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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124004862 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595426868461568 |
|---|---|
| author | Shota Kunimatsu Hiroyuki Kurota Soyoka Muko Seiji Ohshimo Takeshi Tomiyama |
| author_facet | Shota Kunimatsu Hiroyuki Kurota Soyoka Muko Seiji Ohshimo Takeshi Tomiyama |
| author_sort | Shota Kunimatsu |
| collection | DOAJ |
| description | 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 violated, leading to potential biases in CPUE-based abundance indices (AI). In the present study, we focused on chub mackerel stock in the East Asian Marginal Seas (EAMS), where shifting fishing grounds and ocean warming have raised concerns regarding the reliability of the nominal CPUE trend. We developed a spatiotemporal machine learning approach to predict the CPUE values while taking into consideration environmental variables and changes in fish distribution. Our model accounts for unexploited areas, thereby addressing the sampling biases inherent to traditional CPUE analyses. The results suggest that recent declines in the nominal CPUE observed in Japan do not reflect the actual stock declines but instead reflect biases due to shrinking fishing areas. These findings highlight the need for more sophisticated methods in fisheries management to ensure sustainable practices and highlight the importance of considering environmental and distributional changes in fish stock assessments. |
| format | Article |
| id | doaj-art-d6c61030cef043e0bf35ccfb092aa3e3 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-d6c61030cef043e0bf35ccfb092aa3e32025-01-19T06:24:36ZengElsevierEcological Informatics1574-95412025-03-0185102944Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contractionShota Kunimatsu0Hiroyuki Kurota1Soyoka Muko2Seiji Ohshimo3Takeshi Tomiyama4National Research and Development Agency, Japan Fisheries Research and Education Agency, Fisheries Stock Assessment Center, Nagasaki Station, 1551-8 Taira-Machi, Nagasaki 851-2213, Japan; Corresponding author.National Research and Development Agency, Japan Fisheries Research and Education Agency, Fisheries Stock Assessment Center, Nagasaki Station, 1551-8 Taira-Machi, Nagasaki 851-2213, JapanNational Research and Development Agency, Japan Fisheries Research and Education Agency, Fisheries Stock Assessment Center, Nagasaki Station, 1551-8 Taira-Machi, Nagasaki 851-2213, JapanNational Research and Development Agency, Japan Fisheries Research and Education Agency, Fisheries Stock Assessment Center, Nagasaki Station, 1551-8 Taira-Machi, Nagasaki 851-2213, JapanGraduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima 739-8528, JapanIn 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 violated, leading to potential biases in CPUE-based abundance indices (AI). In the present study, we focused on chub mackerel stock in the East Asian Marginal Seas (EAMS), where shifting fishing grounds and ocean warming have raised concerns regarding the reliability of the nominal CPUE trend. We developed a spatiotemporal machine learning approach to predict the CPUE values while taking into consideration environmental variables and changes in fish distribution. Our model accounts for unexploited areas, thereby addressing the sampling biases inherent to traditional CPUE analyses. The results suggest that recent declines in the nominal CPUE observed in Japan do not reflect the actual stock declines but instead reflect biases due to shrinking fishing areas. These findings highlight the need for more sophisticated methods in fisheries management to ensure sustainable practices and highlight the importance of considering environmental and distributional changes in fish stock assessments.http://www.sciencedirect.com/science/article/pii/S1574954124004862Abundance indexHyperstabilityFisheries sampling biasFishing ground contractionModel stackingSpatiotemporal cross-validation |
| spellingShingle | Shota Kunimatsu Hiroyuki Kurota Soyoka Muko Seiji Ohshimo Takeshi Tomiyama Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction Ecological Informatics Abundance index Hyperstability Fisheries sampling bias Fishing ground contraction Model stacking Spatiotemporal cross-validation |
| title | Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction |
| title_full | Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction |
| title_fullStr | Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction |
| title_full_unstemmed | Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction |
| title_short | Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction |
| title_sort | predicting unseen chub mackerel densities through spatiotemporal machine learning indications of potential hyperdepletion in catch per unit effort due to fishing ground contraction |
| topic | Abundance index Hyperstability Fisheries sampling bias Fishing ground contraction Model stacking Spatiotemporal cross-validation |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124004862 |
| work_keys_str_mv | AT shotakunimatsu predictingunseenchubmackereldensitiesthroughspatiotemporalmachinelearningindicationsofpotentialhyperdepletionincatchperuniteffortduetofishinggroundcontraction AT hiroyukikurota predictingunseenchubmackereldensitiesthroughspatiotemporalmachinelearningindicationsofpotentialhyperdepletionincatchperuniteffortduetofishinggroundcontraction AT soyokamuko predictingunseenchubmackereldensitiesthroughspatiotemporalmachinelearningindicationsofpotentialhyperdepletionincatchperuniteffortduetofishinggroundcontraction AT seijiohshimo predictingunseenchubmackereldensitiesthroughspatiotemporalmachinelearningindicationsofpotentialhyperdepletionincatchperuniteffortduetofishinggroundcontraction AT takeshitomiyama predictingunseenchubmackereldensitiesthroughspatiotemporalmachinelearningindicationsofpotentialhyperdepletionincatchperuniteffortduetofishinggroundcontraction |