Field testing a novel high residence positioning system for monitoring the fine‐scale movements of aquatic organisms

Abstract Acoustic telemetry is an important tool for studying the behaviour of aquatic organisms in the wild. VEMCO high residence (HR) tags and receivers are a recent introduction in the field of acoustic telemetry and can be paired with existing algorithms (e.g. VEMCO positioning system [VPS]) to obtain high‐resolution two‐dimensional positioning data. Here, we present results of the first documented field test of a VPS composed of HR receivers (hereafter, HR‐VPS). We performed a series of stationary and moving trials with HR tags (mean HR transmission period = 1.5 s) to evaluate the precision, accuracy and temporal capabilities of this positioning technology. In addition, we present a sample of data obtained for five European perch Perca fluviatilis implanted with HR tags (mean HR transmission period = 4 s) to illustrate how this technology can estimate the fine‐scale behaviour of aquatic animals. Accuracy and precision estimates (median [5th–95th percentile]) of HR‐VPS positions for all stationary trials were 5.6 m (4.2–10.8 m) and 0.1 m (0.02–0.07 m), respectively, and depended on the location of tags within the receiver array. In moving tests, tracks generated by HR‐VPS closely mimicked those produced by a handheld GPS held over the tag, but these differed in location by an average of ≈9 m. We found that estimates of animal speed and distance travelled for perch declined when positional data for acoustically tagged perch were thinned to mimic longer transmission periods. These data also revealed a trade‐off between capturing real nonlinear animal movements and the inclusion of positioning error. Our results suggested that HR‐VPS can provide more representative estimates of movement metrics and offer an advancement for studying fine‐scale movements of aquatic organisms, but high‐precision survey techniques may be needed to test these systems.

[1]  C. Holbrook,et al.  Tracking animals in freshwater with electronic tags: past, present and future , 2013, Animal Biotelemetry.

[2]  S. Benhamou How to reliably estimate the tortuosity of an animal's path: straightness, sinuosity, or fractal dimension? , 2004, Journal of theoretical biology.

[3]  N. Metcalfe,et al.  Environmental stressors alter relationships between physiology and behaviour. , 2013, Trends in ecology & evolution.

[4]  Peter Leimgruber,et al.  From Fine-Scale Foraging to Home Ranges: A Semivariance Approach to Identifying Movement Modes across Spatiotemporal Scales , 2014, The American Naturalist.

[5]  R. Arlinghaus,et al.  Behaviour-mediated alteration of positively size-dependent vulnerability to angling in response to historical fishing pressure in a freshwater salmonid , 2016 .

[6]  A. Hearn,et al.  Performance of an ultrasonic telemetry positioning system under varied environmental conditions , 2014, Animal Biotelemetry.

[7]  J. Armstrong,et al.  What causes intraspecific variation in resting metabolic rate and what are its ecological consequences? , 2011, Proceedings of the Royal Society B: Biological Sciences.

[8]  Thomas R. Binder,et al.  Spatial and temporal variation in positioning probability of acoustic telemetry arrays: fine-scale variability and complex interactions , 2016, Animal Biotelemetry.

[9]  Thomas R Binder,et al.  An approach for filtering hyperbolically positioned underwater acoustic telemetry data with position precision estimates , 2013, Animal Biotelemetry.

[10]  G Staaks,et al.  Behaviour in a standardized assay, but not metabolic or growth rate, predicts behavioural variation in an adult aquatic top predator Esox lucius in the wild. , 2016, Journal of fish biology.

[11]  Henrik Madsen,et al.  Geolocation of North Sea cod (Gadus morhua) using hidden Markov models and behavioural switching , 2008 .

[12]  Justin M. Calabrese,et al.  ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process , 2016 .

[13]  A. Parton,et al.  Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time , 2017, 1701.05736.

[14]  Thomas M. Grothues,et al.  Testing an autonomous acoustic telemetry positioning system for fine-scale space use in marine animals , 2013 .

[15]  Kim Aarestrup,et al.  Phenotypic variation in metabolism and morphology correlating with animal swimming activity in the wild: relevance for the OCLTT (oxygen- and capacity-limitation of thermal tolerance), allocation and performance models , 2016, Conservation physiology.

[16]  S. Simpson,et al.  Anthropogenic noise increases fish mortality by predation , 2016, Nature Communications.

[17]  Martin Wæver Pedersen,et al.  Performance Assessment of Two Whole-Lake Acoustic Positional Telemetry Systems - Is Reality Mining of Free-Ranging Aquatic Animals Technologically Possible? , 2015, PloS one.

[18]  Thomas Stieglitz,et al.  The influence of environmental parameters on the performance and detection range of acoustic receivers , 2016 .

[19]  W. Heyman,et al.  Diver ecotourism and disturbance to reef fish spawning aggregations: it is better to be disturbed than to be dead. , 2010 .

[20]  S. Lamarre,et al.  Estimates of metabolic rate and major constituents of metabolic demand in fishes under field conditions: Methods, proxies, and new perspectives. , 2016, Comparative biochemistry and physiology. Part A, Molecular & integrative physiology.

[21]  N. Metcalfe,et al.  Does individual variation in metabolic phenotype predict fish behaviour and performance? , 2015, Journal of fish biology.

[22]  Roland Langrock,et al.  Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling” , 2017 .

[23]  Christopher G. Lowe,et al.  Testing a new acoustic telemetry technique to quantify long-term, fine-scale movements of aquatic animals , 2011 .

[24]  Finn Økland,et al.  Positioning of aquatic animals based on time-of-arrival and random walk models using YAPS (Yet Another Positioning Solver) , 2017, Scientific Reports.