The Annals of Applied Statistics

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

E. Cantoni, J. Mills Flemming, and A. H. Welsh

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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 cluster-specific 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.

Article information

Ann. Appl. Stat. Volume 11, Number 4 (2017), 2178-2199.

Received: June 2015
Revised: June 2017
First available in Project Euclid: 28 December 2017

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Bycatch clustered count data excess of zeros random-effects hurdle models prediction


Cantoni, E.; Mills Flemming, J.; Welsh, A. H. A random-effects hurdle model for predicting bycatch of endangered marine species. Ann. Appl. Stat. 11 (2017), no. 4, 2178--2199. doi:10.1214/17-AOAS1074.

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Supplemental materials

  • Supplementary Material for the paper “A random-effects hurdle model for predicting bycatch of endangered marine species”. The supplementary file contains four sections. In the first section we give a general formulation of the random effects hurdle model. The second section presents a result about maximum likelihood estimation of the model. The third section introduces a fast bootstrap estimator and establishes its asymptotic distribution. Finally, the fourth section gives additional simulation results, as discussed in this paper.