Optimising the performance of acoustic telemetry receiver arrays for tracking aquatic animals using the arrayplanR package

Abstract Strategically deploying acoustic telemetry receiver arrays is vital for tracking the movements of aquatic animals. Nevertheless, there is a lack of tools that provide the capacity to evaluate how well arrays perform in their study area, as well as ways to optimise receiver configurations to best meet study goals. Receiver configurations vary based on the species or behaviours being studied, environmental conditions affecting detectability or the spatial resolution being targeted (e.g. fine‐scale positioning), among other factors. Therefore, optimising detectability of transmitters across receivers—relative to the diverse study objectives that often exist—should be a major priority in acoustic telemetry research. Here, we introduce a simple probabilistic approach through an R package (arrayplanR) to plan and evaluate the detection performances of acoustic telemetry arrays, when combined with user range detection data, specifically with the goal to optimise receiver locations depending on the needs of the study (e.g. transmissions detected on n receivers, largest area covered beyond a detection probability threshold, highest detection probability across entire study area). There are two distinct workflows associated with our approach: (1) ‘DE_array_quantify’ quantifies array performance for receivers at pre‐designated locations (e.g. an existing study); and (2) ‘DE_array_optimize_1overlap’ and ‘DE_array_optimize_3overlap’ enable users to iteratively identify optimal receiver locations based on animal transmissions being detected by one receiver (e.g. planning for a broad‐scale presence study) or detected by more than one receiver (e.g. planning for a fine‐scale positioning study), respectively. Each workflow incorporates several input parameters to allow customisation based on study goals (e.g. number of receivers, minimum distance between receivers, fixed receiver location(s), anticipated animal‐receiver depth differences, habitat‐specific receiver locations). Using a simple case study, we show how optimal receiver configurations vary based on study parameters and highlight how strategic receiver deployments across different habitats or areas can improve detection capacity for telemetry studies. Overall, this work can contribute to better research planning and study design across the global telemetry community and marine, estuarine and freshwater environments.

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Publication Details

Journal
Methods in Ecology and Evolution
Published
2026-11-06
DOI
https://doi.org/10.1111/2041-210x.70443
Primary Topic
Fish Ecology and Management Studies
Type
article
Field-Weighted Citation Impact
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article

Optimising the performance of acoustic telemetry receiver arrays for tracking aquatic animals using the arrayplanR package

Christopher T. Monk, Joshua D. Dennis, Jordan K. Matley, Natalie V. Klinard et al.
Methods in Ecology and Evolution
Fish Ecology and Management Studies
article

Optimising the performance of acoustic telemetry receiver arrays for tracking aquatic animals using the arrayplanR package

Christopher T. Monk, Joshua D. Dennis, Jordan K. Matley, Natalie V. Klinard, Jacob W. Brownscombe, Thomas M. Clarke
article en

Abstract

Abstract Strategically deploying acoustic telemetry receiver arrays is vital for tracking the movements of aquatic animals. Nevertheless, there is a lack of tools that provide the capacity to evaluate how well arrays perform in their study area, as well as ways to optimise receiver configurations to best meet study goals. Receiver configurations vary based on the species or behaviours being studied, environmental conditions affecting detectability or the spatial resolution being targeted (e.g. fine‐scale positioning), among other factors. Therefore, optimising detectability of transmitters across receivers—relative to the diverse study objectives that often exist—should be a major priority in acoustic telemetry research. Here, we introduce a simple probabilistic approach through an R package (arrayplanR) to plan and evaluate the detection performances of acoustic telemetry arrays, when combined with user range detection data, specifically with the goal to optimise receiver locations depending on the needs of the study (e.g. transmissions detected on n receivers, largest area covered beyond a detection probability threshold, highest detection probability across entire study area). There are two distinct workflows associated with our approach: (1) ‘DE_array_quantify’ quantifies array performance for receivers at pre‐designated locations (e.g. an existing study); and (2) ‘DE_array_optimize_1overlap’ and ‘DE_array_optimize_3overlap’ enable users to iteratively identify optimal receiver locations based on animal transmissions being detected by one receiver (e.g. planning for a broad‐scale presence study) or detected by more than one receiver (e.g. planning for a fine‐scale positioning study), respectively. Each workflow incorporates several input parameters to allow customisation based on study goals (e.g. number of receivers, minimum distance between receivers, fixed receiver location(s), anticipated animal‐receiver depth differences, habitat‐specific receiver locations). Using a simple case study, we show how optimal receiver configurations vary based on study parameters and highlight how strategic receiver deployments across different habitats or areas can improve detection capacity for telemetry studies. Overall, this work can contribute to better research planning and study design across the global telemetry community and marine, estuarine and freshwater environments.

Methods in Ecology and Evolution
Fisheries and Oceans Canada (CA), Flinders University (AU), South Australian Research and Development Institute (AU), Government of South Australia (AU), GEOMAR Helmholtz Centre for Ocean Research Kiel (DE), Department of Primary Industries and Regions South Australia (AU), Leibniz Institute of Freshwater Ecology and Inland Fisheries (DE)
Openalex Percentile: Top 19%
Fish Ecology and Management Studies
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