Journal Article

Relating trap capture to abundance: a hierarchical state-space model applied to black sea bass (<i>Centropristis striata</i>)

Kyle W. Shertzer, Nathan M. Bacheler, Lewis G. Coggins and John Fieberg

in ICES Journal of Marine Science

Volume 73, issue 2, pages 512-519
Published in print February 2016 | ISSN: 1054-3139
Published online November 2015 | e-ISSN: 1095-9289 | DOI: https://dx.doi.org/10.1093/icesjms/fsv197
Relating trap capture to abundance: a hierarchical state-space model applied to black sea bass (Centropristis striata)

More Like This

Show all results sharing these subjects:

  • Environmental Science
  • Marine and Estuarine Biology

GO

Show Summary Details

Preview

Traps are among the most common gears used to capture fish and crustaceans. When traps are deployed in surveys, the data gathered are often used to develop an index of abundance. However, trap catches are known to saturate over time for various reasons, such as space limitation of the gear or intraspecific interactions, and these features can dissociate the catch from local abundance. In this study, we develop a hierarchical state-space model of trap dynamics that is fit to data in a Bayesian framework. The model links trap catch to estimated local abundance, and additionally provides direct estimates of capture probability. For demonstration, we apply the model to data on black sea bass (Centropristis striata), which were collected using chevron traps combined with video cameras to give continuous-time observations of trap entries and exits. Results are consistent with the hypothesis that trap catch is generally proportional to local abundance. The model has potential application to surveys where animals not only enter a trap, but also may exit, such that the apparent trap saturation occurs because the system approaches equilibrium.

Keywords: abundance estimation; Bayesian analysis; black sea bass; fish traps

Journal Article.  4706 words.  Illustrated.

Subjects: Environmental Science ; Marine and Estuarine Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.