A spatial point process model to estimate individual centres of activity from passive acoustic telemetry data

Abstract

Failure to account for time‐varying detection ranges when inferring space use of marine species from passive acoustic telemetry data can bias estimates and result in erroneous biological conclusions. This potential source of bias is widely acknowledged but often ignored in practice due to a lack of available statistical methods. Here, we describe and apply a spatial point process model for estimating individual centres of activity (COAs) from acoustic telemetry data that can be modified to account for both receiver‐ and time‐specific detection probabilities. We use simulation testing to evaluate the suitability of the proposed models for estimating COAs and compare their performance to that of the popular mean‐weighted COA method for a variety of scenarios. We illustrate how the approach can be applied to correct for variable detection ranges by integrating data from moored test tags and demonstrate how accounting for time‐varying detection probabilities can impact space use estimates by fitting the model to data collected from a black sea bass (Centropristis striata) on a receiver array off the east coast of the United States. The proposed model reduced bias in COA estimates, particularly when tagged individuals occurred along the periphery of the receiver array. The test tag‐integrated model largely corrected the bias associated with receiver‐ and time‐specific detection probabilities. When applied to the black sea bass detection data, the model revealed fine‐scale movements not apparent when detection ranges were assumed constant. Spatial management practices for coastal marine species are often based on trends in space use inferred from passive acoustic telemetry data, which can be misinterpreted when factors influencing detection ranges are not accounted for. Our approach provides a general framework for estimating individual COAs that can be modified on a study‐specific basis to ensure resulting patterns of space use reflect a species’ movements and behaviour, rather than variation in receiver detection ranges.

Space use of juvenile and subadult yellow perch (Perca flavescens) in the Detroit River using acoustic telemetry: incorporating variable detection ranges in vegetated areas

“…The ability to incorporate potential error in space use estimates due to variable detection patterns (e.g., SAV or other environmental variables) helps interpret animal movements from acoustic tracking (Winton et al 2018;Brownscombe et al 2020). Seasonal and inter-annual changes in the aquatic environment often create highly variable acoustic areas (Jossart et al 2017, Klinard et al 2019) that can alter the ability of receivers to detect animals uniformly.…”

“…Fine-scale positioning techniques that use triangulation of simultaneous detections on multiple hydrophones (e.g., Espinoza et al 2011) are not typically feasible in areas with high vegetation due to costs and logistical constraints of deploying enough receivers that are close enough to achieve simultaneous detections. Other studies have aimed to develop methods to account for variable detection efficiency within a study area by weighting seasonal activity space estimates (Brooks et al 2019), correcting the relative number of site-specific detections (Payne et al 2010;Brownscombe et al 2020) or by improving the accuracy of position estimates (e.g., Charles et al 2016;Winton et al 2018).…”

A Novel Framework to Predict Relative Habitat Selection in Aquatic Systems: Applying Machine Learning and Resource Selection Functions to Acoustic Telemetry Data From Multiple Shark Species

“…For future studies aimed at examining relative selection, we suggest grid array designs (Heupel et al, 2006;Kraus et al, 2018) to achieve proportionally representative coverage of areas rather than deployments guided by a priori beliefs of animal space use (Brownscombe et al, 2019b). In addition, detection range and efficiency, should be considered during the array design (Brownscombe et al, 2019b), when constructing COAs (Winton et al, 2018b), when defining available resource units around receivers, or even explicitly in the modeling process (see Brownscombe et al, 2019a). Detection efficiency and range, often limited by physical structure, wind, currents, animal noise, or by human activities may vary greatly across a given study area (Gjelland and Hedger, 2013;Kessel et al, 2014).…”

Current and emerging statistical techniques for aquatic telemetry data: A guide to analysing spatially discrete animal detections

“…Those that do may use it to pre‐process their data (e.g. Hoenner et al., ; Kessel et al., 2014a), or directly incorporate measurement error into the statistical method (Pedersen & Weng, ; Simpfendorfer et al., ; Winton, Kneebone, Zemeckis, & Fay, ). Measurement error can additionally be used to help numerically optimize the spatiotemporal design of a receiver array before deployment, resulting in a study design with enhanced ability to acquire high‐quality data (Pedersen, Burgess, & Weng, ).…”

“…State‐space models are hierarchical models that can pair a measurement equation with a model for animal movement, and simultaneously estimate both processes (Auger‐Méthé et al., ). Two notable examples with detection data include: (a) a non‐parametric function for detection probability paired with an Ornstein–Uhlenbeck movement process to estimate the home range of a humphead wrasse ( Cheilinus undulatus ; Pedersen & Weng, ); and (b) a Gaussian decay measurement equation coupled with a binomial spatial point process to estimate centres of activity of a black sea bass ( Centropristis striata ; Winton et al., ). State‐space models have gained popularity for analyzing spatially continuous animal movement data, likely because of their flexibility—multiple measurement error distributions can be included and matched specifically to the tracking technology (e.g.…”