Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/55711
Title: Using referential alarm signals to remotely quantify 'landscapes of fear' in fragmented woodland
Contributor(s): McDonald, Paul G  (author)orcid ; Doohan, Samantha J (author); Eveleigh, Kyia J  (author)
Publication Date: 2022
Early Online Version: 2021-12-20
DOI: 10.1080/09524622.2021.2013319
Handle Link: https://hdl.handle.net/1959.11/55711
Abstract: 

Land-use changes have greatly impacted biodiversity and led to new conservation challenges, including greater predation pressure, although this can be difficult to quantify. Here we directly monitor predator encounters in fragmented woodlands by using passive acoustic monitoring (PAM) and a semiautomated assessment protocol to detect functionally referential alarm vocalisations of the noisy miner Manorina melanocephala. We demonstrate that measuring changes in perceived predation pressure, the so-called 'landscape of fear', in a prey species across temporal (dawn, midday, dusk across multiple seasons) and spatial scales (small/large fragments and edge/ centre locations within fragments) is achievable. Vocalisations linked with ground predator presence were rarer during midday recordings, but more commonly detected from the edge rather than centre of smaller fragments. While the probability of detecting aerial alarm calls directed at flying raptors also increased in edge habitat, aerial alarm detections declined from a dawn peak to a minimum during dusk recordings. These patterns did not simply reflect noisy miner occupancy or different sections of monitored patches, but highlighted higher perceived predation risk along edges, particularly for small patches, demonstrating the nuanced insights that PAM can offer when quantifying animal behaviour

Publication Type: Journal Article
Source of Publication: Bioacoustics, 31(6), p. 629-645
Publisher: Taylor & Francis
Place of Publication: United Kingdom
ISSN: 2165-0586
0952-4622
Fields of Research (FoR) 2020: 310901 Animal behaviour
310301 Behavioural ecology
310405 Evolutionary ecology
Socio-Economic Objective (SEO) 2020: 280102 Expanding knowledge in the biological sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science

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