Critiquing the use of Passive Acoustic Monitoring
frameworks to support evidence-informed
conservation for British bats

Perks, Samantha J

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Critiquing the use of Passive Acoustic Monitoring frameworks to support evidence-informed conservation for British bats

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Perks, Samantha J ORCID: https://orcid.org/0000-0003-1893-8059 (2024) Critiquing the use of Passive Acoustic Monitoring frameworks to support evidence-informed conservation for British bats. PhD thesis, University of Gloucestershire. doi:DOI 10.46289/SNXH8515

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Official URL: https://doi.org/DOI 10.46289/SNXH8515

Abstract

It is widely acknowledged that robust and accessible ecological evidence is required to underpin solutions to the current global biodiversity crisis. Reliable data are vital to inform conservation action, but can be challenging to obtain for some taxa, particularly those that are nocturnal, crepuscular, or cryptic. In the United Kingdom, one quarter of the mammal species found nationally are perceived to be at risk of extirpation, yet many species lack sufficient data to enable robust assessment of their distributions and the status of their populations. This thesis examined passive acoustic monitoring frameworks for surveying and monitoring bat species in the United Kingdom, considering their ability to collect reliable data and to provide insights into species ecology, and evaluated their application to ecological research and practice. Passive acoustic bat surveys were found to be most optimal when conducted for full nights, and in the absence of moonlight and/or heavy rain. Moreover, they were found to be more effective in recording comprehensive species assemblages, when compared to active acoustic surveys. Empirical testing of different detectors used in Passive Acoustic Monitoring (PAM) frameworks for bats, found that lower cost, open-source devices can serve as a viable alternative for commercial equipment in certain scenarios (dependent on target species and habitat), and when enough devices are deployed for a sufficient duration. The analysis of large PAM datasets was also examined, finding pairwise disagreement between popular automated bat classifiers, and supporting the recommendation that analysis should not, at present, be fully automated. These findings contribute to the development of best practice and demonstrate the key advantages of PAM approaches, primarily in their potential to aid the up-scaling of both local, and national bat monitoring schemes.

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Thesis (PhD)

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