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Scientific article
Open access
English

A random-effects hurdle model for predicting bycatch of endangered marine species

Published inThe Annals of Applied Statistics, vol. 11, no. 4, p. 2178-2199
Publication date2017
Abstract

Understanding and reducing the incidence of accidental bycatch, particularly for vulnerable species such as sharks, is a major challenge for contemporary fisheries management worldwide. Bycatch data, most often collected by at-sea observers during fishing trips, are clustered by trip and/or vessel and typically involve a large number of zero counts and very few positive counts. Though hurdle models are very popular for count data with excess zeros, models for clustered forms have received far less attention. Here we present a novel random-effects hurdle model for bycatch data that makes available accurate estimates of bycatch probabilities as well as other clusterspecific targets. These are essential for informing conservation and management decisions as well as for identifying bycatch hotspots, often considered the first step in attempting to protect endangered marine species. We validate our methodology through simulation and use it to analyze bycatch data on critically endangered hammerhead sharks from the U.S. National Marine Fisheries Service Pelagic Observer Program.

Keywords
  • Bycatch
  • Clustered count data
  • Excess of zeros
  • Random-effects hurdle
  • Models
  • Prediction
Citation (ISO format)
CANTONI, Eva, MILLS FLEMMING, J., WELSH, A. H. A random-effects hurdle model for predicting bycatch of endangered marine species. In: The Annals of Applied Statistics, 2017, vol. 11, n° 4, p. 2178–2199. doi: 10.1214/17-AOAS1074
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Article (Published version)
accessLevelPublic
Identifiers
ISSN of the journal1932-6157
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