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Modelling multiple fishing gear efficiencies and abundance for aggregated populations using fishery or survey data

Citation

Zhou, S and Klaer, NL and Daley, RM and Zhu, Z and Fuller, M and Smith, ADM, Modelling multiple fishing gear efficiencies and abundance for aggregated populations using fishery or survey data, ICES Journal of Marine Science, 71, (9) pp. 2436-2447. ISSN 1054-3139 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 International Council for the Exploration of the Sea

DOI: doi:10.1093/icesjms/fsu068

Abstract

Fish and wildlife often exhibit an aggregated distribution pattern, whereas local abundance changes constantly due to movement. Estimating population density or size and survey detectability (i.e. gear efficiency in a fishery) for such elusive species is technically challenging. We extend abundance and detectability (N-mixture) methods to deal with this difficult situation, particularly for application to fish populations where gear efficiency is almost never equal to one. The method involves a mixture of statistical models (negative binomial, Poisson, and binomial functions) at two spatial scales: between-cell and within-cell. The innovation in this approach is to use more than one fishing gear with different efficiencies to simultaneously catch (sample) the same population in each cell at the same time-step. We carried out computer simulations on a range of scenarios and estimated the relevant parameters using a Bayesian technique. We then applied the method to a demersal fish species, tiger flathead, to demonstrate its utility. Simulation results indicated that the models can disentangle the confounding parameters in gear efficiency and abundance, and the accuracy generally increases as sample size increases. A joint negative binomial–Poisson model using multiple gears gives the best fit to tiger flathead catch data, while a single gear yields unrealistic results. This cross-sampling method can evaluate gear efficiency cost effectively using existing fishery catch data or survey data. More importantly, it provides a means for estimating gear efficiency for gear types (e.g. gillnets, traps, hook and line, etc.) that are extremely difficult to study using field experiments.

Item Details

Item Type:Refereed Article
Keywords:aggregated distribution, biomass, catchability, catch efficiency, detection, fishing gear, negative binomial
Research Division:Environmental Sciences
Research Group:Environmental Science and Management
Research Field:Environmental Management
Objective Division:Environment
Objective Group:Environmental and Natural Resource Evaluation
Objective Field:Environmental Lifecycle Assessment
Author:Daley, RM (Mr Ross Daley)
ID Code:117019
Year Published:2014
Web of Science® Times Cited:2
Deposited By:Sustainable Marine Research Collaboration
Deposited On:2017-05-29
Last Modified:2017-10-18
Downloads:0

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