat habitat-suitability in New Hampshire based on two spatial scales.
Incidental observations were used to model second-order selection. GPS-telemetry
locations from 18 collared bobcats were used to model third-order habitat selection.
These analyses were then combined to generate the scale-integrated map of habitat
selection. Major freeways are shown, and the White Mountain region of northern New
Hampshire is labeled for reference.
27
DISCUSSION
An animal’s habitat requirements that are both geographically relevant and
biologically detailed can be generated by analyzing habitat selection at multiple scales.
Application of these methods to the analysis of bobcat habitat suitability in New
Hampshire improved results when compared to single scale analyses. For instance,
measuring bobcat response to snow depth is difficult to do with fine-scale analysis
because there is little heterogeneity within a bobcat home range. However, snow depth
and overall winter severity can change suitability drastically over a relatively small area
(Fox 1990). Extrapolation of the telemetry-based model resulted in the northern portion
of the state looking like the best habitat (Fig 2-6), however, when snow was taken into
account this area became one the poorest regions in the state (Fig 2-7). Likewise, the
habitat map based on incidental sightings depicted the entire southern portion of New
Hampshire to be suitable habitat (Fig 2-3). However, when the telemetry-based model
was added, the highly developed areas in the Merrimack Valley and Seacoast were seen
to be low suitability. Using only one method of mapping bobcat habitat suitability would
have resulted in incorrect conclusions about suitability in the state. By using incidental
sightings, coupled with telemetry data, a map that more realistically reflected bobcat
habitat suitability was made, which could then be used to assess connectivity (Chapter
III) and statewide abundance (Chapter IV).
Incidental Sightings to Assess Distribution. Incidental sightings offer an
inexpensive method to track broad-scale habitat use (Palma et al. 1999) and allowed us to
extrapolate fine-scale data statewide. This was especially important because bobcats in
New Hampshire are near the northern extent of their geographic range (Anderson and
28
Lovallo 2003). Since European settlement, their geographic range has expanded
northward (Seton 1925), potentially limiting lynx populations (Parker et al. 1983, Hoving
et al. 2005). Recently, though, breeding lynx have been documented within the state
(NHFG 2011) in areas where the two species ranges overlap. Additionally, large changes
in habitat use are possible across relatively small distances within a state. Fox (1990),
studying bobcats in New York, found that home ranges in the Catskills (µ=36.0 km2 and
µ=31.0 km2, for males and females respectively) were much smaller compared to those in
the Adirondacks (µ= 325.7 km2 and µ=86.4 km2). He believed this was largely due to
differences in winter severity. New Hampshire has a similar change in climate across the
state. Due to this, any fine-scale habitat suitability data had to be examined in the context
of the larger climatic features of the state.
Consistent with previous studies (McCord 1974, Koehler and Hornocker 1989)
second-order habitat maps indicated that bobcats avoided areas of deeper snow pack
(Figs. 2-2). However, historic harvest records in the northern portion of the state saw
some of the highest number of bounties (Litvaitis et al. 2006). Due to high snow depths,
this area is considered the poorest habitat when analyzed at second-order selection (Fig.
2-3). The dissimilarities could be due to a number of reasons.
First, we know the abundance of early-successional habitat has decreased
(Litvaitis 2003), and that forests have matured (Morin and Woodall 2012), potentially
leading to lower prey densities. Because bobcat abundance is related to their prey
(Litvaitis 1986) we should expect a population decline. Throughout the early to mid1900s, land alterations may have increased prey populations significantly enough to
29
support bobcats, despite the greater snowfall. Once those prey populations declined,
bobcat abundance likely did as well.
The alternate explanation is that our sightings data is not sufficient to track
changes in bobcat habitat use in the northern portion of the state. We know the human
population density is much lower there, providing fewer opportunities for sightings.
Additionally, decreased interest in our study from the northern portion of the state may
have led to less reported sightings. However, data from hunter surveys and road-killed
bobcats also show few occurrences (Tate, unpublished data). And while both of these
metrics have similar biases as sightings data, taken together they could highlight an
overall trend in the state. Furthermore, while the sightings may be biased, they still
highlight that snow depth can limit bobcats, and thus habitat is of poorer quality
compared to other parts of the state. And while bobcats certainly exist in the area, they
will be more susceptible to starvation during intense winters, so specific monitoring and
management of their population may be warranted.
Outside of the northern portion of the state and the White Mountains, the state
seemed to be relatively high-quality habitat in all areas. Only one variable was used in
modeling, however, so few conclusions about bobcat habitat use could be made from this
type of data if it was used independently. Bobcat home ranges are also limited by
development (Riley et al. 2003, Riley et al. 2006), yet an analysis of how development
limits bobcats was not possible because the incidental sightings data was associated with
roads and people’s houses (Broman 2012). Furthermore, Broman (2012) showed that if
fine-scale analysis is done using these data incorrect conclusions about bobcat habitat
could be made. The bias that accompanies much of the occurrence data we have is a
30
major drawback (Philips et al. 2009, Broman 2012). However, careful use of occurrence
data to map broad-scale distributions can be useful, as seen here.
Fine-Scale Habitat Selection. The third-order habitat selection model gave a more
detailed view of habitat use by bobcats, and is comparable to other habitat studies in the
area (Donovan et al. 2011). Similar to Broman (2012) the greatest selection was for
wetlands (Fig. 2-4). Wetlands may function as superior hunting areas for bobcats because
they offer good cover for stalking, as well as higher prey densities along their edges.
Additionally, they may act as a refuge from human development, due to their protected
status. Bobcats also showed selection for scrub/shrub habitat. Similar to wetlands, these
areas offer good hunting opportunities because of the combination of good cover and
high prey densities. Deciduous and evergreen forests were shown to be selected for, but
not significantly. The majority of bobcat locations were in forest habitats (>72%), and
bobcats selected for areas closer to forest edges. Edge habitats presumably offer higher
prey abundance whereas forests can provide good cover, therefore while there was not
strong selection for forests, they are still crucial to bobcat habitat suitability.
Bobcats selected against development and high road densities, which was
expected from past studies (Crooks 2002, Riley et al. 2003, Riley 2006). However,
incidental observations indicate that bobcats occasionally use habitat features adjacent to
or in developed areas. Whereas bears and coyotes have adapted to anthropogenic changes
on the landscape (e.g. Grinder and Krausman 2001, Beckamnn and Berger 2003), bobcats
have always been considered to have a “shy and secretive nature” (e.g., Pollack 1951)
and avoided humans. Through reported sightings and accompanying pictures it seems
some individual bobcats have adapted to human settlements. They have been documented
31
exploiting bird feeders to prey on birds and squirrels in the winter
(http://mlitvaitis.unh.edu/Research/BobcatWeb/bobcats.html). Additional studies on the
differences in tolerance for development, and related life history metrics could help us
better understand how bobcats will adapt to increased development in the future.
Topography played a role in determining third-order habitat selection. The model
demonstrates that bobcats are selecting areas that have steeper slopes and greater overall
ruggedness, corroborating bobcat use of rough, broken habitat, interspersed with ledges
(McCord 1974, Broman 2012). South-facing slopes are being selected for, while flat
areas are being selected against compared to northern aspects, further providing evidence
that bobcats use areas with higher sun exposure and decreased snow depth (Koehler and
Hornocker 1989, McCord 1974). Finally, both elevation and distance to stream were
included in the final model, but explained little variability, even though in previous
studies these have been shown to be influential (Kolowski and Woolf 2002).
Comparisons between the third-order habitat selection map (Fig. 2-6) and
historical records of bobcat harvests (see Litvaitis et al. 2006) highlight many
agreements, but also some potential problems when the third-order map is extrapolated
beyond the study areas and surrounding region. First, the northern portion of the state is
shown to be the best habitat by the third order map. It is an area largely devoid of roads,
one of the key limiting factors for bobcats, and has an abundance of wetlands, making it
good habitat throughout most of the year. However, based on current sightings reports,
there do not seem to be an abundance of bobcats in the area. Furthermore, the secondorder habitat (Fig. 2-3) showed that bobcats may be limited by snow depth in this area.
During severe winters, bobcats are probably limited in their mobility (McCord 1974) and
32
thus ability to catch prey, making the area unsuitable at this time of the year. This
disagreement is one instance in which the third-order habitat maps fails when
extrapolated outside of its context.
Second, areas in the southeastern portion of the state, where both road and
population density are highest (Sunquist and Hewes 2010), are intermixed areas of very
high and low suitability according to the third-order habitat map. Bobcats were mostly
absent from the southeast based on historical records, however they have been reported
there lately. Given the relative abundance of wetlands and early successional habitat, this
area may provide good sources of prey for bobcats. However, while the area may be
suitable in many spots, it could also function as a population sink, given the high road
density (Nielsen and Woolf 2002) that often leads to higher mortalities (Litvaitis and
Tash 2008). Extrapolating the third-order habitat map to this area may not fully capture
the degree to which increased road densities effect bobcats in this area.
Throughout the rest of southern New Hampshire historic records and current
assessments largely agree on bobcat habitat suitability. Harvest rates show that the
majority of bobcats were found in in the southwestern portion of the state west of the
Merrimack River, as well as the western border of the state along the Connecticut River
with sporadic higher densities around Lake Winnipesauke and to the northeast of
Concord. The third-order habitat map largely recognizes these as highly suitable habitat,
and numerous sightings have also come from these areas.
Implications of Multi-Scaled Habitat Selection. It is imperative to consider the
issue of scale when modeling habitats (Boyce 2006), and extra care must be used when
33
extrapolating beyond study areas (Johnson et al. 2004, DeCesare et al. 2012). For this
particular study, the third-order habitat suitability map is justified in the southern portion
of the state, where the two study areas took place. However, extrapolation to the rest of
the state was not justified without taking into account selection at larger scales. New
Hampshire varies considerably in both elevation and climate, important drivers of bobcat
ecology that cannot be considered at the third-order level of selection. Conversely, the
second-order habitat model developed with sightings made sense across the state in
regards to snow depth, but lacked the fine scale detail needed for decision making. By
combining the second and third-order models to make a scale-integrated resource
selection probability function better informed habitat selection and subsequent
management decisions at an appropriate scale can be made (Johnson et al. 2004,
DeCesare et al. 2012).
The use of incidental observations allowed insights gained using GPS technology
to be applied at a more regional scale, which is especially important for bobcats at the
edge of their range because changes in climate will directly affect them. In the past 50
years more precipitation in New England is falling as rain instead of snow due to
increasing temperatures (Huntington et al. 2004). These trends are expected to continue
(Huntington et al. 2009). Because bobcats are limited by snow, this may result in them
range expansion northward into lynx territory. Increased range overlap between bobcats
and lynx could lead to more instances of hybridization (Schwartz et al. 2004, Homyack et
al. 2008) or increased interspecific competition (Buskirk et al 2000). Incidental sightings
may help track these changes in distribution. However, understanding the mechanisms
34
that drive the movements, as well competitive interactions, will require more fine-scale
data.
Finally, New Hampshire’s human population is growing, and therefore the
amount of suitable bobcat habitat will likely decline, while previous areas of connectivity
may be severed. Measures must be taken to ensure that areas of high quality habitat, such
as wetlands and scrub/shrub, are maintained. Furthermore, areas that contain good habitat
features, but also high road suitability must be monitored closely for changes in the
population. Incidental sightings will aid in describing where bobcats are, but data
describing movement and survival will be more useful, especially in these potential sink
habitats. Additionally, an abundance of occurrences does not ensure a healthy population,
so other measures of bobcat status should be used. These areas may be most susceptible
to large changes in population numbers because of increased risk associated with
developed areas. In light of this, areas of high suitability that also have relatively less
developments, such as the southwestern region of the state, may contain the most stable
source populations.
35
CHAPTER III
MODELING CONNECTIVITY FOR CARNIVORES IN A DEVELOPED
LANDSCAPE: WHAT IS THE BEST APPROACH?
Connectivity conservation plays a major role in the conservation and management of
many species today (Crooks and Sanjayan 2006). It has been incorporated in numerous
state wildlife action plans (e.g., New Hampshire Wildlife Action Plan 2010, California
Essential Habitat Connectivity Project: A Strategy for Conserving a Connected
California, Washington Wildlife Habitat Connectivity Working Group), become the
driving force behind nongovernmental organizations (e.g., Yukon to Yellowstone
Conservation Initiative, Quabbin-to-Cardigan Partnership), and plays an integral role in
management plans for endangered or imperiled species (e.g., Noss 1987, Ferreras 2001).
At its most basic level, connectivity conservation serves as a focused type of land
conservation, most often with a special emphasis on conserving land fragmented by
development and roads (Theobald et al. 2010). Conserving land for connectivity, whether
through easements or outright protections, or altering highway transportation thruways, is
an expensive proposition in the best of circumstances. Therefore, we must be sure that
our efforts to improve connectivity follow proven and tested methods.
Recent advancements in the ability to assess connectivity via genetic methods has
substantially advanced our understanding of landscape integrity (Cushman et al. 2009,
Schwartz et al. 2009, Wasserman et al. 2012); however genetic methods are not always
36
available or applicable to the temporal or spatial scale with which connectivity must be
considered (Cushman et al. 2010). Because of this, analysis of telemetry-based data with
geographic information systems may be a better asset when exploring the current status
of fragmentation (Cushman et al. 2010, Squires et al. 2013). Furthermore, if more finescale movements by individuals are protected, dispersal could be better facilitated, and it
is reasonable to assume that gene flow will remain high as well.
Unfortunately, both genetic and GPS methods are expensive, and not always
applicable. Often, it is necessary to make connectivity plans for multiple species, and do
it inexpensively, and expert-opinion or literature review efforts provide the most feasible
opportunity (Beier et al. 2008). Additionally, focal species are often implemented in
further attempts to cut costs by reducing workloads. While these economic realities are
unavoidable, we should make an effort to assess the validity of these methods.
Modeling Connectivity
Connectivity is the ability of organisms to move between separated patches of
suitable habitat based on the structure of the landscape (Taylor et al 1993), and corridors
have become a widely cited approach for assuring connectivity in human-altered
landscapes (e. g., Beier and Noss 1998, Hilty et al. 2006, Gilbert-Norton et al. 2010).
Proposed benefits of corridors include: areas for dispersal (Beier 1995), assuring gene
flow (Keller and Waller 2002), re-colonization of uninhabited habitat patches (Hanski
and Gilpin 1991), paths for migration (Berger 2006), movement paths for climate change
induced displacement (Heller and Zavaleta 2009), and avoidance of direct mortality along
roadways (Clevenger and Waltho 2000).
37
Despite the apparent benefits of corridors, they may not be applicable for all
organisms and their associated landscapes. Many connectivity projects seek to connect
large tracts of public land through an intervening matrix of poor or less suitable habitat.
Unfortunately, in many regions these large areas of undeveloped or protected land do not
exist, and ways to increase connectivity may not be so straightforward. In New
Hampshire, for example, bobcats (Lynx rufus) inhabit a variety of land covers with
varying intensities of development interspersed, with relatively small blocks of protected
land. Therefore, identifying one or two major connections between protected lands may
not be a relevant conservation strategy in such landscapes. Instead, it may be more
appropriate to identify where connectivity exists. Such an approach could consider
multiple spatial scales. For example, at the home range level, bobcats are in danger of
vehicular collisions during daily forays. While at the landscape scale, considerations to
assure successful dispersal and gene flow among populations are needed. An integration
of these factors, with attention to differing spatial concerns, is necessary to mitigate
fragmentation for bobcats and wide –ranging species.
Developing connectivity conservation strategies requires a balance between
assumptions, data availability, on-the-ground feasibility, and appropriate modeling
techniques. Noss and Daly (2006) identified three approaches toward corridor design:
intuitive, empirical, and modeling. An intuitive approach uses existing knowledge, expert
opinion, or even best guesses to locate important areas of connectivity. Intuitively
designed corridors are routinely implemented because they can be designed quickly, are
often considered cost effective, and can be applied to numerous animals for which habitat
preferences are known. Conversely, empirical approaches use data to inform decisions on
38
connectivity. As a result, an empirical design can be more easily defended and may be
considered more accurate. Empirically designed connectivity plans often rely on a focal
species, an organism with specific habitat preferences chosen to represent the movement
needs of a broad group of species (Beier et al. 2008). They need information on habitat
associations, which can be based on telemetry, camera trapping, sightings, or sign
surveys (Noss and Daly 2006). Finally, modeling involves the inclusion of either intuitive
or empirical approaches to inform the parameters used in a model. Models are usually
based on movement (Colchero et al. 2011), genetics (Cushman et al. 2009), permeability
analysis (Singleton et al. 2002), least-cost pathways (Squires et al. 2013), sourcedestination points (Cushman et al. 2009, Schwartz et al. 2009), resistant kernels
(Compton et al. 2007), or circuit theory (Anderson et al. 2012). All connectivity models
attempt to represent an organism’s potential movements across the landscape given a
variety of environmental factors and then identify the areas that minimize those
movement ‘costs’. Given the abundance of options to choose from when planning for
connectivity conservation it is important to highlight the potential strengths and weakness
of each tool.
Evaluating Connectivity Models
Models of connectivity have been assessed in the field (Hilty and Merenlender
2004, Driezen et al. 2007, Leoniak et al. 2012) and using GPS-location data (Pullinger
and Johnson 2010), although more widespread validation is needed. Given the increased
use of GPS-telemetry data in connectivity modeling, more opportunities to compare and
validate different models of resistance and connectivity are available. Although, dispersal
events are rare and difficult to observe, identifying movements within home ranges may
39
help identify potential large-scale areas of connectivity. In fragmented areas, animals
may avoid roads, development, or other landscape features. This may provide a smallscale version of how dispersing animals move through a modified landscape. Using
location data, movement paths can be inferred by connecting subsequent locations
(Cushman et al. 2010) or by using Brownian bridge movement models (BBMM; Horne et
al. 2007, Sawyer et al. 2009, Lewis et al. 2011, Sawyer et al 2013). These approaches
move beyond simple point-location data, and acknowledge that we are concerned with
animal movement. Additionally, they provide answers to connectivity issues occurring in
present time that may not be observed with genetic data. Connecting subsequent locations
provides the easiest way to assess how well our connectivity models work, but the
limitations are numerous, including; dependency on acceptable GPS fix rates, the need
for small amounts of time between fixes, and inherent inaccuracies of assuming straightline movement between locations. However, Cushman et al. (2010) demonstrated the
usefulness when parameterizing elephant movement models in relation to human
development.
Building upon connections of subsequent locations, BBMMs use location data to
model potential movement paths as utilization distributions, which takes into account the
error in connecting paths. They have been used to map migratory corridors (Horne et al.
2007, Sawyer et al. 2009), identify barriers to movement (Sawyer et al. 2013), and locate
road crossings (Lewis et al. 2011). Similar to these methods, I believe they can aid in
validating connectivity models by comparing utilization distribution (UD) scores with
modeled connectivity scores from Circuitscape. Applying these types of validation
techniques helps address some of the uncertainty present in connectivity modeling.
40
Focal Species in Connectivity Modeling
Focal species, particularly large carnivores, are often used to plan for connectivity
conservation (Beier 1993, Noss and Daly 2006, Servheen et al. 2001, Singelton et al.
2002). However, given the diverse habitat requirements and movement capabilities of
animals, care must be taken when evaluating the use of focal species (Beier et al 2008,
Chetkiewicz and Boyce 2009). Given the time, expense, and data requirements needed to
design empirically defensible corridors, we may not be able to abandon the use of focal
species (Haddad and Tewksbury 2006, Cushman et al. 2010).
Bobcats may be an appropriate animal to evaluate the focal species approach
because they are a wide-ranging carnivore (Beier 1993, Cushman et al. 2008, Servheen et
al. 2001, Singleton et al. 2002) and rely on specific habitat features (Litvaitis et al. 1986,
Broman 2012). In New England, they select early-successional forests, scrub-shrub, and
wetland habitat types, and avoid agriculture, development, and high road densities
(Litvaitis 2001, Litvaitis et al. 2006, Litvaitis and Tash 2008, Donovan et al. 2011,
Broman 2012). Despite their sensitivity to development and roads (Crooks 2002, Riley
2006), bobcats can adapt to living in fragmented environments (Tigas et al. 2002).
Therefore, they should show some behavioral response to roads and development
resulting in clear areas of suitable and unsuitable habitats that can be used to assess
connectivity for similar species.
Objectives
The objectives for this study were: i.) compare methods used to design
connectivity conservation plans, ii.) validate connectivity modeling for bobcats at the
41
home range scale using telemetry-based data and Brownian bridge movement models,
iii.) extrapolate validated connectivity models statewide and identify potential bottlenecks
of movement, and iv.) assess the applicability of using bobcats as surrogate
mesocarnivores in connectivity planning.
METHODS
Study Areas
New Hampshire is the second most forested state in the nation at 82% forest
cover, but has fallen from a high of 87% in 1960, and is expected to fall to 78.5% by
2030 due to rapid human development and increases in population (Sunquist and Hewes
2010). This is most pronounced in the southeastern portion of the state and along
transportation corridors (Sunquist and Hewes 2010). Major transportation corridors
included Interstate Highways 89, 93, and 95, and State Highways 3, 16, and 101. All
operate as divided expressways. Heaviest development is concentrated in these regions,
especially the seacoast and Merrimack Valley. This alteration from a predominantly rural
to an increasingly fragmented state provides an excellent opportunity to observe the
effects of fragmentation on habitat suitability and connectivity.
Connectivity models were made using bobcats that were outfitted with GPS
collars (n=18) in two portions of the state, the southwest and southeastern portion (Fig. 31). The first study area was selected because of a high historic occurrence of bobcats. The
second study area was chosen to determine how differing levels of road and population
density affected bobcats, in addition to there being an abundance of recent sightings in
42
the area. Road densities are 1.14 km/km2 and 1.21 km/km2 for the southwest and
southeastern studies areas, respectively. The southwestern study area had a higher mean
elevation (308.3 m), compared to SA2 (186.0 m.). Both study areas contain a mix of
forest types including eastern hemlock (Tsuga canadensis), white pine (Pinus strobus),
American beech (Fagus grandifola), yellow birch (Betula allegheniensis), paper birch
(Betula papyrifera), northern red oak (Quercus rubra), red maple (Acer rubrum), sugar
maple (Acer saccharum), and white oak (Quercus alba). Wetlands, agriculture areas, and
varying levels of development are also present in both areas.
Resistance Layers
Two types of resistance layers were compared: an expert-opinion layer developed
from an independently generated model (by members of New Hampshire Fish and Game
and New Hampshire Audubon) and the inverse of a habitat suitability map generated
from GPS-telemetry data from bobcats modeled with Resource Selection Probability
Functions (Chapter II). Expert-opinion layers are often used in connectivity planning
(e.g., Singleton et al. 2004, LaRue and Nielsen 2008) because of their low cost and wide
applicability. Recently, resource selection functions (RSF), have been used to generate a
cost layer for a species, providing an empirically-based layer to model corridors
(Chetkiewicz and Boyce 2009, Pullinger and Johnson 2010, Squires et al. 2013). When
applied to connectivity modeling, the inverse of the habitat suitability map generated
from the RSF is used as the ‘cost layer’ (Chetkiewicz et al. 2006). This method assumes
traversing less suitable habitat is avoided, and that animals have some prior knowledge or
at least the ability to infer the relative cost of moving through a landscape and choose the
path that maximizes chance of survival (Driezen et al. 2007). Unlike expert opinion
43
Fig. 3-1. GPS locations from marked bobcats in the southwest and southeastern portions
of New Hampshire. Bobcats were monitored from 2009-2011. Major highways and
names of regions are shown for reference.
44
models, RSFs that utilize GPS-telemetry data can be expensive, time intensive, and may
be only applicable to the organism for which data are available. Despite these limitations,
it is believed that they are a better representation of an animal’s movement through its
habitat and are a superior method when resources are allowable.
Expert Opinion. Resistance layers based on expert-opinions and literature review
were independently developed by biologists at New Hampshire Fish and Game (NHFG)
and New Hampshire Audubon (NHA). For a complete description of methods, see NH
Audubon and NHFG (2010). Briefly, the expert opinion-based resistance layer used five
habitat variables to model resistance (Fig. 3-2), with each receiving a relative influence
(or weight) score: land cover = 40%, distance to roads = 40%, distance to riparian = 10%,
and slope = 10%. Resistance layers were parameterized with logistic functions: riparian
(Equation 1), slope (Equation 2), and distance to road (Equations 3, 4, 5). Resistance by
roads was determined by average annual daily traffic (AADT) and divided into three
class; low (>100 AADT; equation 3), medium (100-2999 AADT; equation 4), and high
(3000+; equation 5). Each cover type (NLCD 2001, with modifications from NLCD
2006) was assigned a resistance score (Fig. 3-2). Additionally, a ridgeline modifier was
added that subtracted two points from all ridgeline resistance scores.
45
Equation #1:
Equation #2:
Equation #3:
Equation #4:
Equation #5:
Fig. 3-2. Resistance scores for riparian areas are a function of distance from stream (top
left). Resistance scores for slopes are resistance a function of percent slope (top right).
Resistance scores for roads are a function of traffic volume, and separated into three
classes-low, medium, and high (lower left). Land cover variables were assigned
resistance scores from 1-10 with 1 being lowest resistance and 10 being the highest
(lower right) Figures were recreated from NH Audubon and NHFG (2010).
46
Resource Selection Probability Functions. Resource selection probability
functions were fit at the second and third-order of habitat selection using incidental
observations and GPS-telemetry data, respectively (see Chapter II). These models were
refit to test predictability using location data from each individual bobcat. This entailed
withholding data from one bobcat, fitting the model using the data from the remaining
seventeen bobcats, and then using the data from the withheld bobcat to validate the
resistance model. By using the withheld bobcats’ location data to validate each
connectivity model, I had a quasi-independent method of testing connectivity models.
Individual RSPF models were fit using the package ResourceSelection (Lele et al. 2013)
in the statistical program R (R Core Team 2012). Each individual model then served as a
‘conductance’ layer in connectivity modeling using the program Circuitscape (McRae
and Shah 2009), with the assumption that areas of higher suitability would have greater
conductance than areas of lower suitability. Functionally, the conductance layer is the
inverse of a resistance layer.
Connectivity Modeling and Assessment
Circuit Theory. Circuit theory, through the program Circuitscape (McRae and
Shah 2009), uses algorithms that were originally developed for electrical circuit theory,
but have the potential to be applied to a variety of connectivity conservation issues
(McRae et al. 2008, Fig. 3-3). Circuitscape incorporates multiple ‘random walk’
pathways into one model, so instead of modeling a single path, as in least-cost path
modeling, Circuitscape displays the relative cost of moving through the entire landscape.
McRae and Beier (2007) applied this to the gene flow of a plant and an animal population
and found that it performed better than isolation by distance and least-cost-path methods.
47
This could be beneficial in identifying potential ‘pinch-points’ of movement through
high-cost area because random walkers will select these points at a higher probability.
This approach may be more applicable to organisms that inhabit a variety of habitats with
few large blocks of protected land.
F
ig. 3-3. Comparison of circuit theory and least cost pathways. (A) Cost surface layer used
for modeling. The white patches in the lower left and upper right corners are the source
patch patches. Resistance is shown using a gray scale. Areas with low resistance are light
grey, areas of high resistance are dark grey, and black areas are barriers to movement. (B)
Least cost modeled corridor is shown in white. (C) Output from circuit theory. Areas of
blue are have a lower probability of utilization, while areas of red have a higher chance.
Areas in yellow show extremely high utilization points and thus identify ‘pinch-points’ of
movement. These areas are constrictions in the landscape where organisms are forced to
travel, highlighting the area of highest conservation value. A real world example of such
points may be an undeveloped underpass on a major freeway system (from McRae et al.
2008).
Using program Circuitscape (McRae and Shah 2009), connectivity was modeled
for both resistance layers. To ensure that source (start points) and ground (end points) did
not bias the connectivity maps, methods used by Anderson et al. (2012) were adopted
with some modification for bobcats (Fig. 3-4). A minimum-convex polygon (MCP) was
generated around all bobcat locations. The centroid of this was determined in GIS and the
length from the centroid of the MCP to the furthest point on the MCP was used as a
radius to generate a circle around the locations. A second circle was added at twice the
48
radius of the initial circle. Squares were then generated around both circles. The inner
square served as the unbiased analysis area. The outer square defined the full analysis
area. Program CircuitScape was run four times through this area: once using the top side
of the square as a source and the bottom as the ground, once using the bottom side as a
source and the top as the ground, and similarly from left to right and right to left. The
average of these four outputs was used to make the cumulative map. Using this method
resulted in an analysis area unbiased by source or ground points (Anderson et al. 2012).
Bobcat locations were sequentially connected to produce a movement path,
referred to as the actual path. Segments that were not the result of two consecutive GPS
fixes were removed. The actual path was then randomly rotated and shifted 100 times to
generate 100 random paths using the Geospatial Modeling Environment program (GME;
Beyer 2008). Mean conductivity scores were then computed for each path using GME
(Beyer 2008). A one sample t-test was used to test if the actual paths were significantly
different from the mean conductivity score of the random paths, indicating bobcats were
using areas predicted by the connectivity model.
Brownian Bridge Movement Models. Brownian bridge movement models were
generated for each individual bobcat using the package BBMM (Nielsen et al. 2013) in
the statistical program R. Location error was set to 20 m based on collar tests done by
Mallet (2013). Only consecutive locations were used to estimate BBMMs. Utilization
distribution scores were then summed to get cumulative distribution functions for each
individual, and only areas with scores within 95% utilization distribution were used in the
analysis.
49
Fig. 3-4. Modeling scheme used to test connectivity predictions for bobcats. RSPF
generated using all location except from Bobcat #47, with his locations overlaid. This
served as the ‘conductivity’ layer (top left). Sampling protocol adopted from Anderson et
al. (2012) to map connectivity in bobcat home range with minimal source/ground bias
(top right). Output from circuit theory using top as source/bottom as ground, bottom as
source/top as ground, left as source/right as ground, and right as source/left as ground
(middle panels – L to R). Cumulative circuit theory map with actual path of bobcat #47
(bottom left). Actual path overlaid with 100 random paths, rotated and shifted (bottom
right).
50
A roads layer was overlaid onto individual BBMMs and circuit theory models
(both expert opinion and RSPF). The roads layer was divided into sampling points,
spaced every 100 m. Values for BBMM utilization distributions, expert-opinion based
circuit theory, and RSPF based circuit theory models were obtained for each point in GIS.
Spearmann pairwise correlations were then derived between BBMM scores and the two
connectivity models at the road points. Significant negative correlations between BBMM
UD scores and circuit theory scores were used to evaluate agreement between the two
modeling procedures. As the BBMM UD score decreases, the level of estimated use for
that area increases, therefore a negative correlation between the BBMM UD and
connectivity scores indicates that areas of high use and high connectivity are correlated.
Extrapolation of Connectivity Models
After model validation and methods comparison took place at the home range
level, the models were extrapolated to the rest of New Hampshire. The RSPF resistance
layer utilizing both second and third-order selection (see Chapter II) was used. The
expert-opinion model was designed to be applicable statewide and no alterations were
necessary. Due to differences in spatial extent of some layers (i.e., being constrained to
either New Hampshire or the continental U.S.), the RSPF layer and expert-opinion layer
were buffered by random pixels with scores between 0 and 1 (Koen et al. 2010). This
assured that the sources and ground points did not bias the connectivity model (Koen et
al. 2010). Program Circuitscape was run to and from each cardinal direction (as described
in previous section). Connectivity scores were then extracted to major roads in GIS and
divided into degrees of connectivity. Segments of roads that resulted in bottleneck that
could be limiting connectivity were identified.
51
Bobcats as a Surrogate-Species
The use of bobcats as a surrogate species to model connectivity was tested by
using remote cameras and the extrapolated connectivity models. During January to April
2013, 29 sites were sampled with remote trail cameras 1-5 times with a mean sampling
time of 11.5 days (range 2-18 days) along New Hampshire Route 101. Camera sampling
time varied due to camera malfunctions and site visit logistics. The sampling area
spanned from Epping, NH to Auburn, NH where Route 101 is a four-line divided
expressway with a traffic volume of ~40,000 vehicles per day (NH DOT Traffic
Volume). This size road represents a potential barrier to movement for bobcats and other
forest carnivores (Riley et al. 2006), and therefore serves as suitable location to test
connectivity models. Sites were systematically distributed at approximately 1 km
intervals and were mapped in ArcMap 10.0 before being located in the field. Each site
served as a potential crossing point, and it was believed that areas with higher
connectivity scores would better explain carnivore presence. Sites were accessed by
auxiliary roads that bisected Route 101. Only sites located in forested areas were used to
limit the types of habitats sampled, and because they provided structure to mount cameras
and baits. Sites were located between 15-50 m from the highway, depending on highway
shoulder width and available vegetation. Four habitat measurements were taken for each
site (Table 3-1) to assess whether animals were responding to site specific habitat
features versus the landscape level features measured by the connectivity models. These
variables were whether the road was visible from the site, if the site was adjacent to a
stream, whether there was an open or closed-canopy forest, and the distance to the road.
52
Camera sites were baited with both coyote urine and catnip oil, and a compact
disc was hung nearby to act as a visual attractant. Coyote urine was sprayed at the base of
a tree 3-5 m from and within view of the camera. Catnip oil was contained in 15 ml. test
tubes and zip-tied to a sapling or branch 3-5 m from and within view of the camera.
Compact discs were tied to adjacent branches, out of view of the camera to limit
incidental camera activation.
All animals that triggered the camera were noted, but data analysis focused on
carnivore species; including, bobcats, coyotes (Canis latrans), gray foxes (Urocyon
cinereoargenteus), red foxes (Vulpes vulpes) fishers (Martes pennanti), raccoons
(Procyon lotor), and opossums (Didelphi virginiana). Consecutive days within a session
were collapsed to represent one event (i.e. Long et al. 2011, Tempa et al. 2011); therefore
only one detection was counted for each species in each sampling occasion. Probability
of occupancy and detection was modeled in program Presence (Hines 2006). Singleseason models were run for each species for which there was sufficient data (coyotes,
gray foxes, fishers, and raccoons). Model fitting followed a stepwise procedure and was
judged using AIC. First, species specific differences in probability of detection across
sites and sampling periods were tested against the null model with probability of
occupancy held constant. Next, the best fitting model for probability of detection was
then re-fit with all possible variables that affect occupancy, including connectivity scores.
Models only included one variable to model the probability of detection and one variable
to model probability of occupancy. Predictive capability of circuit theory models was
determined by whether they were included in top models.
53
Table 3-1. Variables used to model probability of detection and occupancy at camera
sites along Highway 101 from January-April 2013. Temporal variables measured
differences in deployment order and duration, and were used to model differences in the
probability of detection. Probability of occupancy was modeled using habitat and
modeling variables. Habitat variables were measured in the field during camera
deployment. Modeling variables utilized connectivity scores, with low scores indicating
poor connectivity and high scores indicating good connectivity.
Category Variable
Justification
Units
Range
Temporal # of prior sessions
Knowledge of trap
Count
1-4
Camera days
Sampling time
Count
2 to 18
Session
Temporal variation
Count
1-5
Road visible
Avoidance of roads
Category yes or no
Adjacent to stream
Potential corridors
Category yes or no
Canopy
Provides cover
Category open or closed
Distance to road
Tolerance of roads
Meters
Habitat
Modeling Circuit theory – EO
Circuit theory - RSPF
15-47
Measure of connectivity Unitless
0-5
Measure of connectivity Unitless
0-5
RESULTS
Resistance Layers
The resistance layer developed by NHFG and NH Audubon (2010) was used as
the expert opinion-based approach (Fig. 3-5). The empirically-based resistance layer (Fig.
3-5) was modeled using resource selection probability functions described in Chapter II.
This layer showed habitat selection by bobcats, and resistance was assumed to be
negatively correlated with selection.
54
Fig. 3-5. Resistance models used for mapping bobcat connectivity. The resource
selection probability function (RSPF) model was generated using GPS-telemetry data
from collared bobcats (right). The expert opinion model was generated independently by
agency biologists (NHFG and NH Audubon 2010).
55
Connectivity Modeling and Assessment
Circuit Theory. Connectivity modeling at the home-range level using
Circuitscape was completed for all 18 collared bobcats using both the individual RSPFs
layers for each bobcat, and the expert opinion layer. Scores of actual and random paths
were extrapolated using GME. Circuit theory modeling using RSPFs resulted in 16 out of
18 actual paths scoring higher than random paths (p<0.05; Fig. 3-6; Appendix C),
indicating actual bobcat paths had greater connectivity compared to random paths. The
expert-opinion model resulted in 12 of 18 (p<0.05; Fig. 3-6; Appendix C) actual bobcat
paths scoring higher than random paths.
Fig. 3-6. Differences between actual and random path mean scores for resource
selection probability function and expert opinion connectivity models. The connectivity
score for the actual path was standardized to 0 for each individual bobcat. The difference
between the mean connectivity score of random paths and actual paths was then plotted.
If connectivity was successfully predicted, the mean score of the random paths will be
less (below zero) than the actual path.
56
Brownian Bridge Movement Models. Brownian bridge movement models were
generated using location data from each individual bobcat with the R package BBMM.
Maximum amount of time between points was 5 hours and 7 hours for Lotek and
Siritrack collars, respectively. Location error was set to 20 m (Mallet 2013). BBMM
models and Circuitscape models were overlaid in GIS, and scores for each were extracted
from 10,292 points along roads within 95% UD of each bobcat. Spearman correlations
(Table 3-2) for RSPF models utilizing all locations revealed a significant negative
correlation for all roads (rs = -0.12, p < 0.0001) and major roads (3330 points, rs = -0.09, p
< 0.0001). Spearman correlations (Table 3-2) for expert opinion models utilizing all
locations did not have a significant negative correlation for all roads (rs = 0.02, p <
0.1686) and major roads (rs = 0.04, p < 0.0333). Individual RSPF models were
significantly negatively correlated for 12 of 18 bobcats when utilizing points on all roads,
and 9 of 17 bobcats for points that were only on major roads. Individual expert-opinion
models were significantly negatively correlated for 5 of 18 bobcats when utilizing points
on all roads, and 4 of 17 bobcats for points that were only on major roads. One bobcat
(41) had no major roads within its home range, so it was not included in this analysis.
Extrapolation of Connectivity Models.
Circuitscape models were extrapolated to the entire state using both resistance
layers (Figs. 3-7 & 3-8). Models showed similar low flow through most of the Merrimack
Valley, the southeastern portion/coastal portion of the state, and other highly developed
areas. The RSPF model showed relatively high flow throughout the state, with the
exception of the White Mountains and the northern portion of the state. Potential
bottlenecks of movement were between Concord and Manchester, between Lake
57
Winnipesaukee and the White Mountains, and along the southeastern corner of the White
Mountains. The expert opinion model showed highest flow through the northern part of
the state, the White Mountains, and along the western edge. Circuitscape models
extracted to the roads (Figs. 3-7 & 3-8) highlighted the lack of connectivity in heavily
developed areas, and along higher traffic volume roads, with the latter seen in expert
opinion model in particular.
Table 3-2. Spearmen correlations between BBMM and circuit theory models for all
bobcats and separate individuals, and all roads and major roads. Significant correlations
are highlighted in bold, and indicate agreement between BBMM models and circuit
theory models.
RSPF
Expert Opinion
ID
Roads-All
p-Val
Roads-Major
p-Val
Roads-All
p-Val
Roads-Major
p-Val
All
-0.12
<0.0001
-0.09
<0.0001
0.02
0.1686
0.04
0.0333
26
-0.15
<0.0001
-0.11
0.0737
0.23
<0.0001
0.23
0.0002
27
-0.07
0.0127
0.07
0.1321
0.07
0.0090
0.18
0.0002
28
0.21
0.0001
0.13
0.1308
0.15
0.0042
0.03
0.7637
29
0.07
0.2995
0.30
0.0026
0.08
0.2529
0.02
0.8353
30
-0.19
<0.0001
0.08
0.1099
-0.13
<0.0001
0.15
0.0015
31
0.00
0.9356
-0.55
<0.0001
0.06
0.2955
0.13
0.2463
32
-0.21
<0.0001
-0.19
0.0284
-0.16
<0.0001
-0.41
<0.0001
33
-0.05
0.4726
0.08
0.5610
0.08
0.2695
-0.07
0.5915
34
-0.28
<0.0001
-0.24
0.0003
-0.13
0.0003
-0.25
0.0001
39
-0.16
0.0010
-0.12
0.2025
-0.11
0.0285
0.04
0.6490
40
-0.08
0.0020
-0.05
0.2379
-0.04
0.1297
0.01
0.7863
41
0.22
0.0159
N/A
N/A
0.07
0.4582
N/A
N/A
42
-0.29
<0.0001
-0.35
<0.0001
-0.05
0.1164
-0.06
0.2736
43
-0.22
<0.0001
-0.32
0.0006
-0.05
0.2543
-0.31
0.0010
44
-0.26
<0.0001
-0.31
<0.0001
-0.19
0.0002
-0.16
0.0194
45
0.19
0.0022
-0.03
0.8379
-0.01
0.8526
-0.12
0.4100
46
-0.13
0.0865
-0.01
0.9433
-0.04
0.5654
-0.36
0.0337
47
-0.06
0.4224
-0.34
0.0106
0.09
0.2693
0.15
0.2748
58
Fig. 3-7. Connectivity models for New Hampshire generated using RSPF habitat
suitability models and Program Circuitscape (left). Areas with highest ‘conductance’ or
movement are in yellow and lowest are in dark blue. Connectivity scores for major roads
in New Hampshire (right).
59
Fig. 3-8. Connectivity models for New Hampshire generated using expert-opinion
resistance models and Program Circuitscape (left). Areas with highest ‘conductance’ or
movement are in yellow, and lowest are in dark blue. Connectivity scores for major roads
in New Hampshire (right).
60
Bobcats as Surrogates.
Data from 952 camera trap nights yielded independent detections of; gray fox =
28, raccoon = 27, fisher = 19, coyotes = 17, opossum = 7, red fox = 2, and bobcat = 1.
Pictures were also captured of moose (Alces alces), white-tailed deer (Odocoileus
virginianus), snowshoe hare (Lepus americanus), ruffed grouse (Bonasa umbellus), grey
squirrels (Sciurus carolinensis), and red squirrels (Tamiasciurus hudsonicus). Analysis
was only performed on gray foxes, raccoons, fishers, and coyotes.
For gray foxes, the top model held the probability of occupancy constant, and
detectability varied by the number of days per trapping session (Table 3-3). However, 8
of the 10 models were less than 2 AIC from the top model, including the null model. This
indicates that the habitat variables did a poor job explaining the variance seen in gray fox
probability of detection and occupancy.
For fisher, the top model contained the RSPF based connectivity model for
probability of occupancy, and the number camera days for the probability of detection
(Table 3-4). Multiple other models in the model set received some weight, including the
null model.
For coyotes, the top model contained distance to road for probability of
occupancy, and the session number for probability of detection (Table 3-5). This model
received the majority of weight in the model set (0.82, Table 3-5). The next top model
contained the expert opinion connectivity model. The RSPF connectivity model had no
support.
61
For raccoons, the top model contained road visibility for probability of
occupancy, and the session number for probability of detection (Table 3-6). Two other
models were less than 2 AICc from the top model, including a model containing the
RSPF connectivity model and a model that held probability of occupancy constant. This
indicates little support for the connectivity models.
Table 3-3. Gray fox occupancy model set. Model selection was based on AIC weight
(AICw). The number of parameters (K), Akaike’s Information Criterion adjusted for small
sample sizes (AICc), and the difference in AICc score (∆AICc) are also reported. Ψ = the
probability of occupancy, p = detection probability.
Model
K
AICc
∆AICc
AICw
Detection
ψ(.) p(days)
3
108.05
0
0.51
ψ(.) p(.)
2
109.27
1.22
0.28
ψ(.) p(order)
3
110.13
2.08
0.18
ψ(.) p(session)
6
114.13
6.08
0.02
Occupancy
ψ(.) p(days)
3
108.05
0
0.25
ψ(eo) p(days)
4
109.24
1.19
0.14
ψ(stream) p(days)
4
109.24
1.19
0.14
ψ(rspf) p(days)
4
109.25
1.2
0.14
ψ(dt_rd) p(days)
4
109.59
1.54
0.12
ψ(canopy) p(days)
4
109.76
1.71
0.11
ψ(rd_vis) p(days)
4
109.87
1.82
0.10
62
Table 3-4. Fisher occupancy model set. Model selection was based on AIC weight
Model
K
AICc
∆AICc
AICw
Detection
ψ(.) p(days)
3
92.79
0
0.4453
ψ(.) p(.)
2
93.3
0.51
0.3451
ψ(.) p(order)
3
94.55
1.76
0.1847
ψ(.) p(session)
6
98.56
5.77
0.0249
Occupancy
ψ(rspf) p(days)
4
90.28
0
0.4931
ψ(.) p(days)
3
92.79
2.51
0.1406
ψ(canopy) p(days)
4
93.53
3.25
0.0971
ψ(dt_road) p(days)
4
93.66
3.38
0.091
ψ(stream) p(days)
4
94.06
3.78
0.0745
ψ(rd_vis) p(days)
4
94.78
4.5
0.052
ψ(eo) p(days)
4
94.79
4.51
0.0517
Table 3-5. Coyote occupancy model set. Model selection was based on AIC weight
Model
K
AICc
∆AICc
AICw
Detection
79.84
0
0.9151
ψ(.) p(session)
7
85.29
5.45
0.06
ψ(.) p(order)
2
87.69
7.85
0.0181
ψ(.) p(.)
3
89.63
9.79
0.0068
ψ(.) p(days)
3
Occupancy
ψ(dt_road) p(session)
7
70.13
0
0.8198
ψ(eo) p(session)
7
73.38
3.25
0.1614
ψ(.) p(session)
6
79.84
9.71
0.0064
ψ(stream) p(session)
7
81.11
10.98
0.0034
ψ(rd_vis) p(session)
7
81.18
11.05
0.0033
ψ(rspf) p(session)
7
81.39
11.26
0.0029
ψ(canopy) p(session)
7
81.53
11.4
0.0027
63
Table 3-6. Raccoon occupancy model set. Model selection was based on AIC weight
Model
K
AICc
∆AICc
AICw
Detection
ψ(.) p(session)
7
100.84
0.00
0.48
ψ(.) p(.)
2
102.17
1.33
0.25
ψ(.) p(order)
3
102.72
1.88
0.19
ψ(.) p(days)
3
104.12
3.28
0.09
Occupancy
ψ(rd_vis) p(session)
7
96.92
0
0.39
ψ(rspf) p(session)
7
98.15
1.23
0.21
ψ(.) p(session)
6
98.84
1.92
0.15
ψ(stream) p(session)
7
100.25
3.33
0.07
ψ(canopy) p(session)
7
100.75
3.83
0.06
ψ(eo) p(session)
7
100.83
3.91
0.06
ψ(dt_road) p(session)
7
100.84
3.92
0.06
DISCUSSION
Improving connectivity in fragmented habitats is important to bobcats because
they can be limited in their home range movements by roads and developed areas
(Crooks 2002, Broman 2012, Chapter II), and are susceptible to vehicle collisions (Tash
and Litvaitis 2008). This may result in landscape-level effects where bobcat populations
become isolated due to unsuccessful dispersal, limiting gene flow (e.g., Lee et al. 2012).
Assessment of Connectivity Methods
Using subsequent GPS-locations to make movement paths for collared bobcats
resulted in a relatively straightforward way to assess connectivity models. If the predicted
64
connectivity models, made using Circuitscape, were meaningful, actual paths would
receive higher scores compared to random paths. This would demonstrate that areas
predicted to have greater movement potential were corroborated with actual movements.
RSPF models accurately predicted for 16 out of 18 bobcats, while expert opinion model
predicted 12 out of 18 bobcats; a clear majority for each method used.
Despite the relative success of both models, the RSPF method did a better a job
modeling movement compared to expert opinion models. This should not be surprising,
given the added detail that was incorporated in that model and previous literature
showing improvement that empirical data has over expert opinion models (Clevenger et
al. 2002, Johnson et al. 2010). While the expert opinion model was based on five
variables influencing habitat, the RSPF model was built using eight, and this added detail
is probably part of the observed difference in accuracy. Additional variables included;
distance to forest edge, aspect, and a measurement of ruggedness (VRM), all of which are
important variables in bobcat habitat selection (Chapter II), and may influence
connectivity as well.
Regardless of the marked improvement in the RSPF model, the expert-opinion
model should not be discounted when making resistant layers. For certain management
scenarios, such as state wildlife action plans, it would be cost and time prohibitive to
collect empirical data for all species. Individual based expert-opinion models may be
better than attempting to make a connectivity plan with only one or a handful of focal
species. When the opportunity exists though, empirical data should be utilized. In this
particular study, nothing special was changed to make the connectivity model. If data for
species-specific habitat use is available, it should be utilized to make the resistance
65
model, or at least validate the expert-opinion model. These steps will help ensure better
performing connectivity models.
In addition to using locations to construct paths, models of movement using
Brownian bridges offer an alternative and complimentary approach to assessing
connectivity (Horne et al. 2007, Sawyer et al. 2009, Lewis et al. 2011, Sawyer et al.
2013). When using locations to construct pathways, measures of uncertainty between
locations are not taken into account. Browning bridge movement models (BBMMs) were
designed with knowledge of this uncertainty and resulted in utilization distributions, as
opposed to distinct paths. This may improve assessments because animals rarely travel in
a straight line between GPS fixes, most likely resulting in an inaccurate prediction of a
road crossing. However, if an animal routinely crosses a road in a similar spot, that will
become apparent in its utilization distribution. When an animal is sensitive to roads, it
must make a choice of where to cross. This can be influenced by habitat, topographic
features, and anthropogenic features (Waller and Servheen 2005, Dodd et al. 2007, Lewis
et al. 2011), therefore bottlenecks of movement should be found in fragmented
landscapes. If those spots of high predicted movement by the BBMM are correlated with
high circuit theory scores the model can be assessed.
Results from correlations between BBMMs and connectivity scores were
moderately successful, indicating that connectivity models did a good job of predicting
inter-home range movement. Under the RSPF model, there was significant correlation
between Circuitscape scores and BBMM UD’s for the study population, and 11 of 18
individual bobcats. While a majority of the individual connectivity models predicted
correctly, a ~61% success rate is not optimal.
66
After the original analyses were completed, comparisons were made between the
bobcats that were predicted correctly and those that weren’t. I found that there was no
significant difference between sex, age, home range size, or number of locations used. I
then predicted that connectivity models may be more successful in modeling bobcat
movements in home ranges that contain more land cover altered by anthropogenic means.
I assumed that these landscapes are more likely to contain areas where bobcats are forced
to travel around development, creating bottlenecks of movement. In areas with less
development bobcat movements would not be as constrained. While there is a difference
between the mean percentage of human-altered land covers (either developed or
agriculture) when comparing the two types of bobcat home ranges (µ = 15.1%, σ = 9.6%
vs. µ = 9.6%, σ = 4.7%) it was not significant (ANOVA: F=1.99, p=0.18). This could be
a function of the small sample size. Despite this, it still appears that the RSPF models did
a relatively good job of predicting individual movement within home ranges, and
validation techniques such as this one should be employed when modeling connectivity.
There were much fewer significant correlations for individual bobcats between
BBMM and Circuitscape scores when the expert-opinion model was used (five out of
eighteen). All five correctly predicted by the expert opinion model were also predicted by
the RSPF model, which shows some agreement between resistance layers. And while it
worked comparatively well at the path level analysis, it falls short when identifying
potential bottlenecks at road crossings. Again, this is most likely due to the added detail
that the RSPF model contains compared to the expert opinion model. It remains to be
seen whether the poor performance of the expert opinion model at the home range level
would carry over to landscape level connectivity.
67
Statewide Connectivity Models
It is important to assess models before we implement them, and identifying
movement at the home range level can aid in make wildlife crossing safer, resulting in
less mortalities. However, the greatest benefits and aspirations of connectivity
conservation are to protect and build linkages at the landscape level. This has a much
better chance of assuring gene flow, and will help ensure long term survival of a species.
Unfortunately, testing landscape level connectivity is very difficult to do in real time.
Genetic methods can tell us about connectivity over time (e.g., McRae and Beier 2008),
and whether there are dispersers or migrants between populations (e.g., Spong and Creel
2001), but using genetics to assess the real-time validity of connectivity plans is more
difficult. Furthermore, using GPS data to assess how an animal disperses through habitats
can also be difficult, although not impossible (Driezen et al. 2007). Juveniles, and ones
that will disperse between good patches of habitat, can be difficult to mark and follow.
This is why using home range movements to assess models should be used when
available, but ultimately genetic or field methods designed for landscape connectivity
will perform better and can more fully validate models.
Increased accuracy for road-kill locations would improve connectivity
assessment. These data are of relatively small cost, and if carcasses are spatially
referenced, they can aid in determining where mortalities are occurring. This may
highlight potential roadside features that contribute to vehicular collisions. Furthermore,
genetic data can be collected from carcasses to assess gene flow for the population.
68
Efforts to extrapolate statewide models of connectivity using expert opinion and
RSPF resistant layers resulted in some similarities. Both models showed an avoidance of
roads. Therefore, agreement between models shows low flow through New Hampshire’s
densest populations such as those near the I-93 and I-95 transportation corridors in the
Merrimack Valley and Seacoast portions of the state (Figs. 4-1, 4-7, 4-8). However, the
expert opinion model put a much stronger influence on roads, and therefore areas
between major cities in southeastern New Hampshire have lower flow in the expert
opinion model compared to the RSPF. Bobcats in these more densely populated areas
may function as sinks in the population (Chapter II). Use of connectivity models to plan
road crossing structures may mitigate the negative effects of roads in this area.
Major differences between the two models also existed when extrapolated
statewide. Most notably, there was disagreement through the northern part of the state
and the White Mountains. The expert opinion model predicts little resistance in these
areas and therefore the highest flow is predicted. Whereas the RSPF model predicted this
to be the lowest quality habitat, and therefore there is virtually no connectivity predicted
through this area. This highlights an important distinction between the types of two
models. The expert opinion model was built to show resistance on the landscape – not
habitat suitability. The RSPF model was built to model habitat suitability and only
functions as a good resistance layer if the assumption that bobcats will move through
areas of high suitability more often than low suitability is met. In the instance of northern
portion of the state and the White Mountains, during times with deep snowpack, this area
may be very poor habitat, however during the rest of the year it probably offers much less
resistance to movement. Assessment of the accuracy of these approaches is probably best
69
done with genetic studies. Wasserman et al. (2010) found that genetic flow was best
described by elevation in American marten (Martes americana) in Idaho, and not with
their empirically-based habitat map. This means that the added detail found within the
empirical RSPF may be better at predicting within home range movements, but this may
not extend out to landscape-level genetic connectivity.
Surrogate Species
Species with large area requirements are often selected to be surrogate or focal
species in connectivity conservation (Bani et al. 2002, Beier et al. 2008, Epps et al.
2011); however there is debate about their usefulness and application in the conservation
community (e.g. Lindenmeyer et al. 2002, Wiens et al. 2008). The use of bobcats as a
surrogate species received moderate support from camera trapping data and occupancy
analysis. Bobcats only appeared to be a good surrogate for fishers, which agrees with
findings by Leoniak et al. (2012) in northern New Hampshire. Both raccoon and coyote
occupancy was better predicted by whether the road was visible from the camera site or
the distance from the camera to the road, respectively. None of the models of gray fox
occupancy were significantly better than the null. These findings may be due to a small
sample size, and limited camera trapping field season. Alternatively, the finding could be
a function of the species biology.
Fishers require the most specific habitat features of the group, requiring
coniferous or mixed wood stands and avoiding open areas (Powell et al. 2002). In
contrast, coyotes, raccoons, and gray foxes are considered habitat generalists, and so
significant overlap between them and bobcats may not be so readily observed.
70
Furthermore, coyotes, raccoons and gray foxes have been shown to be very tolerant of
human presence. And while any conservation connectivity program should attempt to
benefit all species, these animals are not in dire need of conservation action. Noss and
Daly (2006) recommended that connectivity plans focus on species for which habitat loss
and fragmentation are of greater concern. So while, connectivity models for bobcats
were only successful in predicting fisher occupancy, this is the species for which further
connectivity measures would most likely be focused. This is highlighted by its inclusion
in the NH Connectivity Plan, while the other species were not (NH Audubon & NHFG
2010).
Nonetheless, given this available data, it is hard to conclude that bobcats may be a
viable, stand-alone surrogate for connectivity planning in the northeast. They may be a
good choice to represent medium-sized carnivores when a suite of species is employed,
as in the analysis done by NHFG and NH Audubon (2010). However more extensive
field testing needs to be done before they can realistically be chosen as surrogate.
Conclusions
We must find solutions to our increasingly fragmented landscape, and
connectivity conservation promises to be a great tool for reaching that goal. However,
given the limited resources involved, careful planning is necessary. By looking at bobcat
connectivity in New Hampshire a number of important concepts were explored; expertopinion vs. empirical connectivity models, validation of connectivity models,
extrapolation of models, the use of surrogate species as a conservation tool.
71
Empirical models of connectivity can be more successful in modeling bobcat
movement at the home-range scale. This was shown using path level analysis and
Brownian bridge movement models, two techniques that can be used to assess
connectivity models. If the goal is to mitigate deaths from vehicle collisions the empirical
model may be a better choice. This could have direct implications when attempting to
make connectivity plans for imperiled species or those for which any mortality has a
strong effect on the overall health of the population.
When models were extrapolated statewide, differences in model output became
more apparent. Expert-opinion models showed high connectivity throughout western
New Hampshire and into the northern region, agreeing with what is considered
historically good bobcat habitat (see Chapter II). Conversely, the empirical model
showed high connectivity throughout most of southern New Hampshire, and to the north
through lower elevation areas, but low connectivity through the White Mountains and the
northern most part of the state. The expert opinion model may better depict overall
connectivity and thus gene flow in the state. When planning for connectivity the overall
goal may help determine which type of resistance layer is best to use.
As for using bobcats as a surrogate species, neither model was adept at describing
occupancy at roads for the full suite of mammalian carnivores in southern New
Hampshire. However, overlap potentially exists between bobcats and fishers. Additional
investigation into this topic is warranted given its wide-spread use in conservation and its
moderate demonstrated successes, nevertheless based off this data, bobcats cannot be
endorsed as a surrogate that will function for a range of species in connectivity modeling.
72
Finally, further validation of the two connectivity models could best be done
using genetic techniques. The empirical model may help plan areas to mitigate direct
mortality to bobcats at road crossings, which will increase connections between
populations. However, to measure the true effect of fragmentation by development in
New Hampshire, an analysis of gene flow is needed. This will demonstrate whether
fragmentation is having a negative effect on species persistence in the state. This could
easily be implemented using genetic data from bobcats killed by vehicle collisions or
incidental captures that have been spatially referenced.
73
CHAPTER IV
POPULATION ESTIMATE FOR BOBCATS IN NEW HAMPSHIRE BASED ON
HOME RANGE SIZE AND COMPOSITION
Estimating population size has been identified as the greatest challenge in bobcat
management (Bluett et al. 2001, Anderson and Lovallo 2003). In a survey of state
wildlife managers, Bluett et al. (2001) found that abundance, along with distribution,
were recognized as the most important research needs. Unfortunately, exact population
counts for bobcats are nearly impossible, and other assessments are generally inaccurate
(Anderson and Lovallo 2003). Bobcats are a territorial species and live a solitary life
(Sunquist and Sunquist 2002). Their home-range size is closely tied to prey species
(Litvaitis et al. 1986), so in areas of low productivity they likely exist at low densities
(e.g., Litvaitis et al. 1986, Fox 1990). Additionally, they often inhabit rugged landscapes
with a dense understory vegetation (Young 1958, Anderson and Lovallo 2003), making
sampling problematic. All of these factors contribute to the difficulty in making an
accurate population estimate.
Estimates of population size help establish a baseline for future management
actions. Efforts to track or estimate populations of bobcats by state agencies are most
often accomplished through hunter/trapper surveys (31 states) and/or harvest data (26
states) (Bluett et al. 2001). Rolley (1987) found that these measures only detect large
changes in populations (i.e., >50%), and therefore are often unreliable. Other inexpensive
74
methods of tracking populations include scents station surveys (Diefenbach 1994), track
counts along transects (Thompson 1989), and scat-based inventories (Long et al. 2007).
However, these methods only offer a crude analysis of population trends, and often
cannot be used to directly estimate population size.
Recently, use of camera surveys (Heilbrun et al. 2006, Larrucea et al. 2007) and
genetic methods (Ruell et al. 2009) in a mark-recapture framework have provided
accurate estimates of density, but only at a relatively small spatial scale. Extrapolating
these densities to areas relevant to management (e.g., statewide or smaller management
units) encounter the same difficulties of extrapolating habitat-suitability measurements
(Chapter II). Additionally, these studies were in areas with dense bobcat populations.
Their applicability to populations at more northerly latitudes may not be feasible.
As a consequence of the difficulties in measuring bobcat populations, methods
utilizing home-range requirements and habitat suitability maps are often employed when
estimating population size (Lovallo 1999, and Nielsen and Woolf 2002, Donovan et al.
2012, Broman 2013). The basic premise of these methods involves first determining the
exclusive home range or territory area requirements of the study species. Burt (1943)
described a territory as the exclusive and protected area of an animal’s home range. Next,
a habitat suitability map is made, with areas of suitable and unsuitable habitat clearly
defined. If the study animal has an exclusive home range, as female bobcats do (Bailey
1974, Berg 1981, Lawhead 1984, Anderson 1987, Donovan et al. 2011), the amount of
suitable habitat is simply divided by the exclusive home range size. In addition, spatial
configurations can be addressed in GIS by aggregating suitability scores to the size of an
75
exclusive home range, and then summing the number of suitable pixels (Boyce and
McDonald 1999).
These methods are relatively easy to implement, especially if fine scale habitat
and home-range information have been collected. They can also give a statewide estimate
of populations if considerations of spatial scale have been factored into the habitat
suitability map. Finally, they allow for changes in population estimates as habitat
alteration occurs or if more accurate estimates of home range size are obtained.
Populations of bobcats have been increasing range-wide (Roberts and Crimmins
2010) and incidental sightings demonstrated a possible increase in New Hampshire, as
well (Litvaitis et al. 2006, Broman 2012). However, prior to these studies there was
minimal data on the bobcat population in New Hampshire because harvest seasons were
suspended in 1989. Due to their apparent population increase, a GPS-telemetry study was
initiated in 2008 to analyze habitat use and estimate abundance. Using habitat-suitability
maps and home-range requirements for 11 bobcats (10 male and 1 female) in
southwestern New Hampshire, Broman (2012) estimated there were between 465 and 952
resident bobcats in the state. An additional 7 bobcats (3 males and 4 female) from the
southwestern study area were used to generate a second-generation habitat suitability
model (Chapter II). With the availability of this revised habitat suitability map (Chapter
II), I constructed estimates of bobcat abundance in New Hampshire and compared these
to estimates generated in adjacent states. The objective of this study is to; i.) estimate
home-ranges for bobcats and compare findings to adjacent states, ii.) update the
population estimate given the additional home-range data and the habitat-suitability map
76
generated in Chapter II, and iii.) compare population estimates with those of neighboring
states in the New England area.
METHODS
Home-Range Estimation
Home ranges were generated using a fixed kernel density estimator (Seamen and
Powell 1996) with least squares cross-validation within the Home Range Extension
(Hooge and Eichenlaub 1997) of ArcView 3.3 (Environmental Systems Research
Institute, Redlands, CA, USA). Through simulations, Seaman and Powell (1996) found
that fixed kernel density estimators gave the least biased prediction of home ranges.
Home ranges were modeled at 50% and 95% utilization distributions (UD), and core
areas were defined as 50% UD. All home-range estimations utilized a minimum of 30
locations. Mean home ranges were computed for each sex, and these figures were used to
estimate the population of resident bobcats. Additionally, home ranges were used to
define ‘available’ locations for individual bobcats in RSPF analysis (Chapter II).
Population Estimation
Population estimates for adult resident bobcats and associated reproduction were
made using home-range area requirements and habitat suitability models. First, a
population estimate of resident adult female bobcats was made based on observed homerange sizes, distribution, and the habitat-suitability model generated in Chapter II.
Consistent with past studies, (Bailey 1974, Berg 1981, Lawhead 1984, Anderson 1987,
Donovan et al. 2011), no intrasexual overlap was observed for marked females, and I
77
used this assumption to make the population estimate. I followed recommendations by
Donovan et al. (2011, 2012) that only females be used for population estimates.
Additionally, Anderson and Lovallo (2003) assert that estimating mean intrasexual home
range overlap when not all bobcats within a study area are captured is meaningless.
Finally, Chamberlain and Leopold (2001) recommend not making estimates of spatial
organization unless relatedness among individuals is known. Because of this, the estimate
of the resident female population and observed sex ratio were used to estimate the
number of male bobcats. Following this, an estimation of kittens was made, and
contributed to overall population estimate at two time periods throughout the year.
To estimate the resident female bobcat population, I first resampled the scaleintegrated habitat suitability map (Chapter II) so that each pixel was equivalent to mean
home-range size of marked female bobcats. In the process of resampling, each pixel was
assigned the mean score of the aggregated pixels it contained. Any pixel that had a mean
habitat suitability score ≥0.5 was considered an occupied territory.
To estimate resident male bobcat population a 50:50 sex-ratio was assumed
(Rolley 1985). Tate (New Hampshire Fish and Game furbearer biologist, personal
communication) collected incidentally captured and road-killed bobcats in New
Hampshire from 2008-2013. In that sample 71 female and 84 male bobcats were
collected, not different from an assumed 50:50 sex ratio (X2 (1, 155) = 1.090, p = 0.291).
Using the assumption that males and females are approximately equal in the population,
the total estimated female population was doubled to estimate the potential resident adult
bobcat population.
78
To estimate the number of kittens per year the mean number of placenta scars was
calculated from the same incidental captures and road-kill carcasses; µ= 2.09 (Tate,
personal communication). It was assumed that there was 100% breeding success for
resident females, therefore the mean placental scar was multiplied by the number of
estimated females. This number gave the estimated number of kittens immediately
following birth in May-June.
An additional population estimate was made for late fall using observed survival
rates from past studies. An observed survival rate for adult bobcats from May 1 – October
31 from Knick (1990) of 0.85 was used to estimate adult bobcats still alive in
October/November. This was one of the few studies to estimate survival rate by season
for an unexploited bobcat population, however Chamberlain (1999) made a similar yearround survival estimate of 0.80 for an unexploited population of bobcats in Mississippi.
An observed survival rate from 0 to 0.5 years of age from Rolley (1985) of 0.36 was used
to estimate the number of kittens still alive in October/November. Total population
estimate represents the number of resident adults and kittens within the population, and
does not include potential transients.
RESULTS
Home Range Estimation
Eighteen collared bobcats were used to estimate home-range size and describe
habitat selection; 11 from the southwestern study area and 7 from the southeastern study
area (Table 4-1; for addition details on individual bobcats see Appendix D). Thirteen
79
male bobcats, utilizing 89-1138 locations, were used to determine mean home-range size
of 81.6 km2 (SD = 70.2). One male bobcat had a home range over twice the size of the
next largest male and when his home range was removed the mean size dropped to 64.1
km2 (SD = 31.8). Five female bobcats, utilizing 152 to 909 locations, had a mean home
range of 23.84 km2 (SD = 7.24) (Table 4-2). Female bobcats exhibited no intrasexual
overlap in home ranges. Male bobcats had observed intrasexual home range overlap from
~0 to 95%; however, Lovallo and Anderson (2002) do not recommend estimating mean
intrasexual home range overlap when not all bobcats within a study area are captured.
Therefore, only mean female home-range size was utilized for the habitat arearequirement method, with the estimate for males and kittens based off the estimate of
female bobcats.
Population Estimate
Estimates of resident female bobcat territories resulted in a prediction of 547 (Fig.
4-1, Table 4-2). The total potential resident adult population was estimated by doubling
the number of females, resulting in a total resident population estimate of 1094 (Table 42). Kittens were calculated by multiplying female bobcats by the observed mean number
of placental scars, µ= 2.09 (Tate unpublished date), resulting in an estimate of 1143
(Table 4-2). Adding resident adults and kittens gives a total population estimate of 2237
(Table 4-2). This estimate is valid for the time immediately following bobcat births in
May/June. Population estimate for October/November was made by multiplying the
resident adult bobcat estimate by the 0.85 survival rate (Knick 1990) and the kitten
estimate by the 0.36 survival rate (Rolley 1985). This resulted in 972 resident adults and
80
414 kittens surviving their first six months, and a total population of approximately 1386
bobcats (Table 4-2).
Table 4-1. Sex, age, weight, study area, number of usable locations, home range (95%
utilization distribution) and core area (50% utilization distribution) of bobcats in the
study. Bobcats from the southwest (SW) study area were trapped and collared in 200910, and bobcats in the southeast (SE) were trapped and collared in 2011.
ID
Sex
Study Area
Age
Weight (kg)
Locations
95% UD
50% UD
26
Male
SW
4
13.5
860
72.59
5.26
27
Male
SW
2
8.5
848
126.59
5.72
28
Female
SW
10
12.3
433
29.69
2.47
29
Male
SW
7
16.8
233
54.37
14.03
30
Male
SW
5
14.5
705
103.05
9.79
31
Male
SW
9
12.7
94
61.57
10.45
32
Male
SW
8
14.1
205
56.41
2.33
33
Male
SW
5
11.5
89
59.83
9.98
34
Male
SW
3
16.0
416
80.18
8.59
39
Male
SW
3
11.5
381
28.69
1.85
40
Male
SW
5
12.3
319
292.07
47.8
41
Male
SE
3
12.8
252
16.36
1.37
42
Male
SE
5
14.5
1138
27.27
0.94
43
Male
SE
4
11.5
371
81.95
7.64
44
Female
SE
4
9.1
884
14.05
1.29
45
Female
SE
1
7.8
818
24.74
1.2
46
Female
SE
1
6.0
909
19.28
0.67
47
Female
SE
4
7.7
152
31.46
5.07
81
Fig. 4-1. Habitat suitability map generated from incidental observations and marked
bobcats. Pixels in the map have been re-scaled to the size of the average marked females
home range (23.8 km2) and habitat suitability scores (range 0-1) of the aggregated cells
were averaged (left). Home-ranged size cells were classified as either occupied (≥0.5) or
unoccupied (<0.5) based on average habitat suitability scores. Occupied cells were
summed to obtain an estimate of resident female bobcat territories.
Table 4-2. Population estimates of bobcats in New Hampshire generated using home
range requirements and habitat suitability. Total population estimate is the sum of
resident adults and kittens. A survival rate from May/June to October/November of 0.85
(Knick 1990) was assumed for adults, and a survival rate of 0.36 (Rolley 1985) for
kittens.
Time
Female
Male
Resident Adults
Kittens
Total
May/June
547
547
1094
1143
2237
October/November
486
486
972
414
1386
82
DISCUSSION
Potential Population Estimate
Observations on exclusive home-range size requirements, coupled with the scaleintegrated habitat suitability map enabled me to make predictions about the potential
bobcat population for the state at the present time. These methods utilize a variety of
assumptions and can be difficult to verify. Furthermore, this estimate is of the potential
number of bobcats the state could support given the current habitat configuration, not an
actual census of the population. Without determining if bobcats are present in territories
estimated to be occupied, I cannot say for certain what the actual population is in New
Hampshire. Despite this, the estimates give a straightforward assessment of the
population, allowing decision-makers to consider the implications of future land use and
management decisions on the population.
I used the observed mean home range size of marked female bobcats to re-scale
the habitat suitability map. Each home range sized pixel was then classified as either
suitable or unsuitable, and then summed to get an estimate of 547 female bobcat
territories in the state. Obviously, these potential home ranges have not been sampled
themselves, so total occupancy of these home ranges is not known. However, this could
be validated by using spatially referenced sightings, road kills, incidental captures, or
remote cameras.
I chose to use female bobcats because they are most important to the population
demographically, and zero home range overlap was observed between marked females.
83
Identification of exclusive area requirements is important when using these methods of
population estimation (Boyce and McDonald 1999), and this estimates could not be
reliably attained for male bobcats. Therefore, to get an estimate of male bobcats a 50:50
sex-ratio was assumed and corroborated with road kill and incidental sightings data.
Furthermore, our own trapping record had a sex ratio of 2.6:1 males to females. Despite
this, accurate assessments of sex ratios are difficult to obtain (Anderson and Lovallo
2003). And while sex ratios are usually 1:1 at birth (Anderson 1987, Stys and Leopold
1993) susceptibly to trapping, and thus survival differ between sexes can alter this ratio
(Fuller et al. 1995). Males are believed to be more susceptible to harvest because they
have bigger home ranges and traverse a larger area increasing their probability of being
trapped (Anderson and Lovallo 2003). This may also increase their risk of mortality from
vehicle collisions. Because of this, the data from incidental captures and vehicle
mortalities may not be a true reflection of the sex ratio. However, given this is a relatively
unexploited population the ratio may be skewed towards males. Lembeck and Gould
(1979) observed a sex ratio of 2:1 males to females in the unexploited population of
bobcats they studied in southern California. Furthermore, Zezulak and Schwab (1979)
suggest that in dense populations of bobcats, males may have competitive advantages.
Based on this, I feel that sex ratio of 1:1 is justified, but the estimate of adult bobcats
could be dramatically different if the sex ratio is inaccurate.
To estimate bobcat litter size I used observed placental scars. Bobcat litter size is
estimated a variety of ways, based on; corpora lutea, placental scars, embryos, and live
litters. These estimates often differ (Anderson and Lovallo 2002), are affected by age
(Koehler and Aubry 1994), and prey availability (Knick 1990) which complicates
84
extrapolations beyond resident adults. Furthermore, pregnancy rates can decrease
markedly during times of lower prey availability (Knick 1990). This will be an important
consideration during severe winters in New Hampshire. Nonetheless, the litter rate used,
2.09, was smaller than the average of 2.7 that Anderson (1987) computed across 21
studies.
Estimates of kitten survival over the first half year (S = 0.362) were made from
Rolley (1985). Unfortunately, estimates of survival during this time are difficult to
obtain. Rolley’s estimates come from a harvested population in Oklahoma, which differs
in management and geography from New Hampshire, but it coincides with estimates
from Blackenship and Swank in Texas (S = 0.26; 1979) and Hoppe in Michigan (S =
0.33; 1979). Bobcats experience the lowest survival rate during their first year, and
therefore it was important to estimate this effect on the population. Furthermore, bobcats
at 0.5 years bobcats are about to enter their first winter, the most energetically taxing
season for them. It is also the time of most trapping seasons, so an estimate of population
is useful for this time.
It is important to note that this estimate does not explicitly consider transient
bobcats. Transient bobcats, usually yearlings or subadults, do not have a defined home
range and most often have recently dispersed from their natal home range (Blankenship
et al. 2006). While transients can be either male or female, Blankenship et al. (2006)
suggested that the majority are male. Males may spend more time as transients because
females exhibit greater philopatry, often establishing home ranges in or adjacent to their
natal home range. The population model only considered resident females, but used this
number to predict male bobcats based on sex ratios. So while, transients were not
85
explicitly considered in female population estimate, there may be some present in the
male estimate, which should be considered when attempting to apply the population
estimate.
It should also be noted that more detailed methods of habitat area-requirement
population estimates are available (e.g., Downs et al 2008, Donovan et al. 2012).
Donovan et al. (2012) used a technique, maximum clique analysis, to estimate female
bobcat potential carrying capacity for a study area in Vermont. This method found the
maximum number of non-overlapping, suitable pseudo-home ranges in a defined area
using the program Clique (Niskanen and Ostergard 2003). Potential home ranges are
allowed to vary in size and shape, which is a more realistic representation of populations.
However, the technique is computationally intense, and suffers from many of the same
limitations of any habitat-area requirement method. Namely, our ability to estimate
exclusive territories for individuals, as well as, correctly gauge habitat requirements
based largely on land cover, topographic, and anthropogenic features. Given that the two
methods have similar constraints and assumptions, I chose to go with the more
straightforward approach. In the end, both methods provide a baseline estimate to
measure the effects of future land use changes and management actions on the
population.
Implications of Home Range Size
In the two study areas, male and female bobcats utilized home ranges with a mean
of 81.6 km2 and 23.8 km2, respectively. Removing the largest male home ranges from the
samples (292.1 km2) resulted in a reduction to 64.1 km2 (SD = 31.8). Although
86
differences are apparent, these values are contained within estimates in Vermont (70.9
km2 and 22.9 km2; Donovan et al. 2011), Maine (95.7 km2, 31.2 km2; Litvaitis et al.
1986), and Wisconsin (60.4 km2, 28.5 km2; Lovallo and Anderson 1996). Home range
sizes are believed to be functions of the energetic needs of an animal (McNab 1963,
Harestad and Bunnel 1979). Supporting this, Litvaitis et al. (1986) found that bobcat
home range size in Maine was inversely correlated with snowshoe hare density, the main
prey item in the study area. However, it is clear from the supporting chapter, that habitat
suitability, and thus most likely prey availability, changes across the state.
Relatively small linear distances can have large effects on home-range size if
climatic variables influencing vegetation and prey are markedly different. For example,
Fox (1990) found vastly different home range sizes between the Catskills (36.0 km2 and
31.0 km2, for males and females, respectively) of southern New York and the
Adirondacks in northern New York (325.7 km2 and 86.4 km2, for males and females,
respectively). He attributed this difference partly due to a greater energy stress for
populations in the Adirondacks. Likewise, Knick (1990) saw home ranges in Idaho
increase from 20.4 km2 and 11.6 km2 (males and females, respectively; 1982-1984) to
123.0 km2 and 69.7 km2 (males and females, respectively; 1984-1985) when lagomorph
populations decreased between years. Therefore, more research into what types of prey,
and with what frequency they are being consumed would help facilitate comparisons
across study areas and states.
New England cottontails, one of the bobcats main prey items historically, have
decreased dramatically. Past data on stomach contents (Litvaitis et al. 2006) and current
data from road-killed bobcats (unpublished, Litvaitis) in New Hampshire suggests that
87
they prey on a large variety of animals, mostly made up of small mammals. Anecdotal
evidence from sightings information suggests that turkeys (Meleagris gallopavo) may
also be a food source, although to what importance is unknown (Litvaitis, personal
communication). Additionally, bobcats have been witnessed stalking birds and small
mammals at bird feeders during winter months (Litvaitis, personal communication).
These behaviors could signify bobcat’s adaptation to changing landscapes and prey
availably, and further study would aid in understanding of how increased development
may affect bobcats in the future.
However, these samples and our knowledge of bobcat home-ranges in New
Hampshire are largely focused on the southern region of the state. Home-range size and
prey availability in the north is likely substantially different than in the rest of state. Fox
(1990) observed a sex ratio skewed towards males in northern New York, which he
attributed to their greater ability to hunt white-tailed deer (Odocoileus virginianus)
compared to females. Bobcats in northern New Hampshire may also prey more on deer
and utilize large home ranges, at this time, which should be considered in any
management decisions. However, if snow depths decline and the region becomes more
productive, stark differences home-range size may be reduced.
Comparison to New England States
The current status of bobcats in other New England states compiled by Broman
(2012) and Roberts and Crimmins (2010) was used to make comparisons between states
(Table 4-3). Of the six states, only three (Maine, Massachusetts, and Vermont) currently
have a harvest. In each of these states the population is monitored using an analysis of the
88
harvest. All of the states use multiple methods to monitor abundance and trends over
time. Vehicle collisions were used by every state, and 5 of the 6 states used public
sightings. Improvements of these methods through spatial verification and
documentation, as well as more a systematic approach to their implementation could
improve their validity and application. Only 3 of the 6 states made estimations of the
population, however, Roberts and Crimmins (2010) recommend following trends over
time as an adequate way to manage bobcat populations. Finally, of the states reporting an
overall trend in population, 4 of the 6 noted a stable or increasing population.
Going forward, changes in land use practices, climate, or management could
influence both bobcat home range size and habitat use, thereby altering the potential
carrying capacity. For example, severe winters with deep snow packs may cause losses in
potential prey items and force bobcats to increase home range size, limiting the number
of territories. Additionally, a growing human population will result in decreased habitat
suitability and may cause the overall carrying capacity to decline. Perhaps more useful
than a one-time estimate of population, would be an analysis of the population trend over
time. Habitat analysis could be redone as road densities, land covers, and management
practice change. Additionally, trends in sightings data, whether submitted by the general
public or through the use of hunter surveys could provide a means to assess the changes
in population abundance through time. As development throughout the state increases
habitat suitability may decrease causing the overall carrying capacity to decline.
89
Table 4-3. The current status of bobcat populations in the six New England states. Table
was adapted from Broman (2012) and Roberts and Crimmins (2010). Information was
obtained from state wildlife agencies. Whether there is currently a harvest, monitoring
techniques, estimates of potential abundance, suitable habitat area (km2), and overall
population status are included. Monitoring methods included harvest analysis (HA),
incidental harvest (IH), monitored individuals (MI), public sightings (PS), and vehicle
collisions.
State
Harvest
Monitoring
Abundance
Habitat
Status
Connecticut
Closed
IH, PS, VC
Unknown
11,655
Increasing
New Hampshire
Closed
IH, MI, PS, VC
1,508-2,237
13,126
Increasing
Maine
Open
HA, VC
Unknown
40,000
Stable
Massachusetts
Open
HA, PS ,VC
1,200
12,501
Unknown
Rhode Island
Closed
PS, VC
Unknown
Unknown
Unknown
Vermont
Open
23,310
Increasing
HA, IH, MI, PS, VC 2,500-3,500
By understanding an animal’s spatial and habitat requirements, we can make an
estimate of population size. For bobcats, this addresses one of the greatest needs of
management agencies and can inform decisions about conservation status, harvest
management, and the effects on prey species. Additionally, we can make comparisons
between population status and home-range size across regions to get a proxy for the
habitat and prey available, as well as the overall health of the population.
90
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106
APPENDICES
107
APPENDIX A.
University of New Hampshire Institutional Animal Care and Use Committee letter of
approval for bobcat trapping and collaring.
108
109
APPENDIX B.
University of New Hampshire Institutional Animal Care and Use Committee letter of
approval for using remotely-triggered cameras to monitor bobcat populations.
110
APPENDIX C.
Given the large number of potential explanatory variables available to describe
habitat selection, an initial suite of different approaches were used to model bobcat
habitat selection. The first two models used the National Land Cover Database 2006 (Fry
et al. 2011; layers collapsed to open water, light development, heavy development,
barren, evergreen forests, deciduous forests, mixed forests, shrub/scrub, agriculture, and
wetlands) as the main variable, and also included distance to forest edge (excluding open
water/forest edge), distance to stream, elevation, slope, aspect, and a vector ruggedness
measurement (VRM, Sappington et al. 2007). The two models differed in the variable
used for roads. One model used a distance to roads measure, whereas the other used road
density (with a 1 km window). The third model tested used land forms, instead of land
cover, in an attempt to model bobcat habitat. The landform data was made available from
the Ecological Land Unit dataset (The Nature Conservancy 2008). The fourth model used
‘distance to’ measures to all land cover variables that may influence habitat selection.
Land cover variables were extracted from NLCD 2006 (Fry et al. 2011), and included
distance to: forest edge, shrub/scrub, wetland, stream, and development. Additionally, an
unfragmented land variable was used that delineated any natural, unfragmented land (NH
Wildlife Action Plan 2005).
K
NLCD - rd den
NLCD - rd den, no elev
NLCD - dt to rds
NLCD - dt to rds, no elev
Land Forms
Proximity
17
16
17
17
22
12
AICc Delta AICc AICc Wt. Cum. Wt.
24021.41
24023.36
24041.80
24042.81
24363.14
24428.42
0.00
1.95
20.39
21.40
341.74
407.01
111
0.73
0.27
0.0
0.0
0.0
0.0
0.73
1.0
1.0
1.0
1.0
1.0
LL
-11993.69
-11995.67
-12003.88
-12005.39
-12159.54
-12202.20
2
3
4
5
6
7
8
9
3.5
0.5
1.5
2.5
Spearman Correlation
r^2=0.935, StDev=0.046
10
1
2
3
4
5
6
7
8
9
10
Land Cover (rd denstiy) w/o Elevation M odel
3
4
5
6
7
8
9
2
3
1
2
3
4
5
6
7
Bin Number
Land Form M odel
Proximity M odel
r^2=0.599, StDev=0.156
1
1.5
10
Bin Number
4
5
6
7
8
9
10
0.5 1.0 1.5 2.0
2
Area Adjusted Frequency
0.8 1.0 1.2 1.4 1.6
1
r^2=0.935, StDev=0.048
0.5
1.5
r^2=0.914, StDev=0.028
2.5
Land Cover (dist to road) w/o Elevation M odel
Bin Number
2.5
Bin Number
0.5
2.5
1.5
r^2=0.954, StDev=0.026
1
Land Cover (rd denstiy) M odel
0.5
Land Cover (dist to road) M odel
9
10
8
9
10
r^2=0.952, StDev=0.034
1
Bin Number
8
2
3
4
5
6
7
Bin Number
Fig 1.Results of k-fold cross-validation of the six models tested. Correlation between the
area adjusted frequency of used locations in each bin compared to bin number as
determined by the Spearman rank correlation coefficient, as well as standard deviation
for the five folds tested within each model is displayed.
112
APPENDIX D
Scores for ‘actual’ paths and the mean of ‘random’ paths. One-sided t-tests used to
compare differences between ‘actual’ and ‘random’ paths existed. Bobcats that exhibited
significant differences, and thus successfully predicted connectivity, are in bold.
RSPF
Expert Opinion
ID
Actual
Random
Std.Dev
T Stat
p Val
Actual
Random
Std.Dev
T Stat
p Val
26
4.31
4.23
0.07
11.44
<0.0001
4.11
4.17
0.18
-3.42
0.9995
27
4.35
4.15
0.14
14.50
<0.0001
5.06
4.70
0.29
12.38
<0.0001
28
5.31
5.21
0.08
14.28
<0.0001
4.18
4.29
0.24
-4.62
1.0000
29
4.21
4.18
0.11
2.66
0.0045
4.22
4.31
0.31
-2.79
0.9969
30
4.24
4.08
0.10
16.19
<0.0001
4.46
4.28
0.19
9.67
<0.0001
31
4.18
4.16
0.12
1.44
0.0761
4.19
4.47
0.31
-9.20
1.0000
32
4.11
4.03
0.10
8.04
<0.0001
4.40
4.34
0.17
3.68
0.0002
33
4.16
4.12
0.12
3.82
0.0001
4.81
4.64
0.16
10.63
<0.0001
34
4.35
4.20
0.08
19.26
<0.0001
4.76
4.47
0.27
10.47
<0.0001
39
4.20
4.10
0.07
13.54
<0.0001
4.31
4.36
0.19
-3.04
0.9985
40
4.08
4.13
0.07
-7.41
1.0000
4.28
5.01
0.66
-11.09
1.0000
41
4.68
4.54
0.15
8.81
<0.0001
5.00
4.91
0.24
3.91
0.0001
42
5.18
4.16
0.50
20.50
<0.0001
4.16
3.87
0.54
5.42
<0.0001
43
4.45
4.36
0.07
14.09
<0.0001
5.01
4.71
0.19
16.40
<0.0001
44
5.97
4.84
0.64
17.66
<0.0001
3.86
3.43
0.21
20.27
<0.0001
45
4.33
4.18
0.09
16.78
<0.0001
4.91
4.48
0.28
15.06
<0.0001
46
4.57
4.43
0.07
21.11
<0.0001
5.06
4.71
0.17
21.05
<0.0001
47
4.27
4.13
0.10
13.48
<0.0001
4.55
4.38
0.26
6.35
<0.0001
113
Bobcat
ID
26
27
28
29
30
31
32
33
34
39
40
41
42
43
44
45
46
47
Study Site
Southwest
Southwest
Southwest
Southwest
Southwest
Southwest
Southwest
Southwest
Southwest
Southwest
Southwest
Southeast
Southeast
Southeast
Southeast
Southeast
Southeast
Southeast
Capture
Location
Gilsum
Westmoreland
Hancock
Antrim
Nelson
Harrisville
Harrisville
Alstead
Jaffrey
Alstead
Walpole
Gilamanton
Gilford
Gilamanton
Gilford
Barrington
Barrington
Milton
Sex
M
M
F
M
M
M
M
M
M
M
M
M
M
M
F
F
F
F
Age at
Capture
4
2
10
7
5
9
8
5
3
3
5
3
5
4
4
1
1
4
Weight
At
Date of
Capture
Date of
Collar
(kg)
Capture
Recovery
13.5
11/22/2009 On bobcat
8.5
1/13/2010
On bobcat
12.3
1/16/2010
2/17/2011
16.8
1/19/2010
9/26/2010
14.5
2/3/2010
???
12.7
2/13/2010
11/4/2010
14.1
2/13/2010
9/9/2010
11.5
2/22/2010
9/16/2010
16.0
3/1/2010
1/15/2011
11.5
3/8/2010
12/25/2010
12.3
3/12/2010
11/4/2010
12.8
1/7/2011
On bobcat
14.5
1/11/2011
On bobcat
11.5
1/23/2011
On bobcat
9.1
1/25/2011
On bobcat
7.8
1/28/2011 12/26/2011
6.0
2/11/2011
3/7/2012
7.7
3/5/2011
8/4/2011
Usable
Locations
860
848
433
233
705
94
205
89
416
381
319
252
1138
371
884
818
845
152
Last Known Fate
Alive - Not Recaptured
Recaptured; Collar Removed
Dead - Incidental Capture
Alive - Collar dropped off
Dead - Vehicle Collision
APPENDIX E.
Status of bobcats used for analysis in southeastern and southwestern New Hampshire.
114

understanding the relationships between habitat suitability

Transcription

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