HOME!RANGE!USE,HABITAT!SELECTION,!AND!STRESS!PHYSIOLOGY!OF ...
Transcript of HOME!RANGE!USE,HABITAT!SELECTION,!AND!STRESS!PHYSIOLOGY!OF ...
HOME RANGE USE, HABITAT SELECTION, AND STRESS PHYSIOLOGY OF EASTERN
WHIP-‐POOR-‐WILLS (ANTROSTOMUS VOCIFERUS) AT THE NORTHERN EDGE OF
THEIR RANGE
A Thesis Submitted to the Committee on Graduate Studies
in Partial Fulfillment of the Requirements
for the Degree of
Master of Science
in the Faculty of Arts and Science
TRENT UNIVERSITY
Peterborough, Ontario, Canada
(c) Copyright by Gregory J. Rand 2014
Environmental and Life Sciences Graduate Program
May 2014
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Abstract
Home range use, habitat selection, and stress physiology of eastern whip-‐poor-‐wills
(Antrostomus vociferus) at the northern edge of their range
Gregory J. Rand
The distribution of animals is rarely random and is affected by various
environmental factors. We examined space-‐use patterns, habitat selection and stress
responses of whip-‐poor-‐wills to mining exploration activity.To the best of my
knowledge, fine scale patterns such as the habitat composition within known home
ranges or territories of eastern whip-‐poor-‐wills have not been investigated. Using a
population at the northern edge of the distribution in an area surrounding a mining
exploration site, we tested whether variations in habitat and anthropogenic
disturbances influence the stress physiology of individuals. We found no effect of
increased mining activity on the stress physiology of birds, but found a significant
scale-‐dependent effect of habitat on their baseline and stress-‐induced corticosterone
levels, and we suggest that these are the result of variations in habitat quality. The
importance of other factors associated with those habitat differences (e.g., insect
availability, predator abundance, and microhabitat features) warrants further
research.
Keywords: home ranges, habitat selection, corticosterone, eastern whip-‐poor-‐will,
Antrostomus vociferus, radio-‐telemetry, anthropogenic disturbances
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Acknowledgements
I’d like to start off by thanking my co-‐supervisors Dr. Joe Nocera and Dr. Gary
Burness for taking me on as a graduate student and giving me the opportunity to do
this research. Your advice and guidance were invaluable and I count myself
fortunate to have had you as my supervisors. I’d also like to thank my committee
member Dr. Jim Schaefer for his advice and ideas throughout the project.
Thank you to New Gold (formerly Rainy River Resources) and the Ontario Ministry
of Natural Resources for providing funding and help, which made this research
possible.
My thanks to John Van den Broek and Matt Myers (OMNR), for their assistance with
some of the logistics and giving me an idea of where to look for birds. Otherwise who
knows where I’d be, probably still wandering the roads at night trying to find birds. I
have to thank Andy, Peter, and Alyson for helping me out with the placement and
maintenance of sound monitoring equipment, there weren’t enough hours in the
night and who knows how many tick bites you saved me from! I must also thank all
the field assistants that helped me out no matter for how long or short a time: Lisa,
Andy, Larissa, Kat (Kate), Val, Rhiannon, Niki, and Mackenzie. I truly am grateful for
all of you that defied your circadian rhythm and helped me out on those late nights
(or were they early mornings)!
A great thanks to all the landowners that allowed me to access their properties (and
John/Peter again for talking to them), as well as providing me with a wealth of local
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knowledge about the area. A special thanks to the many Neilsons for their
hospitality, wi-‐fi, tips and what I came to believe were sanity checks.
To all the people I’ve met at Trent and that have helped me in one way or another;
thank you. Thank you to Lanna, Nick, and Yasmine for tolerating “clever remarks”
and occasional bouts of contagious procrastination. Thanks to Eunice for your help
with figuring out how to run the assay (and running my first RIAs). Thank you to
Erica Nol, Chris Risley, and Walter Wehtje for reminding me that I should be
working on my thesis but still inviting me to birding excursions.
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Table of Contents
Abstract ............................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Figures ....................................................................................................................................vi
List of tables ..................................................................................................................................... vii
Introduction ........................................................................................................................................1
Methods ................................................................................................................................................8
Study area and treatment ............................................................................................................................8
Field procedures ..............................................................................................................................................9
Home Range Analysis..................................................................................................................................11
Habitat Analysis ............................................................................................................................................12
Corticosterone Assay ..................................................................................................................................13
Statistics............................................................................................................................................................14
Results................................................................................................................................................ 17
Discussion......................................................................................................................................... 20
Literature Cited............................................................................................................................... 30
Figures and Tables......................................................................................................................... 39
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List of Figures
Figure 1. Kernel utilization distribution estimates for the (A) home range and (B) core range of a male whip-‐poor-‐will in the Rainy River district, Ontario, Canada, 2012. Red dots represent locations of birds based on triangulation.
Figure 2. Kernel utilization distribution estimates for (A) home ranges and (B) core ranges of 5 neighbouring whip-‐poor-‐wills in the Rainy River district, Ontario, Canada, 2012. Dots represent triangulated locations and individuals are identified by colour.
Figure 3. Effect of distance from the mining exploration site on baseline (circles) and stress-‐induced (squares) corticosterone levels in whip-‐poor-‐will.
Figure 4. Strip plot illustrating the effect of different habitat proportion on baseline corticosterone at the core range. Dots represent corticosterone values for individuals.
Figure 5. Strip plot illustrating the effect of different habitat proportion on baseline corticosterone at the home range level. Dots represent corticosterone values for individuals.
Figure 6. Strip plot illustrating the effect of different habitat proportion on stress-‐induced corticosterone at the home range level. Dots represent corticosterone values for individuals.
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List of tables
Table 1. Mean habitat composition within the home and core ranges of birds, and mean habitat composition within randomly selected areas associated with the core (50 % available) and full (95%) available ranges.
Table 2. Percent overlap of whip-‐poor-‐will home range estimates between adjacent birds and within pairs (matched by colour) for the 95% isopleths (home range) and 50% isopleths (core range) estimates in the Rainy River district, Ontario, 2012. Individuals are labeled with their band number preceded by M for males and F for females.
Table 3. Competing conditional logistic regression models with variables, coefficients (+-‐ SE), and p-‐values for whip-‐poor-‐will habitat selection at the core range scale in the Rainy River district, Ontario in 2012. The model with the best parameter estimates is displayed in bold.
Table 4. Competing conditional logistic regression models with variables, coefficients (± SE), and p-‐values for whip-‐poor-‐will habitat selection at the home range scale in the Rainy River district, Ontario in 2012. The model with the best parameter estimates is displayed in bold.
Table 5. Multiple linear regression model with variable coefficients, standard error, t-‐value and p-‐value evaluating the effects of habitat within the core range on baseline corticosterone levels in whip-‐poor-‐wills
Table 6. Multiple linear regression model with variable coefficients, standard error, t-‐value and p-‐value evaluating the effects of habitat within the home range on baseline corticosterone levels in whip-‐poor-‐wills
Table 7. Multiple linear regression model with variable coefficients, standard error, t-‐value and p-‐value evaluating the effects of habitat within the core range on stress-‐induced corticosterone levels in whip-‐poor-‐wills
Table 8. Multiple linear regression model with variable coefficients, standard error, t-‐value and p-‐value evaluating the effects of habitat within the home range on stress-‐induced corticosterone levels in whip-‐poor-‐wills
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Introduction
The spatial distribution of animals is rarely random and is affected, at the landscape
scale, by habitat availability and heterogeneity (Dunning et al. 1992, Kie et al. 2002,
Bailey and Thompson 2007, Leonard et al. 2008, Wilson and Watts 2008). On a
smaller scale, intra-‐ and inter-‐specific interactions (Cody 1981, Fletcher 2007),
available resources (Burke and Nol 1998, Rush et al. 2010), vegetation structure
(Dearborn et al. 2001, Bailey and Thompson 2007), breeding condition (Marra and
Holberton 1998), and local factors can govern distributions.
Anthropogenic activity can have profound effects on the landscape and on ecological
communities. Such activity that results in habitat loss is clearly linked to reduced
biodiversity, through activities such as agriculture (Donal et al. 2001, Gaston et al.
2003), forestry, and mining (Wilcove et al. 1998, Prugh et al. 2010). Resultant effects
such as habitat fragmentation (Fahrig 2003), reduction in food availability (Burke
and Nol 1998), and introduction of environmental pollutants (Kight and Swaddle
2011, Crino et al. 2011) can further negatively affect animal populations.
Anthropogenically-‐altered landscapes can create a mosaic of different habitat types
with relatively natural areas interspersed among highly modified sites (Andren
1994). Such alterations in habitat structure (Suorsa et al. 2003) and quality (Marra
and Holberton 1998) can have significant effects on a species’ space use patterns
(Leonard et al. 2008). However, because not all species respond to anthropogenic
disturbance in the same way (Hamilton et al. 2011), understanding how habitat is
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used on a species-‐by-‐species basis can help create more effective conservation
policies (Dearborn et al. 2001, Bailey and Thompson 2007).
Anthropogenic activity is increasingly prevalent in remote areas; exposing wildlife
to threats that can result in direct mortality (Trombulak and Frissell 2000), habitat
loss (Fahrig 2003), and environmental pollution (Franceschini et al. 2009, Barber et
al. 2010). One such activity increasingly occurring in remote areas is mining. Even in
the initial stages, mining operations pose a number of potential challenges to
wildlife; the creation of roads to access sites, traffic, habitat destruction, operation of
heavy machinery, blasting and the introduction of environmental pollutants can
have a effects on nearby individuals and populations (Trombulak and Frissell 2000,
Canaday and Rivadeneyra 2001). Noise pollution is increasingly recognized as a
potential problem to wildlife due to its prevalence and pervasiveness (Leonard and
Horn 2008, Ortega 2012), and it may be especially problematic with low frequency
sounds, which can travel great distances unimpeded by dense vegetation (Habib et
al. 2007). The deleterious effects of noise have been demonstrated in many
situations and can cause individuals to avoid noisy areas (Canaday and Rivadeneyra
2001), a reduction in perceived habitat quality (Habib et al. 2007, Bayne et al. 2008),
lowered breeding success (Halfwerk et al. 2011), disruption of normal behaviour,
reduced immune capacity, and changes to endocrine function (Kight and Swaddle
2011, Crino et al. 2013). Due to acoustic masking (i.e., when a sound disrupts or
blocks another sound), noise may become especially problematic for birds and other
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animals that rely on vocalizations to communicate. Masking can pose a problem for
setting up and maintaining territories, attracting mates, and detecting approaching
threats (Habib et al. 2007, Barber et al. 2010). This may cause individuals to spend
additional energy that may lead to physiological stress (Halfwerk et al. 2011) or
increased predation risk.
To gauge the effect of environmental disturbance on animal health, conservation
physiologists are increasingly relying on quantifying indices of stress (Bonier et al.
2009, Angelier et al. 2009). Environmental stressors may be from natural causes,
including limited food supply (Kitaysky et al. 2007), conspecific interactions
(Bhatnagar and Vining 2003), habitat quality (Marra and Holberton 1998), and
predator risk (Scheuerlein et al. 2001), or can be of anthropogenic origin such as
habitat fragmentation (Johnstone et al. 2012), environmental contaminants
(Franceschini et al. 2009), noise pollution (Crino et al. 2011), and increased human
intrusion from tourism (Müllner et al. 2004, Walker 2006). In response to a stressor,
vertebrates secrete glucocorticoids; in birds and rodents the primary glucocorticoid
is corticosterone, while in fish and most mammals it is secreted in the form of
cortisol. Corticosterone is always in circulation and helps to regulate normal
metabolic functions, thereby allowing animals to cope with normal life events
(Landys et al. 2006). In response to immediate threats, there is an immediate release
of catecholamines which increases heart rate, promotes vasoconstriction and
gluconeogenesis. Following this, an individual’s glucocorticoid levels rise rapidly
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within several minutes, resulting in behavioural and physiological changes that
promote survival (Wingfield and Romero 2001). When exposure to stressors is
prolonged, glucocorticoids can become chronically elevated which can result in
wide-‐ranging effects, including depression of the immune system (Saino and Suffritti
2003, Martin 2009), reduced cognitive ability (reviewed in Schoech et al. 2011), and
behavioural changes, such as redirection away from parental care (Wingfield and
Romero 2001, Breuner et al. 2008). As a result of the sensitivity of glucocorticoids to
environmental perturbations, chronically elevated levels are often interpreted as
indicative of an animal under chronic stress (Romero 2004, Bonier et al. 2009, Busch
and Hayward 2009).
Although environmental stressors often leads to an elevation in circulating baseline
levels of corticosterone (e.g., Birds: Kitaysky et al. 2001, Clinchy et al. 2004, Jenni-‐
Eiermann et al. 2008, Crino et al. 2011, Mammals: Blanchard et al. 1998, Schmidt et
al. 2007; Reptiles: Romero and Wikelski 2001, Cash and Holberton 2005, Sykes and
Klukowski 2009); acute changes in stress-‐induced levels have also been reported
(Bhatnagar and Vining 2003, Crino et al. 2011, Leshyk et al. 2013). Variation among
individuals in stress-‐induced glucocorticoid levels has been suggested to be
correlated with fitness (Breuner et al. 2008) and survival (Hau et al. 2010).
However, a recent meta-‐analysis highlights the complex and variable nature of
physiological responses to stress, especially with respect to stress-‐induced
glucocorticoid levels (Dickens and Romero 2013). In fact, both increased and
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decreased stress-‐induced levels may occur as a result of chronic stress. For example,
while some presumed stressors such as human intrusion from tourism (Walker
2006), captivity (Dickens et al. 2009), and reduced food availability (Kitaysky et al.
1999) resulted in a decreased stress response, other studies and stressors showed
increased stress responsiveness (e.g., tourism, Müllner et al. 2004; reduced food
availability, Kitaysky et al. 2001, 2007; predation risk, Clinchy et al. 2004;
conspecific interactions, Bhatnagar and Vining 2003).
Environmental stressors, and the pursuant effects of stress on individuals, may be
linked to population declines in some animals (Boonstra and Hik 1998, Foley et al.
2001). In recent decades, aerially-‐insectivorous birds (hereafter ‘aerial
insectivores’) have undergone dramatic population declines in North America
(Nebel et al. 2010). Among members of this guild, one of the most poorly understood
groups is the nightjars, due to their crepuscular and nocturnal behaviour, cryptic
camouflage, and large territory size (Cink 2002). One member of the nightjar guild,
the eastern whip-‐poor-‐will (Antrostomus vociferus; hereafter ’whip-‐poor-‐will’), is
listed as threatened in Canada (COSEWIC 2009) and in Ontario (COSSARO 2009) and
has recently undergone significant population declines for which habitat loss and
degradation are possible causes (Cink 2002). Identifying how whip-‐poor-‐wills use
habitat features and respond to disturbances on their breeding ranges should
contribute to more effective conservation policies (Dearborn et al. 2001, Bailey and
Thompson 2007).
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Due to their nocturnal and cryptic habits which render them difficult to study, very
little is known about whip-‐poor-‐will ecology and stress physiology. Our work
represents the first study to look at their habitat use in the northern portion of their
range, and will help guide conservation strategies for this rapidly declining species.
Despite the increase in use of physiological indices as conservation tools (Wikelski
and Cooke 2006), there are surprisingly few studies of the effects of habitat on an
individual’s stress physiology. Although large-‐scale environmental factors, such as
anthropogenic disturbances (Crino et al. 2011, 2013), habitat structure (Suorsa et al.
2003), and habitat quality (Bauer et al. 2013) can affect stress physiology, to the best
of our knowledge our work represents the first investigation of fine scale habitat use
in that regard.
We assessed the habitat/space use and stress physiology of whip-‐poor-‐wills in
northwestern Ontario, Canada, and tested whether these varied in response to
natural and anthropogenic environmental factors. We radio-‐tracked individuals and
determined how birds were selecting habitats on a landscape level, and evaluated
the effects of habitat and anthropogenic activity on their stress physiology.
Specifically, we examined space-‐use patterns, habitat selection and stress responses
of whip-‐poor-‐wills to mining exploration activity. We hypothesized that differences
in habitat composition, and proximity to anthropogenic disturbances, alter patterns
of corticosterone secretion and habitat use. We predicted that birds exposed to
disturbances associated with elevated human activity would differ in their baseline
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corticosterone levels from non-‐disturbed individuals. Similarly, we predicted that
birds residing in less desirable habitat types, such as land dedicated to intensive
human use (logging, agriculture), would also show different baseline corticosterone
levels from individuals in more desirable habitat (Bauer et al. 2013). Owing to
variability in the stress response among species (Busch and Hayward 2009) and
given that there has been no previous work on stress physiology of whip-‐poor-‐wills
or related species, we followed the recommendations of Dickens and Romero (2013)
and Leshyk et al. (2013) and did not make predictions as to the directionality of
stress-‐induced corticosterone levels. However, we predicted that habitat use would
be different in disturbed areas (i.e., the area of mining exploration) as habitat quality
may be reduced, forcing birds to use and/or defend larger areas (Ortega and Capen
1999, Anich et al. 2009).
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Methods
Study area and treatment
We conducted this study in the Rainy River district of Ontario, Canada, May-‐August,
2011 and 2012. The landscape is a mosaic of natural and anthropogenic habitats
consisting primarily of areas characterized by agriculture, forest, logged forest, and
wetlands. Forest sites are dominated by trembling aspen (Populus tremuliodes),
balsam poplar (Populus balsamifera), speckled alder (Alnus sp.) red pine (Pinus
resinosa), jack pine (Pinus banksiana), white spruce (Picea glauca), and black spruce
(Picea mariana).
The distribution of whip-‐poor-‐wills throughout the area was known to be
heterogeneous and clumpy (pers. comm. John Van den Broek, Ontario Ministry of
Natural Resources (OMNR)). As such, in 2011 we captured birds in six separate
areas at distances ranging from 1 to 33 km from a mining exploration site. In 2012,
we focused on a finer spatial scale of two 5 km x 5 km areas, where one area was a
“treatment” site centered directly a mining exploration site. At this stage in the
process there was no mining, however, mine exploration activity can be a
disturbance by creating increased traffic, elevated noise levels, and localized habitat
clearing. The second area was a “control” site situated 10 km from the mining
exploration site.
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Field procedures
In 2011, we located whip-‐poor-‐wills by surveying areas that were known to be
occupied (pers. comm. John Van den Broek, OMNR), and we trapped birds in these
areas during June and July. In 2012, we re-‐visited areas where we had previously
found whip-‐poor-‐wills and detected additional birds through point counts along set
routes. Although we detected birds as early as 1 May 2012, we waited for migrating
individuals to pass through, and then trapped birds in these areas between 11 May-‐6
July. We attempted to capture birds on all nights where it did not rain or winds did
not pose a risk to birds. We used between 1-‐4 continuously monitored mist nets,
which were opened after sunset, and we generally continued until sunrise. After a
bird was captured, or if birds were non-‐responsive, we relocated the nets to a new
site. Nets were placed in areas where whip-‐poor-‐wills had been detected on
previous nights. We attempted to attract birds to the mist nets using playbacks of
calls obtained from a public archive (http://www.xeno-‐canto.org) broadcast
through an mp3 player linked to speakers. We played the recording of a territorial
male continuously at maximum volume and speakers were placed either directly
under a net if one or two nets were being used, or between nets if three or four were
being used. Around the mining exploration site, birds were captured while
machinery continued to operate, however we only attempted to capture birds
outside of active areas for safety purposes. We captured 13 birds between 4 June -‐12
July in 2011, and 27 between 11 May -‐ 6 July in 2012.
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Once captured, we collected a 50-‐150 ul blood sample from the brachial vein of each
bird within three minutes of it hitting the net, to obtain baseline corticosterone
levels (Romero and Reed 2005). Call playback was shown not to elicit a stress
response for some species ( Angelier et al. 2009, but see Charlier et al. 2009,)
Individuals were then held in a bird banding bag and were re-‐sampled 30 minutes
post-‐capture for stress-‐induced samples. Blood samples were stored in a cooler with
ice packs, and generally within 4 h, (but < 7h) of collection were centrifuged and
stored at -‐20° C before being stored at -‐80° C. Following blood collection, we banded
the birds with standard USGS aluminum leg bands (issued by CWS).
Between 3-‐28 June 2012, we fitted 15 of the 27 birds we captured with Lotek® tail
mount radio-‐transmitters (1.7g, 7mm x 9mm x 24mm) after they were bled and
banded. Radio transmitters were affixed to one of the central retrices with
cyanoacrylate glue and anchored to the neighbouring feathers with thread. All bird-‐
handling and tagging conformed to the rules and regulations of the Trent University
Animal Care Committee under Protocols 22067 and 22494.
We had sufficient bird locations (n=17-‐23) to estimate the full and core ranges for
13 of the 15 birds (N= 3 females and 10 males). We attempted to re-‐locate radio-‐
tagged birds at least once every 24 h period. Using a minimum of three different
detection angles, we triangulated the location of each bird up to 23 times over the
season. Radio-‐tagged birds were detected using a 6-‐element handheld yagi antenna
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and a Lotek® SRX 400 telemetry receiver. If a bird could be observed directly (e.g.,
we found it perched or on a nest) we did not use radio-‐location estimates but
recorded the exact GPS location instead.
Home Range Analysis
We estimated home range sizes for all birds with a minimum of 18 locations using
kernel utilization distribution in the adehabitathr (Callenge 2011) package for R
2.15.1 (R Development Core Team 2012) We estimated the home range using the 95
% isopleths (Figure 1A) and core ranges with the 50 % isopleths (Figure 1B) which
are comparable to other studies (Bloom et al. 1993, Karubian and Carrasco 2008).
The home range represents the area the bird is expected to be found most of the
time. It includes the defended territory, foraging areas, and any area traversed
during normal activities; the core range is where the bird is predicted to be at least
50 % of the time and is considered similar to a territory (Kelley et al. 2011). We used
least-‐squares cross-‐validation with fixed Kernel estimators to avoid overestimating
the home range. Kernel estimates are the preferred method for estimating home
range areas and have been shown to provide reliable estimates (Blundell et al. 2001,
Barg et al. 2004). Home ranges were then exported into QGIS, where we calculated
the areas of overlap between adjacent individuals at the full and core range sizes
following HRi,j = Ai,j /Ai as described in Fieberg and Kochanny (2005) (Figure 2),
where HRi,j is the % if the home range of bird i within the home range of bird j, Ai,j is
the area of overlap between two birds and Ai is the home range area of bird i.
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Habitat Analysis
We used satellite imagery from 2011 provided by New GoldTM to categorize habitat
within home ranges into five groups: rock, field, scrub, wetland, and forest (Table 1).
These were the most recent data available for the area. We defined “rock” as any
area showing exposed bedrock; “field” included all agricultural fields either used for
pasture or hay; “scrub” was any abandoned agricultural fields with recolonizing
woody vegetation or clearcut areas in the early phases on regeneration; “wetland”
was any open water, bog, marsh or riparian area; and “forest” included both
coniferous and deciduous trees. 2011 imagery was not available for the home range
of one bird, and 2006 forest resource inventory (FRI) imagery obtained through
Scholars GeoPortal (Ontario Council of University Libraries) was used instead as this
was the closest time point available. In addition, we randomly selected 13 areas of
136 hectares each to estimate available habitat for home ranges and a 30 hectare
subsample of each area was selected to estimate habitat availability for core ranges.
These areas correspond to the mean full and core ranges, respectively. The random
sites were selected from an area of 261 km2 surrounding the mine site for which we
had the most recent 2011 imagery, the site was divided into a numbered grid and a
we generated random numbers to determine the location of random sites. Habitat
polygons were visually outlined in ArcGIS 10.1. Areas of each habitat type within the
birds’ home range and the random plots were then calculated using the ‘intersect’
function in arcGIS.
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Corticosterone Assay
We measured plasma corticosterone concentrations using a commercially available
double antibody 125I radioimmunoassay (MP Biomedicals 07-‐120103). The
corticosterone antibody has low cross-‐reactivity with cortisol (0.05%),
deoxycorticosterone (0.34%), aldosterone (0.03%), testosterone (0.10%), and 17ß-‐
E2 (< 0.01%). We validated the assay for use in whip-‐poor-‐wills by running serial
dilutions of plasma extracted with dichloromethane (DCM) along with unextracted
samples. We compared values from the unextracted and extracted samples against a
standard curve of known corticosterone concentration. Because extracted plasma
samples had improved parallelism, we subsequently extracted all samples.
Plasma sample volumes were between 0.5 and 2.5 μl, which were then twice
extracted with DCM (2 x 3 ml). To determine recovery rates, pooled samples were
spiked with a known amount of corticosterone (25 pg for the 2011 samples and 50
pg for the 2012 samples) and compared with unspiked samples. We dried the
samples using N2 in a water bath at 37°C and dry extracts were resuspended in
110ul of steroid diluent. We then added 100 μl steroid diluent, 100 μl Iodine125 and
100 μl of corticosterone specific antibody to the resuspended samples. Samples
were then incubated at room temperature for 2 hours and 250 μl of precipitating
reagent was added. We then centrifuged samples at 20°C for 15 minutes at 1000g.
Samples were then decanted and the precipitate was counted in a gamma counter.
Samples were run in duplicate; the 2011 intra-‐assay variation was 1.98% ± 0.22%,
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inter-‐assay variation was 3.67% ± 0.33% and recovery rates were 92.28% ± 7.15%.
In 2012, intra-‐assay variation was 6.87% ± 0.94% inter-‐assay variation was 6.01 %
±0.64% and recovery rates were 89.86% ± 9.04%. We did not correct samples for
recovery. The levels of variation are within the range reported for other studies (e.g,
Breuner et al. 1999, Kitaysky et al. 1999, Washburn et al. 2002).
Statistics
All statistical analyses were performed in R version 2.15.1 (R Development Core
Team 2012). We set α = 0.05 for all tests.
Home range and habitat selection
We first used 2-‐tailed t-‐tests to determine if there were differences between the
home range size of radio-‐tagged birds around the mine site and the “control” sites.
To then evaluate resource selection, we used conditional logistic regression models
(i.e., case-‐control models), where bird full and core ranges were treated as “cases”
and were paired with a randomly selected site, which were designated as “controls”.
We used a backward stepwise approach to determine the best models. Beginning
with a global model with all habitat types, we removed the variable that contributed
the least to the model based on parameter estimates and then tested the model
without the variable to see if the model improved. This was repeated until the model
failed to improve or was reduced to a single variable.
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Corticosterone
Only samples collected in under three minutes and at 30 minutes from time of
capture were retained for analysis of baseline and stress-‐induced levels,
respectively. Samples that deviated > 2.5 S.D. from the mean corticosterone levels
were removed from the analysis (baseline, n = 1; stress-‐induced, n = 1) Values for
excluded birds were 36.56 μg/ml for the baseline and 139.3016 μg/ml for stress-‐
indiced values. Furthermore, three birds undergoing their pre-‐basic moult were
excluded from analysis as production of corticosterone may be reduced during this
period (Romero et al. 2005). Owing to changes in the study design and the small
sample size of birds during the 2011 field season, samples collected during the
initial field season were used only for assay validation purposes.
In 2012, we used a 2-‐tailed t-‐test to determine if there were any sex-‐related
differences in corticosterone. Seven birds were within the mine site plot and five in
the control; the other 15 birds were from areas outside both the control plots and
were used in a separate analysis. We used 2 tailed t-‐tests to determine if there were
any differences in baseline and stress-‐induced corticosterone levels between the
treatment site and control site birds. Stress-‐induced corticosterone levels required
natural log transformation to meet assumptions of normality, being confirmed with
a Kolmogorov-‐Smirnov test (D=0.18, p=0.35).
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To evaluate the possible effect of distance from disturbance, we grouped all birds
captured in 2012 and classed them based on distance from the mine site; 0-‐5 km
(baseline n=7, stress-‐induced n=8), 5-‐10km (baseline n= 4, stress-‐induced n=3), 10-‐
15 km (n=5, stress-‐induced n=4), and beyond 15 km (baseline n=5, stress-‐induced
n=6). Differences between numbers of baseline and stress-‐induced samples are
highlighted, as it was not always possible to collect stress-‐induced blood samples.
We used 5 km increments as these separated the clusters of birds around the mine
while giving some buffer to account for birds moving out of their home ranges in
response to the audio-‐lure. We used one-‐way ANOVAs to determine if distance from
the mine site had an effect on baseline or stress-‐induced samples. Baseline levels
required natural log transformation to meet the assumptions of normality (D = 0.1, p
= 0.84).
We ran linear regression models to determine the effects of habitat composition
area on baseline and stress-‐induced levels of radio-‐tagged birds for both the core
range and home range. Upon examination of a correlation matrix we found no strong
evidence of multicollinearity (where |r| ≥ 0.7) amongst the variables. Using the
stepAIC function (MASS package for R, Ripley et al. 2013) we reduced the number of
variables using a backward stepwise approach.
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Results
Range Size
The size of home ranges varied strongly among individuals, core range areas were
not normally distributed (D = 0.26, p<0.001), although they were normally
distributed at the 95 % home range scale (D = 0.22, p-‐value = 0.07).
Estimated size of core ranges varied from 3.73-‐132.43 ha (mean = 30.99 ha) and
home ranges varied from 19.6-‐499.54 ha (mean = 136.23 ha). Areas used did not
differ significantly between sexes at either the core (t = 2.60, df = 3.01, p = 0.08) or
home (t = 1.67, df = 2.10, p = 0.23) range scales. Home range size did not differ
significantly between birds exposed to mining exploration activity (N=7) and birds
from the control site (N=6) at either the core (t = -‐1.39, df = 9.59 p = 0.19) or home (t
= -‐1.21, df = 6.09, p-‐value = 0.27) range scales.
Birds’ home ranges overlapped with those of ≤4 other individuals including both
males and females; overlap ranged from <1 % of an adjacent bird’s home range to
the complete inclusion of another bird’s home range (Table 2). At the core range
level, overlap occurred primarily within pairs; we detected only one instance of a
male intruding in another male’s predicted core area.
18
Habitat selection
We tested five different models of habitat selection for core and home ranges. At the
core range, the negative coefficient for the best model (Wetland) suggests that whip-‐
poor-‐wills avoid having wetlands within their core range (Table 3). At the home
range, coefficients from the best model (Rock+Scrub+Wetland) suggest that whip-‐
poor-‐wills avoiding having wetlands and scrub in their home range, but have an
affinity for rocky areas (Table 4). Although these were the best candidate models
and may have some biological meaning, it should be noted that neither the variables
nor the overall models were statistically significant.
Corticosterone
Comparisons of samples between birds in control (n=5) and treatment (n=7) plots
showed no significant differences in baseline (t = -‐0.84, df = 5.62, p = 0.43) or stress-‐
induced (t = 1.53, df = 8.49, p = 0.16) corticosterone levels. One-‐way ANOVA
revealed no significant effect of distance from the mine site on baseline (F4, 17=0.941,
p=0.16) or stress-‐induced (F4, 16=0.28, p=0.89) corticosterone levels (Figure 3).
For multiple linear regression of baseline corticosterone, field, rock, scrub, and
wetland were retained as the variables at both the home range and core range. At
the core range scale, scrub (β=1.07, p=0.02) and wetland (β=3.25, p=0.02) showed a
significant effect on baseline corticosterone levels (overall model: R2= 0.84, p=0.01)
19
(Table 5, and Figure 4). At the home range scale, rock (β= -‐0.45, p=0.05), scrub
(β=0.39, p=0.03), and wetland (β=1.1, p=0.01) showed a significant effect on
baseline corticosterone levels (overall model: R2=0.65, p=0.03) (Table 6 and Figure
5).
20
We found no significant effect of habitat on stress-‐induced corticosterone levels at
the core range scale (Table 7). However, the home range model for stress-‐induced
values retained field, rock, scrub, and wetland as variables. In this model, the
presence of field (β=1.32, p=0.01) and wetland (β=3.06, p=0.003) shows birds had
significantly increased stress-‐induced corticosterone levels (overall model: R2= 0.93,
p=0.004) (Table 8 and Figure 6).
21
Discussion
Our study revealed that whip-‐poor-‐wills show different habitat preferences within
their core and home ranges. Moreover, we found habitat composition in whip-‐poor-‐
will home ranges to be associated with corticosterone levels in their blood. Whip-‐
poor-‐wills seem to avoid wetland and scrub type habitats. Scrub habitat is generally
associated with anthropogenic disturbances and are not recognized as suitable for
whip-‐poor-‐will (COSEWIC 2009), whereas wetlands may provide suboptimal
foraging opportunities and are not suitable for nesting (Batzer and Wissinger 1996,
Cink 2002). Conversely, we did not find any detectable differences in corticosterone
levels with respect to distance from the mine site. Although predictions about
corticosterone changes can be difficult (Dickens and Romero 2013), it is surprising
that birds nearest the mine exploration site did not have elevated baseline levels of
corticosterone or show different stress-‐induced levels as a result of living near a
mining exploration site. Our results suggest that whip-‐poor-‐wills did not experience
greater stress from exposure to mining explorationactivity, but rather that their
physiology is more sensitive to ecological than anthropogenic factors at our study
site.
Home Range
The home range size of eastern whip-‐poor-‐wills in our study varied considerably
among individuals, with a mean core range of 31 ha and mean home range of 136 ha.
22
This is considerably larger than the 5.3 ha mean territory estimates reported by
Cink (2002) and the 25 ha mean home ranges found by Garpalow (2007). Previous
methods used to quantify home range size typically surveyed singing birds and
estimated area use with minimum convex polygons. These approaches present two
problems, the first of which is that only mapping song locations can underestimate
space use (Anich et al. 2009). For example, Anich et al. (2009) found that the
territory size of Swainson’s warbler (Limnothlypis swainsonii) derived from singing
location was significantly underestimated compared with estimates derived from
ratio-‐telemetry. In addition minimum convex polygons have been shown to provide
coarse and often inaccurate home range estimates (Cumming and Cornélis 2012).
We overcame both of the above home range estimation problems by analyzing our
comparatively precise radio-‐location data using kernel utilization distribution.
Our mean estimates of whip-‐poor-‐will home range size were 5.4-‐fold greater than
reported previously (Garlapow 2007). Although different estimation methods are
the most parsimonious explanation for the discrepancy in home range size between
our study and previous studies, confounding factors remain subject to conjecture, as
variation in home range size can often be attributed to habitat quality, distribution
of resources, mating status, predation risk, or competition (Leary et al. 1998, Barg et
al. 2004). As our study population is at the northern edge of the whip-‐poor-‐will
range, the habitat may be of generally lower quality than found in the core of the
species’ range (e.g., Sexton et al. 2009). Food availability has been shown to have an
23
effect on space use (northern goshawk [Accipiter gentilis] ,Kenward 1982; ovenbird
(Seiurus aurocapilla), Smith and Shugart 1987; red-‐eyed vireo (Vireo olivaceus)
Marshall and Cooper 2014); territory sizes were found to increase as food
availability decreased. At our study site, temperatures frequently dropped below
freezing until June and low temperatures can reduce the abundance of flying insects
(Goller and Esch 1990, Bale 2002), which may increase the difficulty of finding prey.
As such, whip-‐poor-‐wills in our study area may have required larger home ranges in
order to meet their energetic requirements.
The inter-‐individual variation in home range estimates may also reflect the
heterogeneity of habitat types in the landscape. Increased resource patchiness
(Kelley et al. 2011) and larger proportions of unattractive habitat (Anich et al. 2010)
can lead birds to require a larger home range to access preferred habitat types. In a
fragmented landscape, territorial perches, courting sites, and foraging sites may be
interspersed with large areas of unattractive habitat that would likewise enlarge the
size of home ranges. Anthropogenic activity may also play a role; Anderson et al.
(1990) found that raptors exposed to increased military activity appeared to
increase their home range size. Similarly northern waterthrush (S. noveboracensis)
were found to increase their home ranges up to nine-‐fold when exposed to high
levels of deforestation (Leonard et al. 2008). We found no evidence of this in our
study. It is possible that the levels of anthropogenic activity around the mining
exploration site did not adversely affect whip-‐poor-‐wills.
24
Core Range and Territoriality
The degree of territoriality might be inferred through spacing patterns and overlap
of home ranges (McLoughlin et al. 2000). Home ranges overlapped with those of up
to 4 adjacent individuals and there were multiple instances of birds entering
another bird’s home range. However, at the smaller core range scale, there was
overlap only within pairs, and we found only one instance of a male intruding on
another male’s core range, suggesting some level of conspecific exclusion. As such
our core range estimates might provide an estimation of the area required for a pair
of whip-‐poor-‐will in this environment.
Habitat Selection
Our best models indicated that whip-‐poor-‐wills favour and avoid different habitat at
different spatial scales (Tables 3-‐4). At the home and core range scales, wetlands
were avoided more than would be expected randomly, which was surprising given
that wetlands and aquatic systems are often recognized for their production of
insect biomass (Scheffers et al. 2006, Smith et al. 2007). A study on ovenbirds,
another ground nesting insectivorous bird often found in similar habitats as whip-‐
poor-‐will, showed that they set up territories in the areas of greatest food
abundance (Burke and Nol 1998). Various wetlands produce different types of
insects, the most frequent orders being Hemiptera, Diptera, and Coleoptera (Batzer
and Wissinger 1996). However, the scant dietary information that exists for whip-‐
25
poor-‐wills suggests that their preferred prey items are within the order Lepidoptera
and the family Scarabeidae (Coleoptera) (Cink 2002, Garlapow 2007). Scarab beetles
are primarily terrestrial and a majority of the large moths are also associated with
terrestrial habitats (Triplehorn and Johnson 2005). It is therefore possible that
whip-‐poor-‐wills avoid wetlands because they do not produce an attractive primary
food source. Foraging strategy may also play a role in wetland avoidance. Whip-‐
poor-‐wills use sallying from perches or the ground as their primary foraging
strategy (Mills 1986, Cink 2002), which requires them to have access to adequate
perches and a relatively unobstructed environment to facilitate prey detection (Gall
and Fernández-‐Juricic 2009). The relative lack of suitable perching sites in wetlands
for this feeding strategy may also play a role in wetland avoidance by whip-‐poor-‐
wills. Finally, the nesting site is likely to fall within the core range (Bloom et al. 1993,
Elchuk and Wiebe 2003), and because whip-‐poor-‐wills lay their eggs directly on the
ground (Cink 2002) it is likely that they avoid wet areas for nesting.
Birds in our study showed an avoidance of scrubby habitats at the home range scale.
Contrary to this, in North Carolina whip-‐poor-‐will density increased with the
presence of open areas caused by logging (Wilson and Watts 2008). We suspect that
whip-‐poor-‐wills avoid scrub habitat in our study area for the same reason they avoid
wetlands; regenerating stands can have lower abundance and diversity of many
primary prey moths when compared to forested areas (Summerville and Crist
26
2002). In addition, the dense understory vegetation often associated with scrubby
vegetation might interfere with the detection and/or capture of prey items.
Converse to avoiding wetlands and scrub, whip-‐poor-‐wills showed a positive
association with rocky habitat at the home range scale. Whip-‐poor-‐wills may be
attracted to these sites for foraging and thermoregulatory purposes. Areas with
higher ambient temperature than surrounding sites may occur when heat stored
during the day is released through the night (Kardinal Jusuf et al. 2007). As with
pavement, rock substrates can store energy (Farid et al. 2004) and could increase
the localized temperature. Even minor elevations in temperature may have effects
on insect activity (Goller and Esch 1990) .
Corticosterone
Although we did not find differences in corticosterone based on distance from the
mine site (Figure 3), we did find important scale-‐dependent relationships between
habitat type and corticosterone levels (Tables 5-‐8). Environmental factors can
influence both baseline and stress-‐induced corticosterone levels in wild vertebrates.
Anthropogenic disturbances (Newcomb Homan et al. 2003), modifications to the
habitat (Leshyk et al. 2012, 2013), habitat quality (Bauer et al. 2013), predator
abundance (Clinchy et al. 2011), and food availability (Suorsa et al. 2003) have all
been shown to affect an individual’s response to a stressor. To the best of our
27
knowledge, ours is the first study to address the effects of habitat composition
within an animal’s home range on stress physiology.
We found that individuals with more scrub and wetland habitats (which were
generally avoided) within their home and core ranges had significantly higher
baseline corticosterone levels. In addition, individuals with greater amounts of rocky
habitat, which was preferred by whip-‐poor-‐wills in our models, in their home range
showed decreased baseline corticosterone levels. Elevated baseline corticosterone
levels in birds with more wetland and scrub may indicate chronic stress (Busch and
Hayward 2009); given that whip-‐poor-‐wills avoid these habitat types, scrub and
wetland habitats may be suboptimal.
Several underlying factors such as food availability, habitat structure, predation risk,
or conspecific interactions may be linked with the level of stress. Wetlands and
scrub areas may represent suboptimal areas for foraging as scarabs and moths are
not as abundant in these habitats (Ricketts and Daily 2001, Mengelkoch et al. 2004).
Food availability has been demonstrated to affect baseline corticosterone levels and
we suspect it may be a factor influencing whip-‐poor-‐wills; for example, black-‐legged
kittiwake (Rissa tridactyla) nesting at colonies with reduced food availability were
found to have elevated baseline corticosterone levels (Kitaysky et al. 1999).
Our study area was a mosaic of different habitats ranging from relatively pristine to
heavily altered by human activity. Anthropogenic modifications to habitat have been
28
shown to affect the stress physiology of animals. For example, Leshyk et al. (2012)
demonstrated that in areas that had been subjected to logging, nestling ovenbirds
showed elevated baseline corticosterone levels. Similarly, Suorsa et al. (2003) noted
significantly elevated corticosterone levels in Eurasian treecreeper (Certhia
familiaris) nestlings as a result of varying habitat structure and food availability
caused by forestry practices. They suggested this might be a reflection of the
difference in food availability and quality between forests of different age. They
found no effect of vegetation composition surrounding the nesting site on
corticosterone levels; however, the study used pseudo-‐territories centered near the
nest site. These were based on the mean observed foraging distance of previous
studies. These pseudo-‐territories may not have reflected the actual habitat being
used and may have underestimated the importance of certain habitats while
including habitats that may not be used.
In our study, the only significantly lower baseline corticosterone levels were
detected in birds with home ranges with greater proportions of rock habitat, which
is a relationship that can be interpreted two ways. The first interpretation is that
lower baseline corticosterone can simply indicate lower stress levels (Newcomb
Homan et al. 2003, Bonier et al. 2009). The second, decreased baseline
corticosterone may be the result of chronic stress; for example, the stress associated
with the protracted process of translocation was shown to reduce baseline levels in
chukar (Alectoris chukar) (Dickens et al. 2009), emphasizing the challenge of
29
interpreting directionality of corticosterone (Dickens and Romero 2013). Given that
our habitat selection models showed whip-‐poor-‐wills favour rocky habitats, it seems
most likely that the lower baseline corticosterone levels among individuals were
associated with birds having high quality habitat. Exposed bedrock and rocky areas
may provide whip-‐poor-‐wills with enhanced feeding opportunities when the
temperature in other areas falls below the threshold for flying insects.
Stress-‐induced corticosterone levels were significantly higher for birds having more
wetland and field habitats within their full home range. This response may be
attributable to chronic stress through facilitation, in which individuals may not
necessarily show increased baseline levels but instead will show increased reactivity
to any additional stressor (Romero 2004). Elevated stress-‐induced corticosterone
levels have been noted for common murres (Uria aalge) and red-‐legged kittiwake (R.
brevirostris) in situations of reduced food availability (Kitaysky et al. 2001, 2007), as
a result of predator exposure in song sparrows (Melospiza melodia; Clinchy et al.
2004), and in response to anthropogenic disturbances in adult ovenbirds (Leshyk et
al 2013). It is possible that fields do not provide optimal foraging areas, as fields in
our study area were generally used for pasture or hay production and they are
relatively disturbed as a result of grazing and hay cutting, which may in turn reduce
insect diversity and abundance (Kruess and Tscharntke 2002, Zalik and Strong
2008). In addition, predation and predator abundance may increase in areas
surrounding agricultural areas (Andrén and Angelstam 1988, Chalfoun et al. 2002).
30
Stress associated with lower food abundance and increased predation risk are both
possible explanations for elevated stress-‐induced corticosterone levels.
Although anthropogenic disturbances have been shown to influence the way an
individual responds to a stressor (Romero et al. 2009, Crino et al. 2011, Leshyk et al.
2012), we found no significant effect of proximity to the mining exploration site on
either the baseline or stress-‐induced corticosterone levels. We suspect the levels of
disturbance around the mining exploration site were at a level which was tolerated.
Alternatively, as the mining equipment was locally mobile, it is possible that we did
not capture the birds during the most stressful periods. Predicting how baseline and
stress-‐induced corticosterone will respond to chronic disturbances can be
challenging as baseline levels can differ in directionality depending on conditions
and the stressor (Busch and Hayward 2009, Dickens and Romero 2013).
Conclusion
The distribution of animals is guided by multiple environmental factors. Studies
have shown that habitat quality can affect the stress physiology within individuals
and between populations (Suorsa et al. 2003, Bauer et al. 2013). To our knowledge,
this study is the first linking stress physiology with the known home range use of
birds on their breeding grounds. We have shown that differences in habitat
composition at a landscape level can have significant effects on baseline and stress-‐
induced corticosterone levels of whip-‐poor-‐wills. Elevated baseline corticosterone
31
levels associated with wetland and scrub type habitats and reduced levels in rocky
habitats were likely due to variation in habitat quality.
Although we detected differences in corticosterone levels with variations in habitat
quality, the factors underlying those habitat differences (e.g., insect availability,
predator abundance, and microhabitat features) warrant further research.
Additionally, although we did not detect any differences in corticosterone as a result
of proximity to the mining exploration site, additional work should be done to
monitor any changes in physiology once operations expand beyond the initial
exploration activities.
32
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Figures and Tables
Figure 1. Kernel utilization distribution estimates for the (A) home range and (B)
core range of a male whip-‐poor-‐will in the Rainy River district, Ontario, Canada,
2012. Red dots represent locations of birds based on triangulation.
43
Figure 2. Kernel utilization distribution estimates for (A) home ranges and (B) core
ranges of 5 neighbouring whip-‐poor-‐wills in the Rainy River district, Ontario,
Canada, 2012. Dots represent triangulated locations and individuals are identified
by colour.
44
Figure 3. Effect of distance from the mining exploration site on baseline (circles) and
stress-‐induced (squares) corticosterone levels in whip-‐poor-‐will.
45
Figure 4. Strip plot illustrating the effect of different habitat proportion on baseline
corticosterone at the core range. Dots represent corticosterone values for individual
birds
46
Figure 5. Strip plot illustrating the effect of different habitat proportion on baseline
corticosterone at the home range level. Dots represent corticosterone values for
individuals.
47
Figure 6. Strip plot illustrating the effect of habitat proportion on stress-‐induced
corticosterone at the home range level. Dots represent corticosterone values for
individuals.
48
Table 1. Mean habitat composition within the home and core ranges of birds, and
mean habitat composition within randomly selected areas associated with the core
(50 % available) and full (95%) available ranges .
Core Range % Home Range % 50 % Available 95% Available Field 3.59 13.43 1.68 6.65 Forest 64.76 57.88 64.01 59.94 Rock 8.85 7.85 3.48 4.19 Scrub 19.80 15.24 20.02 19.64 Wetland 3.00 5.59 10.80 9.57
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Table 2. Percent overlap of whip-‐poor-‐will home range estimates between adjacent birds and within pairs (matched by colour)
for the 95% isopleths (home range) and 50% isopleths (core range) estimates in the Rainy River district, Ontario, 2012.
Individuals are labeled with their band number preceded by M for males and F for females.
50 % Isopleth Overlap 95% isopleth overlap
F204 M24 M33 M38 M41 F42 M45 M46 F50 F204 M24 M33 M38 M41 F42 M45 M46 F50
F204 100 0 0 0 0 0 0 047 0 100 2 0 0 0 0 29 69 0
M24 0 100 0 0 0 0 0 0 0 2 100 0 0 0 0 0 3 0
M33 0 0 100 0 0 0 0 0 0 0 0 100 0 3 22 0 0 69
M38 0 0 0 100 0 0 0 0 99 0 0 0 100 <1 3 0 0 100
M41 0 0 0 0 100 82 0 0 0 0 0 1 <1 100 78 0 0 70
F42 0 0 0 0 63 100 0 0 0 0 0 6 5 69 100 0 0 78
M45 0 0 0 0 0 0 100 0 0 67 0 0 0 0 0 100 44 0
M46 67 0 0 0 0 0 0 100 0 86 2 0 0 0 0 23 100 0
F50 0 0 0 17 0 0 0 0 100 0 0 7 20 3 32 0 0 100
50
Table 3. Competing conditional logistic regression models with variables,
coefficients (±SE), and p-‐values for whip-‐poor-‐will habitat selection at the core
range scale in the Rainy River district, Ontario, 2012. The model with the best
parameter estimates is displayed in bold.
Model Variables Coefficients p-‐value
Model 1 Forest Rock Scrub Wetland Field Strata
-‐0.03±0.03 0.27±0.23 -‐0.07±0.07 -‐0.20±0.17 0.38±0.35 -‐0.01±0.13
0.35 0.24 0.33 0.26 0.28 0.90
Model 2 Rock Scrub Wetland Field Strata
0.14±0.14 -‐0.07±0.07 -‐0.20±0.16 0.36±0.34 -‐0.04±0.12
0.33 0.34 0.22 0.3 0.76
Model 3 Rock Wetland Field Strata
0.14±0.14 -‐0.18±0.16 0.18±0.27 -‐0.02±0.12
0.32 0.26 0.52 0.84
Model 4 Rock Wetland Strata
0.13±0.13 -‐0.21±0.15 -‐0.01±0.11
0.35 0.19 0.95
Model 5 Wetland
Strata
-0.22±0.15
-0.001±0.11
0.15
0.99
51
Table 4. Competing conditional logistic regression models with variables,
coefficients (± SE), and p-‐values for whip-‐poor-‐will habitat selection at the home
range scale in the Rainy River district, Ontario, 2012. The model with the best
parameter estimates is displayed in bold.
Model Variables Coefficients p-‐value
Model 1 Forest Rock Scrub Wetland Field Strata
-‐0.02±0.02 0.16±0.13 -‐0.05±0.04 -‐0.04±0.05 0.08±0.16 -‐0.01±0.17
0.32 0.22 0.17 0.62 0.07 0.43
Model 2 Rock Scrub Wetland Field Strata
0.08±0.06 -‐0.06±0.03 -‐0.08±0.07 0.06±0.04 -‐0.13±0.17
0.15 0.07 0.22 0.10 0.45
Model 3 Rock
Scrub
Wetland
Strata
0.11±0.07
-0.03±0.03
-0.07±0.06
-0.04±0.14
0.12
0.21
0.30
0.75
Model 4 Rock Wetland Strata
0.05±0.03 -‐0.10±0.06 -‐0.10±0.12
0.08 0.08 0.41
Model 5 Wetland Strata
-‐0.22±0.15 -‐0.001±0.11
0.15 0.99
52
Table 5. Multiple linear regression model with variable coefficients, standard error,
t-‐value, and p-‐value evaluating the effects of habitat within the core range on
baseline corticosterone levels in whip-‐poor-‐wills.
Variables Coefficients S.E. t-‐value P
Intercept 9.68 2.41 4.02 0.01 Field -‐3.00 1.62 -‐1.86 0.11 Rock -‐0.58 0.48 -‐1.20 0.27 Scrub 1.07 0.33 3.21 0.02 Wetland 3.25 1.07 3.02 0.02
Residual standard error: 5.26 on 6 degrees of freedom Multiple R-‐squared: 0.84 Adjusted R-‐squared: 0.73 F-‐statistic: 7.785 on 4 and 6 DF, p-‐value: 0.02
53
Table 6. Multiple linear regression model with variable coefficients, standard error,
t-‐value, and p-‐value evaluating the effects of habitat within the home range on
baseline corticosterone levels in whip-‐poor-‐wills.
Variables Coefficients S.E. t-‐value P
Intercept 1.56 3.91 0.40 0.70 Field 0.24 0.16 1.48 0.19 Rock -‐0.45 0.18 -‐2.50 0.05 Scrub 0.39 0.14 2.81 0.03 Wetland 1.10 0.32 3.42 0.01
Residual standard error: 6.03 on 6 degrees of freedom Multiple R-‐squared: 0.79 Adjusted R-‐squared: 0.65 F-‐statistic: 5.56 on 4 and 6 DF, p-‐value: 0.03
54
Table 7. Multiple linear regression model with variable coefficients, standard error,
t-‐value and p-‐value evaluating the effects of habitat within the core range on stress-‐
induced corticosterone levels in whip-‐poor-‐wills
Variables Coefficients S.E. t-‐value P
Intercept 37.21 7.98 4.66 0.01 Rock 3.66 1.66 2.20 0.08 Scrub 1.10 0.74 1.49 0.20 Wetland 6.63 3.28 2.02 0.10
Residual standard error: 17.56 on 5 degrees of freedom Multiple R-‐squared: 0.75 Adjusted R-‐squared: 0.60 F-‐statistic: 5.00 on 3 and 5 DF, p-‐value: 0.058
55
Table 8. Multiple linear regression model with variable coefficients, standard error,
t-‐value and p-‐value evaluating the effects of habitat within the home range on
stress-‐induced corticosterone levels in whip-‐poor-‐wills.
Variables Coefficients S.E. t-‐value P
Intercept 24.2941 6.7825 3.582 0.02 Field 1.3205 0.2523 5.233 0.01 Forest -‐0.1789 0.1192 -‐1.502 0.21 Rock 0.639 0.357 1.79 0.15 Wetland 3.0579 0.4652 6.573 0.01
Residual standard error: 7.5 on 4 degrees of freedom Multiple R-‐squared: 0.96, Adjusted R-‐squared: 0.93 F-‐statistic: 26.23 on 4 and 4 DF, p-‐value: 0.01