Using machine learning to inform harvest control rule design in complex fishery settings Article Swipe

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Felipe Montealegre‐Mora , Carl Boettiger , Carl J. Walters , Christopher L. Cahill ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2412.12400

In fishery science, harvest management of size-structured stochastic populations is a long-standing and difficult problem. Rectilinear precautionary policies based on biomass and harvesting reference points have now become a standard approach to this problem. While these standard feedback policies are adapted from analytical or dynamic programming solutions assuming relatively simple ecological dynamics, they are often applied to more complicated ecological settings in the real world. In this paper we explore the problem of designing harvest control rules for partially observed, age-structured, spasmodic fish populations using tools from reinforcement learning (RL) and Bayesian optimization. Our focus is on the case of Walleye fisheries in Alberta, Canada, whose highly variable recruitment dynamics have perplexed managers and ecologists. We optimized and evaluated policies using several complementary performance metrics. The main questions we addressed were: 1. How do standard policies based on reference points perform relative to numerically optimized policies? 2. Can an observation of mean fish weight, in addition to stock biomass, aid policy decisions?

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Fishery

Computer Science

Artificial Intelligence

Biology

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Control (management) Fishery Computer science Artificial intelligence Biology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2412.12400
PDF: https://arxiv.org/pdf/2412.12400
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4405561949

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DOI: https://doi.org/10.48550/arxiv.2412.12400

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Title: Using machine learning to inform harvest control rule design in complex fishery settings

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-12-16

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Authors: Felipe Montealegre‐Mora, Carl Boettiger, Carl J. Walters, Christopher L. Cahill

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Landing page: https://arxiv.org/abs/2412.12400

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PDF URL: https://arxiv.org/pdf/2412.12400

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Open access: Yes

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OA status: green

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Concepts: Control (management), Fishery, Computer science, Artificial intelligence, Biology

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Cited by: 0

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Related works (count): 10

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