Comparative analysis of methods for inferring successful foraging areas from Argos and GPS tracking data

Abstract

Identifying animals' successful foraging areas is a major challenge, but such comprehensive knowledge is needed for the management and conservation of wild populations. In recent decades, numerous specific analytic methods have been developed to handle tracking data and to identify preferred foraging areas. In this study, we assessed the efficiency of different track-based methods on Argos and GPS predators' tracks. We investigated (1) the consistency in the detection of foraging areas between track-based methods applied to 2 tracking data resolutions and (2) the similarity of foraging behaviour identification between track-based methods and an independent index of foraging success. We focused on methods that are commonly used in the literature: empirical descriptors of foraging effort, Hidden Markov Models (HMMs) and first passage time analysis. We applied these methods to satellite tracking data collected on 6 long-ranging elephant seals equipped with both Argos and GPS tags. Seals were also equipped with time depth recorder loggers from which we estimated an independent index, based on the drift rate and the changes in the seals' body condition, as a proxy for foraging success along the tracks. Favourable foraging zones identified by track-based methods were compared to locations where the body condition of the seals significantly increased. With or without an environmental covariate, HMMs were the most reliable for identifying successful foraging areas on both high (GPS) and low (Argos) resolution data. Areas identified by HMMs as intensively used were congruent with the locations where seals significantly increased their body condition given a 4 d metabolisation lag.

Development of an animal-borne “sonar tag” for quantifying prey availability: test deployments on northern elephant seals

“…Previous studies have enjoyed a great deal of success in studying the foraging ecology of elephant and other seal species using indirect indices of foraging based on dive behavior [e.g., 9,10,20,44,51] and horizontal movement patterns [e.g., 3,14,16,18], indices of buoyancy indicative of successful foraging [e.g., 9,10,17,20], measurements of stomach temperature as an indicator of prey consumption [52], and more recently measurements made with accelerometers to measure jaw motion [21] and acoustic transceivers designed to measure the at-sea associations of multiple animals instrumented with acoustic pinger tags [53,54]. The acoustic measurements made by the sonar tag do not themselves provide evidence that observed targets are prey, but they do provide a relative indication of the abundance and potentially size of nearby animals.…”

“…Typically, information on the relative availability of prey has been inferred from changes in the behavior of tagged animals through time [e.g., 10,15,16]. In the case of northern and southern elephant seals (Mirounga angustirostris and Mirounga leonina), for instance, small-scale changes in movement and diving behaviors have been related to changes in oceanographic variables and, presumably, changes in prey availability and perhaps prey type.…”

Motes enhance data recovery from satellite-relayed biologgers and can facilitate collaborative research into marine habitat utilization

“…7 could be construed to depths of less than 200 m (and temperatures higher than 17 °C, not shown), whereas Mote data showed that the shark dove to 300 m (and temperatures of 9 °C). Spatially, Motes logged ~96% of the GPS locations recorded by tags, which means that not only did the additional locations provide a much more complete picture of where, when and how sharks use their environment, but as GPS locations are more accurate than Argos locations, the uncertainty around animal's movements was also reduced [12,24]. Consequently, Motes have the potential to improve our knowledge of whales and sharks' behaviors both quantitatively and qualitatively.…”

Influence of sea ice phenology on the movement ecology of ringed seals across their latitudinal range

“…Traveling behaviour consists of fast, directed movements, whereas resident behaviour is identified by slow, tortuous movements thought to occur when encountering patchily distributed prey to increase foraging effort in these profitable patches (Kareiva & Odell 1987, Dragon et al 2012. Because of this large dataset and computational limitations restricting the fit of 1 large hierarchical SSM, we grouped ringed seals by capture location, tagging year and age class and fit each hierarchical SSM separately.…”