Seasonal swarming behavior of Myotis bats revealed by integrated monitoring, involving passive acoustic monitoring with automated analysis, trapping, and video monitoring

Abstract

Bat abundance, diversity, and behavior can be monitored by capturing bats for identification and measurement in the hand, but this has several disadvantages. These include disturbance to the bats, which limits the frequency with which captures can be made at an individual capture site, and potentially alters the behaviors being studied. Infrared video monitoring, passive acoustic recording and automated analysis and identification of bat calls offers an alternative set of noninvasive methods for monitoring bats. In this study, we examine the effectiveness of acoustic monitoring in comparison with capture‐based and video monitoring of seasonal swarming behavior among several species of Myotis bats in southern Britain. We applied these complementary approaches to describe seasonal, overnight, and species‐specific variation in swarming behavior in a multispecies community of Myotis bats. We show that the three monitoring approaches have advantages and disadvantages for different tasks, but can be viewed as highly complementary methods for addressing different types of research questions. In our study of swarming behavior, capture and examination of bats in the hand was necessary for measuring sex ratios, reproductive status, and even for confirmation of species identification for some difficult to separate taxa. Capture is also an essential aspect of tagging bats for individual identification and tracking studies. Video monitoring is useful for understanding the behavior of bats at swarming sites, and measuring the flux of individuals into and out of roosting or swarming sites. Passive acoustic monitoring is a valuable noninvasive method for continuous monitoring of within‐night, seasonal, and between‐year variation in the abundance of bat calls. These can be used as an index of variation in relative abundance within—but not between—bat species.

An Efficient Neural Network Design Incorporating Autoencoders for the Classification of Bat Echolocation Sounds

“…Another way of improving the audio database is to consider habitat influences and corresponding bat sounds as reported by Findlay et al [ 51 ]. Inspired by the way humans perceive the world via multiple senses, it may be advisable to combine acoustic monitoring with video tracking of bat activity as reported by Thomas et al [ 52 ]. Examination of carcasses in areas where populations are tracked can help to offer probabilities of occurrence of different species for detection models as reported by Chipps et al [ 53 ].…”

Flying by the river side: Survey of bat distributions and environmental contexts along a 1000‐mile river corridor, Green and Colorado Rivers, USA

“…To determine if activity varied by species over time (i.e., nightly activity (hour from midnight), as well as seasonal activity (Julian day)) we ran negative binomial generalised additive models (GAM) using the MASS package (Venables & Ripley, 2002) and the mgcv package (Wood, 2011) following methods described in Davison and Thomas (2017) and Thomas and Davison (2022). We allowed the mgcv package to automatically choose the value for k (degrees of non‐linearity) needed to minimize AIC values.…”

Karyotypic stasis and swarming influenced the evolution of viral tolerance in a species-rich bat radiation

“… 24 At these transient meetings, bats exhibit intense flight activity, chasing, and increased communication, including echolocation and human-audible social calls, which facilitate interspecific communication. 24 , 25 , 26 Additionally, these assemblages are typically 80% male biased, 27 , 28 suggesting intense competition for mates. 29 Although the phenology of swarming varies by species, elevation, and seasonal timing, 26 , 30 , 31 many bat species and genera still overlap at swarming sites.…”