Species-specific spatial and temporal variability in anuran call detection: implications for deploying autonomous recording units

Andrew Hall; Amelia Walcott; Ali Borrell; Dale G. Nimmo; Skye Wassens

doi:10.1071/WR24036

RESEARCH ARTICLE (Open Access)

Next Contents Vol 52(2)

Species-specific spatial and temporal variability in anuran call detection: implications for deploying autonomous recording units

Andrew Hall

^A ^* , Amelia Walcott ^B , Ali Borrell ^B , Dale G. Nimmo

^C and Skye Wassens ^C

+ Author Affiliations

- Author Affiliations

^A Gulbali Institute, Charles Sturt University, Albury, NSW 2640, Australia.

^B New South Wales Department of Climate Change, Energy, the Environment and Water, 480 Weeroona Road, Lidcombe, NSW 2141, Australia.

^C School of Agricultural, Environmental and Veterinary Science, Charles Sturt University, Albury, NSW 2640, Australia.

^* Correspondence to: ahall@csu.edu.au

Handling Editor: Adam Stow

Wildlife Research 52, WR24036 https://doi.org/10.1071/WR24036

Submitted: 14 March 2024 Accepted: 20 December 2024 Published: 21 January 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Ecosystem assessment using acoustic monitoring technologies can be an efficient method for determining species community composition and breeding activity, but many factors affect the quality of acoustics-data and subsequent level of confidence in derived inferences.

Aims

We aimed to assess variability in detection probabilities of five frog species using autonomous recording units (ARUs) deployed across a single 1 km² wetland, comprising a lagoon and surrounding area, and subsequently determine the required number of ARUs with 95% confidence in derived presence–absence data.

Methods

Ten ARUs were deployed in two rings around the lagoon’s centroid close to the water’s edge. Occupancy models were used to derive detection probabilities of species calling in the lagoon from data describing the temporal pattern of calling at each site, which were derived using call recognition software.

Key results

Only two of the five target species were detected by all 10 ARUs. All target species’ non-zero ARU detection probabilities varied by a factor of 14, and the coefficients of variation in individual ARU detection probability for each species varied by a factor of seven. Simulations revealed seven or eight ARUs are required to achieve 95% confidence in confirming presence of either of the two species with the highest observed detection probabilities, given they are present and calling. Even with ten deployed ARUs, the probability of successful detection of the other three species known to be calling on any day was less than 40%.

Conclusions

Effective detection was not achieved for all targeted species by several ARUs during a period when hydrology and season suited recruitment activity. Despite all ARUs being deployed at locations favourable for detecting targeted species, stochastic factors drove spatial variability in detection resulting in markedly different data for each ARU and each species.

Implications

Data describing species presence derived from automated recording units may not be representative due to spatiotemporal variability in detection that varies by species. To improve ARU deployment strategies, a priori knowledge of typical detection probabilities and species spatial variability can be used to determine the required number of call recorders for a set level of confidence.

Keywords: ARU, call recorders, community composition, detection probability, frogs, occupancy, phenology, wetland, wildlife management.

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