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VOLUME 12, ISSUE 1, ARTICLE 17
Wittig, T. W., N. L. Cagle, N. Ocampo-Peñuela, R. Scott Winton, E. Zambello, and Z. Lichtneger. 2017. Species traits and local abundance affect
bird-window collision frequency. Avian Conservation and Ecology 12(1):17. https://doi.org/10.5751/ACE-01014-120117
Copyright © 2017 by the author(s). Published here under license by the Resilience Alliance.
Research Paper
Species traits and local abundance affect bird-window collision
frequency
Thomas W. Wittig 1, Nicolette L. Cagle 1, Natalia Ocampo-Peñuela 1,2, R Scott Winton 1,2, Erika Zambello 1,3 and Zane Lichtneger 4
1Nicholas School of the Environment, Duke University, Durham, North Carolina, USA, 2Department of Environmental System
Science, Ecosystem Management, ETH Zürich, Switzerland, 3Choctawhatchee Basin Alliance, Santa Rosa Beach, Florida, USA,
4SAS Institute Inc., Environmental Program, Cary, North Carolina, USA
ABSTRACT. Studies on bird-window collisions have generally drawn inferences about species’ differential vulnerability from collision
tallies. However, this common methodology is potentially biased because the number of collisions may simply reflect prevalence of
species at the study site rather than species-specific vulnerability. Building on recent studies of abundance and collision rates, we offered
a complementary methodology based on point count data that could be widely applied alongside carcass surveys. Additionally, we
broadened our analysis beyond previously applied taxonomic and migratory classifications to include functional classifications of
feeding guild, breeding status, and synanthropy. Our null hypothesis was that collision frequencies reflect a species’ or classification
group’s prevalence at study sites. To test this possibility, we used collision data collected at three sites in the Research Triangle Area of
North Carolina, United States. At one of these sites, Duke University’s Main Campus, we also gathered relative abundances from the
local bird community to develop a case study assessment of how background prevalence compared to number of collisions. Using the
larger, three-site dataset, we developed an initial picture of collision susceptibility based solely on frequency, the standard practice.
Then, by bootstrapping our Duke abundance data, we generated confidence intervals that simulated collision based on chance versus
prevalence. We identified several instances where collision tallies produced misleading perception of species-specific vulnerability. In
the most extreme case, frequencies from our Triangle Area dataset indicated locally breeding species were highly vulnerable to collisions
while our abundance-based case study suggested this same group was actually adept at avoiding collisions. Through our case study, we
also found that foliage gleaning was linked to increased risk, and omnivory and ground foraging were associated with decreased risk.
Although our results are based on a limited sample, we argue that abundance needs to be incorporated into future studies and recommend
point counts as a noninvasive and adaptable alternative to area-searches and mist netting.
Traits spécifiques à l'espèce et abondance affectent la fréquence des collisions d'oiseaux aux fenêtres
RÉSUMÉ. Les études sur les collisions d'oiseaux aux fenêtres infèrent généralement la vulnérabilité d'une espèce à partir des décomptes
de collisions. Toutefois, cette méthodologie fréquemment utilisée est potentiellement biaisée parce que le nombre de collision reflète
simplement la prévalence de certaines espèces au site d'étude plutôt que la vulnérabilité réelle de l'espèce. À partir des récentes études
d'abondance et de taux de collision, nous offrons une méthodologie complémentaire basée sur des recensements ponctuels qui pourrait
être appliquée à large échelle en parallèle avec les décomptes de carcasses. De plus, nous avons élargit nos analyses au-delà des
classifications taxonomiques et migratoires utilisées auparavant, afin d'inclure les classifications fonctionnelles de guilde alimentaire,
statut de reproduction, et le facteur de synanthropie. Notre hypothèse nulle est que la fréquence des collisions reflète une prévalence
d'une espèce ou d'un groupe de même classe au site d'étude. Afin de tester cette possibilité, nous avons utilisé les données de collision
collectées sur trois sites dans la Zone Triangle de Recherche en Caroline du Nord, aux États-Unis. Sur l'un des sites, le Campus Principal
de l'Université de Duke, nous avons aussi amassé les abondances relatives de la communauté locale aviaire, afin de développer une
étude de cas sur la comparaison de la prévalence en arrière-plan et le nombre de collision. Utilisant la banque de donnée des trois sites,
plus vaste, nous avons développé une image initiale de susceptibilité de collision basée seulement sur la fréquence, la méthode courante.
Ensuite, en utilisant le bootstrap sur nos données d'abondance de Duke, nous avons généré des intervalles de confiance simulant les
collisions basées par chance comparé à la prévalence. Nous avons identifié plusieurs instances où les totaux de collisions ont produit
une fausse perception de vulnérabilité spécifique due à l'espèce. Dans les cas les plus extrêmes, nos données de la Zone Triangle indiquèrent
que les espèces se reproduisant localement étaient grandement vulnérable aux collisions, alors que notre étude de cas basée sur
l'abondance suggérait que ce même groupe était adepte à éviter les collisions. À partir de notre étude de cas, nous avons aussi découvert
que les glaneurs de feuillage étaient liés à un risque plus élevé, alors que l'omnivorisme et l'alimentation au sol étaient associés à une
diminution du risque. Malgré que nos résultats soient basés sur un échantillon limité, nous avançons que l'abondance devrait être incluse
dans les études futures et recommandons l'utilisation de recensements ponctuels comme alternative adaptable et non-invasive au-lieu
de d'aires de décomptes ou de filets japonais.
Key Words: carcass survey; classifications; collision vulnerability; local abundance; point count; window strikes
Address of Correspondent: Nicolette L. Cagle, 9 Circuit Drive, Durham, NC , United States, 27708, nicolette.cagle@duke.edu
Avian Conservation and Ecology 12(1): 17
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INTRODUCTION
Window collisions are the second largest source of human-caused
avian mortality in North America (Loss et al. 2015), killing an
estimated one billion birds annually (Loss et al. 2014). Window
hazards potentially reduce the North American bird population
by 2 to 9% (Loss et al. 2014), and could have cascading effects on
ecosystems and the goods and services they provide (Longcore
and Smith 2013).
Since the foundational work of Klem in the late 1970s (Klem
1989, Klem 1990a,b), researchers have begun asking a wide range
of questions about this phenomenon: What building qualities
increase collision risk? Are habitat conditions surrounding
buildings relevant? How do collision frequencies vary across
seasons? One question continues to trouble researchers and
remains largely unresolved: Which birds are most susceptible?
An improved understanding of susceptibility would have
significant implications for both collision mitigation and broader
conservation efforts. It could inform collision deterrent methods
and facilitate the integration of window collision threats into bird
conservation frameworks. Collision mortality may have additive
effects on species that are already suffering from habitat loss and
degradation (Klem 2009, 2010). Failing to recognize
compounding relationships among threats may harm species’
long-term survival.
To properly address which groups of birds are most susceptible
to collisions with windows, researchers must first deal with the
confounding effect of local abundance. It is clear that collision
frequencies are at least partly attributable to background
prevalence (Kahle et al. 2016, Sabo et al. 2016). This attribute has
the power to inflate collision frequencies among locally abundant
birds and suppress frequencies among scarcer species,
independent of the degree of inherent susceptibility among these
various groups. Many studies have found it difficult to account
for abundance when characterizing species-specific susceptibility
or have entirely neglected its influence. Of those studies that have
acknowledged this issue, some have offered anecdotal evidence of
relationships between local abundance and collision (Blem and
Willis et al. 1998, O’Connell et al. 2001) and others have
systematically observed bird communities near study sites, but in
limited spatial or seasonal contexts (Dunn 1993, Hager et al.
2008).
Some of the geographically broader investigations of abundance
and vulnerability have come from Arnold and Zink (2011) and
Loss et al. (2014). In these studies, the authors gathered collision
rates from other investigations and compared these values with
population estimates from the North American Breeding Bird
Survey to index the relative vulnerability of species. Although
broadly informative, this approach assumes similarity of local
study site communities and regional scale populations and
introduces the possibility of spurious correlations because of
pseudocorrelation with variables related to those upon which data
was not actually collected (see Schaub et al. 2011 for examples).
To date, only a handful of studies have attempted to explore the
association between local abundance and collisions. Kahle et al.
(2016) collected collision data at a museum building in California
and conducted area search surveys adjacent to the museum
multiple times per week for a year to establish an understanding
of local avifauna abundance. Although their results offered
valuable insight into the relationship between abundance and
collision, the study’s West Coast location means the findings are
difficult to relate to the majority of collision literature, which is
based in the eastern United States. Migratory bird behavior is
very different in eastern North America along the Mississippi and
Atlantic flyways, compared to migration in the western half of
the continent. Sabo et al. (2016) produced a similar study at a zoo
in Virginia, using mist nets to survey local abundance. These
studies represent a major advancement in methodology for bird-
window collision surveys. Yet, these studies have only considered
collision susceptibility in terms of demographics (e.g., age, sex),
taxonomy, and migratory status.
In this study, we expand the general knowledge of collision
vulnerability to inform future mitigation and conservation efforts.
We begin by (1) investigating patterns of taxonomy, residency and
breeding status, guild, and synanthropy on bird-window collision
rates at three sites in North Carolina’s Research Triangle region,
hereafter referred to as the Triangle Study. In the Triangle Study,
we replicate the standard research practice of carcass surveys. We
complement this effort by (2) testing whether collision patterns
are the product of differential susceptibilities among taxonomic
and functional groups, or the product of random chance and
relative abundance, using a one-year case study of point counts
and collisions at Duke University, hereafter referred to as the
Duke Case Study.
We seek to test the following specific hypotheses about bird
susceptibility to window-collisions: (1) Resident birds are less
susceptible to collisions than migratory species because they have
familiarity with windows in their local environment; (2)
Synanthropic birds have low susceptibility to window collisions
either as an adaptation to survive in human-dominated
landscapes or because innate resistance to window-collisions
allowed them to become synanthropic; (3) Birds in foliage-
gleaning foraging guilds will be more susceptible than others
because reflected foliage represents a potential food source and
is a strong attractant.
METHODS
Study areas
We conducted our Triangle Study at three locations in the Raleigh-
Durham-Chapel Hill area of North Carolina, United States (Fig.
1A). At each site, we chose buildings with a wide range of stories,
glass coverage, age, and floor area (Ocampo-Peñuela et al. 2016).
We selected six buildings per site.
Carcass surveys
We conducted carcass surveys for the Triangle Study during 21
days of the peak migration period in fall 2015 (19 September–9
October), following methods described by Hager and Consentino
(2014). One day prior to this period (18 September), we conducted
a clean-up survey to free the study area of lingering carcasses that
would otherwise bias our results. Each day during the subsequent
21-day survey period, two observers independently looked for
dead and stunned birds within 2 m of each building. These
observers removed carcasses and recorded collisions to species
level when the carcass allowed for visual identification, as well as
building name and side (compass direction, i.e., N, S, E, W). We
conducted the surveys in the afternoon, as recommended by
Hager et al. 2012 to reduce impacts of scavengers on the dataset.
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Fig. 1. (A) Sites for bird window collision study in the Triangle
region of North Carolina, (B) Duke University study site with
sampled buildings highlighted, (C) 1-m resolution EPA land
cover (US EPA 2013), (D) survey points over aerial imagery.
We also conducted an additional carcass survey at Duke
University in spring 2015, cleaning on 31 March 2015, and
surveying from 1 to 21 April following the same methodology
(Hager and Consentino 2014). We only used this data in the Duke
Case Study. (See Ocampo-Peñuela et al. 2016 for further details
of carcass surveys at Duke University, including methodology
and campus description.)
If observers were unable to identify what species collided, we
excluded the observation from our analysis. Scavenging often
reduces carcasses to an unidentifiable state such as a pile of
feathers or partial remains. Our surveys were timed to limit the
impact of scavenging. Nonetheless, unidentified collisions
accounted for 20% and 14% of total collisions in the Triangle
Study and Duke Case Study, respectively.
Further classifications
To investigate the potential influences of taxonomy, behavior, and
life history on bird species susceptibility to window collisions, we
classified species based on taxonomic family, breeding and
migration status, feeding guild and location, and synanthropy.
We defined breeding status as whether or not the species had been
recorded breeding locally (within the Triangle area, including
Durham, Orange, Wake, and Chatham Counties) by expert local
observers from the Chapel Hill Bird Club (Cook 2008, LeGrande
et al. 2016). By using the term “locally breeding,” we did not mean
to indicate individual birds were observed actively breeding in the
study area; our methods did not include the mark and recapture,
territory mapping, or telemetry necessary to glean this detail.
Rather, we applied this term to identify a behavior exhibited by
at least some members of a given species within our study region.
Migration status (binary variable: migratory, year round) was
determined from seasonal, local fluctuations in abundance
reported by Cook (2008). Birds that were not reported or were
only very rarely reported in some seasons, but were common or
abundant in other seasons, were considered migratory. As with
breeding status, this term does not reflect knowledge of individual
birds, but a broader categorization of species behavior in the
Triangle area.
We defined feeding guilds based on González-Salazar et al.’s
(2014) classification. Broad guilds (e.g., insectivore, granivore)
were then subdivided by feeding strategy, feeding location, and
height.
For synanthropy, the degree of positive association with human
environments, we applied Johnston’s (2001) classification of
North American avifauna. Under this classification, bird species
were defined as full synanthropes, casual synanthropes, tangential
synanthropes, or nonsynanthropes. Synanthropy “includes a wide
degree of relationship to humans” from dependence on human
ecology for survival (full), exploitation without dependence
(causal), occasional exploitation (tangential), to no positive
synanthropic relationship (nonsynanthropes; Johnston 2001:50).
Duke case study: point count survey
We complemented our spring and fall carcass surveys at Duke
with relative abundance data gathered from point counts around
Main Campus (Fig. 1B). To assign survey point locations, we used
a stratified random sampling technique, applied in ArcMap 10.3.1
(ESRI 2015). Beginning with 1-m resolution EPA land cover
classification (US EPA 2013), we used a focal calculation to
remove irrelevant features such as trees in parking lots. Duke’s
Main Campus contained four classes: forest, impervious, grass,
and bare earth (Fig. 1C). The last two classes were combined
because of the negligible area of the latter. We limited the
sampling frame for forest and impervious classes to locations with
at least 85% coverage in a 25-m radius and 30% for the much
scarcer grass class. To avoid surveying habitat outside the purview
of Duke, we removed areas 25 m from the Main Campus
boundary from sampling consideration.
We then randomly placed 21 points to the remaining areas, with
the number of points assigned to each class roughly proportional
to their relative area and separated by at least 90 m to improve
independence of observations. The result was 12 forest, 5
impervious, and 4 grass points (Fig. 2D).
A single observer (TW) conducted point counts during the same
peak migration survey periods as the spring and fall 2015 Duke
University carcass surveys. TW visited every point three times
during both survey seasons, altering the order of visitation to
avoid bias. At survey locations, we reported all birds seen or heard
within a 25-m-fixed radius, not counting flyovers (Bibby et al.
2000). We attempted to avoid counting individuals more than
once per visit by maintaining awareness of individual positioning
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and using a short, 10-minute period of observation. Additionally,
we completed surveys within 2.5 hours of sunrise to take
advantage of higher activity and detectability of birds. We did
not conduct surveys during strong winds or precipitation.
Construction and maintenance projects at Duke required two
alterations to our original survey design. In spring 2015, Duke
University converted approximately 2 ha of forest cover to a water
reclamation pond. We incorporated this water feature and
maintained our ratio of points to cover area by relocating a nearby
forest survey point to the pond shore and reclassifying it as water.
Additionally, between spring 2015 and fall 2015, two survey points
became inaccessible because of construction. We compensated
for this loss by randomly assigning two new survey points for fall
2015 using the same sampling technique. These new points were
similar to the original points in vegetation composition and
structure, as well as degree of development, justifying comparison
across seasons.
Duke case study: analysis of bird-window
collision abundance and susceptibility
To test if species’ local abundance has an effect on number of
collisions at Duke University, we compared the observed rate of
collision in each classification category (e.g., Parulidae, casual
synanthrope, granivore) to a simulated rate for those categories
based on chance and background occurrence rates. This analysis
drew on the point count data from spring and fall 2015 and the
Duke carcass data from those same periods. Our comparison of
random collision used 95% confidence intervals (CIs) based on
distributions of random sampling rates for each bird category in
our point count dataset.
We constructed these random distributions by applying a
bootstrapping sampling procedure to the Duke Main Campus
abundance data in R, v. 3.2.2 (R Development Core Team 2015).
Pulling from a pool of all spring and fall point count observations,
we sampled a number of individuals equal to the number of
collisions observed at Duke during the 2015 spring and fall carcass
surveys. Each individual in the sample pool had an equal
probability of being selected, simulating collision in the absence
of differential vulnerability.
We repeated the sampling 10,000 times and recorded the
proportion of birds sampled from each category after every run.
These 10,000 runs created a frequency distribution of random
sampling rates for each avian category based solely on their
campus abundance. We identified our 95% CIs by pulling out the
275th and 9750th values from the ordered vector of sampling rates
for each bird category. We repeated this procedure on all
classification approaches (e.g., foraging location guild, family,
synanthropy).
We then compared these intervals to the observed rate of collision
among different bird groups. To calculate the observed rates, we
divided the collision frequency in each class by the total number
of collisions. Placing these values alongside the CIs, we noted
whether a class fell below, within, or above the expected rate of
collision. We deemed species with values above the simulated rates
as disproportionately susceptible to collisions and species with
values below as less susceptible to collisions.
RESULTS
Triangle Study frequencies
Between 18 September and 9 October 2015, we recorded 151 birds
colliding with survey buildings at our three study sites in the
Research Triangle Area (Table 1). Among these 138 casualties
(the remaining 13 casualties were not visually identifiable), we
observed 40 species, 14 families, and 4 orders. The vast majority
of collisions were Passeriformes (81%), and the remaining
causalities were nonpasserine orders.
Taxonomy
The Parulidae, Turdidae, and Trochilidae families accounted for
approximately two-thirds of all collisions (Fig. 2A). The
Parulidae had more collisions than Turdidae and Trochilidae
combined. Although the Parulidae collisions represented a wide
diversity of species (15), the Trochilidae consisted of only one,
the Ruby-throated Hummingbird (Archilochus colubris).
Breeding and migration status
Collision frequencies indicated local breeders collided roughly
twice as much as nonlocal breeders (Fig. 2B). Birds that migrate
(i.e., nonyear-round residents) showed an even greater disparity
between its two classes (Fig. 2C). There was an approximately
14:1 ratio of casualties of species known to migrate compared to
year round residents.
Feeding guild
Insectivores collided far more than any other feeding group (Fig.
2D). Nectarivores were also well represented in the collision tally,
but as with the Trochilidae results, this finding was solely
attributable to Ruby-throated Hummingbirds. Of the 114
insectivore collisions, 94 belonged to just 3 subgroups: lower
canopy foliage gleaners, ground gleaners, and upper canopy
foliage gleaners (Fig. 2E).
Synanthropy
Collisions were nearly evenly split between nonsynanthropes and
tangential synanthropes (Fig. 2F). Conversely, the more
synanthropic classes (casual and full) had very few collisions.
Duke case study: local abundance and
collision susceptibility
During carcass surveys at Duke University in spring and fall 2015,
we documented 36 birds, representing 19 species and 11 families.
The majority (72%) of individual casualties classified as
migratory species, 58% were insectivores (22% of which were
lower canopy gleaners), and the majority of birds (89%) were
tangential synanthropes or nonsynanthropic.
During the point count surveys, we recorded 477 birds
representing 40 species and 21 families (Table 2). The five most
frequently observed species were, in decreasing order, Northern
Cardinal (Cardinalis cardinalis, 60 individuals), House Finch
(Haemorhous mexicanus, 51 individuals), Eastern Towhee (Pipilo
erythrophthalmus, 37 individuals), Northern Mockingbird
(Mimus polyglottos, 35 individuals), and Carolina Chickadee
(Poecile carolinensis, 35 individuals). The top 10 most abundant
species accounted for 69% of observations. We observed 17 of the
40 species fewer than four times. These rarer birds included four
Parulidae, two Picidae, and two Sittidae species.
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Table 1. Taxonomic classification, guild, feeding location, synanthropy (SY), local breeding status (LB), and migration status (MG)
of birds that collided with windows on the three Triangle campuses from September 18 until October 9, 2015.
Common Name Scientific Name Family Total #
Birds
Guild†Location†Local Breeders‡Migrants§Synanthropy|
Ruby-throated
Hummingbird
Archilochus colubris Trochilidae 16 nectarivore nectarivore L M T
Common Yellowthroat Geothlypis trichas Parulidae 9 insectivore lower canopy foliage
gleaner
L M T
Northern Parula Setophaga americana Parulidae 9 insectivore upper canopy foliage
gleaner
L M X
Red-eyed Vireo Vireo olivaceus Vireonidae 9 insectivore lower canopy foliage
gleaner
L M T
Swainson’s Thrush Catharus ustulatus Turdidae 8 insectivore lower canopy foliage
gleaner
M T
Yellow-bellied
Sapsucker
Sphyrapicus varius Picidae 7 insectivore bark excavator M X
Black-throated Blue
Warbler
Setophaga caerulescens Parulidae 6 insectivore lower canopy foliage
gleaner
M X
Gray-cheeked Thrush Catharus minimus Turdidae 6 insectivore ground gleaner M X
American Redstart Setophaga ruticilla Parulidae 5 insectivore air hawker under
canopy
L M T
Wood Thrush Hylocichla mustelina Turdidae 5 insectivore ground gleaner L M X
Chestnut-sided Warbler Setophaga pensylvanica Parulidae 4 insectivore lower canopy foliage
gleaner
M X
Gray Catbird Dumetella carolinensis Mimidae 4 insectivore ground gleaner L M T
Pine Warbler Setophaga pinus Parulidae 4 insectivore bark gleaner L M X
American Robin Turdus migratorius Turdidae 3 insectivore ground gleaner L M C
Black-and-white
Warbler
Mniotilta varia Parulidae 3 insectivore bark gleaner L M X
Brown Thrasher Toxostoma rufum Mimidae 3 insectivore ground gleaner L M X
Cape May Warbler Setophaga tigrina Parulidae 3 insectivore lower canopy foliage
gleaner
M X
Mourning Dove Zenaida macroura Columbidae 3 granivore ground to undergrowth
gleaner
L T
Ovenbird Seiurus aurocapilla Parulidae 3 insectivore ground gleaner L M X
Summer Tanager Piranga rubra Cardinalidae 3 insectivore upper canopy foliage
gleaner
L M X
Black-throated Green
Warbler
Setophaga virens Parulidae 2 insectivore lower canopy foliage
gleaner
M X
Carolina Chickadee Poecile carolinensis Paridae 2 insectivore lower canopy foliage
gleaner
L T
Philadelphia Vireo Vireo philadelphicus Vireonidae 2 insectivore lower canopy foliage
gleaner
M X
Rose-breasted Grosbeak Pheucticus ludovicianus Cardinalidae 2 insectivore upper canopy foliage
gleaner
M X
Scarlet Tanager Piranga olivacea Cardinalidae 2 insectivore upper canopy foliage
gleaner
L M X
American Goldfinch Spinus tristis Fringillidae 1 granivore lower to upper canopy
gleaner
L M T
Blue Jay Cyanocitta cristata Corvidae 1 omnivore ground forager L M T
Brown-headed
Nuthatch
Sitta pusilla Sittidae 1 insectivore bark gleaner L X
Canada Warbler Cardellina canadensis Parulidae 1 insectivore lower canopy foliage
gleaner
M X
Golden-crowned
Kinglet
Regulus satrapa Regulidae 1 insectivore lower canopy foliage
gleaner
M X
Indigo Bunting Passerina cyanea Cardinalidae 1 granivore ground to undergrowth
gleaner
L M X
Lincoln’s Sparrow Melospiza lincolnii Emberizidae 1 granivore ground to undergrowth
gleaner
M T
Magnolia Warbler Setophaga magnolia Parulidae 1 insectivore lower canopy foliage
gleaner
M X
Northern Cardinal Cardinalis cardinalis Cardinalidae 1 granivore ground to undergrowth
gleaner
L T
Northern Mockingbird Mimus polyglottos Mimidae 1 insectivore ground gleaner L T
Palm Warbler Setophaga palmarum Parulidae 1 insectivore ground gleaner M X
Ruby-crowned Kinglet Regulus calendula Regulidae 1 insectivore lower canopy foliage
gleaner
M T
Tufted Titmouse Baeolophus bicolor Paridae 1 insectivore lower canopy foliage
gleaner
L T
Worm-eating Warbler Helmitheros vermivorum Parulidae 1 insectivore lower canopy foliage
gleaner
M X
Yellow Warbler Setophaga petechia Parulidae 1 insectivore lower canopy foliage
gleaner
M T
† Feeding guild and feeding location subclass based on González-Salazar et al. (2014) classification scheme.
‡ Local breeding status is gathered from Cook (2008).
§ Migratory status is drawn from Cook’s (2008) seasonal abundances.
| Synanthropy is based on Johnston’s (2001) appended list. “T” designates tangential synanthrope, “C” designates casual synanthrope, and “X” indicates nonsynanthrope. Local breeders
are indicated by “L” and migrants by “M.”
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Table 2. Total observations of each species from point counts during spring and fall 2015 at Duke University.
Common Name Scientific Name Family Code
Name†Main
Campus
Total
Guild‡Location‡Local
Breeders§Migrants|Synanthropy¶
Northern Cardinal Cardinalis cardinalis Cardinalidae NOFL 60 granivore ground to
undergrowth
gleaner
L T
House Finch Haemorhous
mexicanus
Fringillidae HOFI 51 granivore ground to
undergrowth
gleaner
L C
Eastern Towhee Pipilo
erythrophthalmus
Emberizidae EATO 37 granivore ground to
undergrowth
gleaner
L X
Northern Mockingbird Mimus polyglottos Mimidae NOMO 35 insectivore ground gleaner L T
Carolina Chickadee Poecile carolinensis Paridae CACH 35 omnivore arboreal forager L T
American Robin Turdus migratorius Turdidae AMRO 28 insectivore ground gleaner L M C
Gray Catbird Dumetella
carolinensis
Mimidae GRCA 23 insectivore ground gleaner L M T
Tufted Titmouse Baeolophus bicolor Paridae TUTI 22 insectivore lower canopy
foliage gleaner
L X
Blue Jay Cyanocitta cristata Corvidae BLJA 19 omnivore ground forager L M T
Carolina Wren Thryothorus
ludovicianus
Troglodytidae CARW 19 insectivore lower canopy
foliage gleaner
L X
White-throated Sparrow Zonotrichia albicollis Emberizidae WTSP 18 granivore ground to
undergrowth
gleaner
M T
American Crow Corvus
brachyrhynchos
Corvidae AMCR 14 omnivore ground forager L T
Red-bellied Woodpecker Melanerpes carolinus Picidae RBWO 14 insectivore bark excavator L T
Song Sparrow Melospiza melodia Emberizidae SOSP 13 granivore ground to
undergrowth
gleaner
L M T
Downy Woodpecker Picoides pubescens Picidae DOWO 12 insectivore bark excavator L T
European Starling Sturnus vulgaris Sturnidae EUST 9 insectivore ground gleaner L F
Mourning Dove Zenaida macroura Columbidae MODO 8 granivore ground to
undergrowth
gleaner
L T
Pine Warbler Setophaga pinus Parulidae PIWA 7 insectivore bark gleaner L M X
Brown Thrasher Toxostoma rufum Mimidae BRTH 6 insectivore ground gleaner L M X
Wood Thrush Hylocichla mustelina Turdidae WOTH 6 insectivore ground L M X
Rock Pigeon Columba livia Columbidae ROPI 5 granivore ground to
undergrowth
gleaner
L F
American Goldfinch Spinus tristis Fringillidae AMGO 5 granivore lower to upper
canopy gleaner
L M T
Ovenbird Seiurus aurocapilla Parulidae OVEN 5 insectivore ground L M X
Black-and-white Warbler Mniotilta varia Parulidae BAWW 3 insectivore bark gleaner L M X
Red-eyed Vireo Vireo olivaceus Vireonidae REVI 3 insectivore lower canopy
foliage gleaner
L M T
Northern Rough-winged
Swallow
Stelgidopteryx
serripennis
Hirundinidae NRWS 2 insectivore air hawker above
canopy
L M T
Brown-headed Cowbird Molothrus ater Icteridae BHCO 2 granivore ground to
undergrowth
gleaner
L T
Black-throated Green
Warbler
Setophaga virens Parulidae BTNW 2 insectivore lower canopy
foliage gleaner
M X
White-breasted Nuthatch Sitta carolinensis Sittidae WBNU 2 insectivore bark gleaner L M T
Hermit Thrush Catharus guttatus Turdidae HETH 2 insectivore ground gleaner M X
Red-tailed Hawk Buteo jamaicensis Accipitridae RTHA 1 carnivore ground hawker L T
Great Blue Heron Ardea herodias Ardeidae GBHE 1 carnivore freshwater
forager
L T
Blackburnian Warbler Setophaga fusca Parulidae BLWB 1 insectivore upper canopy
foliage gleaner
M X
Yellow-rumped Warbler Setophaga coronata Parulidae YRWA 1 insectivore lower canopy
foliage gleaner
M T
Hairy Woodpecker Picoides villosus Picidae HAWO 1 insectivore bark excavator L T
Northern Flicker Colaptes auratus Picidae NOFL 1 insectivore ground gleaner L M T
Pied-billed Grebe Podilymbus podiceps Podicipedidae PBGR 1 L M T
Spotted Sandpiper Actitis macularius Scolopacidae SPSA 1 M X
Brown-headed Nuthatch Sitta pusilla Sittidae BHNU 1 insectivore bark gleaner L X
Scarlet Tanager Piranga olivacea Thraupidae SCTA 1 insectivore upper canopy
foliage gleaner
L M X
†From American Ornithologists Union.
‡Feeding guild and feeding location subclass based on González-Salazar et al. (2014) classification scheme.
§Local breeding status is gathered from Cook (2008).
|Migratory status is drawn from Cook’s (2008) seasonal abundances.
¶Synanthropy is based on Johnston’s (2001) appended list. “T” designates tangential synanthrope, “C” designates casual synanthrope, and “X” indicates nonsynanthrope. Local breeders
are indicated by “L” and migrants by “M.”
Avian Conservation and Ecology 12(1): 17
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Fig. 2. Frequency of collision victims by (A) family, (B) breeding status, (C) migratory status, (D) feeding guild,
(E) location guild, and (F) synanthropy. Other family = Columbidae (2.6%), Paridae (2.0%), Corvidae (1.3%),
Regulidae (1.3%), Emberizidae (0.7%), Fringillidae (0.7%), and Sittidae (0.7%). Other location = ground to
undergrowth gleaner (5.3%), bark excavator (4.6%), air hawker under canopy (3.3%), ground forager (1.3%), and
lower to upper canopy gleaner (0.7%).
Taxonomy and collision susceptibility
Of the 40 species we recorded during the campus point count
survey, 36 had a collision total on or within their confidence
intervals (CI), meaning their number of collisions could not be
differentiated from chance. However, many of these results (31)
occurred simply because the species was never observed during
the collision survey and their background abundance was too low
to lift their lower CI above zero. Additionally, 11 species collided
with windows, but were not seen or heard during point counts.
Despite these limitations, the CI results provided cases of
noteworthy deviation from expected number of collisions (Fig.
3A). Three species, American Goldfinch (Spinus tristis), Ovenbird
(Seiurus aurocapilla), and Red-eyed Vireo (Vireo olivaceus),
collided more than their abundance-based collision estimates,
indicating susceptibility to collisions. Conversely, one species, the
House Finch, showed a lower than expected number of collisions.
Scaling up to taxonomic family, Parulidae and Vireonidae had
collision numbers above their CI, representing a level of
susceptibility that could not be attributed to chance (Fig. 3B).
However, Vireonidae vulnerability simply reflected the
susceptibility of one species, the Red-eyed Vireo. Paridae offered
a contrasting case of collision, an observed collision number
below the CI. Additionally, the Mimidae had collision numbers
equal to their lower CI, but the family was prevalent enough that
this lower value was not equal to zero. Of the 21 families observed
during point counts, 11 were not detected as collision victims and
had a lower CI of zero. Only two families, Trochilidae and
Regulidae, were recorded in the collision survey, but not in the
point count survey. Six families collided more than zero, but
remained within their confidence intervals, i.e., collided at rates
expected based on abundance, including Cardinalidae, Turdidae,
and Picidae. All taxonomic orders occurred within their
confidence intervals.
Breeding and migration status
Although they accounted for a substantial proportion of
collisions at Duke in 2015 (81%), species with populations that
breed locally still collided less often than expected when factoring
in their local abundance. This result is in contrast to nonlocally
breeding species that collided more than expected based on
background prevalence (Fig. 4A). A similar, though more
pronounced pattern emerged between resident and migratory
species. Year round residents’ collision numbers were well below
the CI and migrants’ well above (Fig. 4B). Migratory species were
disproportionally vulnerable to collisions, regardless of their
abundance.
Feeding strategies
Grouping based on feeding guild indicated that omnivores, which
with the exception of one Blue Jay (Cyanocitta cristata), were
entirely absent from carcass surveys, were abundant enough to be
considered less susceptible to colliding with windows (Fig. 4C).
In the location classification, the ground to undergrowth gleaners
had lower than expected collision numbers (Fig. 4D), despite
relatively high observed numbers of collisions. Conversely,
multiple types of lower to upper canopy foliage gleaners collided
at rates higher than the 95% CI.
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Fig. 3. Confidence intervals (bars) and observed rates (dots) for
(A) 9 species. The following species were omitted with a lower
confidence interval equal to zero and no collisions during the
Spring and Fall 2015 Duke collision surveys: AMCR, BAWW,
BHCO, BHNU, BLJA, BLWB, BTNW, CACH, CARW,
DOWO, EATO, EUST, GBHE, GRCA, HAWO, HETH,
NOFL, NOMO, NRSW, PBGR, PIWA, RBWO, ROPI,
RTHA, SCTA, SPSA, TUTI, UYRW, WBNU, WOTH, and
WTSP (see Table 2 for AOU codes). (B) 21 families observed
during campus point count survey.
Synanthropy
Our ordinal measure of positive human association did not give
any clear indication of susceptibility or immunity. Casual,
tangential, and nonsynanthropes’ collision numbers all reflected
their prevalence on Duke’s Main Campus. Full synanthropes were
not observed in the carcass survey and were not prevalent enough
to lift their lower CI above zero (Fig. 4E).
DISCUSSION
The findings of the Duke Case Study provide evidence counter
to the hypothesis that collision numbers across species and other
categories were simply a reflection of relative abundances,
corroborating the results of other recent studies (Kahle et al 2016,
Sabo et al. 2016). We witnessed several instances where locally
abundant birds rarely collided and opposite cases where locally
scarce birds frequently collided. Therefore, we conclude that there
are significant differences in a species’ susceptibility to collide
against or avoid windows. These qualities emerged at nearly every
level of classification: species, family, breeding status, migratory
status, feeding guild, and feeding location. The recurrence of
vulnerability and resistance indicates the complex nature of
collisions; they are not reducible to a single behavior or trait, but
are related to diverse and likely interacting factors.
Fig. 4. (A) Comparison of expected and observed collision
rates for local and nonlocal breeders, as defined by Cook
(2008). (B) Similar comparison for migratory status derived
from weekly checklist (Cook 2008). (C) Confidence interval
comparison based on González-Salazar et al.’s guild
classification (2014). Nectarivores were absent from this
comparison despite making up 14% (n = 5) of collisions at
Duke in spring and fall 2015 because they were not observed
during the campus point survey. (D) Finer guild comparison
using González-Salazar et al.’s subclassification related to
feeding location (2014). Guilds were loosely sorted left to right
on foraging height. (E) Comparison of simulated versus
observed collision rates among Johnston’s (2001) synanthropic
classes.
There were also many instances in our analyses of individual bird
groups where we could not find evidence to suggest that the
observed number of collisions was driven by factors other than
chance and abundance. These cases neither disprove the general
role of species traits nor prove an entirely random nature to
window collisions. Instead, these findings underscore the
importance of considering local species prevalence. Kahle et al.
(2016) and Sabo et al. (2016) found many similar instances where
number of collisions was not discernable from chance and
abundance. This observation has major implications for how
collision tallies are considered and any subsequent understanding
of collision vulnerability. Factoring in local abundance provides
Avian Conservation and Ecology 12(1): 17
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greater appreciation for the inherent uncertainty behind collision
frequency data in terms of species-specific vulnerability.
Within the context of our case study, there were multiple
occasions where collision frequencies alone did not give enough
information about the collision patterns of a species. For example,
the Northern Cardinal, which accounted for 6% of collisions and
has often been reported as a frequent victim (Dunn 1993, Blem
and Willis 1998, Hager et al. 2008), was actually within the
expected range of collision numbers. Sabo et al. (2016) reported
this same species colliding less often than expected based on their
mist netting captures. Complementing the standard carcass
survey method, we found that the vulnerability of ground to
undergrowth gleaners is better informed by putting number of
collisions in the context of relative local abundance. Although
this group had the third highest number of collisions, it was also
so abundant around Duke’s Main Campus that its collision
numbers were lower than expected, indicating a tendency to avoid
collisions.
Although differences in spatial extent, timing, and survey effort
limited how much direct comparison we could make between the
Triangle Study and Duke Case Study, we did find two major
discrepancies in results worth noting. In the Triangle Study, locally
breeding species appeared to collide disproportionately with
buildings. However, the Duke Case Study showed this same group
actually collided less often than expected. Additionally, the near
absence of full synanthropes on Duke University’s Main Campus
suggested the Triangle Study’s lack of full synanthrope casualties
was not due to any acclimation to urban settings, but to a general
scarcity of these birds.
In addition to illustrating the importance of putting bird collision
data in the context of local species abundance, our results also
indicate categories of birds that appear to be especially vulnerable
to window collisions. For example, the finer guild classification
analysis suggests that the vertical location of foraging affects the
vulnerability of birds. Although leaf gleaners exhibited notably
high numbers of collision relative to their abundances, ground-
based feeders fared as well as chance would predict or better.
Species adapted to foraging under the canopy, like foliage
gleaners, fly through small spaces in the thick, forested understory.
This behavior may increase their vulnerability to window
collisions as they potentially confuse windows for understory
openings or are simply unable to cope with the unfamiliar
obstacles of urban settings.
Additionally, the more transitory groups of birds, migrants and
nonlocal breeders, showed considerable susceptibility to window
collisions in the case study. This finding on migratory status is
also supported by several previous studies (Arnold and Zink 2011,
Loss et al. 2014), including those that accounted for the local
abundances (Kahle et al. 2016, Sabo et al. 2016). These relatively
high numbers of collisions may be due to unfamiliarity with the
local landscape and human structures, though investigation into
the causality underlying these relationships is beyond the scope
of this study. We consider the vulnerability of migrants to be of
particular concern because this group contains many species that
already face a large number of threats such as land development,
invasive species, and climate change (Kirby et al. 2008).
Limitations of analysis
There were several limitations to our survey of campus birds that
may have affected our confidence interval analysis. First, we
assumed that we surveyed at the correct spatial scale to capture
an accurate impression of the potential pool of collision victims.
However, it is possible that birds outside our sampling frame, from
neighboring Duke Forest for example, may have shared some of
the risk of collision with our six surveyed buildings. It is also
possible that abundance at a finer spatial scale could be important
for collisions, for example birds may be susceptible to collisions
because they are attracted to ornamental vegetation surrounding
buildings. Second, we observed high levels of ambient noise at
several survey points. It was difficult in these cases to disentangle
the effect of ambient noise as a distraction to observation and a
deterrent to birds. Third, and possibly most significantly, we did
not count flyovers during our surveys. This technique almost
certainly led to an underestimation of species that forage on the
wing such as hummingbirds, swallows, and hawks. Fourth, we
were not able to carry out scavenging experiments, so our carcass
surveys might be underestimating the number of collisions.
Overall, our analyses are not able to estimate the relative impact
of collisions on species’ populations; our results are a hint of
which species might be most affected, but we recommend detailed
population studies for better risk assessments.
Another phenomenon in the data suggests further limitations.
Ten out of 18 species observed during spring and fall 2015 carcass
surveys at Duke were not observed during point counts. This trend
could be caused by several factors. These species may be so rare
their prevalence is too low for detection with standard point count
methods, but their susceptibility to collisions is high enough to
reveal their presence in the collision survey. Alternatively, species
observed only during the collision survey may be relatively
abundant, but secretive, with low detection probabilities.
Nonetheless, there was substantial and often complete overlap
between the carcass and point count datasets when considered in
the context of our other classification methods.
Finally, it is possible that the susceptibility of various bird groups
may be related to characteristics of the buildings themselves and/
or the habitats immediately proximate to the buildings. In a
previous study assessing relationships between building
characteristics and collisions we found that buildings with large
surface areas of glass contribute disproportionately to collisions
on Duke’s campus (Ocampo-Peñuela et al. 2016). We emphasize
that the bird groups we find to be susceptible to collisions in this
study, are specifically susceptible to the dominant buildings in our
study area. Other types of buildings, such as single-family
residences, may be more threatening to different types of birds.
The presence of feeders at residences has been shown to have a
strong influence on collision risk (Kummer et al. 2016), but we
observed no feeding stations at any of the study buildings
throughout the course of the study.
CONCLUSIONS AND FUTURE DIRECTIONS
Our research demonstrates that local abundance and species traits
together offer a better explanation of collision frequencies than
either factor taken alone. We found evidence for susceptibility to
window collisions among both taxonomic and functional bird
classes, even when accounting for the potentially confounding
Avian Conservation and Ecology 12(1): 17
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effect of abundance. We hope other researchers will incorporate
considerations of functional groups into future analyses of
collision vulnerability. Additionally, we recommend conducting
bird surveys in parallel with carcass surveys when evaluating
collision vulnerability in different groups of birds. Once larger
datasets with paired relative abundance and carcass data become
available, we suggest researchers analyze the interactions and
relative influence of the species traits we have linked to collision.
Point counts may be preferable to other bird survey techniques in
this research context. Although mist-net surveys may capture
many of those birds traveling at window level (Sabo et al. 2016)
and area searches may better reveal secretive species (Kahle et al.
2016), point counts are adaptable, easily implemented, and
noninvasive. The flexibility of this survey approach allowed us to
adjust easily for construction, local events, and other
inconveniences likely to be encountered in future suburban and
urban collision studies. Relative to Kahle et al. (2016) and Sabo
et al. (2016), point counts allowed us to develop a comparable
understanding of the influence of local abundance without the
complications of permitting or additional field hours. Ideally,
point counts can be widely and easily adopted alongside studies
of collision vulnerability.
Taking this approach may help limit the amount of disagreement
between studies, creating a more cohesive understanding of
collision vulnerability. This effort must also involve a move toward
consistent definitions of migratory status and other functional
classifications, otherwise future researchers will have difficulty
comparing results across studies. We also suggest that future
research on window collisions begin to consider the year-to-year
variability in collision frequencies. Many studies, ours included,
have only captured a “snapshot” of the problem. Long-term
datasets are needed to assess the annual variation in collision
frequencies. This insight will reveal how reliable one-year studies
are for understanding general patterns of collision. Finally, we
acknowledge a need for greater understanding of the relevant
scales of local abundance, specifically, an empirical definition of
the distance at which birds are at legitimate risk of collision with
a given building. By addressing these issues and adopting these
methodologies, researchers can better understand bird-window
collisions and consequently improve and accelerate mitigation
efforts.
Responses to this article can be read online at:
http://www.ace-eco.org/issues/responses.php/1014
Acknowledgments:
We would like to thank the many volunteers at all campuses who
contributed carcass data to our study. Additional thanks go to Dr.
Dean Urban and Dr. John Poulsen for their guidance on technical
components of this study.
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