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Empirical harvest strategies for data-poor fisheries: a review of the literature

journal contribution
posted on 2023-05-18, 17:30 authored by Dowling, NA, Dichmont, CM, Haddon, M, Smith, DC, Smith, ADM, Sainsbury, K
Harvest strategy approaches based around empirical indicators and/or control rules are beginning to be accepted in a growing range of data- and capacity-poor fisheries. While there is an increasing body of work around developing empirical indicators and control rules in data-poor contexts, this has typically been done on a case-specific basis. There remains a need for general guidance on formulating control rules that link empirical indicators with suitable management responses. Additionally, in the data-poor context, most literature has focused on empirical indicators and assessments, with less focus on decision rules and the incorporation of indicators and assessments in a harvest strategy framework. This review considers a range of harvest strategy options, focusing on empirical indicators and decision rules available for data-poor species and fisheries. These clearly illustrate that a paucity of information is not a reason to avoid developing harvest strategies, and that a range of pragmatic approaches are available regardless of the available data, life-history of the target species, nature of fishing operations, or the available research capacity. There is considerable scope for further work in this field, but arguably there is a comprehensive repository of approaches and decision rules that, when combined with the guidelines, form a solid foundation and toolkit for all but the most data-poor species and fisheries.

History

Publication title

Fisheries Research

Volume

171

Pagination

141-153

ISSN

0165-7836

Department/School

Institute for Marine and Antarctic Studies

Publisher

Elsevier Science Bv

Place of publication

Po Box 211, Amsterdam, Netherlands, 1000 Ae

Rights statement

Crown copyright 2014

Repository Status

  • Restricted

Socio-economic Objectives

Fisheries - wild caught not elsewhere classified

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