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The distributions of birds during migratory stopovers are influenced by a hierarchy of factors. For example, in temperate regions, migrants are concentrated near areas of bright artificial light at night (ALAN) and also the coastlines of large water bodies at broad spatial scales. However, less is known about what drives broad-scale stopover distributions in the tropics. We quantified seasonal densities of nocturnally migrating landbirds during spring and fall of 2011–2015, using two weather radars on the Yucatan peninsula, Mexico (Sabancuy and Cancun). We tested the influence of environmental predictors in explaining broad-scale bird stopover densities. We predicted higher densities in areas 1) closer to the coast in the fall and farther away in spring and 2) closer to bright ALAN and with lower ALAN intensity in both seasons. We found that birds were more concentrated near the coastline in the fall and away from it in spring around Cancun but not Sabancuy. Counter to our expectations, we detected increased bird densities with increased distance from lights in spring around Sabancuy, and in both seasons around Cancun, suggesting avoidance of bright areas during those seasons. This is the first evidence of broad-scale bird avoidance of bright areas during stopover.
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Remote Sens. 2020, 12, 395; doi:10.3390/rs12030395 www.mdpi.com/journal/remotesensing
Article
Artificial Light at Night is Related to Broad-Scale
Stopover Distributions of Nocturnally Migrating
Landbirds along the Yucatan Peninsula, Mexico
Sergio A. Cabrera-Cruz 1,*, Emily B. Cohen 2, †, Jaclyn A. Smolinsky 1 and Jeffrey J. Buler 1
1 Department of Entomology and Wildlife Ecology, University of Delaware, Newark, DE 19713, USA;
scabrera@udel.edu (S.C.C.); jsmo@udel.edu (J.A.S.); jbuler@udel.edu (J.J.B.)
2 Migratory Bird Center, Smithsonian Conservation Biology Institute, National Zoological Park,
Washington, DC 20013, USA; emily.cohen@umces.edu
Current affiliation: Appalachian Laboratory, University of Maryland Center for Environmental Science,
Frostburg, MD 21532, USA
* Correspondence: scabrera@udel.edu
Received: 1 December 2019; Accepted: 24 January 2020; Published: 26 January 2020
Abstract: The distributions of birds during migratory stopovers are influenced by a hierarchy of
factors. For example, in temperate regions, migrants are concentrated near areas of bright artificial
light at night (ALAN) and also the coastlines of large water bodies at broad spatial scales. However,
less is known about what drives broad-scale stopover distributions in the tropics. We quantified
seasonal densities of nocturnally migrating landbirds during spring and fall of 2011–2015, using two
weather radars on the Yucatan peninsula, Mexico (Sabancuy and Cancun). We tested the influence
of environmental predictors in explaining broad-scale bird stopover densities. We predicted higher
densities in areas 1) closer to the coast in the fall and farther away in spring and 2) closer to bright
ALAN and with lower ALAN intensity in both seasons. We found that birds were more
concentrated near the coastline in the fall and away from it in spring around Cancun but not
Sabancuy. Counter to our expectations, we detected increased bird densities with increased distance
from lights in spring around Sabancuy, and in both seasons around Cancun, suggesting avoidance
of bright areas during those seasons. This is the first evidence of broad-scale bird avoidance of bright
areas during stopover.
Keywords: aeroecology; artificial light at night; stopover distribution; bird migration; Gulf of
Mexico; light pollution; weather radar
1. Introduction
Nocturnally migrating birds travel long distances between their breeding and nonbreeding
grounds, facing conditions along the way that could hinder or halt their journeys. For example,
ecological barriers [1,2] and human alterations to the environment such as the addition of bright lights
at night and wind turbines or other tall structures (e.g.,, communication towers [3]) are likely to
influence the flight trajectories [4], altitudes [5,6], orientation and distributions of migrating birds [7].
Billions of Nearctic–Neotropical birds negotiate large bodies of water during migration including the
Great Lakes, the western Atlantic Ocean, the Caribbean Sea, and the Gulf of Mexico (GOM) [8–12].
Food-rich coastal habitats are critical for migrating birds to fuel up before and after crossing such
barriers. However, coastal ecosystems face increasing losses and degradation from human activities
[13], potentially increasing the challenges that migrating birds may face.
Urban and rural settlements are responsible for light pollution that derives from the excessive
use of artificial light at night (ALAN), making them identifiable through satellite imagery of the Earth
[14,15]. Light pollution is a pervasive pollutant with multiple environmental effects [16–18] such as
Remote Sens. 2020, 12, 395 2 of 32
the disorientation and attraction of nocturnally migrating birds to bright ALAN, a phenomenon that
we have known about for over a century [19]. ALAN has a wide range of effects on nocturnally
migrating birds [20], and is increasingly recognized as a driver of their distributions. Bird stopover
densities during migration at the landscape scale are unexpectedly high in urban areas [10,21], a
result of broad-scale attraction of night migrants to ALAN [11,22]. This association is stronger during
the fall migration season [22], possibly because juvenile birds are more prone to ALAN-related
disorientation [23]. Furthermore, light pollution is a relatively novel stimulus during migration and
is brighter and more ubiquitous along migration routes compared to relatively dark stationary
breeding and non-breeding grounds [24]. Given the confluence of migrating birds around large water
bodies where light pollution also occurs, bird distributions at temperate and sub-tropical latitudes
are related not only to geographic and ecological factors [10,12], but also to light pollution [11].
The GOM is a marginal sea of the Atlantic Ocean with a maximum width of ~1500 km, bordered
by >4000 km of coastline. Two of the major migratory flyways in the Nearctic–Neotropical migration
system converge in the GOM [25], and at least 2 billion birds navigate it every spring [26,27], with
birds using the eastern flyway being more likely to fly across [25,28]. For example, Swainson’s
Thrushes (Catharus ustulatus), Wood Thrushes (Hylocichla mustelina), and Red-eyed Vireos (Vireo
olivaceus) departing from coastal Alabama in fall arrive to the Yucatan Peninsula on average 22 hours
later [29]. Thus, the Yucatan Peninsula potentially constitutes the first landmass that some trans-gulf
migrants reach in the fall after crossing the GOM on their way south, and the last they use before
departing north in spring. Evidence from the northern GOM coast in the US indicates that migrating
birds concentrate near the coastline in spring [30] after arriving energy-depleted from their trans-gulf
flight [31]. However, in the fall, birds traveling south from breeding areas concentrate further inland
in hardwood forests as they prepare for their southbound trans-gulf flight [30]. Furthermore, bird
densities in the northern GOM are typically positively related to a larger proportion of forest cover
[12,30,32]. Along the Atlantic coast of the US in the fall, birds concentrate near the coast presumably
before making a trans-oceanic flight [10,11]. Moreover, along the Great Lakes in the US, birds
concentrate near shorelines as they approach lakes and are less concentrated near shorelines on the
far side of lakes after crossing [8]. Hence, the influence of habitat availability and distance from the
coastline on stopover distributions is seasonally dependent in some geographies and not others.
However, all of the available evidence about broad-scale distributions of migrating birds along the
GOM coastlines comes from studies along the US coastline. This is the first effort at evaluating
seasonal distributions of migrating birds on the southern tropical GOM coast in Mexico.
The coastal municipalities along the Mexican coast of the GOM lost 28.4% of their original land
cover between 1976 and 2000 [33]. Furthermore, the Yucatan Peninsula is among the coastal regions
in Mexico with the greatest losses of natural land cover [33]. A large proportion of the beaches along
the Peninsula’s eastern coast need rehabilitation or restoration after environmental degradation
mainly due to tourism developments and activities [34,35], and two of the three states in the Peninsula
were among the Mexican territories with greater forest loss between 1993 and 2002 [36]. Neotropical
migrants wintering on the Yucatan Peninsula use pristine habitats as well as areas with varying
degrees of human alteration [37–39]. Although Neotropical migrants use urban green areas during
migration throughout Latin America [40], they typically prefer large forest patches [41], which are
expected to occur outside urban areas.
Here, we investigate the effect of light pollution, distance to the coast, and habitat on the seasonal
distributions of nocturnally migrating birds on the coast of the Yucatan Peninsula in the southern
GOM, using data from two weather radars located in the southwest (Sabancuy, Campeche) and
northeast (Cancun, Quintana Roo) corners of the Yucatan Peninsula (Figure 1). Considering the
stronger association of nocturnal migrants to light polluted areas at broad scales during the fall than
in spring [22], we predicted that bird densities would be higher closer to bright areas, and this
relationship would be stronger or more evident in the fall. However, at fine spatial scales, bird density
should be lower in areas of brighter light [11]. Considering the degradation of the coasts around the
Peninsula, we hypothesized that distance to the coast is an important predictor of bird distributions.
Furthermore, given that migrating birds in the Peninsula are preparing for their trans-gulf flight in
Remote Sens. 2020, 12, 395 3 of 32
spring and recovering in fall, the opposite scenario as in the north GOM coast [30], it is possible that
the seasonal relationship between stopover density and distance to coastline is reversed for the south
GOM Mexican coast. Thus, we predicted that bird densities would be higher farther away from the
coast in spring before migrating birds cross the GOM on their way north and higher close to the coast
in the fall after crossing the Gulf on their way south, despite coastal degradation. Finally, we expected
that land cover, forests in particular, would have an important effect on bird densities, as many
migrating birds are forest-associated species and rely on forested habitat for resting and refueling
[32]. This work complements the considerable body of literature on the seasonal movements of
migrating birds through the northern coast of the GOM [42] including the factors governing their
stopover distributions, and increases our understanding of the global influence of light pollution on
migrating birds. This is also the first study to compare the relationship of bird distributions to light
pollution among spring and fall seasons using weather surveillance radar data.
Figure 1. Location (black crosses) and 80 km detection range (black circles) of the Sabancuy (Sab) and
Cancun (Can) weather radars in the Yucatan Peninsula showing the average intensity of artificial light
at night (ALAN) during spring and fall migration of 2012
(https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html). Central inset shows the location
of the radars in relation to the Gulf of Mexico. Scale bar of ALAN intensity is common to both figure
panels and is based on radiance values around Cancun expressed in units of nanoWatts per square
centimeter per steradian (nW cm
2
sr).
2. Materials and Methods
2.1. Radar Data and Processing
We sampled the distributions of birds taking off from terrestrial stopover sites for nocturnal
migration (i.e., evening migration exodus) during the spring (01 March–30 May) and fall (15 Aug–15
Nov) migration seasons of 2011–2015, using data from two weather surveillance radars (Sabancuy
and Cancun) located on the Yucatan Peninsula in the southern GOM (Figure 1). Sampling the
atmosphere at the onset of bird migration with weather radar provides quantitative measures of bird
densities at the ground during migratory stopover [43]. We obtained all available data for dates
within our defined migration periods from Mexico’s National Water Commission.
The Sabancuy and Cancun weather surveillance radars are Doppler C-band units that observe
the weather inland and off the coast, while also detecting echoes from flying animals. These radars
emit bursts of single-polarized electromagnetic energy through a rotating antenna, measuring the
amount of energy reflected back from scattering meteorological and biological bodies (i.e., radar
reflectivity factor), which provides a measure of the volumetric density of scatterers in the air around
Remote Sens. 2020, 12, 395 4 of 32
the radar unit [44]. The radars sample the airspace via a volume scan taken every 15 minutes. A
volume scan consists of multiple 360° sweeps of the atmosphere at a different number of tilt angles
ranging from 0.5° to 20° depending on the radar coverage pattern. Radars measure and store
reflectivity and other measures, such as Radial velocity, for individual sampling volumes within each
sweep of a volume scan. The sampling volumes have boundaries corresponding to the polar spatial
resolution of 200m x 1° (Sabancuy) and 250m x 0.5° (Cancun). We used the Weather Decision Support
System—Integrated Information software [45] to convert radar files from their native format to other
data formats for processing.
The onset of nocturnal bird migration is a highly synchronized mass movement of migrating
individuals in response to sun elevation. Birds typically start their exodus from terrestrial stopover
sites when the sun is ~6° below the horizon [46] or ~30–45 minutes after sunset. Bird migration exodus
looks like sudden blooms of reflectivity centered around the radar (e.g., Video S1), as birds enter the
airspace sampled by the radar [47]. We used UNIDATA’s Integrated Data Viewer [48] to visually
screen radar sweeps from the 0.5° tilt angle (i.e., the lowest elevation angle) around the onset of
migration exodus within a 100 km range from the radar. We considered precipitation as an indication
of adverse weather during the rest of the night, and since precipitation at dusk typically results in
low bird migration intensity [49], we excluded nights when precipitation occurred within 50 km from
the radar (see below), or when precipitation contaminated reflectivity blooms of bird exodus. We also
excluded nights when anomalous radar beam propagation occurred, since this condition implies that
the radar beam curves towards the ground in an unpredictable way due to certain atmospheric
conditions [44], contaminating the data with ground returns. The Sabancuy and Cancun radars
provide reflectivity measures of migrating birds at exodus within a limited range (<50 km), or with
gaps within the radar coverage area (i.e., not all radar sample volumes contain data, see Figure S1).
Radial velocity measures were too sparse to determine airspeeds of flying animals and we thus were
unable to conduct analysis to discriminate sampled nights as insect- or bird-dominated (sensu [10]).
However, Radial velocity standard deviation > 2 m s-1 is indicative of bird presence in C-band radar
data [50]. Hence, as an alternative method to discriminate bird- from insect-dominated nights, we
estimated the Radial velocity standard deviation of over-land sample volumes of ~6 radar sweeps
around time of exodus for each night considered for analysis. The range of nightly average Radial
velocity standard deviations for Sabancuy was 3.15–4.59 in spring and 3.38–10.81 in the fall. Only one
night in Cancun had an average Radial velocity standard deviation of 1.75; otherwise, the ranges
were 2.06–2.4 in spring and 2.22–2.42 in the fall. Thus, we feel confident that the great majority of the
data used for analyses had presence of migrating birds. As an additional conservative measure, we
retained for analysis only nights with blooms of reflectivity around the expected exodus time [47]
and assumed that these were bird-dominated because bird reflectivity typically obscures insects’ [51].
For selected sampling nights, we spatio-temporally interpolated reflectivity measures to the sun
elevation at either “peak” exodus (the instant of greatest rate of increase in reflectivity during
migratory exodus) or 15 minutes after the onset of exodus, whichever occurred first [10,11,47]. This
allowed us to obtain a single snapshot of birds aloft at the same relative point of migration exodus
before they dispersed too far away from their ground sources.
Radar reflectivity measures are subject to range bias: a decline in reflectivity values with distance
from the radar as the radar beam increases in altitude and overshoots scatterers [43,44,47], which
hampers the comparison of reflectivity measures across the radar domain. We minimized range bias
by estimating the Vertically Integrated Reflectivity (VIR), an equivalent measure of reflectivity with
respect to a common height reference across ranges [47]. For this, we estimated the height limits of
the radar beam and a seasonal mean vertical profile of reflectivity at peak exodus for each radar. The
vertical profiles represent how radar reflectivity of birds aloft varies with height above the ground
[52]. We derived vertical profiles by integrating reflectivity data from the five lowest radar tilts for 10
m height intervals in relation to the mean reflectivity in the total airspace sampled by the radar to a
common height reference (0 to 1750 meters above the ground level) [43]. Because our data did not
contain reflectivity data for all tilts for all sampling nights or years, for Sabancuy we estimated a
single representative seasonal mean vertical profile using a subset of sampling nights with
Remote Sens. 2020, 12, 395 5 of 32
comprehensive volume scans (n = 11 and 13 for 2014 and 2015 respectively). For Cancun, we were
able to estimate only one representative mean vertical profile using 9 nights of data. We estimated
radar beam limits of every sampling volume by modeling beam propagation paths assuming
standard atmospheric refraction. For each sampling volume, we divided the interpolated reflectivity
at exodus by the mean vertical profile ratio within the sampling volume’s beam limits, to obtain a
volumetric measure of mean bird density integrated within the reference height (i.e., volumetric VIR)
in units of cm2 per km3. We then “flattened” VIR of birds aloft to the ground by dividing volumetric
VIR by the height reference (1750 m) to derive a VIR estimate in units of cm2 per ha-1, representing
the total cross-sectional area of birds in the air above a one hectare area on the ground [11,47]. For
both radars, the effective detection range was where the vertical profile of reflectivity ratio for the
range correction was greater than 0.05. We summarized VIR for each sample volume by pooling radar
data across days and years and calculating the geometric mean of VIR as a relative measure of the
mean daily density of birds during migration exodus.
For all analysis, we used radar data located between 7.5 km and up to the maximum range with
data from potentially biological scatters. We set the lower range limit because the radar beam in the
first kilometers from the radars is too narrow to produce reliable data [53]. Additionally, we excluded
radar volume samples 1) located off the coast and where the proportion of water estimated from our
land cover data set (see below) was >0.75, 2) where the radar beam is blocked at least 25% by
topographical or human-made features, and 3) dominated by persistent ground-clutter that
contaminates reflectivity measures [10,47]. From the radar data, we identified multiple known bat
roosts ~50-60 km southeast of the Sabancuy radar (Cú-Vizcarra, pers. comm.) that became active
approximately around sunset, sometimes overlapping with bird migration exodus. Hence, to prevent
data contamination from bats, we removed all sample volumes located between 47,100 m and 76,000
m from the radar within an arc between 103° and 137°. We did not identify any roosts around the
Cancun radar.
2.2. Model Covariates
We created polar polygon grids for each radar. Each polar grid consists of polygons radiating
around the radar location up to a distance of 100 km, with polygons having depths and widths of 200
m x 1° (Sabancuy) and 250 m x 0.5° (Cancun). Each polygon corresponds to the Cartesian boundary
of every radar sample volume at the 0.5° tilt angle. We overlaid our polar polygon grids on geospatial
data and extracted measures of 17 different environmental variables expected to affect bird stopover
density to each radar sample volume (Table 1). We used these environmental variables as covariates
in our models to explain variability in bird stopover densities.
Table 1. Names and descriptions of model covariates.
Na
me
Des
crip
tio
n
(un
its)
Lig
ht
pol
luti
on
Dis
tan
Remote Sens. 2020, 12, 395 6 of 32
ce
to
b
rig
ht
ligh
ts
Dis
tan
ce
of
rad
ar
sa
mpl
e
vol
um
es
to
are
as
wit
h
b
rig
ht
arti
fici
al
ligh
ts
at
nig
ht
(m
eter
s,
m
)
Me
an
AL
AN
inte
nsit
y
Av
era
ge
inte
nsit
y of
Arti
Remote Sens. 2020, 12, 395 7 of 32
fici
al
Lig
hts
at
Nig
ht
dur
ing
mig
rati
on
mo
nth
s of
yea
r
201
2
(log
10
+
0.00
1
nan
oW
atts
per
squ
are
cen
tim
eter
per
ster
adi
an,
nW
cm
2
sr)
Coa
st
Dis
tan
ce
to
coa
st
Dis
tan
Remote Sens. 2020, 12, 395 8 of 32
ce
of
rad
ar
sa
mpl
e
vol
um
es
to
GO
M
coa
stli
ne
(m)
Lan
d
cov
er
Eve
rgr
een
fore
st
Pro
por
tion
of
eve
rgr
een
fore
st
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Dec
idu
ous
fore
st
Remote Sens. 2020, 12, 395 9 of 32
Pro
por
tion
of
dec
idu
ous
fore
st
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Mix
ed
fore
st
Pro
por
tion
of
mix
ed
fore
st
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Shr
ubl
and
Pro
por
tion
of
shr
ubl
and
fore
Remote Sens. 2020, 12, 395 10 of 32
st
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Gra
ssla
nd
Pro
por
tion
of
gra
ssla
nd
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Bar
ren
Pro
por
tion
of
bar
ren
lan
ds
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Remote Sens. 2020, 12, 395 11 of 32
We
tlan
d
Pro
por
tion
of
wet
lan
d
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Cro
pla
nd
Pro
por
tion
of
cro
pla
nd
fore
st
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Urb
an
Pro
por
tion
of
urb
an
fore
st
Remote Sens. 2020, 12, 395 12 of 32
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Wa
ter
Pro
por
tion
of
wat
er
cov
er
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Uns
pec
ifie
d
non
-
fore
st
Pro
por
tion
of
uns
pec
ifie
d
lan
d
cov
er
in
are
Remote Sens. 2020, 12, 395 13 of 32
as
of
rece
nt
fore
st
loss
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Me
an
ND
VI
Me
an
sea
son
al
Nor
mal
ize
d
Diff
ere
nce
Veg
etat
ion
Ind
ex
wit
hin
rad
ar
sa
mpl
e
vol
um
es
Cor
rect
ive
Remote Sens. 2020, 12, 395 14 of 32
Dis
tan
ce
to
rad
ar
Dis
tan
ce
of
rad
ar
sa
mpl
e
vol
um
es
to
rad
ar
(m)
Rel
ativ
e
ele
vati
on
Diff
ere
nce
bet
wee
n
rad
ar
ant
enn
a
hei
ght
and
gro
und
hei
ght
(m)
To estimate light pollution related variables, we used the R package ‘Rnightlights’ [54] to
download monthly cloudless satellite composite images of the earth at night from the US National
Oceanic and Atmospheric Administration free data repositories. These images are composited from
imagery originally collected by the Visible/Infrared Imager/Radiometer Suite of the Suomi National
Polar-orbiting Partnership satellite, collecting measures of night light radiance in units of nanoWatts
Remote Sens. 2020, 12, 395 15 of 32
per square centimeter per steradian (nW cm2 sr). Night lights radiance vary seasonally, especially in
areas with seasonal changes in land cover properties such as snow cover and vegetation [55].
Although we do not expect significant seasonal changes in land cover in our tropical study area, we
downloaded available night-light images for the bird migration months of 2012 (April–May, and Sep–
Nov), and averaged them to obtain a single night-light dataset during bird migration months only.
Since night-light data are right skewed, we log10-transformed the averaged radiance, adding 0.001
first to improve data distribution (Figure S2). We then estimated the average night light intensity for
each radar sample volume of each radar. Finally, we identified “bright areas”, defined as areas where
night-light radiance is >=26, approximately equivalent to a sky brightness at the zenith five times
greater than natural light brightness, the level of night sky brightness where the Milky Way is no
longer visible to the human eye [56]. This is the threshold brightness that produces the strongest
relationship between bird stopover distribution and distance to bright light [11]. We then estimated
the Euclidean distance of each radar sample volume to bright areas. Similarly, we used a geospatial
product of the coastlines of the world to estimate the Euclidean distance from each radar sample
volume to the coast.
We considered habitat availability by including 10 land cover variables, a measure of forest loss,
and a measure of vegetation greenness, the Normalized Difference Vegetation Index (NDVI). The
North American Land Change Monitoring System is a 30m resolution land cover dataset identifying
19 land-cover types consistently for Mexico, the US, and Canada, based on 2010 satellite imagery [57].
We reclassified this dataset from 19 to 10 more generalized categories (Table S1). Unfortunately, during
the course of the study we discovered that recent deforestation processes in the Yucatan Peninsula [58] are
not reflected in this dataset. Hence, we used version 1.6 of the Global Forest Change dataset [59] to
update [57] by identifying areas that lost forest cover since 2000 to the most recent year for which we
had suitable nights for each radar and season. Thus, we effectively created a new land cover category
that we call ‘Unspecified non-forest’ since we do not know the land cover type established after
deforestation occurred. We then estimated the proportion of land cover within each radar sample
volume. We also included a measure of vegetation greenness because it relates positively with bird
densities at stopover [11]. We used the R package ‘MODIS’ [60] to download the NDVI from the
MYD13Q1 Vegetation Indices product from the USGS Land Processes Distributed Active Archive
Center for the periods within spring and fall migrations for which we had suitable nights. We
estimated NDVI values at the centroid of each radar sample volume for each suitable night, matching
the date to the closest-in-time NDVI measure, which are generated biweekly. We then estimated a
seasonal average, obtaining a mean NDVI value from dates used for analyses.
We also included two corrective variables. Although we made an attempt to reduce range bias
when processing radar data, we included distance to radar and relative elevation as predictors in the
stopover density models to control for any residual measurement error due to range bias [43]. We
estimated relative elevation as the difference between radar antenna height and ground height, so
that positive values indicate terrain heights below the antenna.
2.3. Model Analysis
We used the gradient boosting method of Boosted Regression Trees to model the log10-
transformed geometric mean of the VIR separately by radar and season as a function of all the
predictor variables (i.e., proportion of land cover classes, distance to bright lights, mean ALAN
intensity, and distance to coast). Boosted Regression Trees is a statistical method that combines
regression trees and boosting algorithms, ultimately fitting a single model in a forward, stagewise
way [61]. The Boosted Regression Tree models (models, hereafter) do not make assumptions about
the data distribution of the response variable. Hence, it can fit non-linear response functions; in
addition, it automatically models interactions among predictors [61]. Detailed accounts of the method
and its application in ecology are available in [61,62]. Modeling was performed in R [63] with the
library ‘dismo’ and function ‘gbm.step’, which optimizes the fit of the model through cross validation
[64]. We used a tree complexity of 2 to allow only two-way interactions among predictors, bag
fraction of 0.5, a Gaussian error distribution, and a learning rate varying by radar and season to
Remote Sens. 2020, 12, 395 16 of 32
produce a minimum of 1000 trees in the optimized model. The learning rate weighted the
contribution of each tree to the model, and the bag fraction specified the proportion of data used to
train the model [61]. We used the ‘gbm.interactions’ function from the R package ‘dismo’ to identify
important two-way interactions between predictors. The analysis ranks predictor variables by their
relative influence explaining the response, as a measure of how many times each variable was
selected for splitting trees [61]. In order to consider only biologically relevant predictors in our
interpretations, we re-casted the importance of each predictor after excluding the influence of
variables controlling for measurement error (i.e., distance to radar and relative elevation).
In ecology, variable correlation is typically considered high when r > |0.7| [65]. However,
analytical methods similar to ours can identify the relationship of highly correlated predictors to the
response when large sample sizes are used [11]. We tested for correlation among all predictors with
a Pearson’s correlation test and dropped those with r > |0.8|, except when correlation was with
relative elevation or with distance to radar. For Sabancuy, we removed proportion of grassland as
predictor in spring because it was negatively correlated with proportion of wetland (r = -0.88; Figure
S3). Distance to bright lights was correlated to distance to radar in the fall season at the Sabancuy
radar (r = -0.83). However, our models included ~6000–~30,000 observations/radar (i.e., radar sample
volumes), and the inclusion of these correlated variables in the models did not change the direction
of any relationship nor the ranking of the relative importance of the predictors explaining the
response.
3. Results
We had access to spring and fall data of 2011–2015, but no usable data were available for 2013.
Data were sparsely archived by radar and year. Hence, the number of dates with data differ by radar.
In spring and fall, we sampled migratory bird exodus for 58 and 39 radar nights across years
respectively, with a range of 17–23 nights in spring and 8–15 nights in the fall (Table 2). There was
little overlap among radars in the years and seasons that provided suitable sampling nights. Thus,
within-year comparisons of bird densities between radars was not feasible. In total, ~ 15.5% of all
available radar nights (n = 626) provided suitable samples. The main reasons for discarding nights
included precipitation (~60%) and missing data (~20%, see discussion); anomalous beam
propagation, clutter, and sea breezes were responsible for ~3% of excluded data.
Table 2. Number of nights used for analysis by radar, season, and year.
Sab
anc
uy
Ca
ncu
n
Yea
r
Spr
ing
Aut
um
n
Spr
ing
Aut
um
n
Remote Sens. 2020, 12, 395 17 of 32
201
1
15
201
2
17
8
201
4
23
8
201
5
18
8
Tot
al
41
31
17
8
The Boosted Regression Trees models identified that the two broad-scale predictors, distance to
the coast and distance to bright lights, were consistently the top two biologically relevant predictors
of bird stopover densities across radars and seasons (Table 3). Other finer-scale predictors that ranked
high included ALAN intensity, NDVI, proportion of water (for Cancun only), wetland, and
unspecified non-forest (for Sabancuy, only in spring). The cumulative relative influence of the top
five predictors at explaining bird densities ranged between 86.54% and 90.33% across radars and
seasons. Local landscape composition variables (i.e., proportions of land covers within sampling
volumes) were consistently among the weakest predictors of bird densities. The relative influence of
all predictors included in the analysis is available in Table S2 of the Supplementary materials. The
cross-validation correlation, a goodness-of-fit measure for models, ranged between 0.47 and 0.86
across radars and seasons. The seasonal distribution of bird densities around each radar in relation
to lights is shown in Figure S1.
Table 3. Relative variable influence of biological predictors (i.e., excluding distance to radar and
relative elevation) of bird stopover densities from the Boosted Regression Tree models by radar and
season, and sum of the top five predictors, indicated in bold font. Also shown are the mean ± standard
error (SE) of cross-validation correlations for each model.
Pre
dict
or
Remote Sens. 2020, 12, 395 18 of 32
Sab
anc
uy
Ca
ncu
n
Spr
ing
Aut
um
n
Spr
ing
Aut
um
n
Lig
ht
pol
luti
on
Dis
tan
ce
to
b
rig
ht
ligh
ts
23.7
3
27.1
5
21.3
4
24.1
8
Me
an
AL
AN
inte
nsit
y
12.8
3
Remote Sens. 2020, 12, 395 19 of 32
13.6
6
19.6
7
17.8
1
Coa
st
Dis
tan
ce
to
coa
st
35.6
9
21.3
7
30.1
2
26.7
5
Lan
d
cov
er
Eve
rgr
een
fore
st
2.34
3.75
4.60
0.45
Dec
idu
ous
fore
st
-
0.00
0.06
0.06
Remote Sens. 2020, 12, 395 20 of 32
Mix
ed
fore
st
-
-
-
-
Shr
ubl
and
-
-
0.00
0.00
Gra
ssla
nd
-
4.00
0.05
0.01
Bar
ren
0.00
0.93
0.07
0.12
We
tlan
d
5.20
9.50
4.76
3.51
Cro
pla
nd
0.00
0.15
-
-
Urb
an
1.58
0.87
2.01
5.43
Wa
ter
0.55
0.83
Remote Sens. 2020, 12, 395 21 of 32
12.7
0
12.4
4
Uns
pec
ifie
d
non
-
fore
st
5.32
2.93
0.49
1.55
Me
an
ND
VI
12.7
6
14.8
7
4.12
7.70
Su
m
90.3
3
86.5
4
88.5
9
88.8
7
Me
an
±
SE
of
cro
ss-
vali
dati
on
corr
elat
ion
0.53
±
0.00
0.56
±
0.00
0.86
±
0.01
0.47
±
0.01
According to our prediction, bird densities closest to bright lights were relatively higher in the
fall than in spring for both radars (Figure 2). Bird densities around Sabancuy had generally opposite
responses in spring compared to fall. However, despite the relatively higher density of birds <3 km
Remote Sens. 2020, 12, 395 22 of 32
from bright lights in fall compared to spring around Cancun, relative bird densities for both seasons
peaked at the farthest distance from the bright lights sampled (i.e., ~25 km in spring and 18 km in the
fall). Thus, opposite to what we expected we detected an increase in bird densities with distance from
lights in spring around Sabancuy, and in both seasons around Cancun, suggesting avoidance of
bright areas during those seasons.
Figure 2. Partial dependency plots of distance to coast, distance to bright lights, and mean intensity
of artificial lights at night (ALAN) in relation to bird densities around the Sabancuy and Cancun
weather radars in the southern Gulf of Mexico in spring and fall migration seasons. Solid lines and
thick curves are the mean ± 95% CI of the smoothed original trend line from the Boosted Regression
Tree models, shown as dotted lines. Numbers in parenthesis show the relative importance of the
predictor for each season after removing non-biologically important predictors. Y-axis represent a
measure of bird densities around radars scaled from zero to one for easy comparison of the
relationships. X-axis of mean ALAN intensity is log
10
-transformed. Rug plots on the x-axis show
deciles of the distribution of predictor values for each radar. Plots of all other predictors included in
models for each radar and season are available in Figures S4 to S7. Color version of figure is available
online.
Counter to our prediction, the relative peak in bird stopover densities with respect to distance
to the coast around the Sabancuy radar in spring occurred closer to the coast in spring (within 5 km
from the coast) than in the fall (at ~13 km from the coast; Figure 2). Furthermore, high bird densities
Remote Sens. 2020, 12, 395 23 of 32
during the fall were spread out within 15 km from the coastline. This is the distance at which spring
densities drop to a relative minimum, indicating higher concentrations near the coast in spring even
though bird densities at the coast are similar in both seasons. According to our prediction, however,
bird densities were relatively more concentrated near the coastline around the Cancun radar in the
fall than in spring (Figure 2). Specifically, although the peak bird densities around this radar occurred
at ~10 km from the coastline in both seasons, they rapidly dropped at a distance of ~15 km in the fall,
while they remained comparatively higher in spring, indicating an overall higher concentration of
birds near the coastline in the fall.
Local-scale factors were less influential in general and we highlight a few of the relatively more
influential. For Sabancuy bird densities increased with increasing mean NDVI, with a higher
importance in the fall (Figures S4 and S5). For Cancun, bird density generally declined with greater
proportion of water (Figures S6 and S7). Interestingly, spring bird densities around Sabancuy were
relatively high in relation to low and high proportions of unspecified non-forest, but low with
moderate proportions; in the fall, bird densities around Sabancuy were relatively high in relation to
low and mid proportions of wetland (Figure S5). For Cancun, bird density reached maximum relative
density at mid and high proportions of wetland.
With regards to fine-scale response to local light intensity, we found a negative relationship
between bird density and ALAN intensity around Cancun during spring, but not during fall. Instead,
bird densities during the fall were relatively constant throughout all light pollution levels around
Cancun. The relationship between bird densities and mean ALAN intensity around Sabancuy was
noisy, with no clear positive or negative trend. The strongest interactions in the models of bird
densities around the Sabancuy radar were between distance to the coast and distance to bright lights,
in both seasons. In spring, bird densities were highest closer to the coast and farther from bright
lights. The opposite pattern occurred during fall migration when densities were higher farther away
from the coast and closer to bright lights (Figure 3). For the Cancun radar, the interaction between
distance to coast and bright lights was the least important in spring, but the most important in the
fall (Table S3). In spring, bird densities were lowest at the coastline regardless of distance to bright
lights but peaked between 5 and 10 km from the coast while also increasing with distance to lights
(Figure 3). In the fall, bird densities were highest at the coastline, particularly closer to bright lights,
but beyond the coastline they were consistently low near bright lights, increasing with distance to
lights. The most important interaction in spring around Cancun was between proportion of water
and distance to bright lights, with greater bird densities related to higher proportions of water
regardless of distance to lights, and to short distances to bright lights regardless of proportion of
water (Figure S8). The relative importance of all identified interactions is provided in Table S3.
Remote Sens. 2020, 12, 395 24 of 32
Figure 3. Partial dependency plots for the interaction between distance to the coast and distance to
bright lights, from Boosted Regression Tree models of bird densities during migration exodus around
two weather radars (Sabancuy and Cancun) located in the Yucatan Peninsula, in spring and fall
migration seasons.
4. Discussion
Broad-scale geographic positions relative to artificial lights and the coast were more important
than fine-scale land cover composition in influencing the distribution of nocturnally migrating birds
during their diurnal stopovers on the Yucatan Peninsula, Mexico. This is consistent with the nature
of spatial scaling in the hierarchical process of stopover habitat selection in birds [32,66], which
narrows down from the broadest geographical scale in which birds migrate through, to the finest
scale in which they make the final selection [32,66,67]. Thus, factors from broad to fine scale have a
differential influence on bird distributions, but landscape-scale features seem to drive broad-scale
distributions [32]. For example, geographic position such as distance to the coast has a dominating
effect on explaining bird distributions at a broad landscape scale [10], including in the northern GOM
coast [12]. Thus, our results corroborate the dominance of landscape-scale factors in the distributions
of migrating birds, and support the scale-dependency of habitat selection around the GOM, including
the northern Yucatan Peninsula [68]. However, our results also contradict previous findings in part
Remote Sens. 2020, 12, 395 25 of 32
regarding the broad-scale attraction of nocturnal migrants to bright urban areas, as we found
evidence of avoidance of light pollution.
There is consistent evidence of broad-scale attraction of migrating birds to the bright lights of
urban areas in the US [11] during both spring and fall [22]. The attraction to lights is stronger during
the fall migration season [22], when juvenile birds migrate for the first time and are prone to greater
disorientation due to light pollution [23]. Thus, it is expected that bird densities are higher closer to
bright urban areas during the fall. Our results extend this general phenomenon to migrants seeking
stopover habitats in tropical America during fall. However, contrary to past studies, our results also
suggest avoidance of bright lights in spring, when we found higher bird densities in relation to
distances far away from lights around both radars. Admittedly, we could not sample the distances
closest to bright lights around Sabancuy, but bird densities at the shorter distances sampled are the
lowest, increasing thereafter. Furthermore, bird densities around Cancun drop to a minimum 2–3 km
from bright lights in both seasons, and then increase consistently throughout the whole range of
distances sampled, indicating relatively high bird densities closest to bright areas, but much higher
away from them. Additionally, bird density declined with greater intensity of artificial lights at a fine
spatial scale in spring. To our knowledge, this is the first radar analysis of broad-scale bird migration
in relation to ALAN during spring, and we provide evidence counter to that from citizen-science-
based observations in the US, which indicates attraction to bright areas during both migration
seasons [22]. Considering the greater attraction of migrating birds to ALAN and urban areas in the
fall [22,23], and that greater mortality due to anthropogenic causes concentrates in the same season
[69], we hypothesize that anthropogenic-related bird mortality during the fall selects for individuals
with low attraction to ALAN. Thus, we propose that naïve and ALAN-attracted individuals are
selected out during their southwards migration in the fall, while a higher proportion of experienced
or ALAN-resistant individuals return north, resulting in the observed avoidance of ALAN in spring,
potentially explaining the lower number of bird collisions with lit buildings in spring [70].
Alternatively, higher bird densities away from bright lights around coastlines of the southern
GOM in spring, when birds are heading north back to their breeding sites, may be related to birds
selecting the best available habitat to refuel before embarking on a long barrier-crossing flight.
Migratory birds preparing for long distance flights require food-rich habitat for refueling, as their
fuel load heavily influences their migratory performance [71]. Some species or individuals prefer to
stopover in the largest forest patches available [41]. Thus, when preparing for a long-distance flight,
migrating birds may concentrate away from urban light-polluted areas, which presumably offer less
vegetated cover compared to larger patches of forests available outside of densely human populated
areas. If this were true, we would expect avoidance of bright lights in the fall around the northern
GOM coast, before migrants embarked on southbound flights across the GOM towards their
wintering sites. An analysis of the influence of ALAN on bird distributions along the northern GOM
could test this hypothesis.
The attraction of birds to bright urban areas reported by M cLaren et al . [11] is based on the h ighly
urbanized and light-polluted northeastern US region. Our results relate to bird densities around
urban areas with similar degrees of light pollution as elsewhere in North America [56], but they are
certainly smaller in extent, particularly around the Sabancuy radar, where rural areas dominate.
Hence, the question arises of whether the evidence of avoidance that we found might be related to
the smaller extent of individual bright areas compared to those in North America, rather than to a
seasonal effect. The only evidence of broad-scale attraction to lights in spring comes from a study that
used bird occurrences at a very large-scale [22], incorporating light pollution data from the largest to
the smallest human settlements from North to Central America. Hence, the potential avoidance of
small urban areas might have been masked by attraction to large bright areas. However, other studies
have found weaker association of migrating birds to urban areas in spring and stronger during the
fall [21], consistent with [22], and observational studies confirm that migrating birds use urban areas
during both seasons in North America and Latin America [40,41]. Nevertheless, we observed
evidence of avoidance not only around Sabancuy but also around Cancun, where the largest bright
areas within our study areas occur and where bird densities were highest away from lights in both
Remote Sens. 2020, 12, 395 26 of 32
seasons. Hence, the observed pattern may relate to different pressures or habitat characteristics and
availability in the tropics. Future work should deepen the analysis of seasonal bird-habitat
associations at a broad scale along the southern GOM.
The Cancun radar is located south of the resort city with the same name, one of the major
touristic destinations in Mexico, in a region where habitat loss has been driven by the growth and
development of the tourism sector mainly along the coastline [36,72]. Coastal development in the
eastern Yucatan has potentially affected the communities of migratory and resident bird species [73].
Consequently, greater bird densities farther away from the coast in spring may also be related to
urban development. However, the proportion of urban cover within radar sample volumes was not
important in explaining bird densities in our models, independently nor interacting with another
predictor (Table S3). Nevertheless, our results suggest that bird densities increase with proportion of
urban cover around the Cancun radar in spring and fall (Figures S6 and S7 respectively). Future work
could explore in further depth how urbanization affects bird distributions during migration along
the southern GOM.
Our results support our prediction that bird densities would be higher closer to the coast in the
fall, but only for the Cancun radar. Thus, we have evidence that bird distributions in relation to
distance to the coast are reversed compared to the north GOM around the northernmost radar in the
Yucatan Peninsula. However, the coastline orientation around both radars in the Peninsula relative
to the expected north–south axis of migration, is complex, and the available data did not allow
estimating direction of migration movement, making it difficult to assess whether the observed
patterns obey the expected movement of migration. Nevertheless, the Cancun radar is ~50 km south
of the north coast of the Peninsula, which is nearly perpendicular to the general north–south axis of
Nearctic–Neotropical bird migration, similar to the north GOM. Hence, we consider that these
characteristics make feasible the mirrored pattern of bird distributions between the north GOM and
Cancun, and possible along all the north coast of the Peninsula. Contrastingly, the Sabancuy radar is
located further south on the Peninsula, and the coastline within its domain runs southwest-northeast,
extending then further north towards the Los Petenes and Ria Celestún Biosphere Reserves, which offer
282,857 ha and 81,482 ha of habitats such as mangroves and tropical deciduous forests, and border
the west coast of the Peninsula [74,75]. Thus, we consider that the difference in coastline orientations
around this radar and the north GOM may preclude the mirrored pattern, particularly if bird
migration around Sabancuy follows the coast from or towards nearby protected areas. Future work
could analyze bird distributions along the north coast of the Yucatan Peninsula, but currently there
are no weather radars surveilling that area.
There is likely some daytime trans-Gulf arrivals of birds to the northern Yucatan coast during
the fall season [29], when ALAN would not influence their stopover habitat selection. However, no
studies have quantified the extent of daytime versus nighttime arrivals of trans-Gulf migrants in this
region during the fall. Daytime landfall in the fall may help explain the weaker response of migrants
to ALAN in Cancun and the difference between radars if daytime arrivals are more common at
Cancun versus Sabancuy. Future studies, including ground surveys, would be valuable for assessing
the distribution of migrating birds along the northern coast of the peninsula, and to test the bird
distribution patterns in relation to the coast and ALAN reported here from weather radar
observations.
Satellite measurements of night lights are affected by factors such as atmospheric conditions and
satellite observation angles. Unless night-light measurements are made exactly at the zenith, radiance
levels recorded for the same geographic extent may differ with the angle of satellite observation, for
example by capturing different facades of buildings, or because light sources may be hidden by
obstacles from a specific viewpoint [76,77]. Aerosols, water vapor, and ozone are some of the factors
that influence the radiance scattered by the atmosphere, ultimately affecting the satellite-measured
radiance from artificial lights [78]. The night-light data used for this research are monthly image
composites averaging satellite observations that were subject to a series of algorithms to remove
clouds and non-electric sources of lighting, and this averaging helps to minimize variations in night
light measurements derived from different angles of satellite observation [79]. These monthly
Remote Sens. 2020, 12, 395 27 of 32
composites, however, do not account for atmospheric conditions and this is in fact a recognized area
of improvement for such datasets [79]. Future investigations using atmospheric-corrected night-light
data or improved models of night sky brightness distribution [80] may be able to elucidate more
subtle patterns between bird distributions and ALAN.
Aeroecological analysis of Mexican weather radar data is challenging due to limited data
availability and quality. The network of weather radars operated by the Mexican Weather
Meteorological Service consists of 13 Doppler units with different technologies, in different status of
operability, and in need of modernization [81]. The Sabancuy and Cancun radars were last updated
in 1999 and 2009 respectively [81] and have been functional on and off in the last decade. Hence, the
gaps in data for both radars and migration seasons. At the time of writing, only three of the thirteen
radar stations operated by SMN seem operational (https://smn.conagua.gob.mx/es/observando-el-
tiempo/radares-meteorologicos). In addition to the sparsity in temporal coverage, we also found a
limited geographic coverage within radars. For example, S-band weather radars from the NEXRAD
network in the United States (US) detect birds in migration out to 120 kilometers, and birds at
migration exodus out to between 50 and 100 km [82,83]. Here, we observed migration exodus
consistently within 30 km from the C-band radars, reaching at most 50 km for birds aloft during
migration in the middle of the night (e.g., Video S1). This, however, might be explained by the
different wavelength that each radar network operates, and the greater atmospheric loss of C-band
radars compared to S-band, derived from greater wavelength attenuation of the former [84,85] .
Future efforts may use new developments to filter out precipitation from weather radar data [86], or
a different processing approach [87], in order to salvage some of the precipitation-contaminated
nights and increase the potential of the existing data to study bird migration around the southern
GOM.
5. Conclusions
Geographic position in relation to bright lights and the coast is the major driver of broad-scale
stopover distributions of nocturnally migrating birds along the Yucatan Peninsula coast and
elsewhere. However, the responses of migrants to these drivers contrast in some important ways
from migrants at more northern latitudes. Specifically, we present the first radar evidence of
inconsistent broad-scale attraction of birds to stopover in bright areas during fall migration and
consistent avoidance of bright areas during spring. These results contradict the pattern of broad-scale
attraction of migrants to bright areas observed at more northern latitudes during both spring and fall,
raising the question of how and why migrant response to light changes between seasons and between
temperate and tropical latitudes. Furthermore, the pattern of coastal concentrations around Cancun
during the fall, when birds arrive to the southern GOM after southbound flights, mirrors the pattern
known from the northern GOM coast, when birds arrive to the coast in the spring. However, there
are stronger coastal concentrations of migrants in the western Yucatan before crossing the GOM in
spring and this is not mirrored in the US where there are higher densities inland before crossing the
GOM in the fall. These results from the first broad-scale investigation of stopover distributions on the
Yucatan peninsula of Mexico highlight the potential differences with other regions and need for
increased study of bird stopover in the tropics.
Remote Sens. 2020, 12, 395 28 of 32
Supplementary Materials: The following are available online at www.mdpi.com/2072-4292/12/3/395/s1.Table
S1: Land cover classes aggregated from the 2010 North American Land Change Monitoring System; Table S2:
Relative influence of all predictors included in the Boosted Regression Tree models for each season; Table S3:
Interactions between predictors of bird densities by radar and season. Figure S1: Seasonal distribution of bird
densities around each radar in relation to ALAN; Figure S2: Original and log-10 transformed intensity of artificial
light at night around each radar; Figure S3: Pearson correlation coefficients of all predictors; Figures S4 to S7:
Partial regression plots of all predictors of bird densities around Sabancuy and Cancun radars in spring and fall;
Figure S8: Partial dependency plot for the most important interaction between predictors of bird densities
around the Cancun radar in spring. Video S1: Animation of scans from the Cancun radar showing the evolution
of reflectivity measures with the typical appearance of bird migration exodus.
Author Contributions: Conceptualization, S.C.C.; methodology, J.J.B.; software, J.A.S.; formal analysis, S.C.C.
and J.J.B.; investigation, S.C.C.; resources, S.C.C., J.J.B., and E.B.C.; data curation, S.C.C.; writing—original draft
preparation, S.C.C.; writing—review and editing, S.C.C., E.B.C., J.A.S., and J.J.B.; visualization, S.C.C.;
supervision, J.J.B. and E.B.C.; funding acquisition, S.C.C. and E.B.C. All authors have read and agreed to the
published version of the manuscript.
Funding: At different stages of this work, S.C.C. was supported by Fulbright—Garcia-Robles and CONACYT—
COVEICyDET scholarships, by a Predoctoral Fellowship at the Smithsonian Conservation Biology Institute
Migratory Bird Center, and by a University of Delaware Doctoral Fellowship.
Acknowledgments: Thanks to the Comisión Nacional del Agua (CONAGUA, Mexico) for providing radar data
and Lucas Walls for assistance with screening radar data. Thanks to Armando Rodriguez-Dávila at CONAGUA
for his support and expert advice regarding the network of Mexican weather radars and data acquisition. Thanks
to José D. Cú-Vizcarra for confirming the nature of bat roosts near the Sabancuy radar.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
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... We predict higher species richness in the interior at the ending of autumn and beginning of spring migration reflecting the arrival and then departure of birds into the interior. A previous study in two urbanized landscapes in the Peninsula found that bird densities measured by weather radar were negatively associated with distance to the coast (Cabrera-Cruz et al., 2020). Here, we predict that distance to the coast is more strongly associated with spatial variation in migratory bird species richness before and after migrating over a geographic barrier than primary productivity. ...
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Stopping-over is critical for migrating birds. Yet, our knowledge of bird stopover distributions and their mechanisms near wide ecological barriers is limited. Using low elevation scans of three weather radars covering 81,343 km2, we quantified large-scale bird departure patterns during spring and autumn (2014–2018) in between two major ecological barriers, the Sahara Desert and Mediterranean Sea. Boosted Regression Tree models revealed that bird distributions differed between the seasons, with higher densities in the desert and its edge, as well as inland from the sea, during spring and a predominantly coastal distribution in the autumn. Bird distributions were primarily associated with broad-scale geographic and anthropogenic factors rather than individual fine-scale habitat types. Notably, artificial light at night strongly correlated with high densities of migrants, especially in the autumn. Autumn migrants also selected sites located close to water sources. Our findings substantially advance the understanding of bird migration ecology near ecological barriers and facilitate informed conservation efforts in a highly populated region by identifying a few high-priority stopover areas of migrating birds.
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Despite using the aerosphere for many facets of their life, most flying animals (i.e., birds, bats, some insects) are still bound to terrestrial habitats for resting, feeding, and reproduction. Comprehensive broad-scale observations by weather surveillance radars of animals as they leave terrestrial habitats for migration or feeding flights can be used to map their terrestrial distributions either as point locations (e.g., communal roosts) or as continuous surface layers (e.g., animal densities in habitats across a landscape). We discuss some of the technical challenges to reducing measurement biases related to how radars sample the aerosphere and the flight behavior of animals. We highlight a recently developed methodological approach that precisely and quantitatively links the horizontal spatial structure of birds aloft to their terrestrial distributions and provides novel insights into avian ecology and conservation across broad landscapes. Specifically, we present case studies that (1) elucidate how migrating birds contend with crossing ecological barriers and extreme weather events, (2) identify important stopover areas and habitat use patterns of birds along their migration routes, and (3) assess waterfowl response to wetland habitat management and restoration. These studies aid our understanding of how anthropogenic modification of the terrestrial landscape (e.g., urbanization, habitat management), natural geographic features, and weather (e.g., hurricanes) can affect the terrestrial distributions of flying animals.
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Bird-building collisions are the largest source of avian collision mortality in North America. Despite a growing literature on bird-building collisions, little research has been conducted in downtown areas of major cities, and no studies have included stadiums, which can be extremely large, often have extensive glass surfaces and lighting, and therefore may cause many bird collisions. Further, few studies have assessed the role of nighttime lighting in increasing collisions, despite the often-cited importance of this factor, or considered collision correlates for different seasons and bird species. We conducted bird collision monitoring over four migration seasons at 21 buildings, including a large multi-use stadium, in downtown Minneapolis, Minnesota, USA. We used a rigorous survey methodology to quantify among-building variation in collisions and assess how building features (e.g., glass area, lighting, vegetation) influence total collision fatalities, fatalities for separate seasons and species, and numbers of species colliding. Four buildings, including the stadium, caused a high proportion of all collisions and drove positive effects of glass area and amount of surrounding vegetation on most collision variables. Excluding these buildings from analyses resulted in slightly different collision predictors, suggesting that factors leading some buildings to cause high numbers of collisions are not the exact same factors causing variation among more typical buildings. We also found variation in collision correlates between spring and fall migration and among bird species, that factors influencing collision fatalities also influence numbers of species colliding, and that the proportion, and potentially area, of glass lighted at night are associated with collisions. Thus, reducing bird collisions at large buildings, including stadiums, should be achievable by reducing glass area (or treating existing glass), reducing light emission at night, and prioritizing mitigation efforts for glass surfaces near vegetated areas and/or avoiding use of vegetation near glass.
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The released VIIRS DNB nightly images, also known as VIIRS DNB daily nighttime images, provide rich information for time series analysis of global socioeconomic dynamics. Anisotropic characteristic is a possible factor that influences the VIIRS DNB radiance at night and its time series analysis. This study aims to investigate the relationship between viewing angles and VIIRS DNB radiance of Suomi NPP satellite in urban areas. First, twenty-nine points were selected globally to explore the angle variation of Suomi NPP satellite views at night. We found that the variation of the satellite viewing zenith angle (VZA) is consistent (e.g. between 0° and 70°) since the range of VZA is fixed depending on the sensor design, and the range of viewing azimuth angle (VAA) increases with the increase of latitude. Second, thirty points in cities of Beijing, Houston, Los Angeles, Moscow, Quito and Sydney, were used to investigate the angle-radiance relationship. We proposed a zenith-radiance quadratic (ZRQ) model and a zenith-azimuth-radiance binary quadratic (ZARBQ) model to quantify the relationship between satellite viewing angles and artificial light radiance, which has been corrected by removing the moonlight and atmospheric impact from VIIRS DNB radiance products. For all the thirty points, the ZRQ and ZARBQ analysis have averaged R 2 of 0.50 and 0.53, respectively, which indicates that the viewing angles are important factors influencing the variation of the artificial light radiance, but extending zenith to zenith-azimuth does not much better explain the variation of the observed artificial light. Importantly, based on the data analysis, we can make the hypothesis that building height may affect the relationship between VZA and artificial light, and cold and hot spot effects are clearly found in tall building areas. These findings are potentially useful to reconstruct more stable time series VIIRS DNB images for socioeconomic applications by removing the angular effects.
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The determination of the all-sky radiance distribution produced by artificial light sources is a computationally demanding task that generally requires intensive calculations. In this paper, we develop an analytical formulation that provides the all-sky radiance distribution produced by an artificial light source as an explicit and analytical function of the observation direction, depending on two single parameters that characterize the overall effects of the atmosphere. One of these parameters is related to the effective attenuation of the light beams, whereas the other accounts for the overall asymmetry of the combined scattering processes in molecules and aerosols. Using this formulation, a wide range of all-sky radiance distributions can be efficiently and accurately calculated in a short time. This substantial reduction in the number of required parameters, in comparison with other approaches that are currently used, is expected to facilitate the development of new applications in the field of light pollution research.
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Remote sensing of night light emissions in the visible band offers a unique opportunity to directly observe human activity from space. This has allowed a host of applications including mapping urban areas, estimating population and GDP, monitoring disasters and conflicts. More recently, remotely sensed night lights data have found use in understanding the environmental impacts of light emissions (light pollution), including their impacts on human health. In this review, we outline the historical development of night-time optical sensors up to the current state of the art sensors, highlight various applications of night light data, discuss the special challenges associated with remote sensing of night lights with a focus on the limitations of current sensors, and provide an outlook for the future of remote sensing of night lights. While the paper mainly focuses on space borne remote sensing, ground based sensing of night-time brightness for studies on astronomical and ecological light pollution, as well as for calibration and validation of space borne data, are also discussed. Although the development of night light sensors lags behind daytime sensors, we demonstrate that the field is in a stage of rapid development. The worldwide transition to LED lights poses a particular challenge for remote sensing of night lights, and strongly highlights the need for a new generation of space borne night lights instruments. This work shows that future sensors are needed to monitor temporal changes during the night (for example from a geostationary platform or constellation of satellites), and to better understand the angular patterns of light emission (roughly analogous to the BRDF in daylight sensing). Perhaps most importantly, we make the case that higher spatial resolution and multispectral sensors covering the range from blue to NIR are needed to more effectively identify lighting technologies, map urban functions, and monitor energy use.
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Avian studies have explored how development and landscape fragmentation leads to a loss of native avifauna, particularly migrants during the breeding season. However, during the winter and migration seasons, cities and surrounding fragmented areas could provide habitat for a variety of migrating birds. Unfortunately, much of the information on the occurrence of migrant species in cities has been concentrated in United States/Canada and little is known about the role of Neotropical cities. We performed a systematic review of the occurrence of forest Neotropical Migrant Bird Species (NMB) in small forest fragments and residential areas with urban tree canopy in Latin American countries during fall/spring migration and winter. We identified a total of 58 forest NMB from 19 studies, including 45 Nearctic Migrants and 12 Austral Migrants, and 54 NMB were found in small urban/ rural fragments (0.5-19.6 ha) and 30 were found in residential areas. In addition, six NMB considered as interior-forest specialists during the breeding season in United States/Canada were found using small forest fragments or residential areas during fall/spring migration and winter. This suggests that for some interior-forest specialists, breeding in large forested areas does not preclude their use of fragmented areas as stopover and wintering sites. Urban and rural forest fragments and residential areas could serve as habitat for NMB in and around Neotropical cities, but more research is needed to determine whether fragmented habitats are used by a variety of NMB during migration and winter seasons.
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1.Urban areas affect terrestrial ecological processes and local weather, but we know little about their effect on aerial ecological processes. 2.Here, we identify urban from non‐urban areas based on the intensity of artificial light at night (ALAN) in the landscape, and, along with weather covariates, evaluate the effect of urbanization on flight altitudes of nocturnally migrating birds. 3.Birds are attracted to ALAN, hence we predicted that altitudes would be lower over urban than over non‐urban areas. However, other factors associated with urbanization may also affect flight altitudes. For example, surface temperature and terrain roughness are higher in urban areas, increasing air turbulence, height of the boundary layer, and affecting local winds. 4.We used data from nine weather surveillance radars in the eastern US to estimate altitudes at five quantiles of the vertical distribution of birds migrating at night over urban and non‐urban areas during five consecutive spring and autumn migration seasons. We fit generalized linear mixed models by season for each of the five quantiles of bird flight altitude and their differences between urban and non‐urban areas. 5.After controlling for other environmental variables and contrary to our prediction, we found that birds generally fly higher over urban areas compared to rural areas in spring, and marginally higher at the mid layers of the vertical distribution in autumn. We also identified a small interaction effect between urbanization and crosswind speed, and between urbanization and surface air temperature, on flight altitudes. We also found that the difference in flight altitudes of nocturnally migrating birds between urban and non‐urban areas varied among radars and seasons, but were consistently higher over urban areas throughout the years sampled. 6.Our results suggest that the effects of urbanization on wildlife extend into the aerosphere, and are complex, stressing the need of understanding the influence of anthropogenic factors on airspace habitat. This article is protected by copyright. All rights reserved.