Residency and space use estimation methods based on passive acoustic telemetry data

Abstract

Acoustic telemetry has helped overcome many of the challenges faced when studying the movement ecology of aquatic species, allowing to obtain unprecedented amounts of data. This has made it into one of the most widely used methods nowadays. Many ways to analyse acoustic telemetry data have been made available and deciding on how to analyse the data requires considering the type of research objectives, relevant properties of the data (e.g., resolution, study design, equipment), habits of the study species, researcher experience, among others. To ease this decision process, here we showcase (1) some of the methods used to estimate pseudo-positions and positions from raw acoustic telemetry data, (2) methods to estimate residency and (3) methods to estimate two-dimensional home and occurrence range using geometric or hull-based methods and density-distribution methods, a network-based approach, and three-dimensional methods. We provide examples of some of these were tested using a sample of real data. With this we intend to provide the necessary background for the selection of the method(s) that better fit specific research objectives when using acoustic telemetry.

Individual residency behaviours and seasonal long-distance movements in acoustically tagged Caribbean reef sharks in the Cayman Islands

“…Residency. The extensive use of acoustic telemetry and typically large data sets have resulted in a wide range of analyses being used to assess movements of tagged sharks, including Residency Index (RI), Site-Fidelity Index (SFI), minimum linear displacement (MLD) and network analysis [2,11,12,42,[62][63][64]. The terms 'residency' and 'site-fidelity' are used interchangeably in the literature referring to either the study area [11,49,65,66] and/or specific…”

Long-distance migrations and seasonal movements of meagre (Argyrosomus regius), a large coastal predator, along the Iberian Peninsula coast

“…An overall residency index (IR) was calculated for each individual, by dividing the number of days a fish was detected (D D ) by the number of days between its release and last detection (i.e., total period; T P ). This index ranges from 0 (no residency) to 1 (full-time resident), representing the percentage of time at liberty that each fish spent within the monitored areas [ 34 ].…”

Comparing acoustic and satellite telemetry: an analysis quantifying the space use of Chelonia mydas in Bimini, Bahamas

“…The package was then used to fit several continuous time movement models (CTMM) to the data of each individual to estimate autocorrelation and positions, where the model with the lowest Akaike information criterion (AIC) score was selected per individual [ 82 ]. To produce results that are reflective of and consistent with previous published literature, particularly acoustic studies [ 62 , 84 ], location errors were not incorporated into the CTMM and resulting RD estimates. Weighted AKDEs were estimated to reduce bias from irregular sampling frequencies and locations over land were not removed, but land boundaries were incorporated into the AKDE function.…”