Estimating population density of insectivorous bats based on stationary acoustic detectors: A case study

Abstract

Automated recording units are commonly used by consultants to assess environmental impacts and to monitor animal populations. Although estimating population density of bats using stationary acoustic detectors is key for evaluating environmental impacts, estimating densities from call activity data is only possible through recently developed numerical methods, as the recognition of calling individuals is impossible. We tested the applicability of generalized random encounter models (gREMs) for determining population densities of three bat species (Common pipistrelle Pipistrellus pipistrellus, Northern bat Eptesicus nilssonii, and Natterer's bat Myotis nattereri) based on passively collected acoustical data. To validate the results, we compared them to (a) density estimates from the literature and to (b) Royle–Nichols (RN) models of detection/nondetection data. Our estimates for M. nattereri matched both the published data and RN‐model results. For E. nilssonii, the gREM yielded similar estimates to the RN‐models, but the published estimates were more than twice as high. This discrepancy might be because the high‐altitude flight of E. nilssonii is not accounted for in gREMs. Results of gREMs for P. pipistrellus were supported by published data but were ~10 times higher than those of RN‐models. RN‐models use detection/nondetection data, and this loss of information probably affected population estimates of very active species like P. pipistrellus. gREM models provided realistic estimates of bat population densities based on automatically recorded call activity data. However, the average flight altitude of species should be accounted for in future analyses. We suggest including flight altitude in the calculation of the detection range to assess the detection sphere more accurately and to obtain more precise density estimates.

Fruit bats adjust their foraging strategies to urban environments to diversify their diet

“…We thus recorded audio continuously onboard nine bats (additional to the 39 above, the “Methods” section) to estimate the density of vocal interactions, as a proxy for bat density at foraging sites. Acoustic monitoring is a common method to assess bat density [ 58 , 59 ], and we could estimate that ~ 90% of the social calls we recorded were emitted by conspecifics who were not interacting with the focal bat carrying the microphones. We could determine this because the intensity of a vocalization differs greatly when it is emitted by the individual carrying the microphone or by a remote conspecific.…”

Combining acoustic localisation and high-resolution land cover classification to study predator vocalisation behaviour

“…Passive acoustic monitoring (PAM), the recording of animal vocalisations using automated acoustic recorders, overcomes many of these shortcomings, complementing other monitoring techniques. It is increasingly used to monitor wildlife, from species to ecosystem levels (Marques et al 2013;Sadhukhan et al 2021), facilitating evaluations of species' distributions (Hagens et al 2018), density (Milchram et al 2020), and habitat use (Rhinehart et al 2020). The location of vocalising animals can be calculated based on the arrival times of the sound's acoustic waves at three or more synchronised devices set at distant points, a process known as acoustic localisation (Wilson et al 2014;Kershenbaum et al 2019).…”

“…2021), facilitating evaluations of species’ distributions (Hagens et al . 2018), density (Milchram et al . 2020), and habitat use (Rhinehart et al .…”

Acoustic monitoring yields informative bat population density estimates

“…The acoustic amplitude was highly correlated with the number of bats counted coming out of the cave using a camera placed at the same site as the microphone (Kloepper et al, 2016). More recently, Milchram et al (2019) adapted a generalized random encounter model (gREM) developed by Rowcliffe et al (2008) to estimate the population density of forest bats from data collected using stationary acoustic detectors. Milchram et al (2019) compared their estimates to published estimates for bat colony sizes and to the output from an evaluation of the same acoustic data with Royle-Nichols' detection/non-detection models (Royle & Nichols, 2003).…”

“…More recently, Milchram et al (2019) adapted a generalized random encounter model (gREM) developed by Rowcliffe et al (2008) to estimate the population density of forest bats from data collected using stationary acoustic detectors. Milchram et al (2019) compared their estimates to published estimates for bat colony sizes and to the output from an evaluation of the same acoustic data with Royle-Nichols' detection/non-detection models (Royle & Nichols, 2003). The generalized random encounter models and Royle-Nichols' models estimated lower densities than literature reports for two of three species, but the estimated density from the generalized random encounter models matched the published colony size density for Pipistrellus (Milchram et al, 2019).…”