Figure 2. Pest abundance (total number of individuals per trap
night) in relation to data collection date. Data were collected at
agricultural fields in Champaign County, IL, in 2021. Red line
represents a ‘best-fitting-line’.
Statistical analysis
Bat activity
Bat activity (i.e., number of bat calls per detector per night per
phonic group, N = 2471, mean ± SD = 17 ± 37, range = 1 – 470) is a
count variable and, as such, we used a negative binomial generalized
linear mixed effects model with quadratic parameterization (glmmTMB) to
analyze the data (Brooks et al., 2017; Hardin and Hilbe, 2007). We
included a two-way interaction between distance to forest and relative
pest abundance, a two-way interaction between distance to forest and
phonic group, and a two-way interaction between phonic group and
relative pest abundance as main predictor variables. We also included
daily minimum temperature (Tmin) and daily precipitation (Precip) as
fixed covariates as these environmental factors have been shown to
significantly affect bat activity (Gorman et al., 2021). Lastly, we
included a quadratic effect of date as a fixed effect, as we expected
bat activity to increase as pups become volant, until mid-August, and
decline towards late-September, as bats migrate to hibernation sites. We
included detector (N = 50) nested within transect line ID (N = 10) as
random effects to control for potential spatial and temporal
autocorrelation biases. Statistical tests with a p-value lower than 0.05
were considered statistically significant. We report means and standard
errors where appropriate.
Bat species diversity
We recognized several species within our dataset. Bat diversity was
defined as the number of species detected per detector per night.
However, in our study area Myotis species – apart from MYLU –
were generally rarely identified through automated ID algorithms, so we
combined all Myotis species with the exception of MYLU into one
category ‘Myotis spp.’ Consequently, bat species diversity in our
dataset ranged from 1 to 8 (i.e., EPFU, LANO, LACI, LANO, NYHU, PESU,
MYLU, and Myotis spp ).
We used a similar negative binomial generalized linear mixed effects
model with a quadratic parameterization to analyze bat diversity (N =
1698 detector-nights). We included a two-way interaction between
distance to forest and relative pest abundance as main predictor
variables. We also included the same covariates in this model: daily
minimum temperature (Tmin), daily precipitation (Precip), and a
quadratic effect of date as a fixed effect. The random effects were
detector (N = 50) nested within transect line ID (N = 10).
Results
Bat Activity
Bat activity or bat calls per detector-night (calls/night) decreased
significantly with increasing distance from forest (χ2= 16.37, df = 1, p < 0.001), such that bat activity decreased
56% from the forest edge (11.4 ± 1.8 calls/night) to 4000m away from
the forest edge (5.0 ± 1.0 calls/night; Fig. 3A). Bat activity decreased
less with increasing distance to forest when relative pest abundance was
high (i.e., 1), compared to when relative pest abundance was low (i.e.,
0; χ2 = 10.04, df = 1, p = 0.002; Fig. 3A). The
relation between bat activity and distance to forest was not affected by
phonic group (χ2 = 5.21, df = 2, p = 0.074; Fig. 3B).
However, bat activity varied among phonic groups (χ2 =
2167.74, df = 2, p < 0.001) such that, on average,
low-frequency bats were most active (20.7 ± 2.8 calls/night),
mid-frequency bats had less activity (4.2 ± 0.6 calls/night), and
high-frequency bats had the least activity (1.2 ± 0.2 calls/night)
throughout the study. Bat activity decreased significantly with
increasing relative pest abundance (χ2 = 14.22, df =
1, p < 0.001), but this effect varied by phonic group
(χ2 = 28.07, df = 2, p < 0.001); while
activity of low-frequency bats decreased with increasing relative pest
abundance, activity of med- and low-frequency bats did not vary with
increased relative pest abundance (Fig. 3C).
Lastly, bat activity increased with increasing daily minimum
temperatures (χ2 = 137.67, df = 1, p <
0.001) and decreased with increasing daily precipitation
(χ2 = 4.26, df = 1, p = 0.039). Modeled bat activity
was also highest in the beginning of July (χ2 =
136.44, df = 2, p < 0.001).