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The effect of urbanization and exposure to multiple environmental factors
on life-history traits and breeding success of Barn Swallows (Hirundo rustica)
across China
Yanyan Zhao
a
,
b
,
1
, Emilio Pagani-Nú~
nez
c
,
*
,
1
, Yu Liu
d
, Xiaoying Xing
e
, Zhiqiang Zhang
f
,
Guangji Pan
a
, Luting Song
a
, Xiang Li
e
, Zhuoya Zhou
f
, Yanqiu Chen
f
, Donglai Li
g
, Yang Liu
a
,
Rebecca J. Safran
h
a
State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, Guangzhou, 510006, China
b
Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, 530007, China
c
Department of Health and Environmental Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, 215028, China
d
Key Laboratory for Biodiversity Science and Ecological Engineering, Ministry of Education, College of Life Sciences, Beijing Normal University, Beijing, 100091, China
e
College of Wildlife and Protected Area, Northeast Forestry University, Harbin, 150006, China
f
Institute of Wildlife Conservation, Central South University of Forestry and Technology, Changsha, 410004, China
g
College of Life Science, Liaoning University, Shenyang, 110036, China
h
Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, USA
ARTICLE INFO
Keywords:
Fitness
Heat-island effect
Latitude
Light pollution
Noise pollution
Parental investment
ABSTRACT
In addition to landscape changes, urbanization also brings about changes in environmental factors that can affect
wildlife. Despite the common referral in the published literature to multiple environmental factors such as light
and noise pollution, there is a gap in knowledge about their combined impact. We developed a multidimensional
environmental framework to assess the effect of urbanization and multiple environmental factors (light, noise,
and temperature) on life-history traits and breeding success of Barn Swallows (Hirundo rustica) across rural to
urban gradients in four locations spanning over 2500 km from North to South China. Over a single breeding
season, we measured these environmental factors nearby nests and quantified landscape urbanization over a 1
km
2
radius. We then analysed the relationships between these multiple environmental factors through a principal
component analysis and conducted spatially explicit linear-mixed effects models to assess their effect on life-
history traits and breeding success. We were particularly interested in understanding whether and how Barn
Swallows were able to adapt to such environmental conditions associated with urbanization. The results show
that there is significant variation in the exposure to environmental conditions experienced by Barn Swallows
breeding across urbanization gradients in China. These changes and their effects are complex due to the
behavioural responses ameliorating potential negative effects by selecting nesting sites that minimize exposure to
environmental factors. However, significant relationships between landscape urbanization, exposure to envi-
ronmental factors, and life-history traits such as laying date and clutch size were pervasive. Still, the impact on
breeding success was, at least in our sample, negligible, suggesting that Barn Swallows are extremely adaptable to
a wide range of environmental features.
1. Introduction
Three environmental factors –artificial light at night, environmental
noise, and the heat-island effect –are commonly invoked in the literature
as drivers of reduced fitness of vertebrates inhabiting urban
environments. Artificial light at night disrupts key traits such as orien-
tation during migration, energy expenditure and sleep (Dominoni et al.,
2013;Da Silva et al., 2014;Ouyang et al., 2017;Welbers et al., 2017),
driving potentially harmful behavioural and physiological changes
(Gaston, 2018). Noise pollution impairs communication and negatively
* Corresponding author.
E-mail address: emilio.pnunez@xjtlu.edu.cn (E. Pagani-Nú~
nez).
1
These authors contributed equally to the paper.
Contents lists available at ScienceDirect
Avian Research
journal homepage: www.keaipublishing.com/en/journals/avian-research
https://doi.org/10.1016/j.avrs.2022.100048
Received 10 March 2022; Received in revised form 27 June 2022; Accepted 27 June 2022
Available online xxxx
2053-7166/©2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Avian Research 13 (2022) 100048
affects the immune and hormonal systems (Barber et al., 2010;Kight and
Swaddle, 2011;Shannon et al., 2016). These two environmental factors
have been linked to reduced nest success, yet the effects of the latter seem
particularly pervasive (Senzaki et al., 2020). Finally, the heat-island ef-
fect –the rise of average temperatures of urban areas –drives body size
shifts (Brans et al., 2017;Merckx et al., 2018) and has been linked to
changes in species' breeding phenology (Møller et al., 2015). High tem-
peratures typical of extreme environments can constrain breeding suc-
cess (Stoleson and Beissinger, 1999), which may be reinforced in urban
areas within hot climates. However, there is a paucity of knowledge on
the impact that increased exposure to these multiple environmental
factors can have on vertebrates’breeding success across urban gradients.
Exposure to multiple environmental factors can result in particularly
challenging conditions for organisms, an issue that has been particularly
well studied in marine and wetland ecosystems (Crain et al., 2008;
Ramírez et al., 2018;Sievers et al., 2018). In terrestrial ecosystems, birds
are often used as a model taxon to investigate the consequences of ur-
banization. Environmental factors, such as temperature, noise or light,
covary across urban gradients and can potentially be linked to variation
in life-history traits and fitness (Sprau et al., 2017). Among these factors,
environmental noise has been often linked to reduced fitness (Halfwerk
et al., 2011;Kight et al., 2012;Schroeder et al., 2012;Injaian et al.,
2018). Other studies examining the combined impact of multiple envi-
ronmental factors in urban environments investigated response variables
others than fitness including oxidative stress (Casasole et al., 2017),
species composition (Ciach and Fr€
ohlich, 2017), and singing behaviour
(Da Silva et al., 2014). A single study assessing the combined impact of
artificial light, noise, humidity and temperature on Great Tits (Parus
major) found that differences in fitness were attributable to urbanization
per se rather than these environmental factors (Sprau et al., 2017).
Moreover, previous research using birds as models have focused on
cavity nesters, such as the Tree Swallow (Tachycineta bicolor), the Great
Tit or the House Sparrow (Passer domesticus), breeding in temperate re-
gions within Europe and the United States of America (e.g., Senzaki et al.,
2020). Thus, there is a need for studies 1) examining the impact of
multiple environmental factors on reproductive fitness, 2) particularly in
species building open nests, which likely are more exposed to the detri-
mental effects of urbanization than cavity nesters whose nesting envi-
ronment is insulated from direct exposure to light and noise, and 3)
across latitudinal gradients encompassing different climates.
Using a multidimensional environmental framework, we investigated
the impact that multiple environmental factors combined (diurnal and
nocturnal light intensity, environmental noise, and air temperature) have
on different life-history traits (laying date, clutch size and breeding
success measured as the ratio between number of fledglings and clutch
size during a single breeding attempt) of Barn Swallows (Hirundo rustica)
breeding across urban gradients over a single breeding season (2018).
This relatively small migratory passerine is a highly suitable model
species to investigate the consequences of urbanization and exposure to
environmental factors. It is found in both urban and rural areas thereby
enabling a natural comparative experiment. Moreover, it is well-adapted
to thrive in human-dominated environments (Zink et al., 2006;Dor et al.,
2010). We conducted this study in China, a country that is experiencing
one of the fastest urbanization rates in the world (Seto et al., 2011), over
a large geographical area encompassing 22of latitude and 9of longi-
tude. Most previous studies have investigated the impact of single envi-
ronmental factors on fitness at the city level. For instance, environmental
noise drives a reduction in clutch size of domestic Atlantic Canaries
(Serinus canaria)(Huet des Aunay et al., 2017), and in breeding success of
Tree Swallows (Injaian et al., 2018). Birds breeding in urban areas
sometimes lay earlier and smaller clutches than those in rural areas, as
e.g., Great Tits (Caizergues et al., 2018), yet the relative importance of
landscape changes and the myriad associated environmental factors have
in shaping such patterns remains unclear.
In this study, we provide a comprehensive study of the consequences
for life-history traits and fitness by examining the interactions between
landscape changes and exposure to environmental factors across a wide
geographical area. We aimed to determine whether breeding Barn
Swallows were able to adapt to such different environmental conditions.
Also, we were interested in determining what behavioural responses
were elicited by multiple environmental factors and urbanization at their
nests. After controlling for the effect of longitude and latitude using a
linear correlation structure in our models, we were interested in
discerning the effects that landscape urbanization and multiple envi-
ronmental factors have on laying date, clutch size, and breeding success
of Barn Swallows.
2. Materials and methods
2.1. Study area and field methods
We performed this study over a single breeding season in 2018 in four
locations across a large geographical area of 22of latitude and 9of
longitude (Fig. 1). These locations, from south to north, were Guangzhou
(Guangdong, South China), Changsha (Hunan, South-Central China),
Panjin (Liaoning, Northeast China) and Harbin (Heilongjiang, Northeast
China) (Appendix Table S1). We worked in urban and rural areas at each
location (Fig. 2). We quantified built land cover around the nests using
ArcGIS 10.1 with data extracted from http://www.earthenv.org
following a standard protocol (Tuanmu and Jetz, 2014). Built areas
refer to the proportion of land surface within a 1-km pixel grid covered by
any artificial constructions, which can be used to measure the urbani-
zation level (Guo et al., 2016).
We searched for Barn Swallow nests under construction in suitable
areas across our sampling locations as soon as Barn Swallows appeared in
the early spring, to make sure we obtained data from first breeding at-
tempts. Due to the wide latitudinal gradient considered in this study, the
onset and duration of the breeding season varied considerably among our
study locations. From south to north, laying dates ranged between March
17th and May 30th in Guangzhou, between April 20th and May 25th in
Changsha, between May 13th and June 17th in Panjin, and between May
17th and June 13th in Harbin. Once an active nest was found, we
assigned it a unique nest identification code and checked it regularly
every three to four days to determine laying date (i.e., date in which the
first egg was laid), clutch size (number of eggs) and breeding success
(ratio of nestlings that fledged vs clutch size). Sample size was slightly
smaller for breeding success because we could not access some nests after
determining clutch size (e.g., because property owners were absent) (n¼
120 vs. n¼103). We assumed that nestlings had successfully fledged if
they were still present in the last visit (12–15 days), less than a week
before all the nestlings left the nest (Grüebler et al., 2010). Since
breeding Barn Swallows may respond negatively to human disturbance
associated with direct nest checking, we recorded our data on environ-
mental factors during the late incubation period to minimize the chances
of nest desertion and to avoid interfering with nestling rearing. We
assumed that exposure to environmental factors would be relatively
constant across the entire breeding attempt and certainly less variable
than differences between nests and locations. Finally, we only used data
from first breeding attempts, as data from second breeding attempts were
not systematically recorded, and to avoid pseudo-replication because we
were unable to band, and thus uniquely identify, the parents in all
locations.
2.2. Recording environmental factors
During the incubation stage, 10–12 days after the laying date, we
attached two data-loggers within a 50 cm radius below the nest to record
light intensity (i.e., illuminance) (lux), environmental noise (dBA) and
air temperature (C) at a total of 120 nests. Barn Swallows resumed their
normal behaviour within 0.5 h after the installation of these devices. Data
were recorded continuously every 2 min for 48 h to ensure continuous
and complete diurnal cycles for both data-loggers.
Y. Zhao et al. Avian Research 13 (2022) 100048
2
Air temperature and light intensity were recorded using a HOBO
Pendant ®Temperature/Light 8K Data Logger-UA-002-08 (Onset, MA,
USA). Measurement range is 20to 70 C for temperature and 0 to
320,000 lux (i.e., lumens per square foot) for light. Accuracy of tem-
perature measurements was 0.53 C from 0to 50 C. The light loggers
measure light levels ranging from infrared to UV (175–1200 nm,
including the 300–700 nm range visible to the bird eye (Håstad and
€
Odeen, 2014)) and thus detect wavelength ranges visible to birds. The
light sensor is designed to detect relative changes rather than absolute
values, which was especially useful for our research objectives.
Furthermore, this instrument is capable of measuring light at very low
levels, which was particularly important to detect artificial light pollu-
tion at night. Light intensity in our sample ranges from 0 lux (no light) to
several thousand lux (very bright light). Since environmental conditions
were highly variable among habitats and locations, we used this infor-
mation to determine dawn (light >0 lux) and dusk (light ¼0 lux) and,
therefore, night duration. Thus, night duration, time during which a nest
did not receive diurnal light, was a relative measure according to the
particular environmental conditions at each nest, rather than actual
dawn and dusk (Dominoni and Partecke, 2015). We used night duration
as an important environmental factor because it is commonly assumed
that excessively short nights may lead to insufficient sleeping time, thus
negatively affecting individual health (Cirelli and Tononi, 2008).
Environmental noise was recorded using a CEM ®Sound Level Data-
Logger DT-173 (CEM, Shenzhen, China). Every 2 min, the data-logger
measured 20 data points in dBA at 50 ms (millisecond) intervals and
stored the average value in fast-response mode (125 ms). Accuracy was
1.4 dBA. Average environmental noise ranged between 35 and 65 dBA
in our sample, often well above of the values described to have a negative
impact on wildlife (Shannon et al., 2016). The maximum acceptable
values for humans recommended by the World Health Organization
(WHO) are 30–45 dB for sleeping, 50–55 dB for outdoor living areas, and
70–85 dB to affect auditory capacity (World Health Organization, 1999).
At each nest, we continuously computed night duration, diurnal and
nocturnal light intensity, environmental noise, and air temperature
during a 48-h sampling period, which we then averaged to characterize
exposure to these multiple environmental factors.
2.3. Statistical analyses
To understand how these multiple environmental factors were related
to each other, we ran a principal component analysis (PCA) using the
seven dependent variables previously described (average diurnal and
nocturnal light intensity [i.e., daylight and artificial light pollution at
night], average night duration, average diurnal and nocturnal environ-
mental noise, and average diurnal and nocturnal temperature) from 120
nests of Barn Swallows. Our main objective was to assess the combined
effects of these factors on fitness, and their correlation with latitude,
urbanization, and life-history traits. Thus, we were particularly inter-
ested in obtaining components integrating variability of these multiple
environmental factors. We performed a PCA of correlation matrix (i.e. a
singular value decomposition of a column-centered and scaled matrix).
Thus, our study variables were centered and scaled, subtracting mean
variable values from each score and dividing these scores by the standard
deviation. This procedure removed heterogeneity of variance associated
with the different ranges of variation and measurement units among our
environmental variables.
Additionally, we performed a Nonmetric Multidimensional Scaling
(NMDS) based on the Bray-Curtis distance of the same study variables.
We applied a Wisconsin double standardization, in which variables
(columns) are first standardized by its maximum value and then nests
(lines) by the total line values. The first axis from the NMDS analysis
showed similar scores to the first component of the PCA (r¼0.8, P<
0.01, Appendix Fig. S1). Moreover, the first ten axes from the NMDS
analysis showed optimal stress scores (all <0.05) (Clarke, 1993), sug-
gesting that a single axis would be sufficient to satisfactorily explain data
variability. This result, and the high similarity between the scores from
both approaches, led us to retain the PCA approach, which is frequently
Fig. 1. Left: Study locations across 22of latitude and 9of longitude in the P. R. of China. Right: Satellite images of study locations in which nest sites are indicated
with yellow points. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Y. Zhao et al. Avian Research 13 (2022) 100048
3
used to reduce the dimensionality of multiple environmental variables
(Badgley and Fox, 2000;Pagani-Nú~
nez et al., 2014).
Using the PCA approach, we extracted three relevant components
with eigenvalues higher than 1. We determined the relative importance
of the different study variables for a component according to their
principal coordinates (i.e., correlation coefficients between variables and
components >0.70). We report correlations among our study variables in
Appendix Table S2, and descriptive statistics for all our study variables by
location in Appendix Table S3.
We first ascertained the relationships between the three PCA
components and built land cover (built area within 1 km
2
of the nest)
using a linear mixed-effect models fit by restricted maximum likelihood
(REML). We ran three models sequentially using each of the three PCA
components as dependent variable and landscape urbanization as inde-
pendent variable. Since variability in artificial light pollution at night was
not captured with any component, we ran an additional model including
this environmental factor as dependent variable. These were spatially
explicit models (Dormann et al., 2013); we included latitude and longi-
tude as correlation structure and location as random factor (Changsha,
Guangzhou, Harbin or Panjin). When two nests had the same coordinates
Fig. 2. Typical nest sites of Barn Swallows in our study locations. In southern China, Barn Swallows (Hirundo rustica) usually nest outside of the first floor of buildings,
which are often low and sparse in rural areas (A), while tall and dense in urban areas (B). In some rural areas, they may also nest in the open main room on the ground
floor (e.g., Changsha). In northern China, Barn Swallow nests in rural areas are usually located in open yards, and these houses are usually low, with only one floor in
both Harbin and Panjin (C). Sometimes, in urban areas, Barn Swallows may nest inside old buildings and enter and exit through the windows of stairwells (D).
Y. Zhao et al. Avian Research 13 (2022) 100048
4
we added a decimal point, which represented a negligible distance, as the
difference between two points cannot be zero using this approach. We
also assessed differences among study locations for these three compo-
nents through an analysis of variance and Tukey multiple comparisons of
means.
We then ran a model following the same procedure but using laying
date (date on which the first egg was laid measured as N
days
since March
1st), as a dependent variable and landscape urbanization and the three
PCA components and artificial light pollution at night as independent
variables. We did the same alternatively using clutch size (N
eggs
) and
breeding success (N
fledglings
/N
eggs
) as dependent variables and adding
laying date as independent variable. We applied model selection and
averaging procedures to these models based on Akaike Information Cri-
terion corrected by sample size (AICc), and retained results of conditional
averages of the selected models (ΔAICc <2) (Symonds and Moussalli,
2011). When performing model averaging, we fit the models by
maximum likelihood, which is the appropriate approach for model
comparisons (Zuur et al., 2009). When only one model was retained, we
reran the model using a REML fit and provide these results. We also
computed variation inflation factors of all models including several in-
dependent variables to assess multicollinearity. All variables gave scores
lower than 3, which suggests absence of multicollinearity (Henseler
et al., 2009). Finally, we retained the residuals accounting for fixed
factors’effects from the models using clutch size and breeding success as
dependent variables. Using these residuals, we assessed differences in
these variables between study locations through an analysis of variance
and Tukey multiple comparisons of means. All variables were scaled by
subtracting the mean variable values from each score and diving these
scores by the standard deviation, to reduce heteroscedasticity and
approximate normal distributions. In the case of laying date, we per-
formed this scaling procedure by subtracting the mean per location and
dividing by overall standard deviation. In doing so, we obtained
normalized values that enabled us performing meaningful comparisons
between locations (labeled “normalized laying date”).
We carried out all the analyses in R software 4.0.5 (R Core Team,
2021), and used the packages FactoMineR v1.41 (L^
e et al., 2008), vegan
v2.5-7 (Oksanen et al., 2007), MuMIn v1.43.17 (Barto
n, 2022), car
v3.0-11 (Fox et al., 2012), and nlme v3.1-152 (Pinheiro et al., 2007)to
run these analyses.
3. Results
3.1. Relationships among environmental factors
We obtained three components with eigenvalues higher than 1
(Table 1). PC1 correlated positively with nocturnal noise (r¼0.81) and
diurnal light (r¼0.76). PC1 explained 30.15% of variance. PC2 corre-
lated positively with diurnal (r¼0.91) and nocturnal temperature (r¼
0.76) and explained 24.28% of variance. PC3 correlated positively with
night duration (r¼0.80) and explained 19.87% of variance. These three
components explained a total of 74.30% of variance. Based on these
scores, we characterized these components as “nest exposure”(PC1),
“nest temperature”(PC2), and “night duration”(PC3).
3.2. Relationships between environmental factors and landscape
urbanization
Spatially explicit linear-mixed effects models accounting for variation
in latitude and longitude showed that landscape urbanization did not
correlate with PC1 (βSE ¼0.15 0.10, t
115
¼1.51, P¼0.13), PC2
(βSE ¼0.10 0.10, t
115
¼1.02, P¼0.31), or artificial light pollution
at night (βSE ¼0.09 0.15, t
115
¼0.61, P¼0.54), but correlated
positively with PC3 (βSE ¼0.36 0.07, t
115
¼4.98, P<0.01)
(Fig. 3A). Locations showed significant differences in PC1 (F
3,116
¼4.83,
P<0.01), yet only Panjin showed significantly higher PC1 scores than
Changsha (P¼0.02, 95% CI ¼0.10–1.53) and Harbin (P¼0.03, 95% CI
¼0.05–1.40) (Fig. 3B). Locations showed significant differences in PC2
(F
3,116
¼4.43, P¼0.01), yet only Changsha displayed significantly
higher values than Panjin (P¼0.04, 95% CI ¼0.04–1.48) and Harbin (P
¼0.04, 95% CI ¼0.02–1.72) (Fig. 3B and C). Locations also showed
multiple significant differences in PC3 (F
3,116
¼29.63, P<0.01), with
Guangzhou showing significantly higher values than all the other cities
(all P<0.04) and Changsha showing higher values than Panjin and
Harbin (all P<0.02) (Fig. 3C). Locations did not show significant dif-
ferences in artificial light pollution at night (F
3,116
¼0.92, P¼0.43).
3.3. Factors shaping life-history traits and breeding success of Barn
Swallows
After accounting for variability in latitude and longitude by
employing a linear correlation structure based on these variables,
normalized laying date correlated negatively with landscape urbaniza-
tion and PC1 and positively with PC2, while it did not correlate with PC3
or artificial light pollution at night (Table 2;Fig. 4A–C). After accounting
for these fixed effects, locations showed significant differences in
normalized laying dates (F
3,116
¼2.92, P¼0.04), yet only birds in
Harbin started breeding significantly later than in Changsha (P¼0.03,
95% CI ¼0.05–1.47). Following the same procedure, clutch size corre-
lated negatively with laying date and PC1 in the averaged model and
showed a non-significant positive relationship with artificial light
Table 1
Principal Component Analysis (PCA) on a correlation matrix using seven environmental factors recorded in 120 nests of Barn Swallows (Hirundo rustica) during 2018 as
dependent variables.
PC1 PC2 PC3
Eigenvalues 2.11 1.70 1.39
% Variance 30.15 24.28 19.87
Cum. Variance 30.15 54.44 74.31
Coord Contr cos
2
Coord Contr cos
2
Coord Contr cos
2
Diurnal light (lux) 0.76 27.33 0.58 0.30 5.18 0.09 0.40 11.30 0.16
Artif. light pollution (lux) 0.01 0.00 0.00 0.28 4.45 0.08 0.13 1.15 0.02
Diurnal noise (dBA) 0.59 16.69 0.35 0.28 4.66 0.08 0.64 29.31 0.41
Nocturnal noise (dBA) 0.81 30.77 0.65 0.23 3.17 0.05 0.34 8.39 0.12
Diurnal temperature (C) 0.13 0.84 0.02 0.91 48.78 0.83 0.23 3.81 0.05
Nocturnal temperature (C) 0.58 15.67 0.33 0.76 33.54 0.57 0.07 0.37 0.01
Night duration (min) 0.43 8.70 0.18 0.06 0.22 0.00 0.80 45.68 0.64
Data was recorded in urban and nearby rural areas of Guangzhou (Guangdong, South China), Changsha (Hunan, South-Central China), Panjin (Liaoning, Northeast
China) and Harbin (Heilongjiang, Northeast China). We are reporting eigenvalues and proportion of variance explained for the three main components (eigenvalues >
1), as well as their cumulative variance. Principal coordinates (Coord, correlations between variables and components), contributions (Contr, proportion of variance in
the component explained by each variable) and square cosines (cos
2
, quality of representation of the variables in the PCA) for each variable and component are also
reported. All the variables were scaled, subtracting mean variable values to each score and diving these scores by the standard deviation, to increase homogeneity of
variance. Significant variable loadings are marked in bold.
Y. Zhao et al. Avian Research 13 (2022) 100048
5
pollution at night, yet it did not correlate significantly with landscape
urbanization, PC2 or PC3 (Table 3;Fig. 4D). After accounting for these
fixed effects, locations showed significant differences in clutch size
(F
3,116
¼3.51, P¼0.02), yet only Barn Swallows from Changsha showing
significantly larger clutch sizes than birds from Guangzhou (P<0.01,
95% CI ¼0.17–1.54). Finally, the averaged model for breeding success
only retained landscape urbanization, yet the relationship was non-
significant (Table 4). Locations showed no significant differences in
breeding success (F
3,116
¼0.56, P¼0.65).
4. Discussion
4.1. Coping with detrimental effects of urbanization and related
environmental factors
Across a wide geographical area spanning 22of latitude and 9of
longitude in China, we recorded the degree of, and environmental dif-
ferences associated with, landscape urbanization and how these influ-
enced life-history traits, such as laying date and clutch size, of open cup
nesting Barn Swallows. We found that urban nests were exposed to
higher diurnal noise and longer times without receiving diurnal light
than rural nests. The fact that we did not record differences in other
environmental factors suggests that urban birds were selecting nesting
sites concealed to a similar or a greater extent than rural birds, amelio-
rating potential negative effects of these factors. Behavioural adaptations
to urban life have previously been reported across multiple taxa (Sol
et al., 2013), including nest site choice. For instance, previous studies
have reported changes associated with urbanization in nesting sites of
European Greenfinches (Carduelis chloris)(Kosi
nski, 2001)or
Black-billed Magpies (Pica hudsonia)(Jokim€
aki et al., 2017;Xu et al.,
2020). Our study expands on this issue by examining the relationships
between urbanization and relevant environmental factors in Barn Swal-
low nests which have been shown to influence parental investment and
fitness (Ambrosini et al., 2006;Ambrosini and Saino, 2010;de Satg
e
et al., 2019).
We found that birds experiencing high nocturnal noise and exposure
to intense diurnal light (PC1), laid small clutches regardless of urbani-
zation intensity. This result is in line with previous studies showing, for
instance, the pervasive negative effects of single environmental factors
such as noise pollution (Schroeder et al., 2012;Strasser and Heath, 2013;
Fig. 3. (A) Linear relationships between landscape urbanization, measured as % of built land cover (built area within 1 km
2
around nests), and three components
(PC1, PC2 and PC3; see Table 1) with eigenvalues higher than 1 extracted from a principal component analysis on seven environmental factors in 120 nests of Barn
Swallows (Hirundo rustica) collected during 2018. Dashed lines represent non-significant relationships. (B) Two-dimensional kernel density estimation based on PC1
and PC2 by study location. The shaded areas depict the inferred probability density function in the two-dimensional space with darker colors meaning a higher
probability to find a nest within these component coordinates. (C) Following the same approach, two-dimensional kernel density estimation based on PC2 and PC3.
(For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Table 2
Linear Mixed-Effects model fit by Restricted Maximum Likelihood using
normalized laying date (date on which first egg was laid measured as N
days
since
March 1st) as dependent variable and built land cover (built area within 1 km
2
around nests), the three components from our PCA, and artificial light pollution
at night as independent variables.
βSE tP
Intercept 0.04 0.15 0.30 0.77
Built land cover 0.24 0.09 2.65 0.01
PC1 0.23 0.08 2.85 0.01
PC2 0.56 0.08 6.67 <0.01
PC3 0.07 0.10 0.76 0.45
Light pollution 0.02 0.08 0.24 0.81
The model was built using a correlation structure based on latitude and longitude
and location (Guangzhou, Changsha, Panjin or Harbin) as random factor. Data
collected from 120 nests of Barn Swallows (Hirundo rustica) collected during
2018 in China. All the variables were scaled, subtracting mean variable values
from each score and diving these scores by the standard deviation, to increase
homogeneity of variance. Normalized laying date was obtained by subtracting
mean values per location.
Y. Zhao et al. Avian Research 13 (2022) 100048
6
Huet des Aunay et al., 2017). Conversely, our results show that landscape
urbanization was only significantly related to earlier laying dates, while
urban and rural birds showed similar clutch sizes and breeding success.
In contrast to a previous study employing a similar approach (Sprau et al.,
2017), this pattern suggests that exposure to these environmental factors
–rather than landscape urbanization per se –is linked to reduced in-
vestment in reproduction by females. In light of these results and previ-
ous studies (Møller, 2010;Teglhøj, 2017), Barn Swallows seemed able to
adapt to increasingly urbanized environments.
Fig. 4. Linear relationships between (A) built land cover (built area within 1 km
2
around nests) and laying date (date on which first egg was laid measured as N
days
since March 1st), (B) PC1 and laying date, (C) PC2 and laying date, and (D) PC1 and clutch size (N
eggs
). Data collected from 120 nests of Barn Swallows (Hirundo
rustica) collected during 2018 at four locations in China (Guangzhou, Changsha, Panjin and Harbin), data points from each location are depicted with different colors.
(For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Table 3
Conditional averaged model based on Akaike Information Criterion corrected by
sample size (AICc) after retaining results of conditional averages of the selected
models (ΔAICc <2).
βAdj. SE zP
Intercept 0.04 0.13 0.33 0.74
Laying date 0.32 0.09 3.50 <0.01
Light pollution 0.15 0.09 1.69 0.09
PC1 0.23 0.09 2.55 0.01
PC2 0.11 0.10 1.05 0.29
Built land cover 0.09 0.09 0.91 0.36
Selected
models
logLik AICc ΔAICc Weight
134 (df ¼7) 158.07 331.14 0.00 0.35
14 (df ¼6) 159.41 331.57 0.43 0.28
1345 (df ¼8) 157.49 332.29 1.15 0.20
1234 (df ¼8) 157.67 332.63 1.49 0.17
Built land cover Laying
date
Light
pollution
PC1 PC2
Term codes 1 2 3 4 5
Model averaging approach was conducted on a linear mixed-effects model fitby
maximum likelihood with a correlation structure based on latitude and longitude
and location (Guangzhou, Changsha, Panjin or Harbin) as random factor, and
using clutch size as dependent variable. The retained independent variables were
normalized laying date (date on which first egg was laid measured as N
days
since
March 1st), artificial light pollution at night, PC1 and built land cover (built area
within 1 km
2
around nests). Data collected from 120 nests of Barn Swallows
(Hirundo rustica) collected during 2018 in China. All the variables were scaled by
subtracting mean variable values to each score and diving these scores by the
standard deviation, to increase homogeneity of variance. Normalized laying date
was obtained by subtracting mean values per location.
Table 4
Conditional averaged model based on Akaike Information Criterion corrected by
sample size (AICc) after retaining results of conditional averages of the selected
models (ΔAICc <2).
βAdj. SE zP
Intercept 0.01 0.10 0.05 0.96
Built land cover 0.03 0.07 0.45 0.65
logLik AICc ΔAICc Weight
Null model (df ¼4) 145.45 299.30 0.00 0.65
1 (df ¼5) 144.97 300.55 1.25 0.35
Built land cover
Term codes 1
A model averaging approach was conducted on a linear mixed-effects model fit
by maximum likelihood using breeding success (N
fledglings
/clutch size) as
dependent variable and using a correlation structure based on latitude and
longitude and location (Guangzhou, Changsha, Panjin or Harbin) as a random
factor. The only retained independent variable was built land cover (built area
within 1 km
2
around nests). Data collected from 120 nests of Barn Swallows
(Hirundo rustica) collected during 2018 in China. All the variables were scaled,
subtracting mean variable values to each score and diving these scores by the
standard deviation, to increase homogeneity of variance.
Y. Zhao et al. Avian Research 13 (2022) 100048
7
4.2. The effect of urbanization and environmental factors on laying date
Similar to our results, previous research has recorded earlier laying
dates in urban than in non-urban environments (e.g. Chamberlain et al.,
2009;Caizergues et al., 2018;de Jong et al., 2018;Seress et al., 2018,
2020; but see Huchler et al., 2020). We also recorded a negative rela-
tionship between laying date and nest exposure to nocturnal noise and
diurnal light (PC1). This is, to our knowledge, a novel result as, for
instance, studies focusing on noise pollution have recorded no effect
(Halfwerk et al., 2011) or a positive relationship between noise and
laying date (Injaian et al., 2018). Interestingly, we found a positive
correlation between temperature around the nests (PC2) and laying date,
although temperature differences were unrelated to landscape urbani-
zation. This is quite surprising as there is evidence that global warming is
producing earlier laying dates in multiple bird species at both national
(Crick et al., 1997) and regional scales (Both et al., 2004). Overall, our
findings suggest that, urbanization, exposure to environmental factors
and increased temperatures, can exacerbate or counteract each other and
simultaneously influence the onset of the breeding season in a complex
fashion. If an early laying date is crucial for fitness, as is the case of Barn
Swallows (Grüebler and Naef-Daenzer, 2008;Raja-aho et al., 2017),
urban areas in which temperature increases are particularly intense, e.g.,
highly urbanized cities in temperate regions of the USA (Imhoff et al.,
2010), or areas in which birds are more exposed to environmental fac-
tors, may thus act as an ecological trap. This risk would be particularly
important for species that are relatively indifferent to urbanization, such
as our study species, and are particularly sensitive to phenological
changes, such as migratory birds (Møller et al., 2008;Jones and Cress-
well, 2010).
4.3. The effect of urbanization and environmental factors on clutch size
and breeding success
We found that clutch size correlated negatively with laying date and
exposure to environmental factors (PC1); larger clutch sizes were laid by
early breeders in areas less exposed to the potential detrimental effects of
noise and low nocturnal temperatures, and high insolation. Our results
suggest that pairs breeding later in the season were more exposed to the
detrimental effects of environmental factors and may have therefore
invested less energy in reproduction. Seasonal declines in reproductive
output have been observed in many bird species such as Barn Swallows
(Ambrosini et al., 2006) and Blue Tits (Cyanistes caeruleus;García-Navas
and Sanz, 2011), and are attributed either to a seasonal decline in the
phenotypic quality of parents or to a seasonal deterioration of environ-
mental conditions (Verhulst and Nilsson, 2008), relationships that we are
not able to tease apart in our study. We also recorded a non-significant
positive relationship between artificial light pollution at night and
clutch size. Our results, and others, suggest that light pollution may
facilitate additional food resources to insectivorous birds (Senzaki et al.,
2020;Wang et al., 2021). Finally, we did not record any significant ef-
fects on breeding success in our spatially explicit models accounting for
variation in latitude and longitude, and no differences between study
locations. These data exemplify how adaptable Barn Swallows are to such
different environmental conditions, showing negligible fitness differ-
ences from densely urbanized city centres in the subtropical South to
remote rural areas in the cold North.
4.4. Caveats
We must acknowledge certain limitations of our study. In addition to
other sources of human disturbance, exposure to multiple environmental
factors and changes in food availability explain patterns of species
persistence and fitness gradients (McKinney, 2008;Schlesinger et al.,
2008;Seress et al., 2018,2020). Unfortunately, we did not have data on
diet composition, which clearly merits further attention. Moreover, we
conducted our study over a single breeding season, and certain effects of
exposure to environmental factors or landscape features could change
across years. For instance, the heat island effect may significantly in-
crease across time within and between breeding seasons (Zhang et al.,
2010), which may affect prey phenology (e.g., Visser et al., 2006) and in
return exacerbate its effect over breeding birds. Southern Barn Swallows
are smaller and have longer breeding seasons than northern birds
(Pagani-Nú~
nez et al., 2016;Zhao et al., 2021), but since we were unable
to take morphological measurements, or systematically record all
breeding attempts in so many locations, we are unable to provide a more
complete picture of the effect on fitness of exposure to urbanization and
multiple environmental factors. Aerial insectivores and other typically
urban taxa such as the House Sparrow are declining worldwide (Sum-
mers-Smith, 2003;De Laet and Summers-Smith, 2007;Nebel et al.,
2010). Our study is, therefore, helpful in narrowing potential drivers of
these declines. Further research is needed to determine how the com-
bined effects of the environmental factors studied here interact with food
availability in determining patterns of species’survival and extinction
across urbanization gradients.
Ethics statement
This study was conducted in accordance with the laws and regula-
tions of the People’s Republic of China.
Availability of data
The datasets used in this study are available from the corresponding
author upon request.
Authors’contributions
Conceptualization: E.P.N., X.X. &R.J.S.; Fieldwork: Y.Z., Yu Liu,
E.P.N., G.P., L.S., X.L., Z.Z., Y.C. Data curation: Y.Z. &E.P.N.; Formal
analysis: E.P.N &Y.Z.; Funding acquisition: E.P.N., X.X. &R.J.S.; Writing:
E.P.N. with contributions from all authors. All authors revised, read and
approved the final version of the manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements
We are very grateful to Wangming Li, Xinyuan Pan, Dan Liang,
Zhenming Yang, Xia Zhan, Minghai Huang, Chengyi Liu, Qingxia Li,
Yujun Feng, Shi Li, Wei Chen, Xuan Wu, Yuhao Sha, Xiaomeng Zhao,
Yajie Que, Xiaopeng Tan, Lei Zhao, Ruihan Chen, Licheng Yuan, and Zi
Yun for their help in the field. We are also very grateful to several
anonymous reviewers who provided helpful comments on previous
versions of the manuscript. This study was funded by the National Nat-
ural Science Foundation of China (31770454 to E.P.N., X.X. and R.J.S.).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://do
i.org/10.1016/j.avrs.2022.100048.
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