Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction

Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction

In fisheries management, accurate estimates of fish stock abundances are crucial for sustainable harvesting practices. Traditional methods often rely on catch-per-unit-effort (CPUE) data, assuming fishing effort is uniformly distributed across the stock range. However, this assumption is often viola...

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Bibliographic Details
Main Authors: Shota Kunimatsu, Hiroyuki Kurota, Soyoka Muko, Seiji Ohshimo, Takeshi Tomiyama
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Ecological Informatics
Subjects:
Abundance index
Hyperstability
Fisheries sampling bias
Fishing ground contraction
Model stacking
Spatiotemporal cross-validation
Online Access:http://www.sciencedirect.com/science/article/pii/S1574954124004862
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