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 (P < 0.001). In this case, the
logarithmic transformation was used to make the data meet a normal
distribution (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 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 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 < 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 ΔAICc as the difference
of AICc of each model between the AICc of the best-fitting model. A
difference of AICc > 2
(ΔAICc > 2) indicated
that the model with the lower AIC value had better explanatory power
(Anderson & Burnham, 2002). If the ΔAICc
was ≤ 2, multimodel inference was performed using the function model.avg
in the package ‘MuMIn’ (Bartoń., 2014) in
R v. 3.5.1. We also calculated Akaike weights (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.