1. Nest site use and ecological partitioning in closely related songbirds
Zachary Kahn
A thesis submitted to the Department of Biology in partial
fulfillment of the requirements for the degree of Bachelor of
Science (Honours)
Queens University
Kingston, Ontario, Canada
March 2015

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ABSTRACT
Closely related species living in sympatry often suffer costs from sharing natural
enemies and competing for limited resources. These costs are thought to promote
asymmetric aggression, with dominant species aggressively excluding subordinate species
from mutually preferred or high-quality habitats and resources. Thus, asymmetric
aggression can lead to resource partitioning between closely related species, with the
dominant using resources preferred by both species. Though these dominance interactions
appear widespread, few studies have tested the relative fitness of dominant and subordinate
species from the perspective of their resource partitioning. We studied the ecological
partitioning of nest site microhabitat (small-scale habitat features) and relative fitness in
two closely related songbird species living in sympatry: Swamp Sparrows (Melospiza
georgiana) and Song Sparrows (Melospiza melodia). We measured nest site microhabitat
and nest survival on study plots where the two species co-occurred, addressing three
questions: (1) Do Song and Swamp Sparrows use nest sites non-randomly with respect to
microhabitat?, (2) Does nest site microhabitat differ between the two species?, and (3) Are
there fitness consequences for birds that use nest sites that are more like the other species?
Both species used nest sites non-randomly: active nest sites were different in their
microhabitat from both suitable sites that were not used (non-use sites) and sites picked at
random from within breeding territories (random sites). Nest sites also differed between the
two species, with Swamp Sparrows using sites with more water and aquatic vegetation.
While Swamp Sparrows had similar reproductive success across nest site types, the
dominant Song Sparrow, surprisingly, had greater reproductive success when they used
nest sites more similar in microhabitat to Swamp Sparrow nest sites. Our results suggest

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that Song Sparrows should choose nest sites more like the sites of subordinate Swamp
Sparrows, rather than the sites that they typically choose in drier microhabitats.

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ACKNOWLEDGEMENTS
I would like to thank Christopher Moser-Purdy, Steph Kim, and Tori Brown for
their immense help with fieldwork. Also thanks to Sophie Gong, Alice Domalik, Sara
Burns, and Vanya Rowher for their supporting contributions. Thanks to Elisabeth Purves,
Haley Kenyon, and the entire Martin Lab. Thanks to Frank Phelan, Veronika Jaspers-Fayer,
and the entire staff at the Queens University Biology Station for supporting this research.
Thanks to Fran Bonier for her contributions as a committee member and mentor. Special
Thanks to my supervisor Paul Martin for his tremendous help with all aspects of this
research and for being a great mentor. Thanks to NSERC Canada and Queens SWEP for
funding this research.

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Table of contents
Abstract ................................................................................................................................. 2
Acknowledgements............................................................................................................... 4
List of Tables and Figures.................................................................................................... 6
Introduction .......................................................................................................................... 7
Methods ............................................................................................................................... 14
Study Species................................................................................................................ 14
Study Site ..................................................................................................................... 15
Nest Monitoring ........................................................................................................... 15
Vegetation Measurements ........................................................................................... 17
Random Forest.............................................................................................................. 19
Cox Proportional Hazards ............................................................................................ 21
Results.................................................................................................................................. 24
Species Differences in Nest Sites................................................................................. 24
Song Sparrow Use, Non-Use, and Random Sites ........................................................ 25
Swamp Sparrow Use, Non-Use, and Random Sites..................................................... 26
Relative Fitness in Different Nest Site Types .............................................................. 27
Discussion ............................................................................................................................ 29
Species Differences in Nest Sites................................................................................. 29
Song Sparrow Use, Non-Use, and Random Sites ........................................................ 30
Swamp Sparrow Use, Non-Use, and Random Sites .................................................... 31
Reproductive Success .................................................................................................. 33
Swamp Sparrow in Wet Microhabitat .......................................................................... 34
Song Sparrow Alternative Explanations....................................................................... 35
Literature Cited.................................................................................................................. 42
Summary ............................................................................................................................. 47
Figures ................................................................................................................................. 48
Appendix A: Supplementary Tables................................................................................. 58

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LIST OF FIGURES AND TABLES
Figure 1: Random Forest classification accuracies for species nest sites.
Figure 2: Classification margin plot for classification of species nest sites.
Figure 3: Variable importance plot for classification of species nest sites.
Figure 4: Partial dependence of water and aquatic vegetation coverage for classification of
species nest sites.
Figure 5: Mean water and aquatic vegetation coverage at Song and Swamp Sparrow nest
sites.
Figure 6: Partial dependence of juniper abundance for classification of species nest sites.
Figure 7: Variable importance plots for classification of Song Sparrow use versus non-use
and random sites.
Figure 8: Partial dependence of shrub coverage and juniper abundance for Song Sparrow
use site classification.
Figure 9: Variable importance plots for classification of Swamp Sparrow use versus non-
use and random sites.
Figure 10: Song Sparrow daily nest survival in Song Sparrow-like and Swamp Sparrow-
like nest sites.
APPENDIX A
Table 1: A description of the microhabitat variables we measured.
Table 2: Best performing Cox regression models predicting Song Sparrow nest survival.
Table 3: Model averaged coefficients for best performing Cox regression models
predicting Song Sparrow nest survival.
Table 4: Best performing Cox regression models predicting Swamp Sparrow nest survival.
Table 5: Model averaged coefficients for best performing Cox regression models
predicting Swamp Sparrow nest survival.
Figure 11: Location of Song Sparrow-like and Swamp Sparrow-like Song Sparrow nests in
our study area.

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INTRODUCTION
Closely related species face many challenges in areas where they co-occur. These
species have recently diverged from a common ancestor and are thus ecologically similar,
causing them to endure costs related to competition for limited resources (Morse 1974;
Schluter 2000). Additionally, closely related species frequently suffer an increased risk of
predation (Holt and Lawton 1994; Martin 1996) and infection (Valera et al. 2003; Dobson
and Hudson 1996) when they co-occur because of density-dependent responses of shared
predators and parasites. As a result, theory suggests that ecologically similar species living
together should diverge from each other with respect to resource use and traits related to
predation, parasitism, or disease in order to reduce the costs of these challenges (Schluter
2000; Valera et al. 2003). Moreover, individuals often act aggressively towards closely
related species, which is thought to be an evolved response to reduce resource overlap and
minimize the ecological costs of co-occurrence (Schluter 2000, Martin and Martin 2001a
).
Aggressive interactions between closely related species are frequently asymmetric,
with one species socially dominant to the other (Morse 1974). The dominant species is
typically larger than the subordinate and wins the majority of aggressive encounters —
which can include chases, displacements, and killings (Morse 1974; Donadio and Buskirk
2006; Peiman and Robinson 2010). Through aggression, the dominant species can gain
priority access to shared resources, exclude the subordinate from mutually preferred
resources and habitats (Morse 1974), and force the subordinate species to use lower quality
resources in order to minimize the costs of aggression (Morse 1974; Orians 2000).

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Resource partitioning as a result of asymmetric aggression in dominant and
subordinate species has been shown in a variety of taxa, including small rodents (Ziv et al.
1993), primates (Houle et al. 2006), fish (Jonsson et al. 2008; Blowes et al. 2013), birds
(Nally and Timewell 2005; Martin and Martin 2001a
), reptiles (Edgehouse et al. 2014) and
insects (Nagamitsu and Inoue 1997). In these cases, a dominant species excluded a
subordinate from temporal and/or spatial resources and asymmetric aggression between
ecologically similar, sympatric species promoted partitioning of shared resources. One of
the best examples of aggression-mediated resource partitioning is found in communities of
desert-dwelling gerbils (Ziv et al. 1993). Ziv et al. (1993) used a controlled enclosure
experiment to elucidate dominance relationships and resource use in two closely related
species of gerbils: Gerbillus allenbyi and G. pyramidium. G. pyramidium were
competitively dominant to allenbyi; on study plots where both species were present,
allenbyi shifted both the timing of when they foraged for seeds and the habitat where they
foraged compared to study plots where pyramidium weren’t present. This partitioning of
resources was a result of asymmetric aggression between the two species which forced
subordinate allenbyi to avoid overlap in temporal and spatial foraging with pyramidium
(Ziv et al. 1993). Similar findings have also been shown in closely related species of
sympatric garter snakes (Edgehouse et al. 2014). In a similar enclosure experiment,
common garter snakes (Thamnophis sirtalis) acted extremely aggressively towards aquatic
garter snakes (T. atratus) and excluded them from mutually preferred aquatic habitat.
Conversely, aquatic garter snakes were never aggressive towards common garter snakes,
and were forced to use less-optimal terrestrial habitat when common garters were present.
Hence, asymmetric aggression in these closely related garter snakes contributed to patterns

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of small-scale microhabitat partitioning. Further, broad-scale resource partitioning has been
shown in closely related species of co-occurring wood warblers (Martin and Martin
2001a,b
). Martin and Martin (2001b
) conducted a playback experiment to show that Orange-
crowned Warblers (Oreothlypis celeta) were competitively dominant to Virginia’s
Warblers (O. virginiae). Consequently, these species partitioned nest site habitat along an
environmental gradient of temperature and moisture: Orange-crowned Warblers occupied
cool, wet areas at low elevations while Virginia’s occupied hot, dry habitat at high
elevations. Removal experiments showed that subordinate Virginia’s preferred to use the
wetter, low-elevation nesting habitat characteristic of dominant Orange-crown’s, which
strongly suggested that asymmetric aggression between species was responsible for the
partitioning of broad-scale nesting habitat in this study system (Martin and Martin 2001a,b
).
Although social dominance and patterns of resource partitioning in ecologically
similar species has been well documented, fitness consequences of resource partitioning
among dominant and subordinate species is not well understood (Martin and Martin 2001a
).
Measuring the relative fitness of dominant and subordinate species in relation to resource
partitioning is important for understanding the mechanisms and trade-offs that directly
influence how such species are able to co-occur. One of the best studies to do this was
conducted by Munday (2001), who investigated the fitness of co-occurring dominant and
subordinate species of coral-dwelling fish on two different species of coral. Though both
the dominant (Gobiodon histrio) and subordinate (G. brochus) fish species competed for a
mutually preferred coral species (Acropora nasuta) as habitat, previous work showed that
G. histrio competitively excluded G. brochus from this habitat and occupied it the majority
of the time, forcing brochus to use another species of coral (A. loripes) in areas where they

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co-occurred. After transplant experiments, G. histrio had higher lifetime survival and both
species had higher growth rates when they used A. nasuta coral in comparison to loripes,
which showed that the dominant species achieved fitness benefits from occupying the
mutually preferred coral habitat. Interestingly, G.brochus did not experience reduced
survival when it used A. loripes instead of A. nasuta, which suggested there was an
ecological trade-off between competitive ability and breadth of resource use in these fish
seen in other dominant and subordinate species (Martin 2015; Munday 2001). Similarly, a
study of closely related snow finches found that dominant white-rumped snowfinches
(Montifringilla taczanowskii) had higher reproductive success and breeding densities than
subordinate rufous-necked snowfinches (M. ruficollis) because of their ability to
outcompete rufous-necked snowfinches for active pika burrow nest sites (Zeng and Lu
2009). These studies showed that a dominant species can achieve fitness benefits over
subordinates by using high quality resources, but subordinates may be able to compensate
for this by using a greater range of resources in their environment (Munday 2001).
Closely related, ecologically similar species must be able to overcome costs of co-
occurrence if they are to establish coexistence over long time frames. Ecological costs of
co-occurrence and resource partitioning have been well demonstrated in guilds of passerine
birds (Martin 1996, 1998). Communities of co-occurring birds suffer costs of co-occurrence
through increased nest predation, which is known to be the main cause of nest failure in
many passerine species (Martin 1996). To demonstrate this, Martin (1996) conducted an
experiment where he placed fake nests in areas that overlapped with seven species of co-
occurring birds, both in terms of location and nest placement. Nest predation increased
significantly for all species when artificial nests were placed in the same area as real nests

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compared to where they did not, and also increased within species when artificial nests
overlapped in habitat with other species. (Martin 1996). Thus, there were strong fitness
costs for birds that used nest sites more like other sympatric species. Martin (1998) then
examined nest site microhabitat use and fitness of these same seven species in a high
elevation snowmelt drainage where they co-occurred. All species showed distinct
microhabitat preferences at their nest sites: nest sites differed in microhabitat from non-use
sites (sites in the same vegetation as the nest) and random sites, and these differences were
also what differentiated each species nest site from all other species (Martin 1998).
Additionally, all seven species had greater fitness when they used their own characteristic
nest sites compared to other nest sites, which suggested that microhabitat preferences were
adaptive and under selection (Martin 1998). Taken together, these studies showed that co-
occurring birds partitioned nest site microhabitat in order to reduce the costs of co-
occurrence associated with increased nest predation. This resource partitioning mechanism
mitigated the density-dependent effect of nest predation and likely allowed these species to
co-occur with each other.
Here, we examine ecological resource partitioning and relative fitness of nest sites
in closely related passerine birds living in sympatry. Our focal species, Song Sparrows
(Melospiza melodia) and Swamp Sparrows (Melospiza georgiana), were suitable for this
study because they are closely related and ecologically similar, live together in sympatry
across much of their range, and have been well studied (Grant 1966; Greenberg 1988).
Song and Swamp Sparrows also exhibit an asymmetric, social relationship where Song
Sparrows are dominant to Swamp Sparrows (Grant 1966; Willson 1972; Greenberg 1988).
Our focal species therefore provided an opportunity to examine resource partitioning and

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relative fitness in co-occurring, closely related, and ecologically similar species with a
dominant and subordinate relationship. We assessed resource partitioning and relative
fitness for nest sites by assessing nest site microhabitat and reproductive success of both
species on study plots where they co-occurred. Small-scale habitat features such as nest
sites are important for birds because they directly influence the success of raising immobile
young in the face of predators and parasites (Chalfoun and Schmidt 2012). Nest sites
should therefore be chosen to minimize the costs associated with a particular set of habitat
conditions, which can include predation and interspecific competition (Chalfoun and
Schmidt 2012; Martin and Martin 2001a
; Martin 1996). Song and Swamp Sparrows have
been shown to behave aggressively towards each other on shared breeding territories
(Greenberg 1988), and this observation, along with evidence from other ecological studies
in passerine birds (Martin and Martin 2001a
; Zeng and Lu 2009), suggested that nest sites
were an important and limiting ecological resource for our focal species.
We assessed nest site microhabitat and reproductive success of our focal species in
order to answer three questions related to their co-occurrence: (1) Do both species use nest
sites non-randomly? (i.e. Does the microhabitat used for nest sites differ from the
microhabitat available to the birds?) (2) Are Song and Swamp Sparrow nest sites different
from each other with respect to microhabitat? (3) Are there fitness consequences for birds
that use nest sites more similar to the other species? To address the first question, we
quantified microhabitat used at each nest site and compared it to non-use sites where birds
could have placed their nests and random sites away from the nest. We predicted that both
species would use nest sites non-randomly and show repeated patterns of microhabitat use,
as has been shown in other guilds of co-occurring birds (Martin 1998). We based this

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prediction on the assumption that nest sites were important ecological features for our focal
species and should be used in a way to maximize fitness by using high-quality microhabitat
available to them (Chalfoun and Schmidt 2012). To address the second question, we
compared microhabitat used at nest sites between species and predicted that Song and
Swamp Sparrows would use nest sites that were different from each other with respect to
microhabitat. Since Song Sparrows are socially dominant to Swamp Sparrows (Grant 1996;
Willson 1972), we predicted that they would be able to exclude Swamp Sparrows from
mutually preferred nest sites and that both species would therefore use nest sites that
differed in microhabitat. Alternatively, Song Sparrows may be restricted in where they can
place their nests, which could lead them to use different nest site microhabitat than Swamp
Sparrows (Greenberg 1988). To address the third question, we compared the reproductive
success of birds that used nest sites characteristic of their own species versus nest sites
characteristic of the other species. We predicted that Swamp Sparrows (subordinate) would
have higher reproductive success when they used Song Sparrow-like (dominant) nest sites
than when they used nest sites characteristic of their own species. Conversely, we predicted
that Song Sparrows would have lower reproductive success when they used Swamp
Sparrow-like nest sites instead of their own characteristic nest sites. We based this
prediction on the assumption that the dominant species should exclude the subordinate
from high-quality nest sites in areas where they co-occur (Martin and Martin 2001a
; Martin
1998; Morse 1974). We predicted that Song Sparrows would have priority access to high-
quality nest sites and both species would have higher fitness when they used these sites.

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METHODS
Study Species
Song and Swamp Sparrows are closely related, ecologically similar species in the
genus Melospiza and family Emberizidae (new world sparrows; Mowbray 1997; Arcese et
al. 2002). Both species commonly nest in shrubs and patches of sedge slightly elevated
from the ground or water, as well as directly on the ground (Peck and James 1987). Song
Sparrows also nest in trees and occasionally in tree cavities and man-made structures (Peck
and James 1987). Both species eat primarily insects and small invertebrates during the
breeding season, with Swamp Sparrows being the more insectivorous of the two species
(Wetherbee 1968). Both species also primarily feed insects to their young, including
Lepidoptera larvae and Hymenoptera (Tompa 1971; Ellis 1980). Song Sparrows are heavier
than Swamp Sparrows (20.0g vs. 16.1g respectively; Dunning 1993) are socially dominant,
and win the majority of aggressive interactions (Grant 1966; Willson 1972; Greenberg
1988). Both species are migratory in the northern part of their range, residing in south-
eastern United States and Mexico during the winter (Mowbray 1997; Arcese et al. 2002).
Song Sparrows arrive earlier on the breeding grounds than Swamp Sparrows and begin to
breed earlier in the season (Weir 2008; Peck and James 1987).
The breeding ranges of Song and Swamp Sparrows overlap extensively. Within
their ranges, the two species use different habitats, but commonly overlap their breeding
territories (Peck and James 1987; Arcese et al. 2002). Swamp Sparrows are restricted to wet
habitats for breeding, and the presence of water is a universal habitat feature (Wetherbee
1968). They commonly breed in wet bogs, fens, marshes, and swamps dominated by
sedges, common cattails (Typha latifolia), and shrubs such as sweet gale (Myrica gale),

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Willow (Salix sp), and Alder (Alnus sp; Wetherbee 1968). In contrast, Song Sparrows
occupy a wide range of breeding habitat across their distribution, ranging from arctic-alpine
and southern desert scrub to tidal marshes and tall grass prairies (Aldrich 1984). Song
Sparrows are somewhat restricted to areas adjacent to fresh water, and often breed in
Swamp Sparrow habitat including marshes and swamps (Wetherbee 1968).
Study Site
We studied Song and Swamp Sparrow breeding biology at the Queen's University
Biology Station (44.5653, -76.322) near Elgin, Ontario during the breeding season from
early May until mid July, 2014. Our study plots were restricted to properties owned by
QUBS and were no more than 15km away from the central station. Study sites consisted of
swamps, marshes, and beaver ponds dominated by low-lying sedges, shrubs, and forbs.
Common plant species on breeding territories included Sweet Gale (Myrica gale),
Winterberry (Ilex verticillata), Speckled Alder (Alnus incana), Common Cattail (Typha
latfolia), and Narrow-leaved Meadowsweet (Spiraea alba). Breeding territories were often
bordered by maple-dominated second growth deciduous forest and rocky clearings with an
abundance of Common juniper bushes (Juniperus communis).
Nest Monitoring
We found and monitored nests of pairs of Song Sparrows (n = 28) and Swamp
Sparrows (n = 40) on a total of 17 study plots where they co-occurred. We defined a co-
occurrence plot as an area of contiguous Melospiza territories separated from other

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Melospiza territories. All of our study plots contained both focal species, and species
commonly overlapped or had neighbouring breeding territories.
To reduce nest-searching bias, we attempted to locate the nests of all breeding pairs
on each of our study plots by carefully observing the behaviour of the female and allowing
her to lead us to the nest. We monitored each nest that we found from the day it was first
located until it became inactive due to depredation, abandonment, or fledging of young. We
monitored each focal nest and recorded its contents every 2-4 days, following standard nest
monitoring protocols (Martin et al. 2007). We classified a nest as successful if it became
inactive at a stage where the young could have fledged, and we located a minimum of one
fledgling near the nest. A nest was classified as unsuccessful if it became inactive before
young could have fledged, if we found evidence of depredation or failure (e.g., the nest was
torn apart or eggs or young were dead in the nest), or if the nest became inactive at a stage
where young could have fledged, but we could locate no fledglings near the nest and the
adults were not actively feeding young. In these latter cases, we confirmed the fate of nests
with a second visit to ensure that the presence of fledglings was not accidentally missed.
The number of fledglings that fledged from a successful nest was estimated by the number
of nestlings seen in the nest at the last nest check prior to fledging. The number of days that
each nest was under observation was calculated as the number of days between the day we
first located the nest to the day it was inactive. When the exact date of inactivity for a nest
was not known, we estimated the day a nest became inactive as the median value between
the last day the nest was checked and was active, and the first day the nest was determined
to be inactive.

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Vegetation Measurements
To quantify microhabitat at nest sites, and to describe how nest sites differed from
sites that were not used for nesting, we measured vegetation on three plots for each focal
nest (use, non-use, and random plots, defined below), following previous established
protocols (Martin et al. 1997). We defined use plots as the area within a 5m radius circle
centered on each focal nest. The goal of use plots was to measure the microhabitat at each
nest for comparison across species, and for comparison with non-use and random sites.
We defined non-use plots as the area within a 5m-radius circle, centered on the
same substrate (i.e., plant species) as what was used for the nest, but was not used. Non-use
plots were located within the territory of the focal breeding pair. To set up a non-use plot,
we walked 10 meters away from the actual nest site in a randomly-selected direction (i.e.,
using a random number generated between 0 and 360 degrees) and located the nearest plant
of the same substrate as the nest site (within 1m), ensuring that the substrate was no closer
than 10m from the nest site (to maintain non-overlapping and thus independent use and
non-use plots). If no suitable substrate was available, then we generated a new random
direction and repeated this procedure until the non-use plot could be established. The goal
of non-use plots was to measure the microhabitat at a site similar to that used for the nest
site (same substrate) that could have been used for a nest, but was not selected for use by
the focal pair of sparrows.
We defined random plots as the area within a 5m radius circle, centered on a
randomly selected point near the focal nest. To set up a random plot, we used a random
number generator to select either 10, 15, or 20 meters distance, and a random direction (i.e.,
using a random number generated between 0 and 360 degrees). We then identified the point

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at the selected distance and in the selected direction away from the actual nest site, and
used this point as our center point for our random plots. We did not place any restrictions
on where this random plot could be placed. The goal of random plots was to measure the
microhabitat at a random site near the focal nest, and compare microhabitat at random sites
relative to nest sites.
For use, non-use, and random plots, we measured vegetation and other microhabitat
characteristics within 5-meter radius circles following established protocols (Martin et al.
1997). We first attached four pieces of five-meter-long polypropylene rope to each end of a
central metal pole and tied each end of the rope down tightly in order to avoid any slack.
We re-measured the lengths of the rope during each week of data collection to ensure that
they did not shrink over time. Within the 5m-radius circles, we measured the diversity and
abundance of every shrub and tree species by counting the number of stems protruding
from the ground. We counted only species with woody stems, with the exception of cattails
(genus Typha). We included cattails in our counts because of their potential importance as a
microhabitat feature for Melospiza nest sites (Peck and James 1987). If the number of stems
of a particular species could not be easily measured due to very high densities, then we
estimated the number of stems by taking the mean of three independent estimates by three
independent observers. On each focal plot, we also estimated the percentage of the plot area
that was covered by standing water, aquatic vegetation, sedges and grasses, and shrubs. For
a detailed description of these microhabitat variables, see appendix table 1. A single
observer estimated these values for every plot to avoid differences between observers. We
also measured water depth (cm) at use and non-use sites using a meter stick and recorded
whether each nest was found in wet or dry soil. We defined wet soil as soil submerged in

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water or damp to the touch, and dry soil as soil that felt dry to the touch and that crumbled
in the hand.
Statistical Analyses using the Random Forest Package
To test our first two hypotheses, we used Random Forest classification models in
the randomForest package in R (Breiman 2001) to assess differences in microhabitat (1)
between the two species' nest sites, and (2) between use and non-use plots, and use and
random plots within each species. Random Forest models use iterative classification trees
to predict the membership of a group (i.e., a categorical dependent variable) using one or
more predictor variables (Carrasco et al. 2014). These models are particularly useful for
ecological questions because they do not make distributional assumptions about the data
and they can easily handle non-linear relationships that are common in nature (Friedl and
Brodley 1997). In the Random Forest algorithm, a user-determined number of bootstrap
samples are taken from the data with replacement, and a classification tree is fitted to each
sample (Cutler et al. 2007). For each sample, a small subset of randomly chosen variables
are used for classifying each observation, the number of which is set by the user and known
as mtry in the R function (Horning 2010). Using only a few predictor variables in each
sample reduces the correlation between trees in successive samples, reducing the overall
model error rate (Horning 2010). Each classification tree is created using a random subset
of the original data (around 63%), with the remaining data, known as the out-of-bag data
(OOB), used to test the accuracy of the model (Cutler et al. 2007). Since the OOB data is
not used in the fitting of each classification tree, they act as a cross-validated accuracy

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measurement for each model run (Cuter et al. 2007). The final class prediction is calculated
as the majority vote of the OOB predictions for that observation (Cutler et al. 2007).
Random Forest models are useful because they show the relative importance of each
predictor variable for classifying observations. Each classification tree that is generated has
a misclassification rate calculated from the out-of-bag data (Cutler et al. 2007). The
importance of a predictor variable for classifying data is estimated by randomly permuting
each variable in the out-of-bag data and then re-running the model (Cutler et al. 2007),
where the importance of a predictor variable equals the difference between the out-of-bag
misclassification rate of the original model versus the randomized model, divided by the
standard error. In other words, the importance of a given predictor is quantified by the
changes in the classification power of the model when the variable is randomized. If the
OOB misclassification rate increases with the randomization of a predictor variable, then
that variable is important for classifying the data.
We used Random Forest models to test the hypotheses that Song and Swamp
Sparrows used different nest sites with respect to microhabitat, and that each species used
nest sites that differed in microhabitat from non-use and random sites. For all models, we
determined the optimal number of random variables to be tested at tree nodes using the
tuneRF function in the randomForest R package. In the first model, we used species as the
response (category) variable and all microhabitat variables (vegetation species counts and
coverage estimates) as the predictors. In the second set of models, we used plot type (use
versus non-use, or use versus random) as the response variable and all microhabitat
variables as the predictors. We created separate models to compare use and non-use sites
and use and random sites for each species. In each model, we excluded continuous

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variables that contained only zeros and categorical variables that did not vary. We
calculated classification accuracy as 1 – OOB error rate and ran each model 100 times in
order to obtain an average classification accuracy with 95% confidence intervals. We also
calculated the classification accuracies and 95% confidence intervals for each grouping of
our response variable (e.g., separate OOB error rates for use and non-use site classifications
for each species).
We used partial dependence plots generated by randomForest to show a graphical
representation of the marginal effects of the most important predictor variables on the
probability of correct classification (Breiman 2001). The vertical axis of these plots
represents the marginal effect of a predictor variable on a given classification (i.e., the
probability of the observations being classified correctly using each predictor variable) and
the horizontal axis represented the value of the variable for which partial dependence was
sought (Carrasco et al. 2014). We constructed partial dependence plots to show the relative
importance of predictor variables for classifying data in our models.
Cox Proportional Hazards
We tested if nest sites with microhabitat more characteristic of the heterospecific
influenced the nesting success of each species using Cox proportional hazard regressions in
the survival package in R. Cox proportional hazard models are semi-parametric models
used for survival data that specify a flexible and nonparametric baseline hazard function for
describing proportional changes in the baseline hazard with changes in covariates (Hosmer
et al. 2008). Cox regressions (an extension of the Cox proportional hazard models) are
usually used to model data associated with a time until an event occurs, with the event often

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being death. In our analyses, time until an event represented the time interval between the
first day of observation of a nest (i.e., the day that the nest was found) until the day that the
nest became inactive, either because it was successful (fledged young) or unsuccessful
(depredated or abandoned).
For Cox regressions, our response variable was a survival function that contained
the number of observations days and the nest fate (failure or success). We created two Cox
regression models to test our hypothesis in each species separately. For both models, we
included nest classification margin and first egg date as predictor variables in a saturated
model. First egg date was included in the model because nest failure increases later in the
breeding season in some passerine birds at our study site (Grant et al. 2005). Classification
margins were calculated for each individual nest in the previous Random Forest models
where the response (grouping) variable was the two different species. Classification
margins are defined as the proportion of votes for the correct classification minus the
proportion of votes for the incorrect classification, where a positive classification margin
indicates a nest was classified as the correct species the majority of the time, while a
negative classification margin indicates a nest was classified as the incorrect species the
majority of the time. Thus, classification margins allowed us to test the hypothesis that
nests that were more similar to the other Melospiza species (i.e., had lower classification
margins) were more likely to fail.
For all Cox regressions, we identified the best-performing models using the dredge
command in the MuMIn package in R, where the best-performing model was the model
with the lowest Akaike Information Criterion value adjusted for small sample size (AICc).
We tested the assumptions of proportional hazards for our original models first and then

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23
again for our best-performing models graphically, following Fox (2002). We used the
cox.zph function in the survival package in R to test for violation of the assumptions of
proportional hazards. We tested for the presence of influential data points by examining the
contribution of each data point to the regression coefficient using the dfbeta function.
Finally, we tested for non-linearity of covariates by plotting Martingale and component-
plus residuals of our best-fitting model against all continuous predictor variables. We found
no non-linear trends of Martingale residuals with covariates, indicating that covariate
predictors met the assumptions of linearity in our best-fitting models.

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RESULTS
Species Differences in Nest Sites
Song and Swamp Sparrow nest sites differed in microhabitat. Our most accurate
model for classifying nest sites by species had an average error rate of 20.6%, giving the
model an average accuracy of 79.4% [95% Confidence Intervals (CI): 78.7 – 80.0].
Average model accuracy (%) for the classification of Swamp Sparrow nests (93.2; [95%
CI: 92.4 – 94]) was significantly higher than the classification accuracy of Song Sparrow
nests (59.7; [95% CI: 58.5 – 60.9]; one-tail t-test, df = 41.3, t = -49.6, p < 0.001; figure 1).
On average, 37.28 out of 40 Swamp Sparrow nests were correctly classified, while 16.72
out of 28 Song Sparrow nests were correctly classified (figure 2). Misclassification of Song
Sparrow nests therefore accounted for the majority of the classification errors in the model.
Microhabitat variables related to water were consistently the most important
predictors of species nest site classification (figure 3). Four of the top five most important
predictor variables were directly related to water: water coverage, water depth, aquatic
vegetation coverage, and soil type. Water coverage and aquatic vegetation coverage were
the best predictors of species and therefore the most important variables in the model
(figure 4). Juniper abundance was the only other highly important predictor variable that
was not directly related to water (figure 6), though it had lower importance than the other
top predictors and is generally indicative of dry soils and the absence of water (figure 3).
Partial dependence plots from the model for the two most important predictor
variables, water coverage and aquatic vegetation coverage, are illustrated in figure 4. The
probability of a nest site being classified as a Swamp Sparrow increased as both predictors
increased, indicating that Swamp Sparrow nest sites had more water coverage and aquatic

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25
vegetation coverage than Song Sparrow nest sites (figure 4). Swamp Sparrow nest sites had
significantly greater water (Kruskal-Wallis, X2
= 23.47, p < 0.0001) and aquatic vegetation
coverage (Kruskal-Wallis, X2
= 19.49, p < 0.0001) than Song Sparrow nest sites. The
probability of a nest site being classified as a Swamp Sparrow increased faster with
increased aquatic vegetation coverage than water coverage (figure 4). Conversely, the
probability of a nest site being classified as Swamp Sparrow greatly decreased with
increased juniper abundance, which indicated that Song Sparrow nest sites had far more
juniper than Swamp Sparrow nest sites (figure 6). Song Sparrow nest sites had significantly
greater juniper abundance than Swamp Sparrow nest sites (Kruskal-Wallis, Χ2
= 20.26, p <
0.0001).
Song Sparrow Use, Non-Use, and Random Sites
Song Sparrow nest sites differed in microhabitat from random sites, but did not
differ much from non-use sites. Our most accurate model for classifying Song Sparrow use
versus non-use sites performed poorly, with an average classification accuracy of 40.0%
[95% CI: 38.6 – 41.4]. Model accuracy for classifying use sites (39.1; [95% CI: 37.1 –
41.2]) was similar to that of non-use sites (40.9; [95% CI: 39 – 42.8]). In contrast, our most
accurate model for classifying Song Sparrow use versus random nest sites performed
relatively better, with an average classification accuracy of 64.8% [95% CI: 63.6 – 65.9].
Model accuracy for classifying use sites (65.1; [95% CI: 63.3 – 67]) was similar to that of
random sites (64.4; [95% CI: 63.5 – 65.3]).
Our model for distinguishing Song Sparrow use versus non-use sites had poor
classification accuracy and resulted in predictor variables with low importance (figure 7).

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The three most important predictor variables for classification were winterberry abundance,
juniper abundance and shrub coverage, and these variables were marginally more important
than any other predictor (figure 5). In our model for classifying use versus random sites,
only two predictor variables were important for classification: shrub coverage and juniper
abundance (figure 7). These two variables were more than three times more important than
any other predictor for classifying use versus random sites for Song Sparrows (figure 5).
Partial dependence plots for dependence of shrub coverage and juniper abundance
on the probability of a Song Sparrow nest site being classified as a non-use and random site
showed that Song Sparrows used nest sites with more shrub coverage and juniper
abundance than non-use and random sites (figure 8). For both predictors, the probability of
classification decreased more sharply in the use versus random models, which indicated
that differences in shrub coverage and juniper abundance were greatest between these site
types.
Swamp Sparrow Use, Non-Use, and Random Sites
Swamp Sparrow nest sites did not differ in microhabitat from non-use sites, and
only marginally did so from random sites. Our most accurate model for classifying Swamp
Sparrow use versus non-use sites performed poorly, with an average classification accuracy
of 35.3% [95% CI: 34.3 – 36.4]. Averaged model accuracy for classifying use sites (38.1%;
[95% CI: 36-4 – 39.8]) was slightly greater than that for non-use sites (32.6%; [95% CI: 31
– 34.2]). Our most accurate model for classifying Swamp Sparrow use versus random sites
also performed relatively poorly, with an average classification accuracy of 46.8% [95%
CI: 46.1 – 47.6]. Averaged model accuracy for classifying use sites was much greater for

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27
use sites (54.7%; [95% CI: 53.2 – 56.2]) than random sites (38.9%; [95% CI: 37.9 – 39.9]).
This indicated that the misclassification of random sites had a proportionally greater effect
on the error rate of the model than the misclassification of use sites.
Our most accurate model for classifying Swamp Sparrow use versus non-use sites
was unable to accurately classify plot types by microhabitat. As a result of the model's
inaccuracy, few variables could be considered to be consistently important predictors for
classification (figure 9). Variables such as water depth, juniper abundance, alder
abundance, and swamp rose abundance were the most important predictors in the model,
but the magnitude of their importance was small (figure 9). Our model indicated that
Swamp Sparrow use versus non-use sites differed little with respect to the microhabitat
variables that we measured. Our most accurate model for classifying Swamp Sparrow use
versus random sites identified several important predictor variables, including blue beech
abundance, marsh vegetation coverage, water depth, and soil moisture, with blue beech
abundance being the most important (figure 9).
Relative Fitness in Different Nest Site Types
Song Sparrows had higher reproductive success when they used nest sites
characteristic of Swamp Sparrows compared to nest sites characteristic of their own species
(figure 10). Our single best performing Cox regression model for predicting Song Sparrow
nest survival included only nest classification margin as a predictor, with nest survival
increasing in nests that were classified as more similar to Swamp Sparrows (β = 1.02, z =
1.86, p = 0.06; figure 10; appendix table 1). Our averaged model, incorporating the results
of all top models (delta AICc < 2), included both nest classification margin and first egg

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28
date as predictors (table 2), again with nest survival increasing for nests that were classified
as more similar to Swamp Sparrows (appendix table 3). Nest survival also declined with
first egg date, though this relationship was not significant (appendix table 3).
Swamp Sparrows did not have differential reproductive success when they used nest
sites characteristic of Song Sparrows or nest sites characteristic of their own species. Our
single best performing Cox regression model for predicting Swamp Sparrow nest survival
was the null model (appendix table 4). Our averaged model, incorporating the results of all
top models (delta AICc < 2), included both nest classification margin and first egg date, but
neither variable was a significant predictor of nest survival (appendix table 5). Though not
significant, nest survival increased in nests that were classified as more similar to Song
Sparrows, and decreased with first egg date.

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DISCUSSION
Species Differences in Nest Sites
Song and Swamp sparrows used nest sites with different microhabitat. Our most
accurate Random Forest model for classifying species nest sites was relatively accurate
(80% classification accuracy), which indicated that there were repeatable differences in the
microhabitat variables that we measured between nest sites of the two species. The main
differences revolved around the presence of water; Swamp Sparrow nest sites had greater
water coverage, aquatic vegetation, and water depth than Song Sparrow nest sites (figure
3). Every Swamp Sparrow nest that we found on our study sites was placed in vegetation
above wet soil or standing water, and the area around each nest consisted of microhabitat
characteristics of swamps and marshes with dense aquatic shrubs, sedges, and forbs. In
contrast, Song Sparrow nests were split between dry and wet soil sites, with half of the
nests we found being in each soil type. Consequently, our Random Forest model was much
more accurate in classifying Swamp Sparrow nest sites compared to Song Sparrows
because there was less variation in microhabitat used by Swamp Sparrows (figure 1). Song
Sparrows used a wide range of nest site microhabitat, ranging from wet areas with thick
aquatic vegetation, dry juniper patches, forest edges, sedges, and ground patches. In
contrast, Swamp Sparrows were more restricted in their use of nest sites and nearly always
used microhabitat with lots of water coverage and abundant aquatic vegetation (figure 5).
Our observations were similar to observations of breeding territory habitat made by
Greenberg (1988), who found that Swamp Sparrow territories contained significantly
greater water coverage and depth than those of neighbouring Song Sparrows. Unlike
Greenberg (1988), however, we found that Song Sparrows also used nest sites in extremely

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30
wet areas, and often had nest sites that were indistinguishable to those of Swamp Sparrows
with respect to microhabitat.
Song Sparrow nest sites also differed from Swamp Sparrow nest sites in having
more juniper (figure 6). Most Song Sparrow nest sites on dry soil (78%) were placed in
juniper bushes that also dominated the vegetation within a 5m radius of the nest site. On
study sites where both wet and dry, juniper-rich habitats were available, Song Sparrows
tended to place their nests in juniper bushes, suggesting that these sites were preferred. In
contrast, we did not find a single Swamp Sparrow nest placed in a juniper bush or within
5m of a juniper bush, despite the fact that juniper was present on all of our study plots.
Song Sparrow: Use, Non-Use, and Random Sites
Song Sparrows consistently used nest sites with more shrub coverage and juniper
compared to non-use and random sites, and these were the biggest differences in
microhabitat between sites detected in our models (figure 7). These differences between use
versus non-use/random sites were largely driven by high juniper shrub coverage at dry nest
sites as species of aquatic vegetation were generally less important for classification of use
versus non-use and random sites (figure 7). Juniper tended to grow in large clusters and was
characteristic of dry, rocky soil on the edge of wetter areas on our study sites. Song
Sparrows often placed their nests in juniper (40% of all nests) and used nest sites with more
juniper nearby compared to non-use and random sites. Hence, juniper was used both as an
important vegetation substrate for nest building and as a microhabitat feature at nest sites.
Song Sparrows may have used juniper at their nest sites in order to reduce nest predation
through negative reinforcement, as searching through large areas of homogenous juniper

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31
bushes could reduce the probability of predators finding nests and deter them from
continued searching (Martin 1996). Alternatively, juniper-rich areas may have simply been
optimal dry habitat for Song Sparrows to place their nests due to factors we did not test for
such as increased local food abundance, optimal microclimate, or increased ability to detect
predators (Chalfoun and Martin 2007; Hafton and Reinertsten 1985). Regardless, our
results suggest that Song Sparrows used nest sites selectively, both compared with suitable
sites that they did not use (non-use sites), and especially compared with sites selected at
random from within their territories (random sites).
Swamp Sparrow: Use, Non-Use, and Random Sites
We did not find measurable differences in microhabitat between Swamp Sparrow
nest sites and sites where they could have conceivably placed their nests, but did not (non-
use sites). Nest sites had greater water depth than non-use sites, but the difference was
small. All other microhabitat variables were either not important for classification or had
extremely low importance (figure 9). Despite this, we cannot completely reject our
hypothesis that Swamp Sparrows used nest sites non-randomly at this small scale due to
several possible alternative explanations.
One possibility is that use sites differed from non-use sites with respect to
microhabitat variables that we did not measure in our study. Other potentially important
microhabitat variables that we did not measure include nest coverage, vegetation height,
and heterogeneity (Chase 2002; Chalfoun and Schmidt 2012), and these could have been
important microhabitat features for Swamp Sparrows. Alternatively, small-scale nest site
microhabitat may have been selected in ways that we did not test for, such as to provide

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32
optimal microclimate for developing eggs and chicks (Wiebe 2001; Haftorn and Reinersten
1985). Further, habitats where Swamp Sparrows placed their nests were homogenous in
nature, with an abundance of similar aquatic shrubs, sedges, and structure for nest sites. It is
possible that Swamp Sparrows were not limited by small-scale abundance of high quality
microhabitat, but instead selected nest sites at larger scales for reasons we could not detect
in our analyses such as increased food abundance (Chalfoun and Martin 2007) or reduced
predation rates (Martin 1996, 1998). Song Sparrow nest sites placed in Swamp Sparrow-
like microhabitat also did not differ much in microhabitat between these site types, which
suggested that potential large-scale nest site selection in wet habitats was independent of
species and a function of habitat type.
Consistent with the idea that habitat use at larger scales was important for Swamp
Sparrow nest site selection, we found evidence that Swamp Sparrow nest sites differed
from random sites with respect to the microhabitat variables we measured. Use sites had
lower Blue Beech abundance and higher aquatic vegetation coverage and water depth than
random sites, which indicated that Swamp Sparrows placed their nests in wetter areas
relative to random areas available to them (figure 9). Blue Beech was a common tree
species on our study sites and its presence on random sites was indicative of the drier,
forested areas that surrounded many of the marshes and swamps that Swamp Sparrows
used for breeding. Additionally, it is likely that increased water depth and aquatic
vegetation were proxies for wetter habitat at use sites despite the fact that water coverage
was not an important predictor for classification (figure 9). These results suggested that the
main differences in microhabitat between Swamp Sparrow nest sites and random sites on or
near their territories involved the use of wet microhabitat. Thus, our results suggest that

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Swamp Sparrows used wetter nest sites compared with other sites that were available to
them, but within wet habitats we found no evidence for the preferential use of any sites.
Reproductive Success of Co-occurring Song and Swamp Sparrows
Song Sparrows had higher reproductive success when they used nest sites more
similar to Swamp Sparrow nest sites (figure 10; appendix table 2). In contrast, reproductive
success of Swamp Sparrows did not vary between sites more similar to Song or Swamp
sparrow nest sites (appendix table 5). These results are different from what we expected,
and suggest that ecological costs of resource partitioning between our focal species differ
from other previously studied dominant and subordinate species (Martin and Martin
2001a,b
; Munday 2001; Zeng and Lu 2009). Martin and Martin (2001a
) suggested that
dominant species typically occupy mutually preferred habitat and are more restricted in
habitat use compared to subordinate species, which are often adapted to a wider variety of
different habitat types in order to compensate. They argued that this pattern could be
widespread among closely related, ecologically similar species, as evidence by their own
results and studies in other taxa (Morse 1974; Connell 1961; Munday 2001). In contrast,
Freshwater et al. (2014) found no pattern of ecological resource breadth and nest site
microhabitat use across closely related dominant and subordinate songbirds. In our study,
we found evidence for the opposite pattern to what Martin and Martin (2001a
) observed,
whereby subordinate Swamp Sparrows were more restricted in their use of nest site
microhabitat than the dominant Song Sparrows, and occupied a higher proportion of high
quality nest sites. Thus, despite being the dominant species, Song Sparrows appeared to be

34.
34
adapted to a wider range of nest site microhabitat and did not appear to exclude Swamp
Sparrows from high quality nest sites.
Song Sparrows experienced higher reproductive success when they used nest sites
more characteristic in microhabitat of Swamp Sparrows, which suggested that these sites
should be preferred by both species. However, the nature of our study did not allow us to
test nest site preference, or if alternative factors shaped the differences in nest site use
between the species. Further, unlike Song Sparrows, Swamp Sparrows did not use nest sites
that could be considered to be truly like the other species, as they never nested on dry soil
(although previous work has documented Swamp Sparrow nests on dry soil in Ontario;
Peck and James 1987). Due to this, we could not determine whether Swamp Sparrows
achieved fitness benefits from using nest sites in extremely wet microhabitat or if they were
simply restricted to these areas for breeding. Nevertheless, our results provide rare evidence
for a subordinate species co-occurring with a dominant and occupying a higher proportion
of higher quality resources, as determined by reproductive success at different nest sites.
Swamp Sparrow Nest Sites in Wet Microhabitat
We found that Swamp Sparrows only used nest sites in extremely wet habitat,
possibly due to a preference for this habitat, or due to other species or constraints restricting
them to wet areas. One possibility is that Swamp Sparrows were restricted to using wet nest
sites because Song Sparrows competitively excluded them from using drier nest sites.
Greenberg (1988) observed that Song Sparrows often initiated aggressive chases with
Swamp Sparrows when birds were first establishing territories early in the breeding season.
He found that Song Sparrows initiated the majority of chases between species and were

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35
able to displace Swamp Sparrows from their territories 63% of the time. We observed
similar levels of interspecific aggression on our study sites, as Song Sparrows would often
initiate aggressive chasing events with neighbouring Swamp Sparrows that could last for
several seconds. Additionally, we observed several instances where Song Sparrows
aggressively sang over top of singing Swamp Sparrows, which acts as signal of competitive
dominance in closely related songbirds (Martin and Martin 2001; Martin et al. 1996).
Greenberg’s (1988) observations and our own anecdotal evidence suggested that Song
Sparrows could have displaced Swamp Sparrows from drier areas before they would have
the chance to breed. However, most of the chases that we observed were within wet habitat,
and we did not observe any attempts by a Swamp Sparrow to build a nest in a dry Song
Sparrow-like nest site. Ultimately, only the experimental removal of Song Sparrows would
allow us to test the idea that Song Sparrows may restrict Swamp Sparrows from juniper
nest sites.
Song Sparrows in Dry Microhabitat: Alternative Explanations
Though Song Sparrows are socially dominant to Swamp Sparrows, they
experienced lower reproductive success when they used nest sites more characteristic of
their own species (figure 10). In addition, Song Sparrows did not prevent Swamp Sparrows
from nesting in wet nest sites that conferred higher reproductive success. These results
suggested that patterns of resource use in ecologically similar Swamp and Song Sparrows
are different from what has been observed in other closely related passerines with
asymmetric relationships (Martin and Martin 2001a
). While our results our consistent with
this view, they are also consistent with six alternative hypotheses: (1) Song Sparrows were

36.
36
constrained from using Swamp Sparrow-like nest sites, (2) Song Sparrows behaved
maladaptively, (3) Song Sparrows that used nest sites characteristic of their own species
experienced higher fitness because of increased offspring quality and/or fledgling or adult
survival offset the costs of higher nest failure, (4) Song Sparrows could not exclude Swamp
Sparrows from wet nest sites due to their high densities, (5) Song Sparrow-like nest sites
experienced increased predation due to temporal variation in predation risk, and (6) spatial
variation in predation on our study sites caused dry nest sites to experience greater
predation risk due to clustering.
(1) Constraints may have limited Song Sparrows' ability to use Swamp Sparrow-like
nest sites most of the time, thus explaining why they used sites that conferred lower
reproductive success. Greenberg (1988) suggested that the smaller body size and longer
legs of Swamp Sparrows made them better suited to foraging in wet microhabitat than Song
Sparrows (Wetherbee 1968; Greenberg 1988), and these traits may have also limited Song
Sparrows' abilities (i.e. foraging and nest building) to nest in wet microhabitats. If Song
Sparrows are morphologically constrained from using wet nest sites, but experienced
reproductive benefits from doing so, then the use of these nest sites could have been limited
to individuals that could overcome these constraints, such as smaller or higher quality birds.
The added costs of interacting with Swamp Sparrows in wet habitats may have
compounded the challenges for Song Sparrows attempting to nest in these microhabitats. In
opposition to this, we often observed Song Sparrows foraging in dense aquatic vegetation,
and did not find them to be noticeably restricted in doing so. Future studies are needed to
determine if Song Sparrows truly have reduced ability to forage or nest in wet microhabitat.

37.
37
(2) Our results could be explained if Song Sparrows used dry nest sites
maladaptively. Animals that favour habitat that reduces their fitness relative to other
available habitat are said to be in an ‘ecological trap’, which is often created by changing
selective pressures associated with anthropogenic disturbances (Latif et al. 2011), or by
strong gene flow from sites where the behaviour is adaptive. Among Song Sparrow nests
placed in juniper, only 2 nests successfully fledged young, and 5 of the nests that failed
were under observation for less than 5 days. Hence, Song Sparrows using juniper on dry
nest sites appeared to suffer extremely high nest predation, making juniper a poor choice
for nest sites. While our sites are protected and relatively undisturbed by humans, we
cannot exclude the possibility that juniper nesting is beneficial to Song Sparrows at other
nearby sites, and is maintained at our site due to gene flow.
(3) In our study, we measured only fledging success associated with nest sites, and
thus other components of fitness may have caused higher overall fitness for Song Sparrows
that nested in juniper nests, despite the higher nest predation in these sites. For example,
juniper nests may have fledged offspring of higher quality, or the structure of juniper
bushes may have increased fledgling or adult female survival rates. One potential
mechanism for higher offspring quality could have been higher local food abundance at dry
nest sites. Juniper-rich habitat on the edges of marshes and swamps could have had more
available food (e.g. insect larvae), and this increased proximity of food near the nest could
have resulted in increased nestling condition and lifetime fitness (Chalfoun and Schmidt
2007; Martin 1987; Roff 1992). Birds that use nesting habitat with higher local food
abundance can also benefit through more nesting opportunities throughout the breeding
season, which is particularly important when nest predation rates are high as they were at

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38
our study sites (Holmes et al. 1992; Nagy and Holmes 2004). However, if nest density
increased as a function of dry habitat preference for these reason, then positive fitness
benefits of higher quality offspring and more nesting opportunities would have been offset
by increased nest predation and reduced nest survival (Chalfoun and Martin 2007; Martin
1996). Nest predation rates were much greater in Song Sparrows nest sites on dry soil,
which suggested that such density-dependent effects could have occurred. Other passerines
that placed their nests in juniper on our study sites, including Field Sparrow (Spizella
pusilla), Eastern Towhee (Pipilo erythrophthalamus) and Common Yellowthroat
(Geothylpis trichas), likely contributed to the high nest predation rates at dry nest sites
(Martin and Martin 2001; Martin 1996).
Song Sparrows may also have used dry, juniper-rich nest sites if using these sites
increased adult survival. Socially dominant songbirds have been shown to have greater
annual adult survival compared to subordinates, which may cause them to invest more in
adult survival than reproduction in a given breeding season relative to subordinates
(Freshwater et al. 2014; Stearns 1992). Anecdotally, we observed that Song Sparrow
females in juniper nests were much quicker to flush from their nests when approached
compared to birds in wet nest sites. This suggested that birds using juniper nest sites were
able to detect approaching predators more easily than other nest sites, which would likely
increase their chances of escaping an attack. Thus, it is possible that Song Sparrows
achieved fitness benefits from using juniper nest sites via increased adult survival at the
cost of nest success, a life history strategy that makes sense in terms of the differential adult
survival rates of other dominant and subordinate songbirds (Freshwater et al. 2014).

39.
39
(4) Song Sparrows may have been unable to exclude Swamp Sparrows from high
quality nest sites in wet habitat, and thus often resorted to nesting in dry nest sites. A
dominant species could be forced to use sub-optimal resources if the energy required to
exclude subordinates from mutually preferred resources exceeds the benefits of using these
resources (Morse 1974). Interspecific clustering, where the subordinate species greatly
outnumbers the dominant, can overwhelm the dominant and cause it to reduce its territory
size or abandon resource use entirely (Martin and Ghalambor 2014). We found some
evidence that this could have occurred in our study; Swamp Sparrows tended to nest in
relatively high densities on our study plots, with nests sometimes no more than several
metres away from each other. Large expanses of marsh or swamp habitat where Swamp
Sparrows nested in high densities were almost always bordered by drier, juniper-rich areas,
which provided Song Sparrows with opportunities to move away from Swamp Sparrows
and still breed in suitable habitat. Thus, if the costs of aggressive defense were greater than
the benefits to using wet nest sites, it may have been beneficial for Song Sparrows to
abandon these areas for other nesting habitat (Martin and Ghalambor 2014). Anecdotally,
we observed one instance where a Song Sparrow established a territory in the middle of a
small marsh early in the breeding season, but left soon after while at least 3 pairs of Swamp
Sparrows remained on the territory. Additionally, Swamp Sparrows could possess unique
adaptations that help them compensate for their smaller size when competing with Song
Sparrows in wet habitat. Evolution of adaptations that increase flight acceleration and
manoeuvrability, as have been shown in other subordinate species (Feinsinger et al. 1979;
Willis 1982), could help Swamp Sparrows navigate through dense aquatic vegetation when
being chased by Song Sparrows. High density of Swamp Sparrows on our study sites along

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40
with potential adaptations to breeding in wet microhabitat could have made it energetically
costly for Song Sparrows to exclude them from wet nest sites, causing Song Sparrows to
shift to using drier nest sites.
(5) Temporal variation in predation pressure at dry nest sites could explain why
these nest sites did so poorly. Nest predation rates can change annually as a result of
fluctuating populations of other prey species influencing the density of nest predators. Nest
predation rates could therefore have been higher at Song Sparrow-like nest sites in juniper-
rich areas if there were simply more predators in these areas responding to higher levels of
other prey species. Since our study spanned only 1 year, we cannot rule out the possibility
that annual variation in predator abundance could have contributed to the increased nest
predation of Song Sparrow-like nest sites.
(6) Finally, spatial variation in predation rates on our study sites may have
influenced the interpretation of our results. Nest predation in birds is heavily influenced by
nest density as nest predation rates increase with increased density of nests (Martin 1996).
A nest in an area with lots of other nests would be expected to experience a greater threat of
predation, regardless of the microhabitat, compared to a nest in an area with fewer nests
around it (Martin 1996). Thus, Song Sparrows using Song Sparrow-like nest sites could
have experienced higher nest predation if these sites occurred in higher density compared to
Swamp Sparrow-like nest sites in our study area. In agreement with this hypothesis, we
found clustering of Song Sparrow-like nest sites (used by Song Sparrows) on our study
sites relative Swamp Sparrow-like nest sites (appendix figure 11). However, Swamp
Sparrow-like nest sites were also somewhat clustered in other areas, and it was unclear if
the spatial differences between the two types of clustered nest sites led to increased

41.
41
predation rates over the spatial scales in our study. Nonetheless, we cannot rule out the
possibility that spatial variation in predation rates influenced our results due to the potential
for increased predation in areas with clustered Song Sparrow-like nest sites and the density
of other bird species nests, which we did not measure.
Both Song and Swamp Sparrows used nest sites non-randomly with respect to
microhabitat. These differences reflected larger scale microhabitat use, as nest sites differed
from random sites but generally did not differ as much from non-use sites. Additionally,
species partitioned nest site microhabitat; Swamp Sparrow nest sites had wetter
microhabitat than Song Sparrow nest sites. In opposition to what we expected, Song
Sparrows had lower reproductive success when they used nest sites characteristic of their
own species (e.g. dry, juniper-rich habitat) compared to Swamp Sparrow-like nest sites in
wetter areas, while Swamp Sparrows had similar fitness across nest sites. Our results
provide one of the only examples of a subordinate species using higher quality resources,
via nest sites that directly influenced fitness, in comparison to a closely-related, co-
occurring dominant species. Further, our results suggest that patterns of resource
partitioning and fitness in closely related, ecologically similar species with dominant and
subordinate relationships are complex in nature and may reflect the unique ecological trade-
offs and natural histories of the species involved. Studies such as these are important for
strengthening our understanding of how ecologically similar species can overcome costs of
co-occurrence, and, consequently, how biodiversity is maintained at local scales. Patterns
of resource partitioning, fitness, and costs of co-occurrence in closely related, ecologically
similar competing species may not always occur in ways we would expect, a notion that
should drive future research towards investigating the breadth of the patterns we observed.

42.
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47.
47
SUMMARY
1. Both species selected non-random sites for nesting, but we found little difference in
microhabitat between sites that were used versus sites that were not used but could
have been for nesting.
2. The greatest differences between the nest sites of the two different species involved
water: Swamp Sparrows consistently used wetter habitat than Song Sparrows.
3. Song Sparrow (dominant) nests were more likely to succeed when they were placed
in Swamp Sparrow-like sites. Swamp Sparrow (subordinate) nests had similar
survival across nest sites.
4. Song and Swamp sparrows select nest sites non-randomly and partition nest sites by
habitat.
5. Surprisingly, the dominant Song Sparrow experienced higher nest success when
they nested in sites more typical of the subordinate species. This result is counter to
previous findings in other dominant and subordinate bird species.
6. Our findings might reflect constrained or maladaptive nest site selection in Song
Sparrows, or be explained by several other alternative hypotheses.
7. Resource partitioning and fitness of closely related, ecologically similar dominant
and subordinate species living in sympatry do not always occur in ways that have
been previously documented.

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48
FIGURES
Figure 1: Average classification accuracies of species nest sites in our Random Forest model. Model classification
accuracy was calculated as 1 – out-of-bag error rate for each species. Swamp Sparrow nest sites were classified as being
the correct species significantly more than Song Sparrow nest sites (p <0.0001).
Swamp Sparrow Song Sparrow
ModelAccuracy(%)
55
60
65
70
75
80
85
90
95

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49
Figure 2: Nest site classification margins for Song Sparrows (left) and Swamp Sparrows (right). Classification
margins were calculated as the proportion of votes for the correct species minus the maximum proportion of
votes for both species in the Random Forest model. Thus, nest sites with a classification margin greater than 0
were correctly classified the majority of the time (points above dashed line). Swamp Sparrows nest sites were
correctly classified significantly more than Song Sparrow nest sites, indicating that Song Sparrows used Swamp
Sparrow-like nest sites more so than Swamp Sparrows used Song Sparrow-like nest sites.
Song Sparrow Swamp Sparrow
0 10 20 30
ClassificationMargin
-1.0
-0.5
0.0
0.5
1.0
0 10 20 30
Nest Number

50.
50
Figure 3: Variable importance plot for the ten most important predictors of species nest site classification. Variables that were
important for classification were those that best predicted what species a nest site belonged to in our Random Forest model. The
best predictors of nest site classification were variables related to the presence of water.
Variable Importance

51.
51
Figure 4: Partial dependence plots showing the marginal effects of water coverage (blue line) and aquatic vegetation coverage
(green line) on the probability of a nest site being classified as Swamp Sparrow. The probability of classifications was positively
related to both variables, indicating that Swamp Sparrow nest sites had greater water coverage and aquatic vegetation coverage
than Song Sparrow nest sites.
Water
Aquatic Vegetation

52.
52
Swamp Sparrow
Song Sparrow
Water Aquatic Vegetation
Coverage(%)
0
20
40
60
80
100
Figure 5: Mean water and aquatic vegetation coverage at Song and Swamp Sparrow nest sites. Swamp Sparrows had
significantly greater water and aquatic vegetation coverage at their nest sites compared to Song Sparrows. Black lines
above each bar represent one standard error from the mean.

53.
53
Figure 6: A partial dependence plot showing the marginal effect of juniper abundance on the
probability of a nest site being classified as Swamp Sparrow. There was a strong negative association
between probability of Swamp Sparrow classification and juniper abundance, which indicated that
Song Sparrow nest sites contained far greater juniper abundance than Swamp Sparrow nest sites.

54.
54
Figure 7: Variable importance plots for classification of Song Sparrow use versus non-use (left) and random sites (right). Juniper
abundance and shrub coverage were important variables in both models, indicating that they were important predictors of Song
Sparrow nest site use on our study plots.
Variable Importance Variable Importance

55.
55
Figure 8: Partial dependence plots showing the marginal effect of shrub coverage (left) and juniper abundance (right) on the
probability of a Song Sparrow nest site being classified as a non-use (solid line) or random site (dashed line). The probability of a
nest site being classified as a non-use and random site decreases with increasing shrub coverage and juniper abundance, indicating
that nest sites had greater shrub coverage and juniper abundance than non-use and random sites.

56.
56
Figure 9: Variable importance plots for classification of Swamp Sparrow use sites versus non-use (left) and random sites (right).
Few variables were important for classification of use versus non-use sites, which indicated that these sites did not differ in
microhabitat. In contrast, Blue Beech abundance, water depth, and aquatic vegetation coverage were moderately important for
classification of use versus random sites.
Variable Importance Variable Importance

57.
57
Figure 10: Daily nest survival for Song Sparrows that used their own type of nest sites (green line) and Swamp Sparrow-
like nest sites (blue line). Song Sparrow-like nest sites were those with a positive classification margin and Swamp
Sparrow-like nest sites were those with a negative classification margin in the Random Forest model. Song Sparrows had
significantly higher daily nest survival when they used Swamp Sparrow-like nest sites than when they used their own type
of nest sites.
Song Sparrow-like
Swamp Sparrow-like

58.
58
APPENDIX
A
Supplementary
Tables

59.
59
Table 1: A description of the microhabitat variables we measured on use, non-use, and random sites
for each nest.
Table 2: Best performing Cox proportional hazard models (ranked by AICc) for predicting daily Song
Sparrow nest survival.

60.
60
Table 3: Full averaged Cox proportional hazard model coefficients for best performing models (delta < 2)
predicting daily Song Sparrow nest survival.
Table 4: Best performing Cox proportional hazard models (ranked by AICc) for predicting daily Swamp
Sparrow nest survival.
Table 5: Full averaged Cox proportional hazard model coefficients for best performing models (delta < 2)
predicting daily Swamp Sparrow nest survival.

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61
Figure 11: A plot showing the location of Song Sparrow nests in our study area. Swamp Sparrow-like nest sites (blue diamonds) were
those with a negative classification margin and Song Sparrow-like nest sites (red squares) were those with a positive classification
margin. Both types of nest sites were clustered in different areas on our study sites.
44.525
44.53
44.535
44.54
44.545
44.55
44.555
44.56
44.565
44.57
44.575
-­‐76.4
-­‐76.39
-­‐76.38
-­‐76.37
-­‐76.36
-­‐76.35
-­‐76.34
-­‐76.33
-­‐76.32
-­‐76.31
Latitude
Longitude
Song
Sparrow-­‐like
Swamp
Sparrow-­‐like