Comparing acoustic and radar deterrence methods as mitigation measures to reduce human-bat impacts and conservation conflicts

Abstract

Where humans and wildlife co-exist, mitigation is often needed to alleviate potential conflicts and impacts. Deterrence methods can be used to reduce impacts of human structures or activities on wildlife, or to resolve conservation conflicts in areas where animals may be regarded as a nuisance or pose a health hazard. Here we test two methods (acoustic and radar) that have shown potential for deterring bats away from areas where they forage and/or roost. Using both infrared video and acoustic methods for counting bat passes, we show that ultrasonic speakers were effective as bat deterrents at foraging sites, but radar was not. Ultrasonic deterrents decreased overall bat activity (filmed on infrared cameras) by ~80% when deployed alone and in combination with radar. However, radar alone had no effect on bat activity when video or acoustic data were analysed using generalised linear mixed effect models. Feeding buzzes of all species were reduced by 79% and 69% in the ultrasound only treatment when compared to the control and radar treatments, but only the ultrasound treatment was significant in post-hoc tests. Species responded differently to the ultrasound treatments and we recorded a deterrent effect on both Pipistrellus pipistrellus (~40-80% reduction in activity) and P. pygmaeus (~30-60% reduction), but not on Myotis species. However, only the ultrasound and radar treatment was significant (when compared to control and radar) in post-hoc tests for P. pipistrellus. Deterrent treatment was marginally non-significant for P. pygmaeus, but the ultrasound only treatment was significant when compared to radar in post-hoc tests. We therefore suggest that acoustic, but not radar methods are explored further as deterrents for bats. The use of acoustic deterrence should always be assessed on a case-by-case basis, with a focus on bat conservation.

Fig 1. Schematic of example experiment set-up at riparian sites.

Schematic of experiment set-up at riparian sites, including treated airspace (textured area) in an area of river/canal with a bridge, flanked by a tree line or hedge on one side. Near infrared (NIR) video camera and laptop were set up at 0 m (a.), the radar unit on a table at 0.5 m from the camera (b.), the acoustic deterrent speaker units on chairs at 1 m (c.) and the SM2 BAT+ bat detector at ~20 m (d.).

Fig 2. Spectrogram and power spectrum of acoustic deterrent speaker output and spectrograms of echolocation calls of bat species recorded.

Spectrogram (A.) and power spectrum (B.) calculated from acoustic deterrent speaker unit output recorded on-axis in an anechoic chamber using a Sanken CO-100K Super Wide Range Microphone and calibrated using a type 4231 Brüel & Kjær calibrator (114 dB SPL at 1 kHz). Power spectrum dB SPL measurements calculated at 1 m and adjusted for spectral sensitivity of microphone and distance (original distance 2.2 m, FFT 128, Hamming window, calculated in Avisoft SASLab Pro). Spectrograms of bat echolocation calls (C.), including two calls from a sequence identified as Pipistrellus pipistrellus (a.), P. pygmaeus (b.) and Myotis species (c.). Both spectrograms were created using Raven Lite 2.0.0 with the same settings (spectrogram window Hann, size 512, brightness 69, contrast 71).

Fig 3. Number of near infrared video (NIR) and acoustic bat passes recorded during control and deterrent treatments.

Bat activity (mean number of passes) during 4 treatments of ultrasound (U), radar (R), ultrasound and radar (U+R) and a control (C), including counts of bat passes recorded on near-infrared (NIR) video footage (a) and acoustic bat passes identified as Pipistrellus pipistrellus (b), P. pygmaeus (c), Myotis spp. (d) and feeding buzzes of all species (e). Including SE error bars and p values from post-hoc Tukey comparisons, presented as significance stars and associated label of treatment comparison (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). NIR video N = 14, acoustic data N = 6.