A Methodological Framework to Estimate the Site Fidelity of Tagged Animals Using Passive Acoustic Telemetry

The rapid expansion of the use of passive acoustic telemetry technologies has facilitated unprecedented opportunities for studying the behavior of marine organisms in their natural environment. This technological advance would greatly benefit from the parallel development of dedicated methodologies accounting for the variety of timescales involved in the remote detection of tagged animals related to instrumental, environmental and behavioral events. In this paper we propose a methodological framework for estimating the site fidelity (“residence times”) of acoustic tagged animals at different timescales, based on the survival analysis of continuous residence times recorded at multiple receivers. Our approach is validated through modeling and applied on two distinct datasets obtained from a small coastal pelagic species (bigeye scad, Selar crumenophthalmus) and a large, offshore pelagic species (yellowfin tuna, Thunnus albacares), which show very distinct spatial scales of behavior. The methodological framework proposed herein allows estimating the most appropriate temporal scale for processing passive acoustic telemetry data depending on the scientific question of interest. Our method provides residence times free of the bias inherent to environmental and instrumental noise that can be used to study the small scale behavior of acoustic tagged animals. At larger timescales, it can effectively identify residence times that encompass the diel behavioral excursions of fish out of the acoustic detection range. This study provides a systematic framework for the analysis of passive acoustic telemetry data that can be employed for the comparative study of different species and study sites. The same methodology can be used each time discrete records of animal detections of any nature are employed for estimating the site fidelity of an animal at different timescales

Field data: Survival curves of CRTs at small timescales.

<p>Survival curves of CRTs obtained for MBP ranging between 10 up to 120 min by intervals of 10 min (see legend) in semi-logarithmic scale for (A) bigeye scad (B) yellowfin tuna.</p

Field data: Survival curves of CRTs at large timescales.

<p>Survival curves calculated for <i>MBP</i>_<i>n</i> ranging between 2 h and 48 h (see caption) for (A) Bigeye scad (B) yellowfin tuna.</p

Scenario 1: Survival curves of CRTs.

<p>The survival curves are obtained for different values of <i>MBP</i>_<i>n</i> (see legend) and different noise parameters: <i>η</i> = 1 (A), 0.1 (B), 0.01 (C) and 0.005 (D). The <i>y</i> axis is in logarithmic scale. Black line: the theoretical survival curve of residence times <i>S</i>(<i>t</i>) = exp(−0.0002<i>t</i>).</p

Experimental data.

<p>Columns from left to right: species, number of tagged individuals, number of instrumented FADs, location and acoustic telemetry equipment (receiver and tag type) for the two datasets used in this study.</p

Scenario 1: Renormalized sum of squared residuals.

<p>The rSSR is calculated among pairs of survival curves of residence times with Δ_<i>MBP</i> = 100 and different values of the noise parameter: <i>η</i> = 1 (A), 0.1 (B), 0.01 (C) and 0.005 (D). The vertical dashed line represent the MBP value at which the survival curve of residence times mostly approached the theoretical curve. Insets: the same in semi-logarithmic scale.</p

Scenario 3: Survival curves of CRT and renormalized sum of squared residuals.

<p>(A) Survival curves of CRT obtained for different values of <i>MBP</i>_<i>n</i> (see legend). (B) rSSR in semi-logarithmic scale calculated among pairs of survival curves of residence times with variable Δ_<i>MBP</i> (see legend). Inset: the same in semi-logarithmic scale.</p