10.5061/DRYAD.HQBZKH1G6
De Groot, Krista
0000-0002-7207-838X
Environment and Climate Change Canada
Year-round monitoring at a Pacific coastal campus reveals similar winter
and spring collision mortality and high vulnerability of the Varied Thrush
Dryad
dataset
2021
FOS: Biological sciences
en
895690 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Bird-window collisions are a leading cause of direct anthropogenic avian
mortality, yet our state of knowledge regarding this threat relies heavily
on eastern North American studies. Seasonal patterns of collision
mortality may differ along the Pacific coast, and western North American
species remain understudied. We therefore surveyed a stratified random
sample of 8 buildings for collisions at the University of British
Columbia, Vancouver, Canada over 45-day periods during 2 winters, 1
spring, 1 summer and 1 fall season between January 22, 2015 and March 15,
2017. After accounting for the rate of scavenging and efficiency of
observers in finding carcasses, we estimated that 360 collision fatalities
(95% C.I.: 281 to 486) occurred over 225 days of monitoring. Collision
mortality was highest in fall, but in contrast to most published research,
collision mortality was intermediate in both winter and spring, and was
lowest in summer. In winter 2017, we performed point count surveys to
assess whether individual species are disproportionately vulnerable to
collisions when accounting for population size, and found that the Varied
Thrush (Ixoreus naevius) was 76.9 times more likely to collide with
buildings, relative to average species vulnerability in winter. To our
knowledge, this is the first study to report the Varied Thrush as a
species that is disproportionately vulnerable to collisions. Further
studies are needed to assess the vulnerability of Western North American
species and subspecies and to determine whether similar patterns of
seasonal collision mortality are found elsewhere.
Study Area We conducted our research at the 420-ha Vancouver campus of the
University of British Columbia (hereafter referred to as UBC), along the
Pacific coast of southern British Columbia (BC), Canada (49.2606° N,
49.2606° W). The campus is situated within the Fraser Lowlands (elevation
0 - 152 m) bordered by the Strait of Georgia and the Coast Mountains
(Figure 1). UBC is located on a peninsula, separated from the City of
Vancouver by Pacific Sprit Park, a 874-ha park comprised mainly of 70 -
130 year-old second-growth forest (Metro Vancouver 2019). Tree canopy at
UBC is predominantly coniferous; dominated by western red cedar (Thuja
plicata), Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga
heterophylla), and grand fir (Abies grandis). ~58% of campus is in
park-like open condition, with mowed grass, a number of coniferous and
evergreen broad-leaf plantings, and scattered deciduous trees; primarily
red oak (Quercus ruba) and maple species (Acer spp.) (Dyck 2016). The
campus and surrounding forest lies within the Coastal Western Hemlock
(CWH) Biogeoclimatic Zone (DataBC 2018) characterized by warm dry summers
and mild wet winters, with a climax forest dominated by western hemlock
(Tsuga heterophylla), as well as Douglas-fir (Pseudotsuga menziesii) and
western red cedar (Thuja plicata) (Pojar et al. 1987, 1991). Broadly, the
region is part of the temperate rainforest biome, also known as the
Northern Pacific Rainforest Bird Conservation Region, composed of similar
climate, habitat types and bird species, extending from the Gulf of
Alaska, through to Northern California (Alaback 1996, North American Bird
Conservation Initiative 2000, Rich et al. 2004). Building Selection We
chose 8 buildings for bird-window collision monitoring at UBC, using a
randomized design, stratified by building height and extent of surrounding
vegetation (Figure 2; see Hager and Cosentino 2014, Hager et al. 2017). We
used a map layer of 229 UBC building footprints and size-related
attributes of all buildings >1 story, excluding parking garages
(University of British Columbia 2015), to divide buildings into two
categories: “short” buildings (2-4 stories) and “tall” buildings (5-12
stories). We used ArcMap 10.3.1 to generate a 50-m buffer around buildings
and digitized all vegetation using the World Imagery base map (ESRI 2015)
to calculate % vegetation within 50 m of each building. Within each
building height class, we then randomly selected 2 buildings with
>50% vegetation cover within 50 m of the building and 2 buildings
with <50% vegetation cover within 50 m, as defined in Hager and
Cosentino (2014). Excluding 1-story buildings eliminated most maintenance
sheds and utility buildings with few windows. We further specified that
study buildings must be a minimum of 100m apart, which resulted in one
building being removed from the initial selection. We excluded a second
building from our initial selection due to construction fencing which
prevented access to the majority of the building perimeter. Collision
Surveys We followed protocols outlined in Hager and Cosentino (2014) and
Hager et al. (2017) to conduct a total of 155 bird-window collision
surveys between January 22, 2015 and March 15, 2017 at our 8 study
buildings. Surveys were conducted over 5 45-day periods consisting of 2
winter seasons (January 22, 2015 - March 7, 2015 and January 23, 2017 –
March 15, 2017), 1 fall season (September 9, 2015 - October 23, 2015), 1
spring season (April 15, 2016 - May 29, 2016 and 1 summer season (June 16,
2016 - July 30, 2016). We aligned our spring and fall collision monitoring
with the calendar dates when a local banding station records the highest
numbers of migrants (WildResearch 2015). We performed a clean-up survey
the day before each monitoring period to remove all evidence of
bird-window collisions that accumulated prior to survey periods, including
carcasses and feathers. In winter 2015 we surveyed every second day for 45
days. For the second winter season and all other seasons we conducted
daily surveys for 21 days followed by surveys every second day for an
additional 24 days; for a total of 45 days. We stopped surveys for 6 days
during the second winter season due to a heavy snowfall that prevented us
from reliably finding collision evidence. Surveys resumed after a clean-up
survey to remove any evidence that accumulated while surveys were halted,
and the survey period was extended accordingly. We conducted collision
surveys in mid-afternoon as recommended by Hager and Cosentino (2014),
since window collisions often peak in the morning and continue at a lower
rate later in the day (Klem 1989, Kahle et al. 2016). Two surveyors worked
concurrently, walking around the perimeter of buildings in opposite
directions, searching for carcasses within 2 m of the building façades. We
defined a façade as the exterior face of a building, generally facing the
same direction. In some cases, the complexity of a building footprint
required us to consider several portions of a building footprint as one
façade, e.g., several sides of an alcove, if we could not reliably discern
which portion of the façade that a bird may have hit. One surveyor, ANP,
remained constant throughout all 5 collision monitoring periods, while the
second surveyor(s) differed seasonally. Evidence of a collision included
whole carcasses and partial carcasses (e.g. wing, feet, bill and/or
feather piles) (Hager and Cosentino 2014, Hager et al. 2017). We
conservatively specified that feather piles must include a minimum of 10
tail, wing or body feathers confined to 50 cm diameter circular area
(Ponce et al. 2010). Only two stunned/injured birds were found during
surveys, and both died shortly after discovery. We removed all carcasses
immediately following each survey to ensure double counting did not occur,
and identified all intact carcasses to species. We also collected partial
carcasses, including all feathers, and where possible these were later
identified to species-, genus- or family-level, using online resources
(U.S. Fish and Wildlife Service Forensics Lab 2018), hard copy guides
(Scott and McFarland 2010), or by comparing to specimens from the UBC
Beaty Biodiversity Museum. Correcting For Biases Due To Scavenging And
Missed Carcasses By Observers Collision frequency estimates can be
negatively biased due to carcass removal by humans and scavengers prior to
their observation by surveyors, and imperfect detection of carcasses by
surveyors (Klem et al. 2004, Bayne et al. 2012, Riding and Loss 2018).
Therefore, it is important to design trials to estimate bias parameters
when comparing building mortalities among studies, or among seasons and
regions of interest (Loss et al. 2015). To account for carcass removal and
imperfect detection, we conducted carcass persistence and searcher
efficiency trials, then incorporated the results into statistical models
using the Generalized Mortality Estimator in R package GenEst (Dalthorp et
al. 2018a, b) to correct our collision mortality estimates (details
below). We tested both intercept-only and season-specific models, but had
insufficient data for both carcass persistence and searcher efficiency
trials to allow us to apply bias corrections at the species-level, by
building perimeter substrate or by other potential covariates. Carcass
persistence (CP). We conducted 103 carcass persistence (CP) trials to
quantify bias due to scavenging of carcasses. Trial periods were chosen
based on field worker availability and logistics of conducting frequent
carcass checks; February 16 - March 7 in winter 2017 (n = 33), April 20 -
April 28 in spring 2016 (n = 28), July 12 - July 30 in summer 2016 (n =
28), and September 10 - October 16 in fall 2015 (n = 14). We used
carcasses from a range of previously window-killed birds, ranging in size
from 4g (Anna’s Hummingbird, Calypte anna) to 128g (Steller’s Jay,
Cyanocitta stelleri). Carcasses were stored in deep freeze and held under
our Canadian Wildlife Service Salvage Permit, as outlined in the Ethics
Statement. We placed thawed carcasses at a range of times throughout the
day at a randomly selected study building and at a randomly-selected
façade. The location along each façade was also chosen randomly, but was
no closer than 2m from a corner or edge of an adjacent façade. Carcasses
were placed on a variety of substrates, depending on the randomized
location chosen, including concrete, asphalt, river rock, bark mulch,
grass, or under bushes and ground cover. Collision monitors were alerted
to the carcass persistence study and to the location of CP trial
carcasses. However, as a precaution, data were checked carefully following
all collision surveys to ensure that carcasses from CP trials were not
inadvertently included in the collision mortality dataset. We checked
carcasses every 5 – 6 hours after initial placement on the first day until
early evening, in early morning and mid-day on the second day, and at
mid-day on subsequent days until carcasses were no longer present
(presumably removed by scavengers or campus maintenance staff), or until
the end of the study period, whichever arose first. Carcass removal time
was the interval between placement of a carcass at a façade until it was
removed by a “scavenger” and no longer detectable by observers, as defined
by Riding and Loss (2018). We used bias-correction functions within the R
package GenEst to fit a carcass persistence (or parametric survival) model
to estimate the amount of time a carcass would persist, given the
conditions under which it arrived (Dalthorp et al. 2018a, b). We tested
whether a persistence model that explicitly varies depending on the
season, or one that depends only on the intercept, best described the
detection probability of a carcass over time. We fit one-parameter
exponential and two-parameter (location and scale) Weibull models, and
tested whether persistence parameters varied categorically by season. The
location parameter in the exponential and Weibull models allowed
covariates to shift the mean of the probability density function, whereas
the additional scale parameter in the Weibull model allowed covariates to
affect the degree of spread of the distribution. Since the sample size was
small (n/K < 40) we chose the model with the lowest 2nd-order AIC
(AICc) goodness-of-fit statistic across models. Searcher efficiency (SE).
We estimated searcher efficiency by having a non-surveyor place a total of
29 thawed bird carcasses during the winter 2015 (n = 1), winter 2017 (n =
20), spring 2016 (n = 3), summer 2016 (n = 3), and fall 2015 (n = 2) study
periods. Carcasses ranging in size from 4 g (Anna’s Hummingbird) to 160 g
(Northern Flicker, Colaptes auratus) were placed at randomly-selected
buildings, façades, and façade locations as described above for CP trials.
Carcasses were removed from the trial if they were detected by at least
one of the surveyors. Carcasses that were not detected on the first day
were checked prior to each subsequent survey until they were located by
observers, removed by a scavenger, or until the end of the collision
monitoring period, whichever arose first. We used the R package GenEst to
model searcher efficiency as a function of the probability that carcasses
are located by observers on successive searches (Dalthorp et al. 2018a,
b). The probability of finding a carcass on the first search after carcass
arrival was denoted by the parameter, p, and k was the change in
probability of finding a given carcass on the second and subsequent
searches until the end of the collision monitoring period (Dalthorp et al.
2018a). We fit two models to estimate the observer bias parameters,
including a model that depended only on the respective intercepts, and
another model that allowed searcher efficiency to vary by season. We
selected the best model by comparing the goodness of fit of models using
AICc. Bias-corrected mortality estimation: a two-stage parametric
bootstrap approach. We used the function estM in the R package GenEst to
produce a bias-corrected estimate of mortality (Dalthorp et al. 2018b).
EstM estimated each carcass’ contribution to the total mortality in each
search interval by accounting for the carcasses missed due to
scavenging/imperfect carcass persistence (CP), and imperfect
detection/searcher efficiency (SE) (Dalthorp et al. 2018a, b). We assumed
that most birds that collided with buildings fell within 2 m of building
façades (Hager et al. 2013, Hager and Consentino 2014, Riding et al.
2020), and therefore set the parameter density weighted proportion (dwp)
or the proportion of carcasses expected to arrive in the area searched by
observers (Simonis et al. 2018), to 1. The model uncertainty from the two
bias correction models (carcass persistence and searcher efficiency
models) was propagated into estimates of total mortality using parametric
bootstrap methods (Efron and Tibshirani 1986). Specifically, function estM
in the R package GenEst drew 1000 sets of estimated carcass counts from
the asymptotic distributions of the maximum likelihood estimators of
parameters from the two bias-correction models. The result was a matrix of
carcass contributions where each row corresponded to a carcass, and each
column corresponded to a simulated realisation of that carcass’ estimated
contribution to total mortality. These simulated outputs resulted in
bias-adjusted mortality estimates that included corrections for
differences in search interval (time-increments between searches on the
same building façade), corrections for seasonal difference in carcass
persistence, and a correction for imperfect searcher efficiency.
Assessment of Relative Vulnerability to Collisions in Winter 2017 After 4
seasons of collision monitoring, we noted that some species were
particularly prevalent in the collision mortality data. However,
assessment of relative vulnerability to collisions requires an estimate of
population size of species within the study area, as well as
species-specific mortality counts. Therefore, in winter 2017, we conducted
avian point count surveys during our collision monitoring period, and
corrected these counts for imperfect detection. We used raw collision
mortality counts to calculate relative vulnerability of species to window
collision mortality on UBC campus as we did not have sufficient data to
generate species-specific bias-corrected mortality counts as described
above. To determine how population size influenced the likelihood of
collision by species, we applied a two-step approach: 1) we estimated the
change in total number of birds by species over time in response to
population-level processes of mortality, immigration and emigration, and
then; 2). we regressed mortality estimates on the corrected population
counts to determine a species-specific vulnerability rating. Winter 2017
point count surveys. We conducted 5-minute point counts at a randomly
chosen façade of each building approximately weekly between January 25,
2017 and March 12, 2017, for a total of 72 point counts over 9 survey days
throughout the winter 2017 collision monitoring period. We minimized
factors that could contribute to detection error by: 1) recording all
birds seen or heard by call notes within a small area to maximize the
probability of detecting all individuals (i.e., a 50-m radius semi-circle,
from the edge of building façades), and excluding birds that were only
seen flying over; 2) conducting surveys during the morning hours when
human foot traffic was low, and only in weather conditions favorable to
maximum detection of all individuals (i.e., with no precipitation and
winds below 13 km/hr), and; 3) using the same observer (KLD) to conduct
all point count surveys to minimize variation in observer bias. Population
size estimates. We calculated the population size for each species using
the Dail–Madsen model (Dail and Madsen 2011), which is a generalized form
of the Royle (2004) binomial N-mixture model developed for open
populations (i.e., the closure assumption is relaxed, allowing abundance
to change over time). We chose this model for our winter point count data
because it 1) assumes that abundance patterns are determined by an initial
territory establishment process followed by gains and losses resulting
from mortality, movements into the population (recruitment) and movements
out of the population, and; 2) accounts for imperfect detection
probability. For all species, we assumed that individuals could move
across all sites (building locations) and all days within the winter 2017
collision monitoring and point count period. Thus, the number of gains
across days was directly proportional to the number of survivors
(individuals occupying winter territories), so we applied an
auto-regressive model to recruitment, γ*Ns[i,t-1]; where γ = recruitment,
N = the number of individuals per site, i, for each species, s, on survey
day, t. As a result of the relatively short detection distance, we used a
starting value of 1 for probability of detection. We applied a Poisson
distribution to all species in which the total number of individuals
detected across all 9 surveys, Nstot, was greater than 10. For species
with Nstot < 10, we applied either a negative-binomial or
zero-inflated Poisson distribution to a simple intercept-only model, and
restricted immigration to zero. We then used the parameter estimates from
this simple model as starting values for the more complex, open count
model that allowed immigration across survey locations and days. For all
models, we set the upper bound for discrete integration, K, at the greater
value of Nstot + 1, or 10. We summed across all sites the resulting
site-specific population size estimates corrected for detection
probability and movement into the study area, divided by the number of
survey-days (9), and multiplied by number of buildings surveyed to obtain
population estimates for each species, Ncorr. We calculated the root mean
squared error on the residuals of each model to estimate residual standard
error for each species’ estimate of Ncorr. We estimated all parameters and
obtained corrected population size estimates using the function,
‘pcountOpen’ and back-transformed the estimates for detection probability
from each model using the function, ‘plogis’ in the R package, unmarked
(Dail and Madsen 2011, Fiske and Chandler 2011, Hostetler and Chandler
2015, R Development Core Team 2018). Calculation of relative vulnerability
to collisions by species. Next we used our raw mortality counts per
species for winter 2017 and species population estimates at our 8 study
buildings (Ncorr) to assess relative vulnerability of species to
collisions during the winter 2017 season. We fit a simple linear
regression to log10 (X + 1) of the raw winter 2017 mortality counts on
log10 of the population size estimates, Ncorr (Arnold and Zink 2011, Loss
et al. 2014). The use of the (X + 1) transformation on mortality counts
allowed us to account for zero mortality for species that were observed in
point counts but not in the mortality counts. We set regression
coefficients for population size, Ncorr to 1.0, calculated residuals, then
raised 10 to the power of the absolute value of the residuals, for all
species observed in our point count surveys. A slope of 1.0 assumes that
mortality due to collisions for a given species is proportionate to their
population size, as described by Arnold and Zink (2011). Vulnerability
values represent the factor by which a given species is more (+) or less
(–) vulnerable to collisions relative to the expected value for a species
with average collision risk (residual = 0).