2020
DOI: 10.1111/1365-2664.13720
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Addressing tagging location bias to assess space use by marine animals

Abstract: 1. Estimates of space use derived from animal tracking studies are often biased by where animals are tagged, with areas distant to the tagging site, in both space and time, being under-represented. 2. We develop an approach to overcome this tagging bias by quantifying the likely movements of animals after tags have failed. 3. We illustrate the approach using high accuracy Fastloc-GPS tracking data for 35 adult female green turtles Chelonia mydas equipped with satellite tags within one of the world's largest ma… Show more

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Cited by 15 publications
(12 citation statements)
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“…The density of locations in the GoM biased the KDE map toward the GoM, possibly because of tagging site bias that may underestimate the use of areas farther away from the tagging site (Hays et al. 2020). To address this, a separate KDE map was created for each region and then combined to create a single composite map.…”
Section: Resultsmentioning
confidence: 99%
“…The density of locations in the GoM biased the KDE map toward the GoM, possibly because of tagging site bias that may underestimate the use of areas farther away from the tagging site (Hays et al. 2020). To address this, a separate KDE map was created for each region and then combined to create a single composite map.…”
Section: Resultsmentioning
confidence: 99%
“…Beyond these areas, localized movement suggests that skate probably remain undersampled by recent PIT tagging efforts (2011 to present). The implication is population‐level inferences from a handful of sites may be unrepresentative, and a wider more representative spatial distribution of CR A effort would benefit analyses of habitat preferences and the estimation of population trends (Sollmann, Gardner & Belant, 2012; Sun, Fuller & Royle, 2014; Hays, Rattray & Esteban, 2020). More sampling is also required in the winter months to clarify seasonal movement patterns, especially for males with sparser detection time series, and for the smallest size classes, which remain understudied.…”
Section: Discussionmentioning
confidence: 99%
“…Indeed, much like conventional (non-spatial) capture-recapture, applications of animal movement models typically lack a clearly defined population in both space and time. Furthermore, population-level inferences about animal movement and space use are typically based on a relatively small (and non-random) sample of telemetered individuals (e.g., Hebblewhite and Haydon, 2010;Hays et al, 2020), with no attempt to account for the process by which individuals were tagged or other potential sampling biases. For example, telemetry data are often produced through sampling efforts that focus on the most accessible locations (e.g., near roads or ports) where animals are thought to be present (i.e., convenience sampling; Anderson, 2001).…”
Section: Motivationmentioning
confidence: 99%