\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[ngerman,greek,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{Extrinsic and intrinsic factors influencing the emergence and return of
Asian particolored bats Vespertilio sinensis}



\author[1]{Lei Feng}%
\author[1]{Hexuan Qin}%
\author[2]{Jingjing Li}%
\author[2]{Xin Li}%
\author[1]{Jiang Feng}%
\author[1]{Tinglei Jiang}%
\affil[1]{Northeast Normal University}%
\affil[2]{Jilin Agricultural University}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
The circadian rhythm is an adaptive biological process, allows organisms
to anticipate daily environmental changes and implement appropriate
strategies. Circadian rhythms play a crucial role in the health and
survival of organisms. However, little is known concerning how intrinsic
and extrinsic factors affect animal daily rhythms in the field,
especially in nocturnal animals. Here, we investigated the emergence and
return times of Vesperilio sinensis, and also integrated environmental
conditions (temperature, humidity and light intensity) and biotic
factors (reproductive status and predation risk) to determine causes of
variation in the activity rhythms of the bats. We found that variation
in the first emergence time, the mid-emergence time, and the final
return time were distinct. The results demonstrated that the emergence
and return times of bats were affected by light intensity, reproductive
status, and predation risk in a relatively complex pattern. Light
intensity had the greatest contribution to activity rhythms. Moreover,
we first investigated the effects of actual predators on the activity
rhythms of bats; the results showed that the mid-emergence time of bats
was earlier as predators were hunting, but the final return time was
later when predators were present. This challenges the traditional view
that high predation risk leads to later emergence and earlier return.
Finally, our results also highlighted the importance of higher energy
demands during the lactation period in bats to variation in activity
rhythms. These results improve our understanding of the patterns and
causes of variation in activity rhythms in bats and other nocturnal
animals.%
\end{abstract}%



\sloppy


\textbf{Extrinsic and intrinsic factors influencing the emergence and
return of Asian particolored bats\emph{Vespertilio sinensis}}

Lei Feng\textsuperscript{a,b}\textbar{} Hexuan Qin\textsuperscript{a,b}
\textbar{} Jingjing Li\textsuperscript{c} \textbar{} Xin
Li\textsuperscript{c} \textbar{} Jiang Feng\textsuperscript{a, c*}
\textbar{} Tinglei Jiang\textsuperscript{a,b*}

\textsuperscript{a}Jilin Provincial Key Laboratory of Animal Resource
Conservation and Utilization, Northeast Normal University, Changchun,
China;

\textsuperscript{b}Key Laboratory of Vegetation Ecology of Education
Ministry, Institute of Grassland Science, Northeast Normal University,
2555 Jingyue Street, Changchun, 130117, China;

\textsuperscript{c}College of Animal Science and Technology, Jilin
Agricultural University, Changchun, China

\textsuperscript{*}Corresponding author: Jiang Feng and Tinglei Jiang

E-mail: fengj@nenu.edu.cn; jiangtl730@nenu.edu.cn

Jiang Feng: Telephone: 86-0431-85098097; Fax number: 86-0431-85098097

Tinglei Jiang: Telephone: 86-0431-85098097; Fax number: 86-0431-85098097

\textbf{ABSTRACT}

The circadian rhythm is an adaptive biological process, allows organisms
to anticipate daily environmental changes and implement appropriate
strategies. Circadian rhythms play a crucial role in the health and
survival of organisms. However, little is known concerning how intrinsic
and extrinsic factors affect animal daily rhythms in the field,
especially in nocturnal animals. Here, we investigated the emergence and
return times of \emph{Vesperilio sinensis} , and also integrated
environmental conditions (temperature, humidity and light intensity) and
biotic factors (reproductive status and predation risk) to determine
causes of variation in the activity rhythms of the bats. We found that
variation in the first emergence time, the mid-emergence time, and the
final return time were distinct. The results demonstrated that the
emergence and return times of bats were affected by light intensity,
reproductive status, and predation risk in a relatively complex pattern.
Light intensity had the greatest contribution to activity rhythms.
Moreover, we first investigated the effects of actual predators on the
activity rhythms of bats; the results showed that the mid-emergence time
of bat\emph{s} was earlier as predators were hunting, but the final
return time was later when predators were present. This challenges the
traditional view that high predation risk leads to later emergence and
earlier return. Finally, our results also highlighted the importance of
higher energy demands during the lactation period in bats to variation
in activity rhythms. These results improve our understanding of the
patterns and causes of variation in activity rhythms in bats and other
nocturnal animals.

\textbf{Keywords:} circadian rhythm, abiotic factors, biotic factors,
reproduction status, predation, bats

\textbf{INTRODUCTION}

Circadian rhythms are endogenous patterns in the activity of organisms
over periods of about 24 h, and they are universal, from prokaryotes to
eukaryotes (Bell-Pedersen et al., 2005; McClung, 2000). It is commonly
considered that circadian rhythms are adaptive biological processes that
are crucial to the health and survival of organisms, as they allow
organisms to anticipate the daily environmental changes and implement
appropriate strategies (Caravaggi et al., 2018; Yerushalmi \& Green,
2009). It is well known that global climate change has disrupted the
activity rhythms of many animals (Levy, Dayan, Porter, \&
Kronfeld-Schor, 2018). Environmental conditions and biotic factors have
been considered drivers of variation in the circadian activity rhythms
of many vertebrates (Pita, Mira, \& Beja, 2011). However, little is
known concerning how intrinsic and extrinsic factors influence animal
daily rhythms in the field (Quaglietta, Mira, \& Boitani, 2018).

Activity rhythms are influenced by many abiotic and biotic factors,
including light, moon phase, temperature, humidity, reproductive status,
and predation risk (Tester \& Figala, 1990). Light is an important
modulator of organisms' circadian rhythms (LeGates, Fernandez, \&
Hattar, 2014). Bright light conditions may present a dangerous situation
for nocturnal animals, and the opposite would hold for day-active
animals. For example, some nocturnal animals may decrease their activity
during a full moon; this is known as ``lunar phobia'' (Mougeot \&
Bretagnolle, 2000; R. A. Salda\selectlanguage{ngerman}ña-Vázquez \& Munguía-Rosas, 2013).
Extreme weather conditions could affect animals' activity patterns to
avoid overheating or hyperthermia, causing animals to reduce their
activity during high- or low-temperature conditions (Foà \& Bertolucci,
2001; Frick et al., 2012; Speakman, Hays, \& Webb, 1994). Additionally,
drought could affect animal and plant populations and constrain the
activity of prey, thereby increasing the competition for food (Frick et
al., 2012).

In addition to abiotic factors, biotic factors such as predation risk
and reproductive status also may influence animals' activity rhythms
(Svensson et al, 2018). Predation risk is a strong selection pressure
affecting activity rhythms, and predator avoidance is a major topic in
animal evolution (John, 2013; Lima \& O'Keefe, 2013). Animals face a
tradeoff between predation risk and energy requirements when they decide
to initiate activity during the period of day-to-night transition,
especially for female animals during lactation, an energetically
demanding period (Arndt., O'Keefe., Mitchell., Holmes., \& Lima., 2018;
Shiel \& Fairley, 1999; Shuert, Halsey, Pomeroy, \& Twiss, 2020).
Females must balance increases in foraging time while maximizing food
availability and minimizing the possibility of being captured by
predators (Caro, 2005). In general, an earlier start to foraging
activity may be beneficial (Pavey, Burwell, Grunwald, Marshall, \&
Neuweiler, 2001), but may also expose animals to a higher predation risk
(Fenton et al., 1994; Jones \& Rydell, 1994). Thus, a successful
strategy requires the optimal adjustment between predation pressure and
foraging activity in order to maximize energy intake (Caro, 2005).
However, it remains unclear how animals adjust activity rhythms based on
predation risk and energy requirement, especially during the lactation
period.

Bats are an excellent system for investigating questions concerning the
effects of abiotic and biotic factors on activity rhythms for the
following reasons. First, as one of the most abundant mammal groups in
terms of species and ecological diversity, bats are active almost
exclusively during the night (Rydell \& Speakman, 1995; Speakman, 1991).
In this case, bats are sensitive to environmental changes, especially to
light (Voigt \& Kingston, 2016). Second, bats normally start nighttime
activity for foraging after sunset, but not in total darkness (Lee \&
McCracken, 2001; Pavey et al., 2001). Like other animals, bats would
benefit from an earlier emergence that would allow the bats to follow
the activity peaks of some insects (i.e., Nematocera and other small
flies) during dusk (Rydell, Entwistle, \& Racey, 1996). Moreover,
although earlier onset of activity would increase the amount of foraging
time at dusk and dawn (Pavey et al., 2001), it would increase the chance
of bats encountering diurnal predators that remain active around sunset,
or even during dusk (Fenton et al., 1994; Lima \& O'Keefe, 2013).
However, this view was proposed based on an estimate of predation risk
without actual predators. Thus, it is necessary to clarify the effects
of natural predation and energy requirements for reproduction on the
activity rhythms of bats. Finally, previous studies have focused on the
effects of environmental factors such as temperature (Arndt. et al.,
2018; Irwin \& Speakman, 2003), cloud cover (Arndt. et al., 2018; Lee \&
McCracken, 2001; McAney \& Fairley, 1988), heavy rain (McAney \&
Fairley, 1988) and light (Russo, LucaCistrone, \& GarethJones, 2007) on
the emergence of bats, but little is known concerning the impact factors
affecting the return times of bats, or how interactions of abiotic and
biotic factors influence the activity rhythms of bats.

In this study we monitored the emergence and return times of Asian
particolored bats (\emph{Vesperilio sinensis} ), and integrated
environmental conditions (temperature, humidity and light intensity) and
biotic factors (reproductive status and predation risk) to determine the
sources of variation in activity rhythms of bats. We tested three
hypotheses. First, we hypothesized that light intensity may play a
pivotal role in influencing the emergence and return times of \emph{V.
sinensis.} Second, since bats during the lactation period have greater
energy requirements, we hypothesized that \emph{V. sinensis} would
depart from the roost earlier during the lactation period than during
the post\emph{-} lactation period. Third, since bats should avoid
predators, we hypothesized that \emph{V. sinensis} would depart from the
roost at dusk later, but return to the roost earlier at dawn in the
presence of diurnal predators (e.g., falcons).

\textbf{MATERIALS AND METHODS}

\textbf{Study site and species}

The study was conducted at a highway bridge (126°57\selectlanguage{english}'26\selectlanguage{english}'\selectlanguage{english}' E, 45deg32\selectlanguage{english}'52\selectlanguage{english}'\selectlanguage{english}'
N) in Acheng district, Heilongjiang Province, northeast China. A large
number of \emph{V. sinensis} roost under the bridge\emph{.} In summer,
female \emph{V. sinensis} roost and breed offspring here, forming a
nursery (Wang., BY Shi, X Zhao, Feng., \& Jiang., 2020). The population
of bats arrives in the summer roost in the middle of June and leaves the
summer roost by the end of September. We found that\emph{Falco
amurensis} started to prey on \emph{V. sinensis} during mid-August in
2018; this provided an opportunity to determine the effects of predation
on activity rhythms of\emph{V. sinensis} .

\textbf{Monitoring of activity rhythms}

Roost emergence time and final return time were recorded from the middle
of July to early September, from 2019 to 2021. The monitoring was
stopped when it was raining heavily. In this study, we arrived at the
roost of bats one hour before sunset. The first emergence time was
defined as the first bat to emerge and leave the roost. We also arrived
at the roost of the bats an hour and a half before sunrise. The final
return time was observed and recorded when the last bat returned to the
day roost. Additionally, previous studies showed that median emergence
time was a better parameter to describe the emergence time of the whole
roost than first emergence (Bullock., Combes., Eales., \& Pritchard.,
1987). Therefore, we also included the median emergence time as a
descriptive parameter for emergence time of\emph{V. sinensis} . We
defined the median of the first emergence time and the end time of
emergence as the mid-emergence time. In order to calculate the median
time between the starting and ending of the emergence events, we
recorded the data until the end of emergence. When there were no bats
flying out for five minutes, we considered the emergence activities of
the bats to have finished.

\textbf{Collection ofenvironmental data}

We recorded the sunrise and sunset times through the network
https://richurimo.51240.com/ based on the longitude and latitude of the
roost of \emph{V. sinensis} . Here, the time was accurate to the nearest
second. One researcher recorded the first emergence time and the final
return time. Another researcher simultaneously measured the light
intensity, air temperature, and humidity using a Five in One
Multifunctional Environment Meter (HT-8500, Guangzhou, China). The
environmental data were measured at the same location on the highway
bridge without objects blocking natural light or external artificial
light sources. In this study, the final return times were recorded in
2019 and 2021. Additionally, we recorded the light intensity of natural
light every 30 seconds in 2019 and 2021. Then, we determined the light
intensity of the median of emergence events by selecting the point in
time closest to the median of emergence.

\textbf{The division of reproductive status}

A previous study showed that the lactation period of \emph{V.
sinensis}occurs in July at this roost (Yin, 2020). In this study, in
order to investigate the effects of reproductive state on activity
rhythms of \emph{V. sinensis} we divided the monitoring period into two
periods: a lactation period (during July) and a post-lactation period
(after July) based on our previous observations.

\textbf{Predation risk assessment}

In order to explore the effects of diurnal predators on the activity
rhythms of \emph{V. sinensis} , we defined predation risk as presence or
absence of predators. If the predator \emph{F. amurensis} appeared near
the roost and hunted bats, this was considered as the presence of
predators (high predation risk) when we were monitoring the first
emergence and the final return. If there were no \emph{F. amurensis,} or
\emph{F. amurensis} only flew across the roost without predation
attempts, this was considered as absence of predators (low predation
risk). Since \emph{F. amurensis}arrived at the roost to hunt bats every
day in summer, \emph{F. amurensis} was present during mid-emergence. In
this case, we considered predation risk as ``predators were hunting'' or
``predators were not hunting'' when determining mid-emergence time.
During the monitoring period, we did not observe any other potential
diurnal avian predator near the roost. Therefore, we believed that only
\emph{F. amurensis}exerted diurnal predation pressure on \emph{V.
sinensis} as they were flying out and returning.

\textbf{Data analysis}

In this study, we used differences between sunset time and first
emergence time of bats, and between sunset time and mid-emergence time,
as two variables to quantify the time of emergence of bats. Moreover, we
also used differences between sunrise and final return time as a
variable to quantify the return time of bats. The three variables were
tested for normality by Shapiro-Wilk tests. We found that only
differences between sunrise and final return time did not follow a
normal distribution (\emph{P} \textless{} 0.001). In this case, the
logarithmic transformation was used to make the data meet a normal
distribution (\emph{P} = 0.407).

Here, in order to state and use statistics more easily, we use the
following abbreviations for independent variables measured at dusk,
which were used to construct models to test the effects of these
variables on emergence times of bats: HFE, Humidity at first emergence
of bats; HME, Humidity at median emergence of bats; TFE, Temperature at
first emergence of bats; TME, Temperature at median emergence of bats;
SST, Sunset time; LISS, Light intensity of sunset; LIFE, light intensity
of first emergence; LS, Lactation stages (lactation or post-lactation);
PAPDD, Presence or absence of predators during dusk; LME, light
intensity of mid-emergence; PHNHM, Predators were hunting or not hunting
in mid-emergence. Additionally, variables measured at dawn were
considered independent variables for the analysis of the effects of
factors on final return events: HFR, Humidity at final return of bats;
TFR, Temperature at final return of bats; SRT, Sunrise time; LISR, Light
intensity at sunrise; LS, Lactation stages (lactation or
post-lactation); PAPDA, Presence or absence of predators during dawn;
LIFR, Light intensity at final return.

To test the effects of environmental factors, reproduction, and
predation on the emergence time of \emph{V. sinensis,} we selected
optimized linear models using the dredge function in the package
``MuMin'' in R v. 3. 5.1 (R Core Development Team, 2018). In first
model, we used the difference between sunset time and first emergence
time of bats as a dependent variable; HFE, TFE, SST, LISS, LS, PAPDD and
LIFE as independent variables; and year as a random variable. In the
second model, we used the difference between sunset time and
mid-emergence time as a dependent variable; HME, TME, ST, LISS, LIFE,
LIME, LS and PHNHM as independent variables; and year as a random
variable. Additionally, to test the effects of environmental factors,
reproduction, and predation on the final return time of \emph{V.
sinensis,} we also selected optimized linear models using the dredge
function in the package ``MuMin'' in R v. 3.5.1. In the third model, we
used the difference between sunrise time and final return time as a
dependent variable; HFR, TFR, SRT, LISR, LS, PAPDA and LIFR as
independent variables; and year as a random variable. To test the
multicollinearity of variables, the Variance Inflation Factor (VIF) of
each predicted factor was calculated to determine which predictors could
not be used for subsequent analysis. If VIF was \textless{} 5, the
corresponding predictor variables were included in the models (Lin. \&
Feng, 2008). We compared the models using the Akaike information
criterion corrected for small sample size (AICc). The model with the
lowest AICc indicates the best-fitting model. We caculated \selectlanguage{greek}Δ\selectlanguage{english}AICc as the
difference of AICc of each model between the AICc of the best-fitting
model. A difference of AICc \textgreater{} 2 (\selectlanguage{greek}Δ\selectlanguage{english}AICc \textgreater{} 2)
indicated that the model with the lower AIC value had better explanatory
power (Anderson \& Burnham, 2002). If the \selectlanguage{greek}Δ\selectlanguage{english}AICc was [?] 2, multimodel
inference was performed using the function model.avg in the package
`MuMIn' (Barto\selectlanguage{polish}ń., \selectlanguage{english}2014) in R v. 3.5.1. We also calculated Akaike weights
(\emph{Wi} ) to assess the relative likehood of a model compared with
other models. Additionally, we conducted a hierarchical partitioning
analysis in the `hier.part package' (Walsh. \& Nally., 2020) in R v.
3.5.1 to estimate the independent effect of each predictor variable
(Chevan. \& Sutherland., 1991).

Multiple linear regression analyses were used to assess the
relationships between sunset time and first emergence time and LIFE,
between sunset time and first emergence time and LISS, between between
sunset time and mid-emergence time and LIFE, between between sunset time
and mid-emergence time and LISS, between between sunrise and final
return time and LIFR, and between sunrise and final return time and
LISR. During the analysis, if the relationships between dependent
variables and independent variables were better simulated by a curve, a
curvilinear regression analysis was performed. In this study, we used
curvilinear regression analyses to assess the relationships between
sunset time and first emergence time and the variables LIFE, between
sunset time and mid-emergence time and LIFE, and between sunrise and
final return time and LIFR. All statistical analyses were conducted in R
v. 3.5.1.

\textbf{RESULTS}

\textbf{The impact factors of first emergence time of \emph{V.
sinensis}}

A total of 140 first emergence events of \emph{V. sinensis} were
recorded. There were no significant differences in first emergence time
among the three consecutive years (Kruskal-Wallis test: \emph{H} =
2.804, \emph{P} = 0.246). In 49 records, \emph{V. sinensis} started to
emerge before sunset at dusk. Differences between sunset and first
emergence ranged from -36 to 33 min (4.49 \selectlanguage{ngerman}± 13.23). Here, the negative
values meant that first emergence events started before sunset.

Our analyses indicated that the best model describing the variation in
first emergence time of \emph{V. sinensis} used LIFE, LISS, and LS as
the predictor variables (Table 1). Further, model averaging revealed
that the three predictive variables were also significantly associated
with the first emergence time of\emph{V. sinensis} (Table 2).
Differences between first emergence time and sunset time were
significantly and negatively associated with light intensity of first
emergence (linear regression: \emph{t} = \selectlanguage{english}-11.447,\emph{P} \textless{}
0.001, Fig. 1b; curvilinear regression: \emph{t} = 13.728, \emph{P}
\textless{} 0.001, Fig. 5a). In other words, the higher the light
intensity at first emergence, the earlier the first emergence of
\emph{V. sinensis.} Moreover, differences between first emergence time
and sunset time were significantly and positively associated with light
intensity at sunset (\emph{t} = 7.801, \emph{P} \textless{} 0.001; Fig.
1a). Finally, significant differences in the first emergence time were
detected between lactation and post-lactation (\emph{t} =
\selectlanguage{english}-4.779,\emph{P} \textless{} 0.001; Fig. 1c). During the lactation
period, \emph{V. sinensis} departed earlier from the roost than during
the post-lactation period (Fig. 1c). Specifically, 65.67\% of first
emergence events (44/67) started before sunset during the lactation
period. In contrast, only 6.85\% of first emergence events (5/73) were
observed before sunset during the post-lactation period. A Chi-square
test of independence showed that the first emergence events had a higher
probability of occuring before sunset during the lactation period
(\selectlanguage{greek}\emph{χ} \selectlanguage{english}\textsuperscript{2} = 26.34,\emph{P} =
2.86\selectlanguage{ngerman}×10\textsuperscript{-7}). Consistent with the results of the
hierarchical partitioning analysis, the independent contributions of
LIFE, LISS, and LS were 53.86\%, 25.54\%, and 14.17\%, respectively
(Fig. 4a).

\textbf{The impact factors of the mid-emergence time of \emph{V.
sinensis}}

We recorded 91 mid-emergence events of \emph{V. sinensis} , only two
events occurred before sunset. The difference between mid-emergence time
and sunset ranged from \selectlanguage{english}-4 to 62 min. Here, the positive values meant
mid-emergence events happened after sunset. The light intensity of
mid-emergence events was approximately 0.6--400 lux (40.46+-68.72). In
addition, 90.11\% (82/91) mid-emergence events occurred when the light
intensity was below 100 lux, and 45.05\% (41/91) of mid-emergence events
occurred when the light intensity was below 10 lux.

Our analyses indicated that the best model of variation in mid-emergence
time of \emph{V. sinensis} used LIFE, LISS, LS, and PHNHM as the
predictor variables (Table 3). Further, model averaging revealed that
the four predictor variables were also significantly associated with the
mid-emergence time of \emph{V. sinensis} (Table 4). First, differences
between the mid-emergence time and sunset time were significantly and
negatively associated with light intensity of first emergence (linear
regression: \emph{t} = \selectlanguage{english}-6.73, \emph{P} \textless{} 0.001, Fig. 2b;
curvilinear regression: \emph{t} = \selectlanguage{english}-9.974, \emph{P} \textless{} 0.001,
Fig. 5b). That is, the higher the light intensity of first emergence,
the earlier the mid-emergence time of \emph{V. sinensis.} Second, a
significant and positive correlation between the mid-emergence time and
light intensity at sunset was observed (\emph{t} = 5.882,
\emph{P}\textless{} 0.001; Fig. 2a). Third, a significant difference in
the mid-emergence time was detected between lactation and post-lactation
(\emph{t} = \selectlanguage{english}-2.903, \emph{P} = 0.004; Fig. 2d). During the lactation
period, \emph{V. sinensis} departed earlier from the roost than during
the post-lactation period (Fig. 2d). Specifically, the mid-emergence
events started approximately \selectlanguage{english}-4--40 (15.435+-10.051) min after sunset
during the lactation period. In contrast, the mid-emergence events
started about 12--62 (29.511+-9.201) minutes after sunset during the
post-lactation period. Finally, bats also departed from the roost
earlier when predators were hunting (\emph{t} = \selectlanguage{english}-3.713,
\emph{P}\textless{} 0.001; Fig. 2c). We found that predators were
hunting bats during 36 mid-emergence events. Consistent with the results
of the hierarchical partitioning analysis, the independent contributions
of LIFE, LISS, LS, and PHNHM were 46.41\%, 13.60\%, 22.74\% and 17.25\%,
respectively (Fig. 4b).

\textbf{The impact factors for the final return time of \emph{V.
sinensis}}

We recorded 78 final return events of \emph{V. sinensis} , and all final
return events occurred and ended approximately 7--79 minutes before
sunrise. There were no significant differences in the final return time
between 2019 and 2020 (Paired sample t test, \emph{t} = 1.730, \emph{P}
= 0.093).

Our analyses indicated that the best model of variation in the final
return time of \emph{V. sinensis} used LIFR and PAPDA as the predictor
variables (Table 5). However, model averaging revealed that three
predictive variables (LIFR, LISR, and PAPDA) were also significantly
associated with the final return time of \emph{V. sinensis} (Table 6).
First, differences between the final return time and sunrise time were
significantly and positively associated with light intensity at sunrise
(\emph{t} = 4.054,\emph{P} \textless{} 0.001; Fig. 3a). That is, the
higher the light intensity at sunrise, the earlier the final return time
of \emph{V. sinensis.} Second, differences between sunrise and final
return time were significantly and negatively associated with light
intensity at final return (linear regression:\emph{t} = \selectlanguage{english}-8.194, \emph{P}
\textless{} 0.001, Fig. 3b; curvilinear regression: \emph{t} = \selectlanguage{english}-10.226,
\emph{P} \textless{} 0.001, Fig. 5c). Finally, bats also returned to the
roost later when predators were present (\emph{t} = \selectlanguage{english}-5.746, \emph{P}
\textless{} 0.001; Fig. 3c). We found that predators were often (60\%
records at dawn) present before the ending of the final return events at
dawn. However, the number of successful predation events occurring at
dawn was significantly less than at dusk (Mann-Whitney \emph{U} test, Z
= -1.993, \emph{P} = 0.046). Consistent with the results of the
hierarchical partitioning analysis, the independent contributions of
LIFR, LISR, and PAPDA were 56.34\%, 6.18\%, and 37.49\%, respectively
(Fig. 4c).

\textbf{DISCUSSION}

In our study, we found that light intensity at emergence, return,
sunset, andd sunrise influenced the emergence and return times
of\emph{V. sinensis} in different ways, supporting our first hypothesis.
Moreover, \emph{V. sinensis} departed from the roost earlier regardless
of the first emergence and mid-emergence times during the lactation
period, suggesting support for our second hypothesis. Finally, the
mid-emergence time of \emph{V. sinensis} was earlier when predators were
hunting, but the final return time was later when predators were
present. This result was inconsistent with our third hypothesis.

Factors affecting an animal's energy status and the value of additional
energy intake are major determinants of risk-taking behaviour (Brown,
1988; Caro, 2005; Lima, 1998). In general, the first emergence of bats
occurs after sunset (Acharya, Racey, McNeil, Sotthibandhu, \&
Bumrungsri, 2015; Arndt. et al., 2018; Lee \& McCracken, 2001;
Welbergen, 2006) and return events occur before sunrise (Acharya et al.,
2015; Lee \& McCracken, 2001). During lactation, reproductive female
bats would emerge before sunset because of the higher energy demand
(Arndt. et al., 2018; Lee \& McCracken, 2001). This behaviour has been
noted in hoary bats, \emph{Lasiurus cinereus}(Barclay, 1989), Mexican
free-tailed bats,\emph{Tadarida brasiliensis}(Lee \& McCracken, 2001),
the grey-headed flying fox, \emph{Pteropus poliocephalus}(Welbergen,
2006), Daubenton's bats,\emph{Myotis daubentonii} (Lu\selectlanguage{polish}č\selectlanguage{english}an, 2009), dawn
nectar bats, \emph{Eonycteris spelaea}(Acharya et al., 2015), and
Indiana bats, \emph{Myotis sodalist}(Arndt. et al., 2018). Additionally,
during lactation, reproductive female bats also return later to the day
roost than during post-lactation, such as in \emph{T. brasiliensis}(Lee
\& McCracken, 2001) and Dawn nectar bats, \emph{E} . \emph{spelaea}
(Acharya et al., 2015). Consistent with these previous studies, our
results showed that the lactation period significantly influenced the
first emergence and the mid-emergence of \emph{V. sinensis} during the
lactation period. Specifically, 64.29\% of emergence events started
before sunset during the lactation period, while only 5.88\% of
emergence events started before sunset during the post-lactation period.
The earlier onset of activity and the delay of ending activity increases
the foraging time at dusk and dawn (Rydell et al., 1996). To summarize,
the higher energy demands may prompt bats to depart from the roost
earlier and return to the roost later during the lactation period.

There are many benefits to an early start to night activity, but for
bats, doing so in bright light may be a risky option. Large flocks of
bats emerging from the day roost and returning to the day roost would
attract the attention of diurnal avian predators (Fenton et al., 1994).
Predation on bats by birds is seemingly rare and opportunistic at dusk
and dawn, especially in temperate zones, but predation would affect many
aspects of bats' behaviour, such as roost selection and foraging
activity (Fenton et al., 1994; Lima \& O'Keefe, 2013). Bats emerging
around dusk before sunset under high light intensity would be exposed to
higher predation risk than the bats emerging in true night under low
light intensity. As for earlier emerging bats, later returning bats also
would be exposed to high predation risk from diurnal predators (Fenton
et al., 1994). However, previous studies have mainly assessed predation
risk without observing real predators. In this case, the effects of
predation on activity rhythms of bats may not be universal. In this
study, we found that the mid-emergence time of \emph{V. sinensis} was
earlier when predators were hunting, but the final return time was later
when predators were present. The results may challenge the previous view
that high predation risk leads to later emergence and the earlier
return. This can be explained by the following reasons: First, the
previous studies investigated the effects of predation risk on behaviour
of bats based on empirical estimation or predator models, but here
\emph{F. amurensis} hunt \emph{V. sinensis} every day at this roost.
Thus, the different results may not be inconsistent. Second,\emph{V.
sinensis} usually gives birth to twins in each litter (Jin. et al.,
2012), and the energy demands are greater during the lactation period
than in other periods or in bat species with one offspring per litter.
Thus, even though \emph{V. sinensis} may suffer higher risk of
predation, they may need to depart from the roost earlier and return to
the roost later during the lactation period due to the higher energy
demands. The hierarchical partitioning analysis also confirmed that
lactation periods had a greater contribution to the mid-emergence time
and the final return time in \emph{V. sinensis} than predation
risk\emph{.} Interestingly, predation risk only influenced the
mid-emergence time rather than the first emergence time in \emph{V.
sinensis.} This may be because \emph{V. sinensis} had to depart in
quantity around the mid-emergence time due to\emph{F. amurensis}
successfully preying on bats in dim light (light intensity below 10
lux); this also may reduce the risk of predation on individuals via the
dilution effect of a large flock of bats.

Bright light conditions may present a risky option for nocturnal
animals, especially for bats. In this study, light intensity was the
most important factor affecting the activity rhythm of \emph{V.
sinensis} t (Fig. 4). Specifically, differences between first emergence
time and sunset time, and between mid-emergence time and sunset time,
were significantly and positively associated with light intensity at
sunset (Fig. 1a; Fig. 2a). A similar trend was detected in the
relationship between final return time and sunrise (Fig. 3a). These
results showed that \emph{V. sinensis} may adjust the emergence and
return times based on sunset and sunrise times, respectively. This may
be helpful for avoiding \emph{F. amurensis} for the following
reasons\emph{.} With the decrease of light intensity, the visual
sensitivity of falcons decreases significantly (Fox., Lehmkuhle., \&
Westendorf., 1976). Therefore, it was clearly a safer behavioural
strategy for bats to emerge from the roost under dim light conditions.
Additionally, bright light exerts inhibitory effects on the activity of
bats (A.Gutierrez, F.Pessoa, Aguiar, \& M.A.Pesso, 2014). However, here
the opposite trends of the effect of light intensity of first emergence
on the emergence time (Fig. 1b; Fig. 2b) and of light intensity at final
return on the final return time (Fig. 3b) were observed. With increases
in light intensity of first emergence, both the first emergence and
mid-emergence events became increasingly earlier relative to sunset,
then changed slowly (Fig. 5a, b). Moreover, with increases in light
intensity at final return, the final return events became increasingly
later relative to sunrise, then changed slowly (Fig. 5c). These findings
may be due to the high energy demands in \emph{V. sinensis} during the
lactation period prompting the bats to depart from the roost earlier and
return to the roost later despite the high light intensity. We observed
that 65.67\% of first emergence events (44/67) started before sunset
during the lactation period, supporting this view. Additionally, there
is a threshold of light intensity in bat activity rhythms (Erkert,
2004), which may reflect the fact that leaving the day roost early at
dusk or returning to the roost late at dawn was not the best option for
the bats.

Weather conditions may also affect the emergence behaviour of bats, but
the effects in the previous studies were not severe (Erickson \& West,
2002; Frick et al., 2012; Welbergen, 2008). For example, the
relationship between temperature and the start of emergence of bats
depended on summer climatic conditions; therefore, the influence of
daily temperature in drought conditions may be different than in normal
or unusually moist years (Frick et al., 2012). Below a critical minimum
temperature (8-10\selectlanguage{ngerman}°C), the foraging behaviour of bats would be less
beneficial than remaining torpid (Avery, 1985). Drought conditions were
associated with low insect abundance (Hawkins et al, 1998), and thus
were associated with earlier emergence and displaying risker behaviour
(Frick et al., 2012). Consistent with these previous studies, our
results indicated that temperature and humidity did not influence
emergence or return times of \emph{V. sinensis.} This may be because
environmental temperatures at the roost of \emph{V. sinensis} were
higher than the critical minimum temperature during the monitoring
period, and there was sufficient rainfall in summer. The relatively
stable environment led to the relatively high insect abundance. In this
case, the effects of temperature and humidity on activity rhythms of
bats may be relatively weak.

\textbf{CONCLUSIONS}

In summary, our study demonstrated that the emergence and return times
of \emph{V. sinensis} were affected by light intensity, reproductive
status, and predation risk in a relatively complex pattern, indicating
that the decisions concerning emergence and return of \emph{V.
sinensis}had a high degree of plasticity. Therefore, our study
investigated the effects of real predators on activity rhythms of bats,
and the results challenged the traditional view that high predation risk
leads to the later emergence and earlier return. Our results also
highlighted that higher energy demands during lactation period in bats
may be more important than predation risk in the variation in activity
rhythms. A limitation of this study is that it is difficult to determine
how\emph{V. sinensis} make decisions of emergence and return based on
the intrinsic and extrinsic factors according to our present data.
Further studies will need to determine the causal links between activity
rhythms of bats and their impact factors.

\textbf{ACKNOWLEDGEMENTS}

We are grateful to Liu Yingying, Zhao Xin and Wang Yuze for their help
during field work. We thank LetPub (www.letpub.com) for its linguistic
assistance during the preparation of this manuscript.

This research was supported by the National Natural Science Foundation
of China (Grant Nos. 31922050, 31872680, 32071492).

\textbf{CONFLICT OF INTEREST}

The authors declare that they have no conflict of interest.

\textbf{AUTHOR CONTRIBUTIONS}

\textbf{Lei Feng:} Conceptualization (equal); Data curation (equal);
Formal analysis (lead); Investigation (equal); Methodology (lead);
Visualization (lead); Writing-original draft (lead). \textbf{Hexuan
Qin:} Investigation (equal), \textbf{Jingjing Li:} Investigation
(equal), \textbf{Xin Li:} Investigation (equal), \textbf{Jiang
Feng:}Funding acquisition (equal); Resources (lead). \textbf{Tinglei
Jiang:}Conceptualization (equal); Data curation (equal); Funding
acquisition (equal); Project administration (lead); Supervision (lead);
Writing-review and editing (lead)..

\textbf{DATA AVAILABILITY STATEMENT}

If our paper is accepted for publication, data used in this study will
be archived on the Dryad Digital Repository.

Table 1 Candidate linear mixed models explaining the variation in first
emergence time of\emph{Vespertilio sinensis} based on environmental
parameters (temperature, relative humidity and light intensity) and
biotic factors (reproductive stages and predation risk) at dusk.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Model & Predictive variables & \emph{k} & \emph{df} & Loglik & ^AICc &
\emph{wi}\tabularnewline
\midrule
\endhead
M1 & LIFE(-), PAPDD(+), LS(-), LISS(+) & 4 & 7 & -459.748 & 0.000 &
0.581\tabularnewline
M2 & LIFE(-), LS(-), LISS(+) & 3 & 6 & -462.302 & 2.890 &
0.137\tabularnewline
M3 & LIFE(-), TFE(+), PAPDD(+), LS(-), LISS(+) & 5 & 8 & -460.326 &
3.406 & 0.106\tabularnewline
M4 & HFE(+), LIFE(-), PAPDD(+), LS(-), LISS(+) & 5 & 8 & -460.347 &
3.448 & 0.104\tabularnewline
M5 & HFE(+), LIFE(-), LS(-), LISS(+) & 4 & 7 & -462.817 & 6.138 &
0.027\tabularnewline
M6 & LIFE(-), LS(-), TFE(+), LISS(+) & 4 & 7 & -462.962 & 6.427 &
0.023\tabularnewline
M7 & HFE(+), LIFE(-), TFE(+), PAPDD(+), LS(-),LISS(+) & 6 & 9 & -460.981
& 7.003 & 0.018\tabularnewline
M8 & HFE(+), LIFE(-), TFE(+), LS(-),LISS(+) & 5 & 8 & -463.413 & 9.580 &
0.005\tabularnewline
M9 & LIFE(-), PAPDD(+), LISS(+) & 3 & 6 & -471.697 & 21.681 &
0.000\tabularnewline
M10 & LIFE(-), TFE(+), PAPDD(+), LISS(+) & 4 & 7 & -471.309 & 23.122 &
0.000\tabularnewline
\bottomrule
\end{longtable}

Models are ranked by Akaike' s Information Criterion corrected for small
sample sizes (AICc) values, from the best to the worst model. LIFE:
light intensity of first emergence of bats, LISS: light intensity of
sunset, HFE: humidity as first emergence of bats, TFE: temperature as
first emergence of bats, LS: lactation stages, PAPDD: presence or
absence of predators during dusk.

Table 2 Model-averaged parameter estimates of the best-supported (before
and including the null model) linear mixed models describing the
variation in first emergence time of \emph{Vespertilio sinensis.} The
independent effects (IF) of factors on the first emergence time
of\emph{Vespertilio sinensis} using hierarchical partitioning analysis
are displayed in the last column.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Variable & Estimate & SE & Z value & t value & \emph{p} & 95\% CI &
IF\tabularnewline
\midrule
\endhead
(Intercept) & 5.436 & 1.943 & 2.775 & 3.060 & 0.005 & 1.597, 9.274 &
/\tabularnewline
LIFE & -0.020 & 0.002 & 11.312 & -11.447 & \textless{}0.001 &
\textbf{-0.024, -0.017} & 53.860\tabularnewline
PAPDD & -2.267 & 1.408 & 1.596 & -1.602 & 0.110 & -5.052, 0.517 &
3.436\tabularnewline
LS & -7.466 & 1.550 & 4.773 & -4.779 & \textless{}0.001 &
\textbf{-10.532, -4.401} & 14.168\tabularnewline
LISS & 0.030 & 0.004 & 7.741 & 7.801 & \textless{}0.001 & \textbf{0.022,
0.038} & 25.536\tabularnewline
\bottomrule
\end{longtable}

Table 3 Candidate linear mixed models explaining the variation in
mid-emergence time of \emph{Vespertilio sinensis} based on environmental
factors (temperature, relative humidity, and light intensity), and
biotic factors (reproductive stages and predation risk) at dusk.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Model & Predictive variables & \emph{k} & \emph{df} & Loglik & ^AICc &
\emph{wi}\tabularnewline
\midrule
\endhead
M1 & LIFE(-), PHNHM (+), LS(-), LISS(+) & 4 & 7 & -300.237 & 0.000 &
0.674\tabularnewline
M2 & LIFE(-), PHNHM (+), TME(-), LS(-), LISS(+) & 5 & 8 & -300.818 &
3.569 & 0.113\tabularnewline
M3 & LIFE(-), PHNHM (+), LISS(+) & 5 & 6 & -304.161 & 5.499 &
0.043\tabularnewline
M4 & LIFE(-), PHNHM (+), LS(-), LISS(+), ST(-) & 4 & 8 & -301.814 &
5.561 & 0.042\tabularnewline
M5 & LIFE(-), LIME(-), PHNHM (+), LS(-), LISS(+) & 5 & 8 & -302.01 &
5.952 & 0.034\tabularnewline
M6 & LIFE(-), PHNHM (+), TME(-), LISS(+) & 6 & 7 & -303.849 & 7.225 &
0.018\tabularnewline
M7 & LIME(-), PHNHM (+), LS(-), LISS(+) & 3 & 7 & -303.862 & 7.251 &
0.018\tabularnewline
M8 & LIFE(-), PHNHM (+), LISS(+), ST(-) & 4 & 7 & -303.993 & 7.511 &
0.016\tabularnewline
M9 & LIFE(-), HME(+), PHNHM (+), LS(-), LISS(+) & 5 & 8 & -303.458 &
8.849 & 0.008\tabularnewline
M10 & LIFE(-), PHNHM (+), TME(-), LS(-), LISS(+), ST(-) & 5 & 9 &
-302.461 & 9.320 & 0.006\tabularnewline
M11 & LIFE(-), LIME(-), PHNHM (+), TME(-), LS(-), LISS(+) & 4 & 9 &
-302.616 & 9.631 & 0.005\tabularnewline
M12 & LIME(-), PHNHM (+), TME(-), LS(-), LISS(+) & 6 & 8 & -304.472 &
10.876 & 0.003\tabularnewline
M13 & LIFE(-), PHNHM (+), TME(-), LISS(+), ST(-) & 6 & 8 & -304.528 &
10.988 & 0.003\tabularnewline
M14 & LIFE(-), LIME(-), PHNHM (+), LS(-), LISS(+), ST(-) & 5 & 9 &
-303.599 & 11.596 & 0.002\tabularnewline
M15 & LIME(-), PHNHM (+), LS(-), LISS(+), ST(-) & 5 & 8 & -305.05 &
12.033 & 0.002\tabularnewline
M16 & LIFE(-), LIME(-), PHNHM (+), LISS(+) & 4 & 7 & -306.356 & 12.239 &
0.001\tabularnewline
M17 & LIFE(-), HME(+), PHNHM (+), TME(-), LS(-), LISS(+) & 6 & 9 &
-304.037 & 12.473 & 0.001\tabularnewline
M18 & LIFE(-), PHNHM (+), LS(-), ST(-) & 4 & 7 & -306.824 & 13.175 &
0.001\tabularnewline
M19 & LIFE(-), LS(-), LISS(+) & 3 & 6 & -308.228 & 13.633 &
0.001\tabularnewline
M20 & LIFE(-), LIME(-), PHNHM (+), LISS(+), ST(-) & 5 & 8 & -305.894 &
13.720 & 0.001\tabularnewline
M21 & LIFE(-), LIME(-), PHNHM (+), TME(-), LISS(+) & 5 & 8 & -305.97 &
13.873 & 0.001\tabularnewline
M22 & LIME(-), PHNHM (+), LS(-), ST(-) & 4 & 7 & -307.333 & 14.192 &
0.001\tabularnewline
M23 & LIFE(-), HME(+), PHNHM (+), LISS(+) & 4 & 7 & -307.359 & 14.244 &
0.001\tabularnewline
M24 & LIFE(-), HME(+), PHNHM (+), LS(-), LISS(+), ST(-) & 6 & 9 &
-305.016 & 14.431 & 0.000\tabularnewline
\bottomrule
\end{longtable}

Models are ranked by Akaike' s Information Criterion corrected for small
sample sizes (AICc) values, from the best to the worst model. LIFE:
light intensity of first emergence of bats, LISS: light intensity of
sunset, LIME: light intensity of mid-emergence of bats, HME: humidity as
mid-emergence of bats, TME: temperature at mid-emergence of bats, LS:
lactation stages, PHNHM: predators were hunting or not hunting on bats
at mid-emergence of bats, ST: sunset time.

Table 4 Model-averaged parameter estimates of the best-supported (before
and including the null model) linear mixed models describing the
variation in mid-emergence time of \emph{Vespertilio sinensis.} The
independent effects (IF) of factors on the mid-emergence time
of\emph{Vespertilio sinensis} using hierarchical partitioning analysis
are displayed in the last column.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Variable & Estimate & SE & Z value & t value & \emph{p} & 95\% CI &
IF\tabularnewline
\midrule
\endhead
(Intercept) & 20.365 & 2.750 & 7.299 & 7.440 & \textless{}0.001 &
14.896, 25.833 & /\tabularnewline
LIFE & -0.016 & 0.002 & 6.519 & -6.730 & \textless{}0.001 &
\textbf{-0.020, -0.011} & 46.410\tabularnewline
PHNHM & -5.758 & 1.555 & 3.650 & -3.713 & \textless{}0.001 &
\textbf{-8.849, -2.666} & 17.251\tabularnewline
LS & -4.828 & 1.665 & 2.859 & -2.903 & 0.004 & \textbf{-8.138, -1.152} &
22.736\tabularnewline
LISS & 0.033 & 0.006 & 5.793 & 5.882 & \textless{}0.001 & \textbf{0.022,
0.044} & 13.603\tabularnewline
\bottomrule
\end{longtable}

Table 5 Candidate linear mixed models explaining the variation in final
return time of \emph{Vespertilio sinensis} based on environmental
factors (temperature, relative humidity and light intensity) and biotic
factors (reproductive stages and predation risk) at dawn.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Model & Predictive variables & \emph{k} & \emph{df} & Loglik & ^AICc &
\emph{wi}\tabularnewline
\midrule
\endhead
M1 & LIFR(+), PAPDA(-) & 2 & 5 & 45.617 & 0.000 & 0.552\tabularnewline
M2 & LIFR(+), PAPDA(-), LISR(+) & 3 & 6 & 46.432 & 0.720 &
0.385\tabularnewline
M3 & LIFR(+),TFR(-), PAPDA(-) & 3 & 6 & 43.726 & 6.132 &
0.026\tabularnewline
M4 & LIFR(+), LS(-), PAPDA(-) & 3 & 6 & 43.334 & 6.916 &
0.017\tabularnewline
M5 & LIFR(+), PAPDA(-), LS(-), LISR(+) & 4 & 7 & 44.294 & 7.412 &
0.014\tabularnewline
M6 & HFR(+), LIFR(+), PAPDA(-) & 3 & 6 & 41.256 & 11.072 &
0.002\tabularnewline
M7 & LIFR(+), TFR(-), PAPDA(-), LISR(+) & 4 & 7 & 42.361 & 11.278 &
0.002\tabularnewline
M8 & LIFR(+), TFR(-), PAPDA(-), LS(-) & 4 & 7 & 41.394 & 13.213 &
0.001\tabularnewline
M9 & LIFR(+), TFR(-), PAPDA(-), LS(-) & 4 & 7 & 41.134 & 13.732 &
0.001\tabularnewline
M10 & HFR(+), LIFR(+), PAPDA(-), LS(-) & 4 & 7 & 38.897 & 18.207 &
0.000\tabularnewline
\bottomrule
\end{longtable}

Models are ranked by Akaike' s Information Criterion corrected for small
sample sizes (AICc) values, from the best to the worst model. LIFR:
light intensity at final return of bats, LISR: light intensity of
sunrise, HFR: humidity at final return of bats, TFR: temperature at
final return of bats, LS: lactation stages, PAPDA: presence or absence
of predators during dawn.

Table 6 Model-averaged parameter estimates of the best-supported (before
and including the null model) linear mixed models describing the
variation in final return time of \emph{Vespertilio sinensis.} The
independent effects (IF) of factors on the final return time
of\emph{Vespertilio sinensis} using hierarchical partitioning analysis
are displayed in the last column.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Variable & Estimate & SE & \emph{Z} & t value & \emph{p} & 95\%CI &
IF\tabularnewline
\midrule
\endhead
(Intercept) & 1.637e+0 & 8.400e-2 & 19.489 & 9.846 & \textless{}0.001 &
\textbf{(1.485, 1.777)} & /\tabularnewline
LIFR & -4.011e-3 & 4.429e-4 & 9.056 & -8.194 & \textless{}0.001 &
\textbf{(-0.005, -0.003)} & 56.336\tabularnewline
PAPDA & -2.062e-1 & 3.439e-2 & 5.996 & -5.746 & \textless{}0.001 &
\textbf{(-0.274, -0.141)} & 37.488\tabularnewline
LISR & 3.604e-4 & 7.627e-5 & 4.726 & 4.054 & \textless{}0.001 &
\textbf{(0.000, 0.001)} & 6.177\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1 The relationships between differences (between sunset time and
first emergence time) and light density of sunset (a), and light density
at first emergence (b), and reproductive status (c), ***means\emph{P}
\textless{}0.001.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2 The relationships between differences (between sunset time and
mid-emergence time) and light density of sunset (a), light density at
first emergence (b), predation status (c) and reproductive status (d).
***means \emph{P} \textless{} 0.001.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3 The relationships between differences (between final return
time and sunrise time) and light density of sunrise (a), light density
at final return (b), and predation status (c). ***means
\emph{P}\textless{} 0.001.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4 The independent contribution (\%) of each predictor calculated
with hierarchical partitioning. ***\emph{P} \textless{} 0.001. (a):
effects of predictor variables on the first emergence time, (b): effects
of predictor variables on the mid-emergence time, (c): effects of
predictor variables on the final return time. LIFE: light intensity of
first emergence, LISS: light intensity of sunset, LS: lactation stage
(lactation or post-lactation), PAPDD: presence or absence of predators
during dusk, PHNHM: predation risk stages in mid-emergence, LIFR: light
intensity at final return, PAPDA: presence or absence of predators
during dawn, LISR: light intensity at sunrise.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5 a: The relationship between first emergence time of bats and
light intensity at the first emergence of bats, b: The relationship
between mid-emergence time of bats and light intensity at the first
emergence of bats; c: The relationship between final return time of bats
and light intensity at the final return of bats.

\textbf{REFERENCES}

A.Gutierrez, E. d., F.Pessoa, V., Aguiar, L. M. S., \& M.A.Pesso, D.
(2014). Effect of light intensity on food detection in captive great
fruit-eating bats, Artibeus lituratus (Chiroptera: Phyllostomidae).
\emph{Behavioural Processes, 109} (A), 64-69.

Acharya, P. R., Racey, P. A., McNeil, D., Sotthibandhu, S., \&
Bumrungsri, S. (2015). Timing of cave emergence and return in the dawn
bat (Eonycteris spelaea, Chiroptera: Pteropodidae) in Southern Thailand.
\emph{Mammal Study, 40} (1), 47-52.

Anderson, D. R., \& Burnham, K. P. (2002). Avoiding pitfalls when using
information-theoretic methods.\emph{The Journal of Wildlife Management,
66} (3), 912-918.

Arndt., R. J., O'Keefe., J. M., Mitchell., W. A., Holmes., J. B., \&
Lima., S. L. (2018). Do predators influence the behaviour of
temperate-zone bats? An analysis of competing models of roost emergence
times. \emph{Animal Behaviour, 145} (2018), 161-170.

Barclay, R. M. R. (1989). The effect of reproductive condition on the
foraging behavior of female hoary bats, Lasiurus cinereus.
\emph{Behavioral Ecology and Sociobiology, 24} (1989), 31-37.

Barto\selectlanguage{polish}ń., \selectlanguage{english}K. (2014). MuMIn: multi-model inference. R package version
1.10.5. \emph{R topics documented} , 1-75.

Bell-Pedersen, D., Cassone, V. M., Earnest, D. J., Golden, S. S.,
Hardin, P. E., Thomas, T. L., \& Zoran, M. J. (2005). Circadian rhythms
from multiple oscillators lessons from diverse organisms. \emph{Nature
Reviews Genetics, 6} , 544-556. doi:10.1038/nrd1633

10.1038/nrg1633

Brown, J. S. (1988). Patch use as an indicator of habitat preference,
predation risk, and competition\emph{Behavioral Ecology and
Sociobiology, 22} (1), 37-47.

Bullock., D. J., Combes., B. A., Eales., L. A., \& Pritchard., J. S.
(1987). Analysis of the timing and pattern of emergence of the
pipistrelle bat (\emph{Pipistrellus pipistrettus} ). \emph{Journal of
Zoology, 211} (2), 267-274.

Caravaggi, A., Gatta, M., Vallely, M.-C., Hogg, K., Freeman, M., Fadaei,
E., . . . Tosh, D. G. (2018). Seasonal and predator-prey effects on
circadian activity of free-ranging mammals revealed by camera traps.
\emph{PeerJ, 6} , e5827. doi:10.7717/peerj.5827

Caro, T. (2005). \emph{Antipredator defenses in birds and mammals} .
Chicago: University of Chicago Press.

Chevan., A., \& Sutherland., M. (1991). Hierarchical Partitioning.
\emph{The American Statistician, 45} (2), 90-96.

Erickson, J. L., \& West, S. D. (2002). The influence of regional
climate and nightly weather conditions on activity patterns of
insectivorous bats. \emph{Acta Chiropterologica, 4} (1).

Erkert, H. G. (2004). Extremely low threshold for photic entrainment of
circadian activity rhythms in molossid bats (Molossus molossus;
Chiroptera-Molossidae).\emph{Mammalian Biology, 69} (2004), 361-374.

Fenton, M. B., Rautenbach, I. L., Smith, S. E., Swanepoel, C. M.,
Grosell, J., \& Jaarsveld, J. v. (1994). Raptors and bats threats and
opportunities. \emph{Animal Behaviour, 48} (1), 9-18.

Fo\selectlanguage{ngerman}à, A., \& Bertolucci, C. (2001). Temperature cycles induce a bimodal
activity pattern in ruin lizards masking or clock-controlled event a
seasonal problem. \emph{Journal of Biological Rhythms, 16} (6), 574-584.

Fox., R., Lehmkuhle., S. W., \& Westendorf., D. H. (1976). Falcon visual
acuity. \emph{Science, 192} (4236), 263-265.

Frick, W. F., Stepanian, P. M., Kelly, J. F., Howard, K. W., Kuster, C.
M., Kunz, T. H., \& Chilson, P. B. (2012). Climate and weather impact
timing of emergence of bats.\emph{PLoS One, 7} (8), e42737.
doi:10.1371/journal.pone.0042737.g001

Irwin, N. P., \& Speakman, J. R. (2003). Azorean Bats Nyctalus azoreum ,
Cluster as they Emerge from Roosts, Despite the Lack of Avian Predators.
\emph{Acta Chiropterologica, 52} (2), 185-192.

Jin., L., Wang., J., Zhang., Z., Sun., K., Kanwal., J. S., \& Feng., J.
(2012). Postnatal development of morphological and vocal features in
Asian particolored bat,\emph{Vespertilio sinensis} . \emph{Mammalian
Biology, 77} (2012), 339-344.

John, A. (2013). Animal Behavior: An Evolutionary Approach. In A. John
(Ed.), \emph{Animal Behavior: An Evolutionary Approach} . Sunderland:
Sinauer Associates.

Jones, G., \& Rydell, J. (1994). Foraging strategy and predation risk as
factors influencing emergence time in echolocating bats.
\emph{Philosophical Transactions of the Royal Society B, 346} (1318),
445-455.

Lee, Y., \& McCracken, G. F. (2001). Timing and variation in the
emergence and return of Mexican free-tailed bats, \emph{Tadarida
brasiliensis mexicana} . \emph{Zoological Studies, 40} (4), 309-316.

LeGates, T. A., Fernandez, D. C., \& Hattar, S. (2014). Light as a
central modulator of circadian rhythms, sleep and affect. \emph{Nature
Reviews Neuroscience, 15} (7), 443-454.

Levy, O., Dayan, T., Porter, W. P., \& Kronfeld-Schor, N. (2018). Time
and ecological resilience: can diurnal animals compensate for climate
change by shifting to nocturnal activity? \emph{Ecological Monographs,
89} (1), e01334.

Lima, S. L. (1998). Stress and Decision Making under the Risk of
Predation: Recent Developments from Behavioral, Reproductive, and
Ecological Perspectives. \emph{Advances in the Study of Behavior, 27} ,
215-290. doi:10.1016/s0065-3454(08)60366-6

Lima, S. L., \& O'Keefe, J. M. (2013). Do predators influence the
behaviour of bats? \emph{Biol Rev Camb Philos Soc, 88} (3), 626-644.
doi:10.1111/brv.12021

Lin., A., \& Feng, J. (2008). Solving multicollinearity in the process
of fitting regression model using the nested estimate procedure.
\emph{Quality \& Quantity, 42} (3), 417-426.

Lu\selectlanguage{polish}č\selectlanguage{english}an, R. (2009). Effect of colony size and reproductive period on the
emergence behaviour of a maternity colony of Daubenton's Bat
(\emph{Myotis daubentonii} ) occupying an artificial roost (Chiroptera:
Vespertilionidae). \emph{Folia Parasitologica, 53} , 147-152.

McAney, C. M., \& Fairley, J. S. (1988). Habitat preference and
overnight and seasonal variation in the foraging activity of lesser
horseshoe bats. \emph{Acta Theriologica, 33} , 393-402.

McClung, C. R. (2000). Circadian rhythms in plants: a millennial view.
\emph{Physiologia Plantarum, 109} (4), 359-371.

Mougeot, F., \& Bretagnolle, V. (2000). Predation risk and moonlight
avoidance in nocturnal seabirds.\emph{Journal of Avian Biology, 31} ,
376-386.

Pavey, C. R., Burwell, C. J., Grunwald, J.-E., Marshall, C. J., \&
Neuweiler, G. (2001). Dietary benefits of twilight foraging by the
insectivorous bat\emph{Hipposideros speoris} . \emph{Biotropica, 33}
(4), 670-681. doi:10.1111/j.1744-7429.2001.tb00224.x

Pita, R., Mira, A., \& Beja, P. (2011). Circadian activity rhythms in
relation to season, sex and interspecific interactions in two
mediterranean voles. \emph{Animal Behaviour, 81} (5), 1023-1030.

Quaglietta, L., Mira, A., \& Boitani, L. (2018). Extrinsic and intrinsic
factors affecting the daily rhythms of a semiaquatic carnivore in a
mediterranean environment.\emph{Hystrix, 29} (1), 128-136.

R Core Development Team. (2018). R: a language and environment for
statistical computing, 3.5.1 edn. Vienna (Austria): R Foundation for
Statistical Computing. \emph{Retrieved from: http://www.R-proje ct.org}
.

R. A. Salda\selectlanguage{ngerman}ña-Vázquez, \& Munguía-Rosas, M. A. (2013). Lunar phobia in
bats and its ecological correlates: A meta-analysis. \emph{Mammalian
Biology, 78} (3), 216-219.

Russo, D., LucaCistrone, \& GarethJones. (2007). Emergence time in
forest bats: the influence of canopy closure. \emph{Acta Oecologica, 31}
(1), 119-126.

Rydell, J., Entwistle, A., \& Racey, P. A. (1996). Timing of foraging
flights of three species of bats in relation to insect activity and
predation risk. \emph{Oikos, 76} (2), 243-252.

Rydell, J., \& Speakman, J. R. (1995). Evolution of nocturnality in
bats: Potential competitors and predators during their early history.
\emph{Biological Journal of the Linnean Society, 54} (2), 183-191.
doi:10.1111/j.1095-8312.1995.tb01031.x

Shiel, C. B., \& Fairley, J. S. (1999). Evening emergence of two nursery
colonies of Leisler's bat (\emph{Nyctalus leisleri} ) in Ireland.
\emph{Journal of Zoology, 247} (4), 439-447.

Shuert, C. R., Halsey, L. G., Pomeroy, P. P., \& Twiss, S. D. (2020).
Energetic limits: Defining the bounds and trade-offs of successful
energy management in a capital breeder. \emph{J Anim Ecol, 89} (11),
2461-2472. doi:10.1111/1365-2656.13312

Speakman, J. R. (1991). Why do insectivorous bats in Britain not fly in
daylight more frequently?\emph{Functional Ecology, 5} (4), 518-524.

Speakman, J. R., Hays, G. C., \& Webb, P. I. (1994). Is hyperthermia a
constraint on the diurnal activity of bats. \emph{Journal of Theoretical
Biology, 171} (3), 325-339.

Tester, J. R., \& Figala, J. (1990). Effects of biological and
environmental factors on activity rhythms of wild animals.
\emph{Progress in Clinical and Biological Research, 341} (B), 809-819.

Voigt, C. C., \& Kingston, T. (2016). Bats in the Anthropocene:
Conservation of Bats in a Changing World.\emph{Springer International
Publishing} .

Walsh., C., \& Nally., R. M. (2020). hier.part: Hierarchical
Partitioning. \emph{R topics documented} , 1-13.

Wang., Y., BY Shi, X Zhao, Feng., J., \& Jiang., T. (2020).
Morphological correlates of personality in female asian particolored
bats (\emph{Vespertilio sinensis} ). \emph{Animals, 10} (2), 1-15.

Welbergen, J. A. (2006). Timing of the evening emergence from day roosts
of the grey-headed flying fox,\emph{Pteropus poliocephalus} : the
effects of predation risk, foraging needs, and social context.
\emph{Behavioral Ecology and Sociobiology, 60} (3), 311-322.

Welbergen, J. A. (2008). Variation in twilight predicts the duration of
the evening emergence of fruit bats from a mixed-species roost.
\emph{Animal Behaviour, 75} (4), 1543-1550.

Yerushalmi, S., \& Green, R. M. (2009). Evidence for the adaptive
significance of circadian rhythms.\emph{Ecology letters, 12} (9),
970-981. doi:10.1111/j.1461-0248.2009.01343.x

Yin, Z. (2020). \emph{Study on Intestinal Microbiome during Development
of the Asian Particolored Bat (Vespertilio sinensis).} Jilin
Agricultural University,

\selectlanguage{english}
\FloatBarrier
\end{document}

