Journal article
Rejection sampling and agent-based models for data limited fisheries
- Abstract:
- Many of the world’s fisheries are “data-limited” where the information does not allow precise determination of fish stock status and limits the development of appropriate management responses. Two approaches are proposed for use in data-limited stock management strategy evaluations to guide the evaluations and to understand the sources of uncertainty: rejection sampling methods and the incorporation of more complex socio-economic dynamics into management evaluations using agent-based models. In rejection sampling (or rejection filtering) a model is simulated many times with a wide range of priors on parameters and outcomes are compared multiple filtering criteria. Those simulations that pass all the filters form an ensemble of feasible models. The ensemble can be used to look for robust management strategies, robust to both model uncertainties. Agent-based models of fishery economics can be implemented within the rejection framework, integrating the biological and economic understanding of the fishery. A simple artificial example of a difference equation bio-economic model is given to demonstrate the approach. Then rejection sampling is applied to an agent-based model for the hairtail (Trichiurus japonicas) fishery, where an operating model is constructed with rejection/agent-based methods and compared to known data and analyses of the fishery. The usefulness of information and rejection filters are illuminated and efficacy examined. The methods can be helpful for strategic guidance where multiple states of nature are possible as a part of management strategy evaluation.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.0MB, Terms of use)
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- Publisher copy:
- 10.3389/fmars.2024.1243954
Authors
+ Gordon and Betty Moore Foundation
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- Funder identifier:
- https://ror.org/006wxqw41
- Publisher:
- Frontiers Media
- Journal:
- Frontiers in Marine Science More from this journal
- Volume:
- 11
- Article number:
- 1243954
- Publication date:
- 2024-01-19
- Acceptance date:
- 2024-01-03
- DOI:
- EISSN:
-
2296-7745
- Language:
-
English
- Keywords:
- Pubs id:
-
1625688
- Local pid:
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pubs:1625688
- Deposit date:
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2025-02-23
- ARK identifier:
Terms of use
- Copyright holder:
- Carrella et al.
- Copyright date:
- 2024
- Rights statement:
- Copyright © 2024 Carrella, Powers, Saul, Bailey, Payette, Vert-pre, Ananthanarayanan, Drexler, Dorsett and Madsen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Licence:
- CC Attribution (CC BY)
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