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ABSTRACT
WINIARSKI, JASON MATTHEW. Breeding Ecology and Space-use of Bachman’s
Sparrow (Peucaea aestivalis) at the Northern Periphery of its Range. (Under the direction of
Christopher E. Moorman).
Longleaf pine (Pinus palustris) forests provide habitat for unique assemblages of
flora and fauna, and contribute significantly to biodiversity in the southeastern United States.
Historically, the longleaf pine ecosystem covered ~30 million ha from Virginia south through
Texas, but less than 5% remains as scattered and degraded remnants due to extensive landuse change and fire suppression. Strategies for restoring and conserving this ecosystem
include a variety of habitat management approaches that occur at the site-level, but less
consideration has been given to approaches at the landscape-scale. Knowledge of certain
species’ natural history and demographics is essential for directing such multi-scale
strategies. Bachman’s sparrow (Peucaea aestivalis) is a declining ground-nesting bird that is
strongly associated with fire-maintained longleaf pine forests, and has been considered to be
a surrogate species for this system because of its reliance on frequent fire regimes, open
canopy, and diverse groundcover characterizing healthy longleaf pine communities. Despite
the species’ importance, relatively little is known about the demographic mechanisms driving
Bachman’s sparrow declines, or its breeding season ecology at the species’ northern range
limits where some of the greatest declines are occurring. To address this knowledge gap and
inform management in this region, we investigated nest-site selection, space-use, and
reproductive success of Bachman’s sparrows across a fragmentation gradient in southeastern
North Carolina. Despite clear differences in nest-site selection by physiographic region
(Coastal Plain and Sandhills), we documented no significant predictors of nest survival in the
Sandhills, but nest survival declined later in the nesting season in the Coastal Plain. Daily
nest survival rates (Coastal Plain = 0.953, Sandhills = 0.938) were similar between
physiographic regions, and were consistent with published studies from the species’ core
range. Within home ranges, Bachman’s Sparrows used locations with a greater density of
woody vegetation and forbs, and intermediate levels of grass density. Home ranges varied
from 2 ha to 18 ha, but we documented no significant predictors of variation in home range
size. Bachman’s sparrow home ranges at our study sites were larger than previously reported
estimates for the southern portion of the species’ range, likely because of methodological
differences among studies. Finally, we examined whether local- or landscape-scale factors
influenced two components of reproduction: pairing success and fledging offspring. Percent
habitat within 3 km of a male’s territory positively influenced pairing success, with males in
highly fragmented landscapes remaining unpaired throughout the breeding season.
Conversely, habitat amount did not affect the probability of fledging offspring for paired
males, indicating that reduced connectivity may be hindering dispersal. Overall, this study
suggests that creating and maintaining regionally-specific habitat characteristics via
prescribed fire should increase the amount of nesting cover and microhabitat features, but
may not influence nest survival rates or home range sizes. More importantly, increasing
landscape-level habitat amount is the most effective means of promoting breeding
opportunities for Bachman’s sparrow and potentially other dispersal-limited species in the
longleaf pine ecosystem.
© Copyright 2016 Jason M. Winiarski
All Rights Reserved
Breeding Ecology and Space-use of Bachman’ Sparrow (Peucaea aestivalis) at the Northern
Periphery of its Range
by
Jason Matthew Winiarski
A thesis submitted to the Graduate Faculty of
North Carolina State University
in partial fulfillment of the
requirements for the degree of
Master of Science
Fisheries, Wildlife, and Conservation Biology
Raleigh, North Carolina
2016
APPROVED BY:
_______________________________
Dr. Christopher Moorman
Committee Chair
_______________________________
Dr. Christopher DePerno
_______________________________
Dr. George Hess
DEDICATION
This work is dedicated to my parents for their love and support.
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BIOGRAPHY
Jay Winiarski grew up in North Kingstown, Rhode Island. As a child he developed a passion
for wildlife and the outdoors while feeding and identifying backyard birds, and during many
days spent fishing with his father and brother in Narragansett Bay. He pursued a BS in
Wildlife & Conservation Biology at the University of Rhode Island, where he graduated in
2008. Upon graduation, he worked for several years as a seasonal research technician on
ecological field projects in Rhode Island, New York, Jamaica, Costa Rica, and Oregon. In
January 2014, he entered the Fisheries, Wildlife, and Conservation Biology Program at North
Carolina State University.
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ACKNOWLEDGMENTS
I thank my advisor, Dr. Chris Moorman, for this opportunity, and for all of his
support, sound advice, and patience through all aspects of my graduate research and
education. Also, I appreciate the feedback, insight, and support provided by my committee
members, Drs. Chris DePerno and George Hess. Several Bachman’s sparrow researchers
including John Carpenter, Alex Fish, Paul Taillie, Jeff Marcus, and Scott Anderson provided
helpful knowledge, feedback, and guidance on this project. Foremost, I am extremely
grateful to John Carpenter, who offered invaluable advice and field support during this
project. Alex Fish also provided much expertise from his concurrent research, helpful
conversation, and collected much of the data and answered many questions that made the
first chapter of this thesis possible.
Many thanks to the North Carolina Wildlife Resources Commission and North
Carolina State University for providing research funding for this project. The Department of
Defense and the Endangered Species Branch at Ft. Bragg provided funding for some of the
nest data collected and used in the first chapter of my thesis. I am grateful to all my research
assistants that made this work possible: April Bartelt, David Bernasconi, Amber Bledsoe,
Taylor Root, and Mike Wallgren. Susie Masecar, Mark McAlister, and Mikayla Seamster
also volunteered for several long days to help complete vegetation surveys. The staff of
Holly Shelter Game Land provided use of their facilities, which were invaluable for planning
field logistics and entering data, and I thank them for their flexibility and cooperation.
Several land owners and land managers made this research possible by graciously allowing
access to their properties and providing fire history data: Angie Carl (The Nature
Conservancy), Bobby Harrelson (Compass Pointe), Kiley Hinson (Molpus Timberlands),
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Ray Hudson (Sleepy Creek Farms), Hervey McIver (The Nature Conservancy), and Leslie
Starke (NC Plant Conservation Program). Jeff Humphries and Henry Wood also let us
borrow some of the radio-telemetry equipment used to track sparrows.
I thank Cindy Burke who worked hard to help with purchases, housing, and paying
the bills. Also, I thank Sarah Slover for guiding me through all of the graduate student
procedures, and quickly responding to and resolving the many questions I had for her.
Last but not least, thank you to my family (Jayce, Lenny, Kris, Sarah, and Fletcher)
for their love and support on the path toward completing this degree and during my previous
endeavors. I am deeply indebted to my partner, April Bartelt, for embarking on this journey
with me to North Carolina, assisting with many aspects of this project, and her love and
encouragement.
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TABLE OF CONTENTS
LIST OF TABLES ............................................................................................................... viii
LIST OF FIGURES ................................................................................................................ x
CHAPTER 1: NEST-SITE SELECTION AND NEST SURVIVAL OF BACHMAN’S
SPARROWS IN CONTRASTING LONGLEAF PINE ECOSYSTEMS ......................... 1
ABSTRACT ....................................................................................................................... 1
INTRODUCTION ............................................................................................................ 3
METHODS ........................................................................................................................ 5
Study area ..................................................................................................................... 5
Nest-searching and monitoring..................................................................................... 6
Nest-site vegetation measurements .............................................................................. 7
Landscape .................................................................................................................... 8
Statistical analyses ....................................................................................................... 8
RESULTS ........................................................................................................................ 11
Nest-site selection ...................................................................................................... 11
Nest survival ............................................................................................................... 11
DISCUSSION .................................................................................................................. 12
MANAGEMENT AND CONSERVATION IMPLICATIONS .................................. 16
LITERATURE CITED .................................................................................................. 18
CHAPTER 2: SPACE-USE AND HABITAT SELECTION BY BACHMAN’S
SPARROW AT THE NORTHERN RANGE PERIPHERY ............................................ 32
ABSTRACT ..................................................................................................................... 32
INTRODUCTION .......................................................................................................... 33
METHODS ...................................................................................................................... 35
Study area ................................................................................................................... 35
Field data collection ................................................................................................... 36
Statistical analyses ..................................................................................................... 38
RESULTS ........................................................................................................................ 41
Home range characteristics ........................................................................................ 41
Microhabitat selection ................................................................................................ 41
Home range variation ................................................................................................ 42
DISCUSSION .................................................................................................................. 42
CHAPTER 3: REPRODUCTIVE CONSEQUENCES OF HABITAT
FRAGMENTATION IN A DECLINING RESIDENT LONGLEAF PINE BIRD ........ 60
ABSTRACT ..................................................................................................................... 60
INTRODUCTION .......................................................................................................... 62
METHODS ...................................................................................................................... 64
Study area ................................................................................................................... 64
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Field data collection ................................................................................................... 65
Statistical analyses ..................................................................................................... 69
RESULTS ........................................................................................................................ 73
Territory and landscape characteristics ..................................................................... 73
Reproductive success .................................................................................................. 73
DISCUSSION .................................................................................................................. 74
APPENDICES ....................................................................................................................... 91
APPENDIX A .................................................................................................................. 92
APPENDIX B .................................................................................................................. 93
APPENDIX C .................................................................................................................. 94
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LIST OF TABLES
Chapter 1: Nest-site Selection and Nest Survival of Bachman’s sparrows in Contrasting
Longleaf Pine Ecosystems
Table 1. Description of variables considered for examining nest-site selection and nest
survival by Bachman’s sparrows in North Carolina, USA, 2014–2015. ................................ 23
Table 2. Summary statistics (mean ± SD) for nest and reference plot vegetation variables, and
Mann-Whitney U statistics comparing nest-site vegetation between the Coastal Plain and
Sandhills physiographic regions in North Carolina, USA, 2014-2015. ................................. 24
Table 3. Mixed-effects logistic regression models of nest-site selection by Bachman’s
sparrows in the Coastal Plain and Sandhills physiographic regions of North Carolina, USA,
2014-2015. Paired plot ID was used as a random effect in all models. Models were compared
using Akaike’s Information Criterion corrected for small sample size (AICc). ..................... 25
Table 4. Parameter estimates for the top nest-site selection models for the Coastal Plain and
Sandhills physiographic regions of North Carolina, USA, 2014-2015. ................................. 26
Table 5. Bachman’s sparrow nest fates by physiographic region and study site (n = total nests
found, A = abandoned, S = successful, F = failed, U = unknown fate, P = parasitized, B =
burned by prescribed fire or wildfire, and T = total nests used for data summaries and
analyses). ................................................................................................................................ 27
Table 6. Bachman’s sparrow nest survival models for the Coastal Plain and Sandhills
physiographic regions of North Carolina, USA, 2014-2015. Models were compared using
Akaike’s Information Criterion corrected for small sample size (AICc). ............................... 28
Chapter 2: Space-use and Habitat Selection by Bachman’s Sparrow at the Northern
Range Periphery
Table 1. Summary statistics (mean ± SD) for used and reference plot vegetation variables for
37 radio-tracked Bachman’s Sparrows in the Coastal Plain physiographic region in North
Carolina, USA, 2014-2015. .................................................................................................... 53
Table 2. Top 10 mixed-effects logistic regression models of microhabitat selection by
Bachman’s sparrows in the Coastal Plain region of North Carolina, USA, 2014-2015. BirdID
was used as a random effect in all models. Models were compared using Akaike’s
Information Criterion corrected for small sample size (AICc). .............................................. 54
Table 3. Model-averaged parameter estimates for the top microhabitat selection models
(∆AICc ≤ 2) for 37 radio-tracked male Bachman’s Sparrows in the Coastal Plain region of
North Carolina, USA, 2014-2015. Shaded cells indicate parameter estimates that do not
overlap zero. ........................................................................................................................... 55
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Table 4. Linear models examining home range variation for Bachman’s sparrows in the
Coastal Plain region of North Carolina, USA, 2014-2015. Model sets tested habitat and nonhabitat predictors. Models were compared using Akaike’s Information Criterion corrected for
small sample size (AICc). ....................................................................................................... 56
Table 5. Summary of published studies of Bachman’s Sparrow home-range size in
comparison to findings presented here. .................................................................................. 57
Chapter 3: Reproductive Consequences of Habitat Fragmentation in a Declining
Resident Longleaf Pine Bird
Table 1. Variables used in candidate models testing the effects of local- and landscape-scale
factors on reproductive success of male Bachman’s sparrows in southeastern North Carolina,
2014-2015. .............................................................................................................................. 84
Table 2. Summary of Bachman’s sparrow territories monitored in the Coastal Plain of North
Carolina, USA, 2014-2015. Percent of males that fledged young included paired males only.
Note that 8 banded males were monitored in both years. ....................................................... 85
Table 3. Top (∆AICc ≤ 2) mixed-effects logistic regression models of pairing and fledging
success for Bachman’s sparrows in the Coastal Plain region of North Carolina, USA, 20142015. Site was used as a random effect in all models. Models were compared using Akaike’s
Information Criterion corrected for small sample size (AICc). Model parameters: Habitat
(percent habitat within 3 km of a territory centroid), TSF (time since fire), PC2 (habitat
principal component 2), and Wing (wing length). .................................................................. 86
Table 4. Parameter estimates and 95% confidence intervals for model-averaged variables
(∆AICc ≤ 2) used to model pairing and fledging success for 96 male Bachman’s Sparrows in
the Coastal Plain region of North Carolina, USA, 2014-2015. Model parameters: Hab
principal component 1), PC2 (habitat principal component 2), and Wing (wing length). ..... 87
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LIST OF FIGURES
Chapter 1: Nest-site Selection and Nest Survival of Bachman’s sparrows in Contrasting
Longleaf Pine Ecosystems
Figure 1. Locations of study sites and physiographic regions where nest-site selection and
nest survival data were collected during 2014-2015. Physiographic region designation
follows level III classifications defined by Bailey (1995). The Sandhills is a distinct level IV
subregion of the Southeastern Plains. Study sites are indicated with four-letter abbreviation;
from west to east, Fort Bragg Military Installation (FTBR; n = 85 nests), Green Swamp
Savanna Preserve (GSPR; n = 3 nests), Compass Pointe (COPO; n = 1 nest), Holly Shelter
Game Land (HSGL; n = 42 nests), and Shaken Creek Savanna Preserve (SCPR; n = 1 nest).
................................................................................................................................................. 29
Figure 2. Predicted probability of selection of a nesting site for Bachman’s sparrows by
physiographic region in North Carolina, USA, in 2014–2015. ‘Hits’ refers to the total number
of vegetation contacts on the entire pole averaged across a plot. Shaded regions indicate 95%
confidence intervals. .............................................................................................................. 30
Figure 3. Predicted probability of Bachman’s sparrow daily nest survival rate in relation to
Julian date for the Coastal Plain of North Carolina, USA, in 2014–2015. Shaded region
indicates 95% confidence interval. ........................................................................................ 31
Chapter 2: Space-use and Habitat Selection by Bachman’s Sparrows at the Northern
Range Periphery
Figure 1. Locations of study sites and home ranges of 37 Bachman’s Sparrows that were
radio-tracked in North Carolina, USA, 2014-2015. ............................................................. 58
Figure 2. Predicted probability of microhabitat use by Bachman’s sparrows in the Coastal
Plain physiographic region, North Carolina, USA, in 2014–2015, for woody vertical density,
grass vertical density, and forb groundcover density. Shaded regions indicate 95%
confidence intervals. ............................................................................................................ 59
Chapter 3: Reproductive Consequences of Habitat Loss and Fragmentation in a
Declining Resident Songbird
Figure 1. Study sites in southeastern North Carolina where reproductive data were collected
for 96 Bachman’s sparrow territories, 2014-2015. Study sites are indicated with four-letter
abbreviation: Boiling Spring Lakes Preserve (BSLP), Compass Pointe (COPO), Green
Swamp Preserve (GSPR), Holly Shelter Game Land (HSGL), Molpus Timberlands (MOLP),
Pinch Gut Game Land (PGGL), Shaken Creek Preserve (SCPR), and Sleepy Creek Farms
(SCFA). .................................................................................................................................. 88
x
Figure 2. Mean (± SD) habitat amount (%) within 3 km of male territory centroids for 8 sites
in the Coastal Plain region of North Carolina, USA, 2014-2015. Sample size of males at each
site is listed below the site code. ............................................................................................ 89
Figure 3. Predicted probability of pairing in relation to percent habitat within 3 km. Shaded
regions indicate 95% confidence intervals. ........................................................................... 90
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CHAPTER 1
NEST-SITE SELECTION AND NEST SURVIVAL OF BACHMAN’S SPARROWS IN
CONTRASTING LONGLEAF PINE ECOSYSTEMS
ABSTRACT
Longleaf pine (Pinus palustris) communities of the southeastern United States have
experienced high rates of habitat loss and fragmentation, coinciding with dramatic population
declines for a variety of taxa that inhabit the system. Bachman’s sparrow (Peucaea
aestivalis) is closely associated with the longleaf pine ecosystem and is listed as a species of
conservation concern across its entire range. Understanding aspects of Bachman’s sparrow
breeding biology is crucial to mitigating population declines and directing restoration and
management of remnant longleaf pine forest, but factors such as nest-site selection and nest
survival have received little attention due to difficulty in locating the species’ nests. To
address this knowledge gap, we located 132 Bachman’s sparrow nests in the Coastal Plain
and Sandhills physiographic regions of North Carolina during 2014-2015, and modeled nestsite selection and nest survival as a function of vegetation characteristics, temporal factors,
and landscape-level habitat amount. There were distinct differences in nest-site selection
between regions, with Bachman’s sparrows in the Coastal Plain region selecting greater
woody vegetation density and lower grass density at nest sites than at non-nest locations.
Conversely, sparrows selected nest sites with intermediate grass density and relatively high
tree basal area in the Sandhills. Despite clear differences in habitat selection, we detected no
predictors of nest survival in the Sandhills, but nest survival declined later in the nesting
season in the Coastal Plain. Daily survival rates were similar between regions, and were
consistent with published studies from the species’ core range. Creating and maintaining
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regionally-specific vegetation characteristics should increase the amount of nesting cover,
but may not increase nest survival rates for Bachman’s sparrows.
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INTRODUCTION
Although knowledge of habitat selection is important for avian conservation, perhaps
more critical is understanding how habitat factors drive demographic processes such as nest
survival. Nest survival is an important component of avian fitness (Clark and Martin 2007),
and predation is the primary cause of nest failure in birds (Martin 1993). Nest predation often
results in complete reproductive failure (Ricklefs 1969, Martin 1993), and nest success
directly impacts a bird’s fitness. Therefore, it generally is assumed that nest-site selection is
adaptive, such that selected habitat features should minimize predation risk and maximize
reproductive output and fitness (Martin 1998). Theoretically, birds that nest in locations that
increase nest survival should pass their genotypes on to subsequent generations, and their
offspring would be adapted to select more productive habitat features (Jaenike and Holt
1991).
Despite the prevailing theory that habitat selection should be adaptive, empirical
studies often demonstrate a lack of congruence between nest-site selection and nest survival
(Chalfoun and Schmidt 2012). Although nest-site selection can lead to improved nest
survival through concealment (Martin 1998, Moorman et al. 2002), it may ultimately be
overwhelmed by factors occurring at multiple spatial and temporal scales. For example,
landscape context (Thompson et al. 2002), edge effects (Chalfoun et al. 2002), nest initiation
date or year (Grant et al. 2005, Bulluck and Buehler 2008), nest age (Dinsmore et al. 2002,
Grant et al. 2005), and weather patterns (Rotenberry and Wiens 1989, Conway and Martin
2000, Morrison and Bolger 2002) can be more important drivers of nest survival than site
selection. Also, anthropogenic habitat change may lead to mismatches between previously
adaptive nest-site selection and nest survival (i.e., ecological traps; Misenhelter and
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Rotenberry 2000, Robertson and Hutto 2007). Additionally, predator communities could be
so diverse that birds may not be able to select for nest sites that conceal their nests from
varied predator search strategies (Filliater et al. 1994, Davis 2005). Although nest-site
selection and nest survival rarely are straightforward, an improved understanding of the
factors that influence nest survival is critical for species of conservation concern.
Bachman's sparrow (Peucaea aestivalis) is a species of conservation concern
primarily inhabiting longleaf pine (Pinus palustris) forests of the southeastern US.
Bachman’s sparrows nest on the ground in areas with a diverse understory of low shrubs,
grasses, and forbs maintained by frequent fires (Dunning and Watts 1990, Plentovich et al.
1998). Populations of Bachman’s sparrows are declining throughout the species’ range due to
fire suppression and habitat loss in an ecosystem with ~3% of its original extent remaining
(Noss et al. 1995, Dunning 2006). Breeding Bird Survey data indicate that populations have
declined by ~ 3% per year survey-wide since 1966, with greater declines reported at the
species’ contracting range margins (Sauer et al. 2014). These population declines are leading
to extirpation of the species from areas that have supported Bachman’s sparrows during the
past ~125 years (Dunning 2006). Consequently, Bachman’s sparrow is a state-listed species
of conservation concern in every state it inhabits (Cox and Widener 2008).
Understanding aspects of Bachman’s sparrow reproductive success is crucial to
mitigating population declines. Yet, published data on the breeding biology of Bachman’s
sparrows is limited due to the difficulty in finding the species’ cryptic nests (Dunning 2006).
Previous studies have estimated nest survival (Haggerty 1988, Stober and Krementz 2000,
Perkins et al. 2003, Tucker et al. 2006, Jones et al. 2013) and evaluated nest-site selection
(Haggerty 1988, Jones et al. 2013). Of these studies, only one has sought to quantitatively
4
link Bachman’s sparrow nest-site selection to nest survival (Jones et al. 2013) and none have
included the effects of space or time in nest survival models. Additionally, there is a high
degree of geographic variation in longleaf vegetation depending on local climate, soils, and
topography (Peet 2006). Given the lack of nesting information and variability of longleaf
pine communities, research regarding Bachman’s sparrow responses to local environmental
conditions is needed to guide conservation efforts.
We modeled Bachman’s sparrow nest-site selection and survival within contrasting
longleaf pine ecosystems of the Middle Atlantic Coastal Plain and Sandhills physiographic
regions of North Carolina, USA. Specifically, the objectives of our research were to: 1)
identify important vegetation features selected for nest-sites, and 2) determine whether there
was a relationship between nest survival and temporal, local-, or landscape-scale covariates
within each region. Using a unique dataset with the largest sample size of Bachman’s
sparrow nests published to date, we provide one of the first comprehensive reproductive
assessments for the species.
METHODS
Study Area
In 2014 and 2015, we located and monitored nests at five sites within the Sandhills
and the Middle Atlantic Coastal Plain (hereafter, Coastal Plain) physiographic regions in
North Carolina, USA (Figure 1). The Sandhills physiographic region is located in central
North Carolina and is characterized by rolling, hilly terrain dissected by abundant blackwater
streams (Sorrie et al. 2006). We focused our work in the Sandhills at a single study site, Fort
Bragg Military Installation (hereafter, Ft. Bragg), which occupies 73,469 ha in portions of
Cumberland, Harnett, Hoke, and Moore counties. Ft. Bragg and other adjacent areas in the
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Sandhills comprise the largest contiguous block of longleaf pine-wiregrass (Aristida stricta)
ecosystem remaining in North Carolina. Wetlands (i.e., streamhead pocosins and sandhill
seeps) are interspersed within the xeric longleaf pine uplands, and are dominated by
switchcane (Arundinaria gigantea), ferns, or dense shrubs, depending on fire intensity
(Sorrie et al. 2006). Ft. Bragg is divided into >1000 burn units, which are managed with
prescribed fire on a 3-year return interval, with burns usually occurring between February
and August. In contrast, the Coastal Plain consists of flat terrain with little topographic
relief. Four study sites within the region were primarily composed of mesic longleaf pine
forests bordered by pocosin wetlands. Wet pine savannas in this region were characterized by
seasonally-saturated soils, and the understory was more or less dominated by grasses, forbs,
low shrubs, and insectivorous plants depending on soil moisture and fire frequency. Most
prescribed fires in this region were implemented with a less frequent return interval (~3-5
years) than fire prescriptions at Ft. Bragg. Study sites in the Coastal Plain were imbedded in a
matrix of urban and rural residential development and agricultural lands, and were managed
by The Nature Conservancy, the North Carolina Wildlife Resources Commission, and one
private landowner.
Nest Searching and Monitoring
Nest searching and monitoring efforts were conducted from 1 April - 10 August, 2014
- 2015. Nests were located by: 1) observing adults carrying food and nest material, or
exhibiting behaviors elicited when predators were near nests (i.e. flushing from the nest and
performing distraction displays) during weekly reproductive index surveys and territorymapping of color-banded male Bachman’s sparrows (Vickery et al. 1992); and 2) radiotagging and tracking male (Coastal Plain) and female (Sandhills) sparrows to nests for
6
concurrent studies. All Bachman’s sparrows (both sexes) that we captured were banded with
a USGS band and a unique combination of colored leg bands. For a subset of birds, we
attached a 0.64-g (~3.5% of body mass) radio-transmitter (Blackburn Transmitters,
Nacogdoches, Texas, USA) using a figure-eight leg loop harness (Rappole and Tipton 1991,
Streby et al. 2015). We used hand-held antennas and radio receivers to track males and
females to nests. Following discovery by either nest-searching method, nests were flagged
from 5-15 m away and monitored every 1– 4 days using standard nest-monitoring methods
that minimize disturbance to nests (Martin and Geupel 1993). During each nest visit, we
recorded the status of nest contents (i.e., number of eggs, number of nestlings, and estimated
age of nestlings) until nests fledged or failed. We considered a nest successful if at least one
Bachman’s sparrow young fledged, and confirmed fledging by observing parental behavior
(i.e., alarm calls and feeding) around the nest during checks and reproductive surveys.
Because of differences in nest-searching methods, nests in the Coastal Plain typically
were located by observing adults attending nests, which resulted in finding nests mostly
during the nestling stage (73% of nests). Conversely, nests in the Sandhills were located
mainly by following radio-tagged females, and 59% of nests found were in the nest-building
or incubation stage.
Nest-Site Vegetation Measurements
We measured vegetation structure at every meter along two perpendicular 10-m
transects centered at the nest location. Vegetation categories included grass, woody vineshrub (hereafter woody vegetation), and forb-fern following Taillie et al. (2015). We
quantified vegetation structure using indices of density by recording the number of ‘hits’
(i.e., contacts) by each vegetation category on 0.1-m sections of a 1.5-m vertical pole.
7
Vertical density of the vegetation was measured the number of hits on the entire pole, while
groundcover density was calculated using the number of hits on the first 0.1-m section of the
pole (Wiens and Rotenberry 1981). Maximum height of understory vegetation was recorded
as the tallest 0.1-m section of the pole with a vegetation contact (Wiens and Rotenberry
1981, Moorman and Guynn 2001, Taillie et al. 2015). Canopy closure was estimated by
recording the presence or absence of live canopy using an ocular sighting tube at every
meter. Lastly, we used a 10-factor prism from the plot center to determine the total basal area
of pines surrounding the nest.
We repeated these same measurements at a reference plot (i.e., a non-nest location)
50 m away from the nest in a random direction. We chose this distance to eliminate overlap
between nest and reference points, yet constrain reference locations within a bird’s home
range (7.92 ± 4.05 ha, mean ± SD, J. Winiarski, unpublished data). Vegetation sampling was
completed within 14.6 ± 11.4 days (range = 0-69 days) of a nest fledging or failing to
account for short-term changes in vegetation structure.
Landscape
A binary Bachman’s sparrow species distribution model (Pickens et al., in prep.) was
used to quantify landscape-level habitat amount. We used the spatialEco package (Evans
2015) in R to derive the amount of habitat within a 1-km buffer around each nest. We chose
this metric because it offered a simple yet comprehensive interpretation of different
landscape attributes (Fahrig 2013) and has been shown to be an important predictor of
Bachman’s sparrow occupancy (Taillie et al. 2015).
Statistical analyses
8
Based on previous studies as well as our own field experience, we developed a list of
variables that might influence selection at the nest-site scale (Table 1). We used nonparametric Mann-Whitney U tests to compare nest-site vegetation variables between the
Sandhills and the Coastal Plain. For all descriptive statistics of vegetation variables we
present means ± SD, and we considered probability tests significant at P ≤ 0.05.
To assess the importance of individual vegetation variables on nest-site selection, we
used an information-theoretic approach (Burnham and Anderson 2002) in R (R Core
Development Team 2015). We constructed mixed-effects logistic regression models in the
package lme4 (Bates et al. 2015) to determine variables that differentiated nest sites from
reference locations. Because of strong regional variation in vegetation structure, we
developed distinct model sets for each physiographic region. Prior to analysis, we tested for
correlations (r > |0.60|) between all pairwise combinations of variables. To determine the best
model for each region, we used a manual forward-selection approach (Burnham and
Anderson 2002). We began by comparing the null model with univariate models, and
retained variables if they had a lower AICc value compared with the null model. We
continued to build more complex models by adding covariates that decreased the AICc value.
In addition to a single linear effect of grass, we included a quadratic effect of vertical grass
density because dense grass can restrict movement on the ground in the absence of fire
(Brooks and Stouffer 2010, Taillie et al. 2015). For all models, we included ‘plot pair ID’ as
a random effect to account for non-independence between each nest and paired reference
plot.
Nest observation data were formatted for logistic exposure analysis (Shaffer 2004),
where a nest’s exposure was calculated as the number of days between nest visits, and its
9
survival during the observation period was coded as a binary variable (0 = failed, 1 =
survived). The logistic exposure model is a generalized linear model that specifies a binomial
error distribution and a logit-link function that accommodates variable exposure length for
each nest, thus eliminating potential bias due to different exposure periods (Shaffer 2004).
We applied the same manual forward-selection approach to model daily survival rate in
relation to 13 variables (Table 1) for each region. We included a year effect because annual
variation in survival may occur due to weather events, cyclical increases in predator or prey
abundance, and resource availability (Rotenberry and Wiens 1989). Linear and quadratic
models of Julian date on nest survival were considered to assess temporal effects potentially
correlated with factors such as weather, food availability, and predators (Grant et al. 2005).
In addition, we modeled daily survival rate in relation to nest stage (incubation or nestling)
because predation risk may vary between stages. We included vegetation characteristics
selected by Bachman’s sparrows at nest sites because we expected that they were adaptive
and increased nest survival. We tested the effect of time since fire because it can alter
predator communities (Jones et al. 2004) or food and cover availability, and has been shown
to limit reproductive performance of Bachman’s sparrows when time since fire exceeds 3
years (Tucker et al. 2006). Also, we included an effect of habitat amount to determine the
influence of landscape on nest survival. Daily nest survival rates were used to derive
estimates of nesting success for each region by exponentiating the respective daily survival
rate to the 22-day nesting period (incubation and nestling stages) reported by Haggerty
(1988) and Tucker et al. (2006).
We determined the top models of nest-site selection and nest survival for each region
by selecting models that were < 2 ΔAICc using the MuMIn package (Barton 2016). For each
10
of these model sets, a parameter was considered as strongly affecting the response variable if
the 95% CI did not include 0, and was considered to have no relationship if the 95% CI
overlapped 0.
RESULTS
Nest-site selection
We located 132 Bachman’s sparrow nests between 2014 and 2015. We used 130 in
the nest-site selection analysis because two nests were destroyed by prescribed fire and could
not be measured for vegetation characteristics. Several nest-site selection variables differed
between physiographic regions (Table 2). Maximum height of forbs (U = 2506.0, P ≤ 0.005)
was greater in the Coastal Plain, and woody vegetation measurements including vertical
density (U = 3429.0, P ≤ 0.005), groundcover density (U = 3802.0, P ≤ 0.005), and max
height (U = 842.0, P ≤ 0.005) were greater at nest-sites in the Coastal Plain than in the
Sandhills. Top mixed-effects logistic regression models differed for each region (Table 3).
Nest sites had lower grass vertical density and greater vertical density of woody vegetation
than reference locations in the Coastal Plain (Table 4; Figure 2). In the Sandhills, sparrows
selected nest-sites with intermediate vertical grass densities and greater tree basal area
compared with reference locations (Table 4; Figure 2).
Nest survival
We included 114 active nests in the nest survival analysis. We excluded 2 nests
destroyed by prescribed fire, 4 nests discovered the same day the nest fledged, 3 nests with
unknown fates, and 7 nests considered abandoned by adults (Table 5). The best-fit model for
nest survival in the Coastal Plain included a negative correlation between increasing Julian
date and daily survival rate (DSR; β = -0.02; 95% CI: -0.048, -0.004; Figure 3). The next11
best model was competitive (ΔAICc ≤ 2; Table 6), and differed from the top model only by
the addition of a quadratic term for Julian date. However, 95% confidence intervals around
the parameter estimates for this model overlapped zero. For the Sandhills region, there were
no predictors significantly affecting daily survival rate (the 95% CI of the parameter
estimates of all the predictor variables included 0), and the null model ranked among
competing models (ΔAICc = 1.41, Table 6). Daily survival rate was 0.953 (95% CI: 0.919 0.973) from the best-fit Coastal Plain model and 0.938 (95% CI: 0.916 - 0.955) from the null
Sandhills model. Nest survival derived from these models was 35% (95% CI: 16 – 55%) for
the Coastal Plain, and 24% (95% CI: 15 – 36%) for the Sandhills. Nest survival did not
significantly differ between study regions.
DISCUSSION
Our results emphasize the importance of examining nest-site selection and survival in
different habitat types and geographic regions across the species’ range. Bachman’s sparrows
selected distinct nest sites with specific vegetation characteristics, and these relationships
differed among study regions. Our results for the Coastal Plain were consistent with recent
work in the Red Hills region of Georgia, showing that Bachman’s sparrows used nest sites
with greater woody vegetation cover and lower grass cover and standing crop than random
locations (Jones et al. 2013). Greater woody vegetation structure has been hypothesized to
improve access to nest sites and create favorable microclimate conditions (Haggerty 1988).
Moreover, Haggerty (1988) and Jones et al. (2013) noted the presence of bare ground at the
entrances of nest sites, which may facilitate nest attendance or fledgling movement upon
leaving the nest. We documented similar features (e.g., bare ground and sphagnum moss) for
nests in this study, and likewise noted that adults carrying food frequently landed within
12
close proximity of nest-sites and continued on the ground (Haggerty 1988, Jones et al. 2013).
Recent studies examining factors that influence Bachman’s sparrow distribution have
documented decreasing occupancy beyond a threshold of grass (Brooks and Stouffer 2010,
Taillie et al. 2015), and dense grass may restrict movement and foraging activity near the
nest (Haggerty 1998). Also, dense grass has been hypothesized to negatively impact nest
microclimate because it retains water from dew and rain events (Jones et al. 2013). Woody
vegetation may provide cover from solar radiation and protect nests from heat stress (With
and Webb 1993), but quickly outcompetes herbaceous vegetation in the absence of fire
(Jones et al. 2013).
Most differences in Bachman’s sparrow nest-site selection between physiographic
regions likely can be attributed to the available understory plant composition. Like the Red
Hills region, mesic soils in the Coastal Plain are relatively nutrient-rich compared to most
soil types associated with longleaf pine forests (Jones et al. 2013), and support a diverse plant
community (Peet 2006). In contrast, soil productivity of longleaf pine uplands at Ft. Bragg
and other locations in the Sandhills is relatively low (Perry and Amecher 2007, Lashley et al.
2015). Grasses tend to dominate the groundcover layer in these upland areas, and the
occurrence of woody stems often is limited (Lashley et al. 2014, Just et al. 2015), perhaps
due to the homogeneous application of prescribed fire (Lashley et al. 2014). Compared to the
Coastal Plan, vertical woody density at Ft. Bragg was approximately 2.5 times lower and
groundcover density 7 times lower, suggesting the availability of woody vegetation for nestsite selection was much lower at Fort Bragg. Also, woody vegetation height also was greater
at Ft. Bragg, and tall shrubs are less likely to be selected for nest sites (Jones et al. 2013).
Lastly, woody vegetation at Ft. Bragg was composed primarily of re-sprouting oaks (Quercus
13
spp.), which are not characterized by the same growth structure as gallberry (Ilex glabra),
huckleberry (Gaylussacia frondosa), blueberry (Vaccinium spp.), and swamp redbay (Persea
palustris) that were dominant at Coastal Plain sites.
Despite clear selection of habitat features, vegetation structure at Bachman’s sparrow
nests did not influence nest survival. This is not uncommon in many nest survival studies
(e.g., Davis 2005, Bulluck and Buehler 2008), and is consistent with previous research
examining the breeding biology of Bachman’s sparrows (Haggerty 1995, Jones et al. 2013).
Similar results have been explained by random patterns of nest predation (Filliater et al.
1994) or the absence of ‘safe’ nest sites due to myriad search behaviors by diverse predator
communities (Davis 2005). Hence, birds may not be able to select nest-sites that deter
predation from all possible sources. Rather, important vegetation variables from this study
may be related to nest access and microclimate hypotheses proposed previously. Regardless
of the mechanism or degree to which nest-site selection is adaptive, Bachman’s sparrows
appear to select nest-sites with specific habitat attributes, and ensuring the presence of these
nest-site characteristics in the landscape certainly is important for the conservation of this
species.
We detected few significant effects for temporal covariates on nest survival. We
hypothesized that older Bachman’s sparrow nests containing nestlings were more likely to be
depredated than nests in the incubation stage, but found no support for this relationship in
either study region. However, nest survival in the Coastal Plain declined significantly later in
the breeding season, likely because of changes in the predator community. Other studies have
demonstrated a similar trend of nest survival decreasing over time within a single breeding
season (Grant et al. 2005, Davis et al. 2005), which may correspond to an increase in
14
predator abundance or activity later in the season as temperatures increase (Grant et al.
2015). Snakes were abundant at our study sites (A. Fish and J. Winiarski, personal
observation), and are the most important nest predators in the southeastern US (Thompson
and Ribic 2012, DeGregorio et al. 2014). Minimal disturbance at most of the depredated
nests suggests that snakes might be major nest predators in our study, and snakes were a
significant cause of predation during the post-fledging period in a fledgling survival study at
Ft. Bragg (A. Fish, personal communication). Snake activity peaks seasonally in response to
temperature changes or mating behavior, and can drive seasonal variation in nest survival
(Sperry et al. 2008).
Time since fire did not influence nest survival, which was unexpected given the
importance of frequent fire for increasing the abundance and reproductive success of
Bachman’s sparrows (Tucker et al. 2004, 2006). Specifically, reproductive success has been
shown to quickly decline when time since fire exceeds 3 years (Tucker et al. 2006); however,
it is important to note that 89% of nests in this study were located in stands last burned ≤ 3
years, effectively excluding longer fire intervals from our analyses.
We expected nest survival to increase with greater habitat amount in the surrounding
landscape. Most of the nests located in both physiographic regions were in found in relatively
continuous landscapes, and lack of a landscape effect may have been due to the limited
sample size of nests from more isolated sites. In the Coastal Plain, few male Bachman’s
sparrows in isolated locations showed indications of pairing, suggesting that pairing success
may be of greater concern to reproductive success in more fragmented landscapes (Winiarski
2016).
15
We estimated daily survival rates that were within the range of those previously
reported. Estimates of daily survival were 0.919 – 0.965 in Arkansas pine plantations
(Haggerty 1988), 0.952 in the Coastal Plain of South Carolina (Stober and Krementz 2000),
and 0.899 – 0.960 in Florida dry prairie (Perkins et al. 2003). More recent work by Jones et
al. (2013) showed a daily survival rate of 0.960 for nests in mature longleaf pine forest in
Georgia (Jones et al. 2013). Bachman’s sparrow populations are declining throughout their
range, with the greatest declines occurring at the species’ shrinking range margins (Sauer et
al. 2014). Yet, our results and those from core Bachman’s sparrow populations suggest that
reduced nest survival may not pose the most significant threat to this declining species at the
current northern extent. Recent work by Taillie et al. (2015) suggests that Bachman’s
sparrows are negatively affected by habitat fragmentation, but the demographic factors
driving these responses are not well understood. Hence, further research is needed to
determine the influence of habitat, landscape, and temporal factors on other components of
reproductive success (i.e., pairing success and fledgling survival) and adult survival rates.
MANAGEMENT AND CONSERVATION IMPLICATIONS
Based on our results, the frequent application of prescribed fire should be promoted
as an essential management tool to improve nest-site conditions for Bachman’s sparrows.
Frequent prescribed fire (i.e., 2-3 year cycle) can be used to maintain both low grass density
(Coastal Plain and Sandhills) and low-statured woody vegetation (Coastal Plain; Glitzenstein
et al. 2003). While our study indicated that Bachman’s sparrows exhibit strong patterns of
nest-site selection, vegetation structure did not improve nest survival as we expected.
Bachman’s sparrow conservation depends partly on a better understanding of factors related
to other components of reproductive success (i.e., pairing success and fledgling survival).
16
Therefore, until the mechanisms influencing reproductive success of Bachman’s sparrows are
fully understood, habitat-based management approaches alone may be ineffective at
increasing breeding productivity.
17
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22
Table 1. Description of variables considered for examining nest-site selection and nest
survival by Bachman’s sparrows in North Carolina, USA, 2014–2015.
Variable
Description
Nest-site selection
Nest survival
2
-1
PineBA
Pine basal area (m /ha )
x
x
Canopy
Canopy cover (%)
xa,b
xa,b
VerGR
Grass vertical density (hits)
x
x
VerWD
Woody vertical density (hits)
x
x
VerFB
Forb vertical density (hits)
x
x
GrdGR
Grass groundcover density (hits)
xa,b
xa,b
a
GrdWD
Woody groundcover density (hits)
x
xa
GrdFB
Forb groundcover density (hits)
x
x
MaxGR
Max grass height (m)
x
x
MaxWD
Max woody height (m)
x
x
MaxFB
Max forb height (m)
xa
xa
Habitat
Habitat amount within 1 km (%)
x
Fire
Time since fire (years)
x
Date
Julian date (linear and quadratic)
x
Year
Study year (2014 or 2015)
x
Stage
Nest stage (incubation or nestling)
x
a
Variables that were omitted because of correlations with other variables (Coastal Plain).
b
Variables that were omitted because of correlations with other variables (Sandhills).
23
Table 2. Summary statistics (mean ± SD) for nest and reference plot vegetation variables, and
Mann-Whitney U statistics comparing nest-site vegetation between the Coastal Plain and
Sandhills physiographic regions in North Carolina, USA, 2014-2015.
Variable
PineBA
VerGR
VerWD
VerFB
GrdGR
GrdWD
GrdFB
MaxGR
MaxWD
MaxFB
*** P ≤ 0.05
Coastal Plain
Nest-site
Reference
11.78 ± 6.90
11.36 ± 5.76
5.61 ± 3.47
7.90 ± 4.57
2.90 ± 1.34
2.11 ± 1.12
0.65 ± 0.86
0.35 ± 0.53
1.40 ± 0.90
1.94 ± 1.04
1.33 ± 0.64
1.00 ± 0.65
0.12 ± 0.12
0.07 ± 0.12
0.37 ± 0.11
0.42 ± 0.13
0.24 ± 0.09
0.23 ± 0.10
0.27 ± 0.11
0.25 ± 0.12
Sandhills
Nest-site
Reference
13.39 ± 5.12
11.43 ± 5.57
5.29 ± 2.45
4.08 ± 2.88
1.11 ± 0.62
1.12 ± 1.00
0.52 ± 0.77
0.63 ± 0.83
1.48 ± 0.75
1.18 ± 0.85
0.18 ± 0.17
0.18 ± 0.20
0.12 ± 0.20
0.18 ± 0.35
0.39 ± 0.11
0.39 ± 0.17
0.37 ± 0.15
0.43 ± 0.26
0.18 ± 0.15
0.19 ± 0.15
U
1539.5
1922.0
3429.0***
2167.0
1775.0
3802.0***
2106.0
1724.0
842.0***
2056.0***
24
Table 3. Mixed-effects logistic regression models of nest-site selection by Bachman’s
sparrows in the Coastal Plain and Sandhills physiographic regions of North Carolina, USA,
2014-2015. Paired plot ID was used as a random effect in all models. Models were compared
using Akaike’s Information Criterion corrected for small sample size (AICc).
Model
Coastal Plain
VerWD + VerGR + GrdFB
VerWD + VerGR
VerWD
Null
Sandhills
MaxWD + PineBA + VerGR + VerGR2
PineBA + VerGR + VerGR2
MaxWD + VerGR + VerGR2
MaxWD + PineBA + VerGR
VerGR + VerGR2
PineBA + VerGR
MaxWD + VerGr
VerGR
Null
K
Loglik
AICc
∆AICc
wi
5
4
3
2
-54.22
-56.04
-59.34
-63.77
119.14
120.54
124.95
131.67
0.00
1.40
5.81
12.54
0.64
0.32
0.04
0.00
6
5
5
5
4
4
4
3
2
-101.06
-102.88
-105.86
-106.38
-107.81
-108.25
-110.14
-112.19
-116.45
214.63
216.13
222.08
223.13
223.86
224.75
228.53
230.52
236.97
0.00
1.50
7.45
8.50
9.23
10.11
13.89
15.89
22.34
0.65
0.31
0.02
0.01
0.01
0.00
0.00
0.00
0.00
25
Table 4. Parameter estimates for the top nest-site selection models for the Coastal Plain and
Sandhills physiographic regions of North Carolina, USA, 2014-2015.
Parameter
Coastal Plain
VerWD
VerGR
GrdFB
Sandhills
MaxWD
PineBA
VerGR
VerGR2
β
SE
95% CI
P
0.55
-0.14
3.68
0.20
0.06
1.20
(0.17, 0.96)
(-0.27, -0.04)
(-0.10, 7.95)
0.006
0.012
0.066
-1.68
0.10
0.91
-0.06
0.90
0.03
0.24
0.02
(-3.52, 0.04)
(0.04, 0.17)
(0.47, 1.41)
(-0.10, -0.02)
0.063
0.003
<0.001
0.002
26
Table 5. Bachman’s sparrow nest fates by physiographic region and study site (n = total nests
found, A = abandoned, S = successful, F = failed, U = unknown fate, P = parasitized, B =
burned by prescribed fire or wildfire, and T = total nests used for data summaries and
analyses).
Region
Site
n
A
S
F
U
P
B
T
Coastal Plain
COPO
1
0
1
0
0
0
0
1
Coastal Plain
GSPR
3
0
3
0
0
0
0
3
Coastal Plain
HSGL
42
2
25
14
0
5
1
39
Coastal Plain
SCPR
1
0
1
0
0
0
0
1
Sandhills
FTBR
85
7
32
38
3
3
1
70a
a
4 nests were found on their fledge date (exposure = 0), and were excluded from the nest
survival analysis
27
Table 6. Bachman’s sparrow nest survival models for the Coastal Plain and Sandhills
physiographic regions of North Carolina, USA, 2014-2015. Models were compared using
Akaike’s Information Criterion corrected for small sample size (AICc).
Model
Coastal Plain
Date
Date + Date2
Habitat
Null
Sandhills
Stage
Null
Year
Habitat
MaxWD
VerWD
VerFB
HorWD
VerGR + VerGR2
Date
VerGR
Fire
MaxGR
HorFB
MaxFB
Date + Date2
K
Loglik
AICc
∆AICc
wi
2
3
2
1
-42.78
-42.69
-43.85
-45.38
89.67
91.60
91.81
92.80
0.00
1.93
2.14
3.13
0.52
0.20
0.18
0.11
2
1
2
2
2
2
2
2
3
2
2
2
2
2
2
3
-92.87
-94.59
-93.76
-94.10
-94.18
-94.22
-94.28
-94.42
-93.40
-94.43
-94.44
-94.50
-94.54
-94.54
-94.57
-93.84
189.79
191.20
191.59
192.26
192.43
192.50
192.63
192.91
192.92
192.93
192.94
193.06
193.14
193.14
193.20
193.81
0.00
1.41
1.80
2.47
2.64
2.71
2.83
3.12
3.13
3.13
3.14
3.26
3.35
3.35
3.41
4.02
0.21
0.11
0.09
0.06
0.06
0.06
0.05
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.03
28
Figure 1. Locations of study sites and physiographic regions where nest-site selection and
nest survival data were collected during 2014-2015. Physiographic region designation
follows level III classifications defined by Bailey (1995). The Sandhills is a distinct level IV
subregion of the Southeastern Plains. Study sites are indicated with four-letter abbreviation;
from west to east, Fort Bragg Military Installation (FTBR; n = 85 nests), Green Swamp
Savanna Preserve (GSPR; n = 3 nests), Compass Pointe (COPO; n = 1 nest), Holly Shelter
Game Land (HSGL; n = 42 nests), and Shaken Creek Savanna Preserve (SCPR; n = 1 nest).
29
Figure 2. Predicted probability of selection of a nesting site for Bachman’s sparrows by
physiographic region in North Carolina, USA, in 2014–2015. ‘Hits’ refers to the total number
of vegetation contacts on the entire pole averaged across a plot. Shaded regions indicate 95%
confidence intervals.
30
Figure 3. Predicted probability of Bachman’s sparrow daily nest survival rate in relation to
Julian date for the Coastal Plain of North Carolina, USA, in 2014–2015. Shaded region
indicates 95% confidence interval.
31
CHAPTER 2
SPACE-USE AND HABITAT SELECTION BY BACHMAN’S SPARROWS AT THE
NORTHERN RANGE PERIPHERY
ABSTRACT
Bachman’s sparrow (Peucaea aestivalis) populations have declined range-wide
during the past 40 years. Peripheral populations show some of the worst declines, and the
species is now rare or absent over the northern extent of its range. Although home range size
and habitat selection have been quantified in the sparrow’s core range, northern populations
have been largely unstudied. To better describe space-use and habitat relationships in this
region of sparrow decline, we quantified microhabitat selection and the influence of body
condition and reproductive status on Bachman’s sparrow home range size. We captured and
radio-tracked 37 male Bachman’s sparrows between April and July 2014-2015 in the Coastal
Plain of North Carolina, USA. Within home ranges, Bachman’s sparrows used locations with
greater density of woody vegetation and forbs, and intermediate levels of grass density.
Home range size varied from 2.6 ha to 18 ha, but we documented no significant predictors of
variation in home range size. Because home range size was variable and not linked with
reproductive status, it should not be used as an indication of habitat quality without more
detailed studies on the factors that affect space-use in this species.
32
INTRODUCTION
Knowledge of habitat selection and its influence on home range size are important for
understanding a species’ ecology and natural history, and for implementing better
conservation and management actions (Anich et al. 2012). Landscapes with more abundant
resources are expected to contain individuals with smaller, high quality home ranges and
support larger populations (Anich et al. 2010, Tingley et al. 2014). Moreover, habitat
selection may vary across a species’ range (e.g., Zhu et al. 2012, Tack and Fedy 2015), and
space-use may change in accordingly. In particular, populations at the edge of a species’
range experience strong limiting factors in the form of atypical habitat types or reduced
habitat quality (Sexton et al. 2009). Yet, peripheral populations are important for species’
survival and evolution in the context of increasing environmental change, and are often of
high conservation value (Lesica and Allendorf 1995). Thus, understanding geographicallyspecific estimates of habitat use and home range size could identify resources limiting
species of management concern and guide more successful management and restoration
activities (van Beest et al. 2011, Tingley et al. 2014).
One such species of concern is the Bachman’s sparrow (Peucaea aestivalis), a
ground-nesting bird that is strongly associated with fire-maintained longleaf pine (Pinus
palustris) ecosystems in the southeastern United States. Extensive study has shown that
Bachman’s sparrows select specific vegetation conditions that reflect a frequent fire regime
(≤ 3 years), in particular the presence of open canopy cover, and dense ground cover of
grasses and forbs (Dunning 2006, Tucker et al. 2006). Due to extensive habitat loss and firesuppression, the longleaf pine ecosystem has been reduced to ~3% of its historic range (Frost
2006), and ranks among the most endangered ecosystems in North America (Noss et al.
33
1995). Consequently, Bachman’s sparrow populations have steadily declined in recent
decades, particularly along the periphery of the species’ range (Dunning and Watts 1990,
Sauer et al. 2014).
Given their status as an indicator species for functional longleaf pine forests (Taillie
et al. 2015), Bachman’s sparrows have become the focus of numerous studies in recent years.
Extensive research has documented Bachman’s sparrow-habitat relationships (Plentovich et
al. 1998, Tucker et al. 2004, Cox and Jones 2007, Brooks and Stouffer 2010); yet, only a few
studies have been conducted outside of the species’ southern range extent (e.g., Dunning and
Watts 1990, Haggerty 1998, Taillie et al. 2015). Although prior research has been valuable
for describing second-order habitat selection, only a handful of studies have examined habitat
features at the home range-level (Haggerty 1998, Jones 2008, Cox and Jones 2007, Brown
2012). For Bachman’s sparrow, no studies have examined habitat selection at finer spatialscales (i.e., used locations within the home range) to quantify microhabitat characteristics,
which likely are more informative than coarser home-range-level selection (Anich et al.
2012).
Habitat selection is likely to influence Bachman’s sparrow home range size, with
smaller home ranges in areas with better habitat quality (Stober and Krementz 2006). In the
species’ core range, home ranges average 3-5 ha (Stober and Krementz 2006, Cox and Jones
2007, Brown 2012). However, home range size appears to vary considerably depending on
pairing status (Jones 2008), vegetation characteristics (Haggerty 1998), habitat type (Stober
and Krementz 2006), weather conditions (Brown 2012), and study methodology. Most
studies of Bachman’s sparrow home range size have relied on spot-mapping singing perches
of unmarked (McKitrick 1979, Meanley 1990) or color-banded males (Haggerty 1998, Cox
34
and Jones 2007), and these estimates can be substantially lower than home range estimates
from radio-tagged birds (Anich et al. 2009, Streby et al. 2012). Further, Bachman’s sparrow
home-range size has never been empirically estimated in North Carolina.
Given recent extirpations (e.g., Virginia) and the increasing rarity of Bachman’s
sparrow populations at the species’ shrinking range limits (Dunning 2006), we addressed
information needs for Bachman’s sparrow conservation and longleaf pine forest restoration
and management at the northern periphery of the species’ range in North Carolina. Our
primary objectives included 1) quantifying microhabitat selection within Bachman’s sparrow
home ranges, 2) estimating home-range sizes, 3) determining which factors influence
variation in home-range size, and 4) comparing our results to those of previous studies
conducted in the core of the species’ range.
METHODS
Study area
We studied Bachman’s sparrows at four sites within the Middle Atlantic Coastal Plain
(hereafter, Coastal Plain) physiographic region of southeastern North Carolina, USA (Figure
1) from 2014-2015. Bachman’s sparrows are uncommon across most of the region, so we
relied on Bachman’s sparrow presence and accessibility to locate study sites. We selected
Boiling Spring Lakes Preserve, Green Swamp Preserve, Holly Shelter Game Land, and
Sleepy Creek Farms as study sites (Figure 1). Study sites in the Coastal Plain were imbedded
in a matrix of urban and rural residential development and agricultural lands, and were
managed by a variety of state and private landowners.
The Coastal Plain recently has been recognized as a global biodiversity hotspot, and
longleaf pine communities within this region have among the highest levels of plant richness
35
per square meter of any ecosystem in North America (Noss et al. 2015). Study sites were
primarily composed of mesic longleaf pine forests bordered by pocosin wetlands. Wet pine
savannas in this region were characterized by an open tree canopy, seasonally saturated soils,
and a herbaceous layer dominated by graminoids, forbs, and low shrubs. In addition to
longleaf pine, tree species dominating Bachman’s sparrow habitat included loblolly (P.
taeda) and pond pine (P. serotina). Dominant groundcover species included wiregrass
(Aristida stricta), switchcane (Arundinarea gigantea), bracken fern (Pteridium aquilinum)
and inkberry (Ilex glabra).
Field data collection
Capture and radio-telemetry
We documented Bachman’s sparrow home range and microhabitat selection by fitting
VHF radio-transmitters to adult males. We initially targeted Bachman’s sparrows for capture
by playing audio recordings of territorial calls at potential study sites. Once located, we
captured sparrows with audio playback of conspecific vocalizations and mist nets. Upon
capture, we measured wing length and mass, and attached a small transmitter (~0.64 g,
Blackburn Transmitters, Nacogdoches, Texas, USA) using an elastic leg loop harness
(Rappole and Tipton 1991, Streby et al. 2015). Bachman’s sparrows captured during the
course of this study weighed 18.78 ± 0.636 g (mean ± SD), and transmitters weighed ~3.5%
of an average adult bird’s mass. Additionally, we fit all birds with a unique combination of
colored plastic leg bands and a uniquely numbered U.S. Geological Survey aluminum leg
band.
We collected home range data on Bachman’s sparrows between April and July, 20142015. We located sparrows 1 - 2 times daily for 5 - 6 days per week to ensure ≥ 30 telemetry
36
locations necessary to estimate home range size before transmitters failed (Seaman et al.
1999). All telemetry locations were gathered ≥ 2 h apart, which ensured independence
between successive locations because sparrows could move across the largest home range
during this time interval. We located sparrows via homing and typically confirmed their
locations visually or aurally. When birds did not sing or were visually obscured by dense
groundcover, the strength of the telemetry signal allowed us to determine the bird’s
approximate location (i.e., within 5 m). When sparrows were located, we recorded spatial
coordinates (Universal Transverse Mercator Zone 17N or 18N) using a hand-held GPS unit
and marked the location with a labeled flag for future vegetation sampling.
Reproductive status
We were interested in whether reproductive status influenced home range size. To
determine reproductive status of each radio-tagged sparrow, we collected behavioral
information at one-week intervals using a protocol modified from Vickery et al. (1992) and
Tucker et al. (2006). During each visit, one observer spent a minimum of 60 minutes on a
birds’ territory and determined the reproductive status of the male by noting behaviors such
as associating with a female, feeding nestlings, or feeding fledglings. Bachman’s sparrows
can produce 3 broods (Dunning 2006), and we recorded the number of nesting attempts or
successful broods. Also, we located nests during visits and tracking activities, and
determined nest fates using established protocols (Martin and Geupel 1993). After the
completion of radio-tracking, males were assigned a reproductive score based on weekly
observations and the fates of nests (Appendix A).
Vegetation measurements
37
We measured vegetation structure within a 5-m radius plot centered on a subset of 10
randomly-selected telemetry locations prior to the conclusion of each season (24 ± 16 days
after completion of radio-tracking each male). Vegetation categories included grass, woody
vine-shrub (hereafter woody vegetation), switchcane, and forb-fern (Taillie et al. 2015). We
quantified vegetation structure using indices of density by recording the presence of each
vegetation category (i.e., ‘hits’ or vegetation contacts) on 0.1-m sections of a 1.5-m vertical
pole. Vertical density included vegetation hits along the entire length of the pole, while
ground density was calculated as the number of hits on the first 0.1-m section of the pole
(Wiens and Rotenberry 1981). Maximum vegetation height was obtained by recording the
tallest hit on the pole and rounding to the nearest 0.1-m section (Wiens and Rotenberry 1981,
Moorman and Guynn 2001, Taillie et al. 2015). Measurements were taken at every meter
along four 5-m transects running in each cardinal direction. At the same 1-m increments
along each transect, we estimated canopy closure by recording the presence or absence of
live canopy using an ocular sighting tube. We used a 10-factor prism from the plot center to
determine the basal area (m2/ha) of pines surrounding each used location.
We repeated these same measurements at a reference plot (i.e., an available non-use
location) 50 m away from each telemetry location in a random direction. We chose this
distance to eliminate overlap between used and available points, and to constrain reference
locations within a bird’s home range.
Estimating home range size
All statistical analyses were performed in R 3.2.2 (R Development Core Team 2015).
We estimated home-range size using kernel density techniques (Worton 1989) and the ‘ks’
38
package (Duong 2007), which assumes a bivariate normal density fixed kernel. We used the
‘plug-in’ method for calculating the bandwidth parameter (Millspaugh et al. 2006). The plugin method generally outperforms other bandwidth estimators (e.g., reference and least-square
cross validation; Gitzen et al. 2006), and has been increasingly used in avian telemetry
studies (e.g., Rota et al. 2014, Stanton et al. 2014, Lorenz et al. 2015). Home range size
estimates were based on 95% utilization distribution contours.
Modeling microhabitat selection
Microhabitat selection was analyzed with the lme4 package (Bates et al. 2015) and
generalized linear mixed effects models specifying a logit link function and binomial
response (where 1 = used location, 0 = reference location). Prior to analysis, we tested for
correlations (r > |0.60|) between all pairwise combinations of variables. Among 17 potential
vegetation variables, maximum height measurements were correlated with vertical density
measurements, and we omitted all maximum height variables in final models. Grass vertical
density was highly correlated with grass groundcover density, so we omitted the latter
variable. Also, we included a quadratic effect of vertical grass density because dense grass
may restrict movement on the ground in the absence of fire (Brooks and Stouffer 2010,
Taillie et al. 2015). For all models, we included ‘BirdID’ as a random effect to account for
repeated measures of each individual and potential variation in error associated with tracking
different sparrows. We examined models with BirdID nested within a random ‘Site’ effect,
but determined that models with BirdID performed better. To explore the most important
vegetation variables, we looked at all possible linear combinations of variables.
Home range variation
39
Variation in home range size was investigated using linear models. Home-range size
was log-transformed prior to analyses to meet assumptions for statistical analysis. For the
home range size analysis, we selected variables based on published literature and our
observations of Bachman’s sparrow space-use, along with habitat, temporal, and individual
characteristics important in space-use for other bird species (e.g., Mazerolle and Hobson
2004, Anich et al. 2010, Lorenz et al. 2015). Following Anich et al. (2010), we used 2 model
sets examining the effects of 1) habitat and 2) non-habitat predictors on home range size. Due
to limited sample size, we tested univariate models in each model set. For the habitat model
set, we tested the same vegetation variables used in the microhabitat selection analysis, and
time since fire. The non-habitat model set included 5 predictors: body condition (wing
length), reproductive status (i.e., reproductive index score), mean tracking date, site, and
year. For vegetation variables, Bachman’s sparrow locations and the associated reference
locations were combined into a single mean value for each individual to characterize
vegetation within a home range.
Model selection
Habitat selection and home range variation models were selected using Akaike’s
Information Criterion (AICc; Burnham and Anderson 2002) using the ‘MuMIn’ package
(Barton 2016). Models were ranked according to their AICc values, and model averaging was
performed across the top candidate models (∆AICc ≤ 2) when appropriate. A parameter was
considered as strongly affecting microhabitat selection if the 95% CI did not include 0, and
was considered to have no relationship if the 95% CI overlapped 0.
40
RESULTS
Home range characteristics
We collected 1199 locations on 37 male Bachman’s sparrows from 2014-2015. We
obtained ≥ 30 telemetry locations for 27 sparrows for an average of 36 ± 5 points (range: 30
– 46). For the 27 birds, there was no statistically significant correlation between home-range
size and the number of location points (r = 0.36, P = 0.06). Mean 95% kernel home range
size was 7.92 ± 4.05 ha, and varied from 2.59 – 18.18 ha.
Microhabitat selection
We collected vegetation data for 37 sparrows between 2014 and 2015 (Table 1). The
top models showed strong support for an influence of woody vegetation, grass, and forbs on
microhabitat selection. Specifically, the top models (∆AICc ≤ 2) included the effect of woody
vertical density, a quadratic effect of grass vertical density, and groundcover forb density
(Table 2). Also, the confidence limits of the model-averaged parameter estimates for these
terms did not overlap zero (Table 3). Four other terms appeared in our top models, but
confidence limits of the model-averaged parameter estimates overlapped zero (pine basal
area, groundcover density of woody vegetation, and both density measurements of dead
vegetation; Table 3). Of the three significant predictors, vertical woody density had the
strongest relationship with microhabitat selection. The relationship between woody plants
and forbs and microhabitat selection was positive, with the probability of use increasing with
an increase in the density of woody vegetation and forbs (Figure 2). Also, selection
probability increased with increasing grass density, but there was a threshold beyond which
relative probability of use began to decrease (Figure 2).
41
Home range variation
Home range size predictors were poorly supported in model sets, with all models
ranking lower than the null model (Table 4). In the habitat model set, models containing
vertical density of dead vegetation, time since fire, and vertical and groundcover density of
forbs were competitive with the null model (Table 4). However, confidence intervals for
these parameters overlapped zero and were not considered significant. Similarly,
reproductive index score and mean tracking date were among the top predictors in the nonhabitat model set, but these variables had weak effects on home range variation (Table 4).
DISCUSSION
Bachman’s sparrow showed consistent selection for several vegetation characteristics
within home ranges. Although the importance of high grass density often has been
emphasized (Dunning and Watts 1990, Plentovich et al. 1998) when describing Bachman’s
sparrow habitat, our results are consistent with more recent studies documenting threshold
responses to grass density (Brooks and Stouffer 2010, Taillie et al. 2015). This threshold for
grass density suggests that Bachman’s sparrows select patchy grass cover, possibly because
too much herbaceous cover can hamper foraging efficiency (Haggerty 1998). In the absence
of frequent fire, the formation of dense grass may restrict movement of Bachman’s sparrows,
rendering locations within home ranges unsuitable.
Sparrows selected microhabitat with more forb groundcover. Bachman’s sparrows
primarily glean prey from the ground (Dunning 2006), and forbs may represent a valuable
foraging substrate and could reflect the availability of arthropods as a prey resource.
However, the forb species most important as hosts for arthropods consumed by Bachman’s
42
sparrows are not known. Additionally, forbs may further facilitate open ground for
movement.
Contrary to previous studies, greater woody vegetation density was an important
variable for microhabitat selection. Woody vegetation, particularly tall shrubs, has been
considered to be detrimental to Bachman’s sparrow occupancy (Brooks and Stouffer 2010,
Taillie et al. 2015). Indeed, as more time elapses without fire, fire intolerant woody species
become taller and often shade herbaceous vegetation (Engstrom et al. 1984, Glitzenstein et
al. 2003). However, in frequently burned longleaf pine forest, woody vegetation at the homerange level likely provides several important benefits. Numerous studies have documented
the importance of song-perch structures for Bachman’s sparrows (Meanley 1959, Dunning
and Watts 1990, Jones et al. 2013), and we frequently observed males singing from
horizontal and terminal branches of woody shrubs. Also, woody shrubs may provide escape
cover from predators (Pulliam and Mills 1977, Dunning 2006), and constitute important
features for Bachman’s sparrow nest-site selection (Jones et al. 2013, Winiarski 2016).
Interestingly, we observed male Bachman’s sparrows frequently using the edges of
densely vegetated swales that had wetter soils than surrounding longleaf pine forest, as well
as ecotones representing the gradient between longleaf pine woodlands and pocosins. In
these areas, forbs and woody vegetation dominated, and grass density was low. Thus, the
microhabitat relationships we observed in this study may reflect the high use of these areas
within home ranges. Although this habitat association has not been reported elsewhere in the
species’ range, it appears to be consistent with habitat use exhibited by Bachman’s sparrows
in the Sandhills physiographic region of central North Carolina (Alex Fish, personal
43
observation), suggesting that habitat use may differ for the species at the northern limits of its
range.
Home range sizes in this study were larger than most previously reported estimates
for Bachman’s sparrows. Home-range estimates across the species’ range have varied
substantially, perhaps due to differences in habitat quality, home range estimation techniques,
and methods of gathering location points (Table 5). Several early studies relied on spotmapping male locations and singing perches (McKitrick 1979, Haggerty 1998, Cox and
Jones 2007). However, home ranges are generally larger than song territories, and standard
observational methods can underestimate home range size compared to telemetry-based
methods (Anich et al. 2009, Streby et al. 2012). For example, Golden-winged Warbler
(Vermivora chrysoptera) home ranges estimated using radio-telemetry were 3 times larger
than those estimated from spot-mapping (Streby et a. 2012). Our estimated home range sizes
for some individuals were much larger than previously had been documented for Bachman’s
sparrows, which suggests that spot-mapping may have underestimated home range size (but
see Stober and Krementz 2006). Elsewhere, the 95 or 100% MCP was used in 4 of 6
published studies, despite considerable biases associated with this estimator (Börger et al.
2006). In addition to the work presented here, only one other study has implemented both
radio-telemetry and fixed kernel estimators to determine home range size for Bachman’s
sparrows (Brown 2012). Mean home range size (7.92 ± 4.05 ha) presented here was ~2.1
times larger than an estimate provided by Brown (2012), but ~1.2 times smaller compared
with home range size during a severe drought year in the same study (Table 5). Further
research, particularly radio-tracking studies using kernel density estimators, are needed to
44
obtain more robust estimates of Bachman’s sparrow home range size and to compare spaceuse across studies.
Despite clear selection for vegetation features within home ranges, home range size
was variable (2.59 – 18.18 ha) and not associated with any measured habitat or individual
characteristics. Little is known about factors influencing variation in Bachman’s sparrow
home-range size, but Stober and Krementz (2006) reported that home ranges were smaller in
young pine stands (≤ 4 years old) than mature stands. In pine plantations in Arkansas,
Bachman’s sparrow home range size was negatively related to percent forb cover, ground
cover, forb height, percent grass cover, and density of vegetation between 0 - 90 cm
(Haggerty 1998). Paired males were shown to have larger home ranges than unpaired males
in Georgia, perhaps due to long-distance movements to locate and deliver food to nestlings
and young (Jones 2008). Jones (2008) similarly showed that increases in forb cover and time
since burn were positively related to the home-range size of male Bachman’s sparrows,
although parameter estimates indicated weak effects of these relationships. Severe drought
may cause significant increases in daily movements and home range size due to decreased
habitat quality (Brown 2012), but drought conditions did not occur over the course of this
study.
Home range size was not correlated with pairing success or fledging young (i.e.,
reproductive index scores), which are important indicators of habitat quality. However, the
correlation between home range size and years since fire was positive, indicating that habitat
quality may decline as time since fire elapses, but parameter estimates were not significant.
Most (90%) males in this study occurred in stands which were burned ≤ 3 years prior, which
is considered to be an optimal fire-return interval to promote Bachman’s sparrow abundance
45
and productivity (Tucker et al. 2004, 2006). Consistent with male home ranges in clearcut
pine stands (Haggerty 1998), home range size decreased with greater forb cover, though this
effect was weak. In contrast, forb cover was positively associated with Bachman’s sparrow
home range size and breeding success in Georgia (Jones 2008). Successful males in that
study had large home ranges, and often occurred in upland areas where forb cover was lower.
The effect of forbs on Bachman’s sparrow home range size remains unclear, but likely
warrants further study. Other factors, such as relationships to neighbors or mates, may
influence space-use (Anich et al. 2010), but we lacked sufficient data to evaluate the
influence of neighboring individuals. Body size and age can influence territory sizes (i.e., the
ideal dominance model of habitat selection; Adams 2001), with larger, older individuals
typically holding smaller, high quality home ranges. While we accounted for body size (i.e.,
wing length) when modeling variability in home range size, we were not able to determine
precise estimates of age because Bachman’s sparrow adults can only be categorized as afterhatch-year (Pyle 1997).
Bachman’s sparrows have been declining in abundance, with the greatest declines at
the periphery of their range (Sauer et al. 2014). In North Carolina, we measured several
structural vegetation characteristics associated with both sparrow use and prescribed fire at
the home range level. In particular, the microhabitat characteristics we documented support
the value of variable-intensity prescribed fire that maintains small-statured shrubs, high forb
cover, and patchy grass cover (Glitzenstein et al. 2003, Thaxton and Platt 2006). Further
study is needed to determine whether these habitat associations have a biologically
significant link to Bachman’s sparrow vital rates. Also, we provide the first estimate of home
range size for Bachman’s sparrow at the northern limits of its range, but factors that influence
46
home range size in this species are not yet clear. The idea that home range size varies with
habitat quality may not be an appropriate assumption for this species, and we believe that
further studies are needed to understand factors that influence space-use in Bachman’s
sparrows.
47
LITERATURE CITED
Adams, E.S. (2001). Approaches to the study of territory size and shape. Annual Review of
Ecology, Evolution, and Systematics 32: 277–303.
Anich, N. M., T. J. Benson, and J. C. Bednarz (2009). Estimating territory and home-range
sizes: do singing locations alone provide an accurate estimate of space use? The
Auk 126:626-634.
Anich, N. M., T. J. Benson, and J. C. Bednarz (2010). Factors influencing home-range size of
Swainson’s warblers in eastern Arkansas. The Condor 112:149–158.
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52
Table 1. Summary statistics (mean ± SD) for used and reference plot vegetation variables for
37 radio-tracked Bachman’s sparrows in the Coastal Plain physiographic region in North
Carolina, USA, 2014-2015.
Variable
Used
Reference
Canopy
35.76 ± 25.4
35.85 ± 25.49
PineBA
11.14 ± 6.62
10.85 ± 7.00
VerWD
1.40 ± 0.75
1.08 ± 0.65
VerGR
2.09 ± 1.17
2.39 ± 1.29
VerFB
0.32 ± 0.35
0.28 ± 0.36
VerSC
0.02 ± 0.09
0.02 ± 0.09
VerDD
0.11 ± 0.17
0.08 ± 0.11
GrdWD
0.49 ± 0.23
0.44 ± 0.24
GrdGR
0.51 ± 0.26
0.58 ± 0.27
GrdFB
0.07 ± 0.11
0.07 ± 0.10
GrdSC
0.00 ± 0.01
0.00 ± 0.01
GrdDD
0.01 ± 0.03
0.01 ± 0.03
MaxWD
0.21 ± 0.14
0.16 ± 0.11
MaxGR
0.28 ± 0.15
0.31 ± 0.16
MaxFB
0.07 ± 0.08
0.06 ± 0.08
MaxSC
0.01 ± 0.03
0.01 ± 0.03
MaxDD
0.04 ± 0.06
0.02 ± 0.04
Canopy = Canopy cover (%); PineBA = Pine basal area (m2/ha-1); VerWD = woody vertical
density (hits); VerGR = grass vertical density (hits); VerFB = forb vertical density (hits);
VerSC = switchcane vertical density (hits); VerDD = dead vertical density (hits); GrdWD =
woody groundcover density (hits) GrdGR = grass groundcover density (hits); GrdFB = forb
groundcover density (hits); GrdSC = switchcane groundcover density (hits); GrdDD = dead
groundcover density (hits); MaxWD = woody max. height (m); MaxGR = grass max. height
(m); MaxFB = forb groundcover density (hits); MaxSC = switchcane max. height (m);
MaxDD = dead max. height (m).
53
Table 2. Top 10 mixed-effects logistic regression models of microhabitat selection by
Bachman’s sparrows in the Coastal Plain region of North Carolina, USA, 2014-2015. BirdID
was used as a random effect in all models. Models were compared using Akaike’s
Information Criterion corrected for small sample size (AICc).
Model
GrdFB+VerDD+VerGR+VerGR2+VerWD
GrdFB+VerGR+VerGR2+VerWD
GrdFB+PineBA+VerDD+VerGR+VerGR2+VerWD
GrdFB+VerDD+VerFB+VerGR+VerGR2+VerWD
GrdDD+GrdFB+VerDD+VerGR+VerGR2+VerWD
GrdFB+GrdWD+VerDD+VerGR+VerGR2+VerWD
GrdFB+VerDD+VerGR+VerGR2+VerWD
GrdFB+GrdSC+VerDD+VerGR+VerGR2+VerWD
GrdDD+GrdFB+VerGR+VerGR2+VerWD
GrdFB+GrdWD+VerGR+VerGR2+VerWD
K
7
6
8
8
8
8
8
8
7
7
Loglik
-481.09
-482.96
-480.92
-481.04
-481.06
-481.06
-481.06
-481.09
-482.55
-482.84
∆AICc
0.00
1.69
1.70
1.94
1.98
1.98
1.98
2.04
2.91
3.50
wi
0.24
0.10
0.10
0.09
0.09
0.09
0.09
0.09
0.06
0.04
54
Table 3. Model-averaged parameter estimates for the top microhabitat selection models
(∆AICc ≤ 2) for 37 radio-tracked male Bachman’s sparrows in the Coastal Plain region of
North Carolina, USA, 2014-2015. Shaded cells indicate parameter estimates that do not
overlap zero.
Confidence limits
Variable
Parameter estimate a
2.5%
97.5%
(Intercept)
0.186
-0.01
0.38
GrdFB
0.253
0.07
0.43
VerDD
0.146
-0.01
0.34
VerGR
-0.139
-0.31
0.04
VerGR2
-0.186
-0.32
-0.06
VerWD
0.451
0.27
0.63
PineBA
0.006
-0.11
0.20
VerFB
-0.003
-0.22
0.16
GrdDD
0.002
-0.14
0.18
GrdWD
-0.003
-0.23
0.18
VerSC
-0.002
-0.18
0.14
a
Parameter estimates and 95% CIs were derived from standardized variables.
55
Table 4. Linear models examining home range variation for Bachman’s sparrows in the
Coastal Plain region of North Carolina, USA, 2014-2015. Model sets tested habitat and nonhabitat predictors. Models were compared using Akaike’s Information Criterion corrected for
small sample size (AICc).
Model
Habitat models
Null
Dead vertical density
Time since fire
Forb groundcover density
Forb vertical density
Woody groundcover density
Switchcane vertical density
Switchcane groundcover density
Woody vertical density
Grass vertical density
Pine basal area
Dead groundcover density
Grass vertical density (quadratic)
Non-habitat models
Null
Reproductive index score
Mean tracking date
Year
Wing length
Site
K
Loglik
AICc
∆AICc
wi
2
3
3
3
3
3
3
3
3
3
3
3
4
-20.59
-19.46
-20.05
-20.07
-20.10
-20.35
-20.44
-20.46
-20.49
-20.52
-20.55
-20.57
-20.06
45.68
45.96
47.14
47.19
47.24
47.74
47.92
47.96
48.02
48.08
48.14
48.19
49.95
0.00
0.28
1.46
1.51
1.56
2.07
2.24
2.28
2.35
2.40
2.46
2.51
4.27
0.18
0.16
0.09
0.08
0.08
0.06
0.06
0.06
0.06
0.05
0.05
0.05
0.02
2
3
3
3
3
4
-20.59
-20.20
-20.22
-20.35
-20.48
-20.58
45.68
47.44
47.48
47.75
48.00
50.98
0.00
1.76
1.80
2.07
2.32
5.30
0.39
0.16
0.16
0.14
0.12
0.03
56
Table 5. Summary of published studies of Bachman’s Sparrow home-range size in comparison to findings presented here.
Study
Habitat
State
Haggerty 1998
Plantation
AR
McKitrick 1979
Unknown
FL
Dean & Vickery 2003 a
Dry prairie
FL
b
e
Stober & Krementz 2006
Mixed
SC
Cox & Jones 2007
Old-growth
GA
Cox & Jones 2007
Old-growth
GA
Brown 2012 c
Sandhills
FL
c
Brown 2012
Sandhills
FL
Brown 2012 c
Sandhills
FL
d
Brown 2012
Sandhills
FL
Brown 2012 d
Sandhills
FL
Brown 2012 d
Sandhills
FL
Present study
Flatwoods
NC
Present study
Flatwoods
NC
a
Conducted during the non-breeding season
b
Sample includes male and female home ranges
c
First year of study (2010)
d
Second year of study (2011)
e
Mature longleaf pine and young pine plantations
N
25
6
8
4
46
46
8
8
7
23
23
22
27
27
Method
Mapping
Mapping
Telemetry
Telemetry
Mapping
Mapping
Telemetry
Telemetry
Telemetry
Telemetry
Telemetry
Telemetry
Telemetry
Telemetry
Estimator
MCP
MCP
MCP
MCP
MCP
Fixed kernel
MCP
Fixed kernel
Convex hull
MCP
Fixed kernel
CH
MCP
Fixed kernel
Percentile
100th
100th
95th
95th
100th
95th
100th
95th
95th
100th
95th
95th
95th
95th
Mean
2.50
5.10
0.65
2.95
1.80
3.10
4.10
3.80
2.90
33.00
9.60
5.20
3.47
7.92
SE
0.22
0.49
0.10
0.57
1.40
3.50
0.76
0.41
0.43
10.20
0.82
0.68
0.36
0.78
57
Figure 1. Locations of study sites and home ranges of 37 Bachman’s sparrows that were
radio-tracked in North Carolina, USA, 2014-2015.
58
Figure 2. Predicted probability of microhabitat use by Bachman’s sparrows in the Coastal
Plain physiographic region, North Carolina, USA, in 2014–2015, for woody vertical density,
grass vertical density, and forb groundcover density. Shaded regions indicate 95%
59
CHAPTER 3
REPRODUCTIVE CONSEQUENCES OF HABITAT FRAGMENTATION IN A
DECLINING RESIDENT LONGLEAF PINE BIRD
ABSTRACT
The longleaf pine (Pinus palustris) ecosystem is among the most imperiled in North
America, primarily as a result of land conversion and fire suppression. Of its original extent,
less than 5% remains as scattered or degraded patches. Consequently, longleaf conservation
efforts recently have expanded, but a better understanding of local- and landscape-scale
effects on demographic responses for wildlife species associated with longleaf pine is needed
to guide effective conservation strategies. To address this knowledge gap, we studied the
breeding ecology of Bachman’s sparrows (Peucaea aestivalis), a species of conservation
concern that is closely associated with longleaf pine forests in the southeastern United States.
We investigated the effects of local- and landscape-scale factors on different components of
reproductive success (i.e., pairing success and probability of fledging ≥ 1 offspring) for 96
male sparrows at 8 sites in the Coastal Plain of southeastern North Carolina. We used a
‘species-centered’ approach and species distribution modeling to quantify Bachman’s
sparrow habitat amount across our study area. We then constructed mixed effects logistic
regression models for each reproductive component as a function of male body size,
territory-level vegetation structure and time since fire, and habitat amount in the surrounding
landscape. Pairing success was lower in highly fragmented longleaf pine landscapes
compared to more continuous landscapes. In contrast, habitat amount did not affect the
probability of fledging offspring for paired males. Overall, our work indicates that low
pairing success is inhibiting successful reproduction in highly fragmented longleaf pine
60
landscapes, perhaps due to disrupted dispersal. Low pairing success may be partly
responsible for Bachman’s sparrow declines, but further research is needed to quantify the
effects of habitat fragmentation on other vital rates (i.e., adult and juvenile survival) that may
be more influential in population declines. Nevertheless, to promote breeding opportunities
for Bachman’s sparrows and other longleaf-pine associated wildlife, managers should
prioritize resources to patches near large, pre-existing longleaf pine forest.
61
INTRODUCTION
Anthropogenic change in the amount and configuration of native vegetation
communities is hypothesized to be a major driver of bird population declines across
landscapes (Lampila et al. 2005). Habitat destruction and fragmentation transform landscapes
by reducing habitat area, increasing distance and isolation between fragments, and increasing
the amount of edge (Haddad et al. 2015). In addition to diminishing available habitat, these
processes can negatively affect a suite of demographic rates (Lampila et al. 2005) that drive
bird density, distribution, and richness (McGarigal and McComb 1995, Villard et al. 1999,
Donovan and Flather 2002). While it is well known that landscape changes can have
widespread negative effects on bird distribution and abundance, the specific demographic
parameters involved in these declines often remain unknown for many species of
conservation concern (Lampila et al. 2005).
Several hypotheses have been proposed to explain bird population declines in
response to habitat loss and fragmentation. First, changes in landscape composition (e.g.,
patch size reduction and greater exposure to edges) can initiate changes in predator and brood
parasite abundance (Chalfoun et al. 2002), leading to elevated nest predation or brood
parasitism (Robinson et al. 1995), and lower adult survival (Lampila et al. 2005). Second, as
fragments become more isolated and surrounded by a matrix of human-transformed land
cover types, connectivity is reduced. Hence, mobility becomes inhibited in highly fragmented
landscapes, potentially impacting pairing success and survival of dispersing females (Dale
2001, Cooper and Walters 2002, Lampila et al. 2005, Robles et al. 2008). Third, smaller
patches may be of lower quality and contain less food resources and nest sites (Burke and
Nol 1998, Zanette 2001, Lampila et al. 2005), which can negatively affect demographic
62
parameters such as nest success, clutch size, and fledgling condition. Lastly, habitat loss and
fragmentation often lead to a reduction in abundance of males in the remaining habitat
patches (Dale 2001). Because females may use territory density as a habitat selection cue,
this change may lead to a reduction in male pairing success (Villard et al. 1993, Ward and
Schlossberg 2004). Given the myriad impacts of landscape change on bird populations, a
better understanding of the specific demographic mechanisms responsible for declines is
crucial to address adverse fragmentation effects (Lampila et al. 2005).
We focused on the breeding response of the Bachman’s sparrow (Peucaea aestivalis)
to habitat loss and fragmentation as a possible explanation for population declines.
Bachman’s sparrow is a ground-nesting bird that is strongly associated with fire-maintained
longleaf pine forests in the southeastern United States (Dunning 2006). Due to extensive
habitat loss and fire suppression, this ecosystem has been reduced to 3-5% of its historic
range (Frost 2006), and much of the remaining longleaf pine ecosystem exists as scattered
and degraded remnant patches (Van Lear et al. 2005). Consequently, Bachman’s sparrow is
listed as a species of conservation concern throughout its range (Cox and Widener 2008), and
habitat loss and fragmentation likely are serious threats to its population persistence
(Dunning and Watts 1990) and other longleaf pine biota (Van Lear et al. 2005). Additionally,
the species has been considered to be a surrogate species for longleaf and open-pine
ecosystems because of its reliance on frequent fire regimes (typically ≤ 3 years; Tucker et al.
2004, Tucker et al. 2006), open canopy, and diverse groundcover characterizing these forests
(Shelton 2014). Therefore, understanding the effects of habitat fragmentation on Bachman’s
sparrow demographics may have important implications for the conservation of other
longleaf pine-associated species.
63
Suitable patches of longleaf pine forest often are unoccupied by Bachman’s sparrows
(Dunning and Watts 1990), and previous research suggests that habitat loss and
fragmentation play an important role in determining the distribution of this species. Indeed,
isolated patches of habitat are less likely to be colonized than connected patches, possibly
due to limited dispersal ability (Dunning et al. 1995, Dunning and Kilgo 2000). Taillie et al.
(2015) found that landscape-level habitat amount was the most influential predictor of
Bachman’s sparrow occupancy, suggesting a strong effect of habitat loss on this species.
Despite the emergent patterns of Bachman’s sparrow occupancy that have been demonstrated
by these studies, the demographic mechanisms underlying these landscape-scale relationships
remain largely unknown.
In this paper, we focused on three of the four hypotheses discussed above: 1) lower
productivity in response to increased nest predation and brood parasitism, and reduced
pairing success due to 2) disrupted landscape connectivity or 3) low habitat and male quality
in isolated locations. Specifically, we examined the influence of habitat loss and
fragmentation on these key components of reproductive success by comparing Bachman’s
sparrows residing in highly fragmented and relatively continuous longleaf pine landscapes in
North Carolina.
METHODS
Study area
We studied Bachman’s sparrows at 8 sites within the Middle Atlantic Coastal Plain
physiographic region (hereafter, ‘Coastal Plain’) of southeastern North Carolina (Figure 1).
This region is part of the North American Coastal Plain, which has recently been recognized
as a global biodiversity hotspot because of considerable plant endemism and habitat loss
64
(Noss et al. 2015). Longleaf pine ecosystems represented < 20% of the study area, while
development, agricultural lands, and pine plantations were the most abundant land cover
types (Southeast GAP Analysis Program 2008). The majority of longleaf pine woodlands
occurred on public landholdings, which comprised approximately 17% of the study area and
were managed by the US Department of Defense, North Carolina Forest Service, North
Carolina Wildlife Resources Commission (NCWRC), and the North Carolina Plant
Conservation Program (NCPCP). The Nature Conservancy (TNC) also managed 2% of
landholdings. Because of the species’ rarity in the study area, we relied on previously
published information on Bachman’s sparrow distribution in North Carolina (NCWRC,
unpublished data), reconnaissance surveys, and accessibility to select study sites.
Bachman’s sparrow habitat primarily was composed of mesic longleaf pine flatwoods
bordered by pocosin wetlands. Wet pine savannas in this region are characterized by an open
canopy, seasonally saturated soils, and a herbaceous layer dominated by graminoids, forbs,
and low shrubs. In addition to longleaf pine, tree species dominating sparrow habitat
included loblolly (P. taeda) and pond pine (P. serotina). Dominant groundcover species in
these vegetation types included wiregrass (Aristida stricta), switchcane (Arundinarea
gigantea), bracken fern (Pteridium aquilinum) and inkberry (Ilex glabra). Prescribed fire
typically was applied on a 3-5 year cycle, but sometimes exceeded 5 years on privately
owned sites where longleaf pine management was not an objective.
Field data collection
Reproductive status and territory mapping
We captured adult male Bachman’s sparrows at the beginning of each breeding
season from 2014-2015. Female Bachman’s sparrows are not easily observed or captured, so
65
we focused our study on the reproductive behaviors of male sparrows. We lured males into
mist nests using playback of Bachman’s sparrow vocalizations following methods described
by Jones and Cox (2007), and by placing nets in frequently used flight paths. Once captured,
we measured unflattened wing length and fitted individuals with a unique combination of
three colored leg bands and a US Geological Survey aluminum band to allow for visual
identification of individual sparrows. Also, we studied a small subset of males that we were
unable to capture, and their identity was confirmed through weekly territory mapping and the
presence of neighboring color-banded males (described below).
After banding, male territories were visited for 60 min between 5:30 and 13:00 EST
once per week between mid-April and the end of July to determine reproductive success for
each male (hereafter, ‘reproductive visit’). Territories were visited at different times of the
day and by different observers throughout a season. Males were considered territorial if they
remained in a given territory for ≥ 4 weeks (Vickery et al. 1992); we used this time span to
ensure that non-territorial ‘floaters’ were not included in the analyses. We considered males
to be paired if we observed a female sparrow in close proximity to the male, and if we
recorded mating behaviors (e.g., mate-guarding, copulation, or nest-building) during a
reproductive visit. For territories containing a mated pair, we located nests by observing
nesting behaviors of males and females (e.g., distraction displays and carrying nest material,
food, or fecal sacs) or opportunistically. When discovered, we checked nests using standard
nest monitoring protocols until they fledged or failed (Martin and Geupel 1993). Nests were
considered unsuccessful if they were depredated, abandoned, destroyed by weather or fire, or
otherwise produced no fledglings. Bachman’s Sparrows are to able raise multiple broods per
season (Dunning 2006), so we recorded the number and fate of nesting attempts per territory.
66
Based on weekly observations, we determined three components of reproductive success for
males at the end of each field season: 1) pairing success (i.e. paired vs. unpaired), 2) if
paired, was a male successful at fledging ≥ 1 offspring, and 3) the number of successful
broods produced. Because nests and fledglings are difficult to locate and observe for this
species (Dunning 2006), we were not able to precisely estimate productivity (i.e., number of
nestlings fledged) for each male.
During reproductive visits, we mapped territory boundaries by marking male
locations with handheld GPS units (± 5 m) and flagging. We defined a territory as the entire
area used during the breeding season, and recorded locations where males were observed
foraging, singing, following mates, and feeding fledglings. During visits to a territory, we
followed males from a distance to avoid influencing routine behaviors, and recorded 1-5 GPS
points of male locations until we lost track of the bird. GPS locations were taken only when a
male had moved a substantial distance (>15 m) from its previous location to avoid taking
points repeatedly within the same area. To delineate territory boundaries for each male, we
used the adehabitatHR package (Calenge 2006) to create 95% minimum convex polygons
(MCP) from territory mapping locations (20 ± 12 SD GPS points).
Vegetation measurements
Territory-level vegetation structure was quantified for each male. We measured
vegetation structure within a 5-m radius plot centered on five randomly selected male
locations recorded during weekly reproductive visits. Vegetation categories included grass,
woody vine-shrub (hereafter ‘woody vegetation’), and forb-fern following Taillie et al.
(2015). We quantified vegetation structure using indices of density by recording the presence
of each vegetation category (i.e., ‘hits’ or vegetation contacts) on 0.1-m sections of a 1.5-m
67
vertical pole. Vertical density included the number of vegetation hits along the entire length
of the pole, while groundcover density was calculated as the presence of vegetation on the
first 0.1-m section of the pole (Wiens and Rotenberry 1981). Also, maximum vegetation
height was obtained by recording the tallest hit on the pole and rounding to the nearest 0.1-m
section (Wiens and Rotenberry 1981, Moorman and Guynn 2001, Taillie et al. 2015).
Measurements were taken at every meter along four 5-m transects running in each cardinal
direction. At the same 1-m increments along each transect, we estimated canopy closure by
recording the presence or absence of live canopy using an ocular sighting tube. We used a
10-factor prism from the center point to determine the total basal area of pines surrounding
the plot center. Measurements were then averaged across plots for each male.
We obtained time since fire for each male’s territory from GIS databases (NCWRC,
NCPCP, and TNC, unpublished data) or by speaking with private land managers. Data
substitution was necessary for 3% of territories with missing data. For a single territory
monitored in 2014 and 2015, time since fire was unknown, but to our knowledge had not
occurred in ≥ 10 years. Therefore, we coded time since fire as 10 years to prevent the
inclusion of outliers in our models. For another 2 territories, the most recent fire occurred 7 –
10 years prior (K. Hinson, personal communication), so we selected the median date for
those males. For territories that spanned multiple burn units, we used the average time since
fire across burn units within the territory. Averaging time since fire was necessary for 29% (n
= 28) of males included in the analyses.
68
Territory characteristics
All statistical analyses were carried out using program R, version 3.2.2 (R Core Team
2015) and we report mean ± SD unless noted otherwise. To reduce the number of variables
used to measure territory-level vegetation characteristics, we used principal component
analysis (PCA). PCA reduces a large number of interdependent variables to 2 or 3
dimensions that extract the underlying environmental gradients (McGarigal et al. 2000). We
described the ecological characteristics of principal components based on significant factor
loadings (≤ -0.3 and ≥ 0.3; McGarigal et al. 2000). We retained two principal components
describing vegetation structure within male Bachman’s sparrow territories, which explained
> 85% of the original variance. Principal component 1 (PC1) represented increasing vertical
density and max height of grass, and principal component 2 (PC2) represented increasing
vertical density and max height of woody vegetation (Appendix B).
Landscape-level habitat amount
We used landscape-scale habitat amount (hereafter ‘habitat amount’) surrounding
male territories as a metric to assess the effects of habitat loss and fragmentation on
Bachman’s sparrow reproductive success. We chose this metric because it offers a simple
interpretation of both patch size and isolation (Fahrig 2013), and habitat amount is an
important predictor of Bachman’s sparrow occupancy (Taillie et al. 2015). To quantify
habitat amount surrounding each male Bachman’s sparrow territory, we adopted a ‘speciescentered’ approach (Shirley et al. 2013, Betts et al. 2014). Most landscape studies quantify
habitat using human-defined cover types, which may be inaccurate or have little relation to
how species respond to a landscape (Betts et al. 2014). For example, longleaf pine habitat
69
type classified for the Southeast GAP analysis dataset is sometimes incorrect (J. Winiarski,
personal observation), and Bachman’s sparrow can use other habitat types such as open-pine
woodlands, clearcuts, and powerline right-of-ways (Dunning 2006). Alternatively, it is
possible to accurately quantify landscape-level habitat amount using species’ distribution
data and unclassified Landsat reflectance bands (Shirley et al. 2013) that do not suffer from
classification biases. This species-centered approach is a promising method that has been
used to reveal the effects of landscape-scale habitat loss and fragmentation on species’
occupancy and vital rates (reviewed in Betts et al. 2014).
To quantify a species-centered view of habitat amount within the study area, we
constructed a Bachman’s sparrow species distribution model (SDM) using maximum entropy
modeling (Maxent; Phillips et al. 2006). Maxent generally performs well and is considered to
be one of the most effective approaches to creating SDMs with presence-only data (Elith et
al. 2011). Maxent uses environmental data from known occurrence locations to predict the
expected distribution of a species, and produces a raster map where each grid cell represents
the predicted habitat suitability (Elith et al. 2011). We used Bachman’s sparrow occurrence
data obtained from 119 point counts conducted across the study area during April-July, 20072014 (NCWRC and NCSU, unpublished data), and Landsat reflectance bands and
normalized difference vegetation index (NDVI) calculated from an April 2014 Landsat
image of the study region (Shirley et al. 2013, Betts et al. 2014). We used 2014 because it
closely represented the landscape conditions that existed during our study period. To assess
model performance, we performed 10-fold cross-validation and calculated the area under the
curve of the receiver-operator plot (Phillips et al. 2006; Appendix C). The resulting SDM
70
was converted to a binary habitat model by classifying values ≥ 0.5 to a value of 1 (habitat)
and values < 0.5 to a value of 0 (non-habitat; Betts et al. 2014).
To obtain habitat amount from the binary habitat model, we first used the rgeos
package (Bivand and Rundel 2015) and the ‘gCentroid’ function to extract coordinates of
each male’s territory centroid from its 95% MCP. Finally, we used the ‘land.metrics’
function in the spatialEco package (Evans 2016) and the binary habitat model to derive
percent habitat amount within a 3-km buffer around each male’s territory centroid. This 3-km
scale was important for predicting the effects of habitat amount on Bachman’s sparrow
occupancy in North Carolina (Taillie et al. 2015), and corresponds with the estimated
dispersal distance for this species based on home range size (Cox and Jones 2007, Brown
2012, Winiarski 2016) and the proportional relationship between territory size and median
dispersal described by Bowman (2003).
Reproductive success
We conducted analyses of reproductive success using generalized linear mixed effects
models (GLMM) with the lme4 package (Bates et al. 2015). We included ‘Site’ as a random
effect in all models to account for the spatial interdependence of male territories within study
sites. For each component of reproductive success, we built separate models as a function of
vegetation structure variables from the principal component analysis (PC1 and PC2), time
since fire, wing length (i.e., body size), and habitat amount (Table 1). We used the median
value for wing length for 10 birds missing this measurement (~10% of our sample). In a
concurrent study investigating Bachman’s sparrow habitat selection and space-use (Winiarski
2016), we affixed radio transmitters to a subset of males (n = 37), which can negatively affect
productivity (Barron et al. 2010). Therefore, we first tested whether radio-tagged males had
71
reduced pairing (χ2 = 3.17, df = 1, P = 0.07) or reproductive success (χ2 = 0.03, df = 1, P =
0.86) compared to untagged males. Because there were no differences between groups, we
discarded this variable in subsequent analyses. Due to our study design (more isolated sites
were sampled in 2015), we could not include year as a predictor variable in the models.
While we acknowledge that reproductive success may vary annually in response to
fluctuating weather and predator abundance, we did not detect a difference in the number of
males that paired (χ2 = 1.14, df = 1, P = 0.29) or fledged young (χ2 = 0.00, df = 1, P = 1.00) at
sites that we were able to sample in both years of the study (n = 64 territories).
Pairing success (0 = unpaired, 1 = paired) and fledging success (0 = no offspring
fledged, 1 = at least 1 offspring fledged) were fitted to binomial distributions. We conducted
additional analyses examining 1) number of successful broods fledged as the response
variable using the same variables as above (fitted to a Poisson distribution), and 2) daily nest
survival in response to temporal, local-, and landscape-scale covariates in a separate study
(Winiarski 2016); however, we had similar results as the binomial fledging success analysis
and do not include these analyses here. For both model sets, only territorial males (males
present ≥ 4 weeks) were included in the analyses, and only paired males were included in the
fledging success models. To explore the most important variables for each model set, we
tested all possible linear combinations of variables. The top pairing and fledging success
models were selected using Akaike’s Information Criterion corrected for small sample sizes
(AICc; Burnham and Anderson 2002) using the ‘MuMIn’ package (Barton 2016). Models
were ranked according to their AICc values, and model averaging was performed across the
top candidate models (∆AICc ≤ 2). A parameter was considered significant if the 95% CI did
72
not include 0, and was considered to have no relationship with the response variable if the
95% CI overlapped 0.
RESULTS
Territory and landscape characteristics
We monitored 112 Bachman’s sparrow territories, and were able to record
reproductive success for 96 Bachman’s sparrow territories where males were present for ≥ 4
weeks: 43 males at 5 sites in 2014, and 53 males at 5 sites in 2015 (Table 2). Of the 96
males, 89 (93%) were color banded, and 8 of these banded males were monitored in both
years of the study (Table 2). Local- and landscape-scale factors were variable across
territories, with time since fire ranging from 0.2 – 10 years (2.0 ± 1.95 years) and habitat
amount ranging from 3 – 25% (15.9 ± 7.45%).
Reproductive success
Pairing success
Approximately 69% of males were paired across all years and sites combined (Table
2). The GLMM analysis for pairing success included three top candidate models with ∆AICc
≤ 2 (Table 3), and habitat amount appeared in all of the top models. Following model
averaging, habitat amount did not overlap zero (Table 4), reflecting the consistently lower
pairing rate in locations with low habitat amount within 3 km (Figure 3). Two other terms
featured in the two top models, time since fire and PC2 (Table 3), but the confidence limits
of the model averaged parameter estimates overlapped zero. Neither PC1 or wing length
appeared in models with ∆AICc ≤ 2 (Table 3).
Fledging success
73
We documented no support for any of the variables to predict the probability of
fledging ≥ 1 offspring. Sixty-six paired males were included in the fledging success GLMM
analysis. Of these 66 males, approximately 23% produced zero offspring, 64% fledged a
single brood, and 14% raised two broods. No males were observed to successfully fledge
more than two broods. There were eight top candidate models in the fledging success model
set, with the null model ranking the highest (Table 3). PC1, PC2, wing length, time since fire,
and habitat amount appeared in the top models, but none of these variables were significant
following model averaging (Table 4).
DISCUSSION
As expected, we saw lower pairing success in areas with less habit within 3 km of
male territories, but the probability of fledging offspring was unaffected. Thus, differential
pairing success as a function of habitat amount may be partly responsible for the lower
probability of occurrence of this species in landscapes with low levels of habitat (Taillie et al.
2015). Although local vegetation conditions (i.e., herbaceous groundcover) and frequent fire
are often emphasized as the most critical components of Bachman’s sparrow breeding habitat
(Dunning and Watts 1990, Haggerty 1998, Tucker et al. 2004, Tucker et al. 2006), we
conclude that pairing success was more strongly influenced by habitat amount at the
landscape-scale.
Our results indicated no relationship between habitat amount and fledging success.
This result is supported by previous work, which found no indication of reduced nest survival
for Bachman’s sparrows in more fragmented areas compared to relatively continuous
landscapes in the Coastal Plain of North Carolina (Winiarski 2016). Rather, reproductive
success of males in this study that were able to pair was high, regardless of habitat amount in
74
the surrounding landscape. Thus, it appears the mechanisms responsible for negative effects
of habitat fragmentation (i.e., elevated nest predation and brood parasitism) for many species
(Robinson et al. 1995, Rodewald and Yahner 2001) differ for Bachman’s sparrow. For
example, Winiarski (2016) found that parasitism by brown-headed cowbirds (Molothrus
ater) occurred for only 11% of Bachman’s sparrow nests, and low levels of nest parasitism
have been reported elsewhere (Haggerty 1988, Stober and Krementz 2000, Tucker et al.
2006). Little is known of the predator community responsible for Bachman’s sparrow nest
predation, or whether the predator community is differentially affected by habitat loss and
fragmentation. Future research on Bachman’s sparrow nest predators will help clarify the
relationships observed in this study.
Reduced pairing success for males in isolated habitat patches has been observed in
migratory (Gibbs and Faaborg 1990, Burke and Nol 1998, Bayne and Hobson 2001) and
resident species (Cooper and Walters 2002, Robles et al. 2008) in a variety of fragmented
ecosystems. There are several explanations for limited breeding opportunities for males in
highly fragmented landscapes. One hypothesis is that males in highly isolated patches may
fail to pair because these patches are rarely colonized by potential mates (the ‘disrupted
dispersal’ hypothesis; Dale 2001, Cooper and Walters 2002, Lampila et al. 2005, Robles et
al. 2008). Female-biased natal dispersal is common in birds (Greenwood 1980), and it has
been proposed that females in fragmented landscapes have 1) a limited ability to find mates,
and 2) may effectively be ‘lost’ from the breeding population if they disperse into areas
devoid of conspecifics (Dale 2001). This disruption of female dispersal would result in maleskewed populations, which is often the case for small and isolated bird populations in decline
(Dale 2001, Donald 2007). In agreement with this hypothesis, we observed zero females at
75
two sites and only one female at four sites, all of which were small and isolated. Disrupted
dispersal seems a plausible explanation for low pairing success of Bachman’s sparrows in
our study, and these movements have been negatively impacted by habitat fragmentation in a
more mobile longleaf pine specialist, the red-cockaded woodpecker (Picoides borealis;
Kesler et al. 2012). However, removal or translocation experiments are needed to test this
hypothesis (e.g., Cooper and Walters 2002) and the resistance of different matrix conditions
to Bachman’s sparrow movement (e.g., Jones 2013).
Alternatively, females might not select isolated patches due to the potentially lower
quality of these areas and the males residing within them (Cooper and Walters 2002).
However, we accounted for measures of habitat quality (i.e., vegetation structure principal
components and time since fire) and male quality (i.e., body size) in the pairing success
analysis, and showed no confounding effects of these factors. Earlier work showed that
breeding productivity was greatest for Bachman’s sparrows in areas burned 1-3 years
previously, and declined significantly after 3 years (Tucker et al. 2006). Although time since
fire exceeded 3 years for ~30% of unpaired males and was longer for unpaired males (2.71 ±
2.60 years) than paired males (1.6 ± 1.49 years), differences were not significant. Male age
and breeding experience can be an important determinant of pairing success (e.g., Bayne and
Hobson 2001, Dale 2011), but we were unable to evaluate the importance of male age
because adult Bachman’s sparrows can only be classified initially as after-hatch-year (Pyle
1997). However, several males of known age or at least after-second-year did not indicate
that experience was related to reduced pairing success in this study; 2 males banded as
nestlings in 2014 bred successfully as adults the following breeding season, and
approximately 19% of unpaired males (n = 6) banded in 2014 and monitored in 2015 were at
76
least 3 years old. Active habitat-selection by females may be a possible explanation for the
low pairing success of sparrows in isolated patches, but more research is needed to
understand the effects of food availability, male age, and conspecific attraction on pairing
success in Bachman’s sparrow.
Although reduced pairing success in highly fragmented landscapes may be partly
responsible for Bachman’s sparrow declines, it remains unclear which demographic
parameters are contributing the most to population growth rates, and how they are affected
by landscape continuity. Adult survival is generally the most important vital rate affecting
population growth for a wide diversity of birds (Sæther and Bakke 2000), and previous work
suggests this relationship may apply to Bachman’s sparrow populations. Pulliam et al. (1992)
concluded that adult and juvenile survival were the primary parameters influencing growth
rates in Bachman’s sparrows using a spatially-explicit simulation approach. Likewise, adult
survival had a larger influence on population growth rates than annual productivity for
Bachman’s sparrows in an old-growth longleaf forest in Georgia (Cox and Jones 2010).
However, both studies selected reproductive rates from the literature and assumed that
juvenile survival produced a stable population growth rate (λ = 1.0). Therefore, obtaining
accurate estimates of reproductive success and survival is necessary to gain a better
understanding of Bachman’s sparrow population dynamics and implement effective
conservation strategies.
In the rapidly urbanizing southeastern US, wildlife species associated with open pine
woodlands, such as Bachman’s sparrow, are likely to experience the highest rates of habitat
loss under future land-use scenarios (Martinuzzi et al. 2015). Urban sprawl in this region is
expected to double or triple within the next 50 years, putting already vulnerable species at
77
further risk by decreasing habitat amount and hindering management actions (i.e., prescribed
fire) that are necessary to maintain open pine ecosystems (Terando et al. 2014). Therefore,
understanding the effects of landscape transformation on Bachman’s sparrow demographics
will become increasingly critical. Promoting breeding opportunities, and perhaps long-term
persistence of Bachman’s sparrow, depends on protection and management of all remaining
patches of longleaf pine (especially via prescribed burning; Tucker et al. 2004, Tucker et al.
2006), and – even more importantly – increasing landscape-level habitat amount. Managers
can ensure a probability of pairing close to 100% by coordinating restoration and
management activities in landscapes comprised of ≥20% habitat (~560 ha) within 3 km.
Otherwise, restoration efforts may fail to accommodate Bachman’s sparrow and other
longleaf pine inhabitants, despite suitable conditions at the local-level.
78
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Table 1. Variables used in candidate models to test the effects of local- and landscape-scale
factors on reproductive success of male Bachman’s sparrows in southeastern North Carolina,
2014-2015.
Variable
Wing length
Vegetation PC1 & PC2
Time since fire
Habitat amount (%)
Hypothesis
Larger, higher quality males will have a higher probability of
pairing and fledging offspring.
Vegetation structure variables may indicate habitat quality.
Males in recently burned stands will have high quality territories
with more resources, and will have better reproductive success.
Males in more contiguous landscapes will have a better chance
of pairing and producing young due to increased connectivity
and decreased nest predation and parasitism.
84
Table 2. Summary of Bachman’s sparrow territories monitored in the Coastal Plain of North
Carolina, USA, 2014-2015. Percent of males that fledged young included paired males only.
Note that 8 banded males were monitored in both years.
N focal males
Study site
County
Year Banded Unbanded Paired (%)
Holly Shelter
Pender
2014 31
3
32/34 (94)
Holly Shelter
Pender
2015 28a
0
24/28 (86)
Compass Pointe Brunswick 2014 1
0
1/1 (100)
Compass Pointe Brunswick 2015 1b
0
0/1 (0)
Sleepy Creek
Bladen
2014 4
0
1/4 (25)
Shaken Creek
Pender
2014 1
1
1/2 (50)
Molpus
Brunswick 2014 2
0
1/2 (50)
Green Swamp
Brunswick 2015 13
3
6/16 (38)
Pinch Gut
Brunswick 2015 2
0
0/2 (0)
Boiling Spring
Brunswick 2015 6
0
0/6 (0)
a
includes 7 color banded males monitored in both 2014 and 2015
b
same color banded male monitored in both 2014 and 2015
Fledged (%)
24/32 (75)
19/24 (79)
1/1 (100)
0/0 (0)
0/1 (0)
1/1 (100)
0/1 (0)
6/6 (100)
0/2 (0)
0/6 (0)
85
Table 3. Top (∆AICc ≤ 2) mixed-effects logistic regression models of pairing and fledging
success for Bachman’s sparrows in the Coastal Plain region of North Carolina, USA, 20142015. Site was used as a random effect in all models. Models were compared using Akaike’s
Information Criterion corrected for small sample size (AICc). Model parameters: Hab
principal component 1), PC2 (habitat principal component 2), and Wing (wing length).
Model
K
Loglik
AICc
∆AICc
wi
Pairing success
Hab
3
-41.68
89.63
0.00
0.21
Hab + TSF
4
-41.00
90.44
0.81
0.14
Hab + PC2
4
-41.41
91.26
1.63
0.10
Fledging success
Null
2
-35.37
74.94
0.00
0.11
TSF
3
-34.33
75.05
0.11
0.11
Hab + TSF
4
-33.47
75.60
0.66
0.08
PC2
3
-34.84
76.07
1.13
0.06
Wing
3
-34.88
76.15
1.21
0.06
TSF + Wing
4
-33.92
76.49
1.55
0.05
Hab
3
-35.08
76.54
1.60
0.05
PC1
3
-35.26
76.91
1.97
0.04
Notes: K = degrees of freedom, Loglik = log liklihood, ∆AICc = change in AICc in relation to
the highest-ranked model, and wi = AICc weight.
86
Table 4. Parameter estimates and 95% confidence intervals for model-averaged variables
(∆AICc ≤ 2) used to model pairing and fledging success for 96 male Bachman’s Sparrows in
the Coastal Plain region of North Carolina, USA, 2014-2015. Model parameters: Hab
principal component 1), PC2 (habitat principal component 2), and Wing (wing length).
Variable
Parameter estimate a
2.5%
97.5%
Pairing success
Hab
1.46
0.88
2.03
TSF
-0.09
-0.79
0.21
PC2
-0.04
-0.76
0.34
Fledging success
TSF
-0.23
-1.32
0.21
Hab
-0.13
-1.70
0.59
PC2
-0.03
-0.80
0.25
Wing
-0.05
-0.81
0.29
PC1
0.01
-0.46
0.74
a
Parameter estimates and 95% CIs were derived from standardized variables.
87
Figure 1. Study sites in southeastern North Carolina where reproductive data were collected
for 96 Bachman’s sparrow territories, 2014-2015. Study sites are indicated with four-letter
abbreviation; Boiling Spring Lakes Preserve (BSLP), Compass Pointe (COPO), Green
Swamp Preserve (GSPR), Holly Shelter Game Land (HSGL), Molpus Timberlands (MOLP),
Pinch Gut Game Land (PGGL), Shaken Creek Preserve (SCPR), and Sleepy Creek Farms
(SCFA).
88
Figure 2. Mean (± SD) habitat amount (%) within 3 km of male territory centroids for 8 sites
in the Coastal Plain region of North Carolina, USA, 2014-2015. Sample size of males at each
site is listed below the site code.
89
Figure 3. Model-averaged predicted probability of pairing for male Bachman’s sparrows in
relation to percent habitat within 3 km of a territory centroid. Shaded regions indicate 95%
90
APPENDICES
91
Appendix A
Appendix A. Reproductive index scores modified from Vickery et al. (1992) and Tucker et
al. (2006) used as an index of Bachman’s Sparrow reproductive success.
Score
1
2
3
4
5
6
7
8
9
Description
Male was on territory for ≥ 4 weeks but showed no indication of pairing.
Male seen consorting with a female, but no evidence of nesting.
Female seen carrying nesting material or nest found.
Adults seen carrying food or found feeding nestlings.
Direct observation of fledglings or nest known to have successfully fledged.
Renesting after successful fledging of a first brood.
Direct observation of fledglings for a second brood.
Active nest after successful fledging of a second brood.
Direct observation of fledglings for a third brood.
92
Appendix B
Appendix B. Results of principal component analysis of 15 vegetation variables measured at
Bachman’s sparrow territory locations. The first two components explained over half of the
variance and were included as covariates in the pairing and fledging success models. Values
indicate factor loadings for each component. Principal components were interpreted using
significant (≤ -0.3 and ≥ 0.3) factor loading values (bold).
Habitat characteristic
Eigenvalue
Percent of variance explained
Vertical woody density
Vertical grass density
Vertical forb density
Vertical switchcane density
Vertical dead density
Groundcover woody density
Groundcover grass density
Groundcover forb density
Groundcover switchcane density
Groundcover dead density
Maximum woody height
Maximum grass height
Maximum forb height
Maximum switchcane height
Maximum dead height
PC1
1.41
62.49
-0.148
0.614
-0.007
0.005
-0.021
-0.023
0.123
0.014
0.000
-0.002
-0.291
0.699
-0.064
0.018
-0.082
PC2
0.86
23.08
0.446
0.244
0.050
0.026
0.046
0.042
0.023
0.003
0.000
0.000
0.779
0.237
0.163
0.099
0.184
93
Appendix C
Appendix C. The Bachman’s sparrow Maxent model performed better than random, and had
high predictive success based on area under the curve values (AUC; training AUC = 0.934,
test AUC = 0.942, SD = 0.010). Variables with the highest predictive contributions were
reflectance band 5 and NDVI (41.8% and 26.9%, respectively; Figure 1).
Figure 1. Percent contribution of 8 predictors used in Bachman’s sparrow Maxent model for
the Coastal Plain of North Carolina, USA.
94

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