{"title":"利用机器学习方法预测鱼类繁殖情况:阿拉伯青鱼案例研究","authors":"Hiroshi Okamura , Shoko Morita , Hiroshi Kuroda","doi":"10.1016/j.fishres.2024.107096","DOIUrl":null,"url":null,"abstract":"<div><p>Fish recruitment prediction is one of the most challenging topics in fisheries science. The recruitment of arabesque greenling in northern Hokkaido, Japan, which has been annually assessed for population size, greatly fluctuates. Whether the cause of fluctuation is environment or overfishing is controversial. We use a machine learning method for predicting the recruitment of arabesque greenling. Biological, fisheries-related, and environmental factors were included in the predictive models as feature variables. A gradient boosting model (GBM) showed better predictive performance compared with a simple hockey-stick stock-recruitment curve (HS), linear regression model (LRM), generalized additive model (GAM), and a random forest model (RFM) in terms of relative bias and relative root mean square error for recruitment prediction in the last 5 years. The most influential feature for GBM was spawning stock biomass in the last year, followed by the fishing rate for older fish and recruitment at the last year. The sea temperatures (STs) at the depth of 0, 50, 100, and 200 m were unimportant predictors in GBM. The difference in important predictors among models suggests the importance of nonlinearity and incorporating multiple variables simultaneously. This study highlights the potential usefulness of GBM for fish recruitment forecast and thereby sustainable fisheries management.</p></div>","PeriodicalId":50443,"journal":{"name":"Fisheries Research","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting fish recruitment using machine learning methods: A case study of arabesque greenling\",\"authors\":\"Hiroshi Okamura , Shoko Morita , Hiroshi Kuroda\",\"doi\":\"10.1016/j.fishres.2024.107096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Fish recruitment prediction is one of the most challenging topics in fisheries science. The recruitment of arabesque greenling in northern Hokkaido, Japan, which has been annually assessed for population size, greatly fluctuates. Whether the cause of fluctuation is environment or overfishing is controversial. We use a machine learning method for predicting the recruitment of arabesque greenling. Biological, fisheries-related, and environmental factors were included in the predictive models as feature variables. A gradient boosting model (GBM) showed better predictive performance compared with a simple hockey-stick stock-recruitment curve (HS), linear regression model (LRM), generalized additive model (GAM), and a random forest model (RFM) in terms of relative bias and relative root mean square error for recruitment prediction in the last 5 years. The most influential feature for GBM was spawning stock biomass in the last year, followed by the fishing rate for older fish and recruitment at the last year. The sea temperatures (STs) at the depth of 0, 50, 100, and 200 m were unimportant predictors in GBM. The difference in important predictors among models suggests the importance of nonlinearity and incorporating multiple variables simultaneously. This study highlights the potential usefulness of GBM for fish recruitment forecast and thereby sustainable fisheries management.</p></div>\",\"PeriodicalId\":50443,\"journal\":{\"name\":\"Fisheries Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fisheries Research\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165783624001607\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FISHERIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fisheries Research","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165783624001607","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FISHERIES","Score":null,"Total":0}
Forecasting fish recruitment using machine learning methods: A case study of arabesque greenling
Fish recruitment prediction is one of the most challenging topics in fisheries science. The recruitment of arabesque greenling in northern Hokkaido, Japan, which has been annually assessed for population size, greatly fluctuates. Whether the cause of fluctuation is environment or overfishing is controversial. We use a machine learning method for predicting the recruitment of arabesque greenling. Biological, fisheries-related, and environmental factors were included in the predictive models as feature variables. A gradient boosting model (GBM) showed better predictive performance compared with a simple hockey-stick stock-recruitment curve (HS), linear regression model (LRM), generalized additive model (GAM), and a random forest model (RFM) in terms of relative bias and relative root mean square error for recruitment prediction in the last 5 years. The most influential feature for GBM was spawning stock biomass in the last year, followed by the fishing rate for older fish and recruitment at the last year. The sea temperatures (STs) at the depth of 0, 50, 100, and 200 m were unimportant predictors in GBM. The difference in important predictors among models suggests the importance of nonlinearity and incorporating multiple variables simultaneously. This study highlights the potential usefulness of GBM for fish recruitment forecast and thereby sustainable fisheries management.
期刊介绍:
This journal provides an international forum for the publication of papers in the areas of fisheries science, fishing technology, fisheries management and relevant socio-economics. The scope covers fisheries in salt, brackish and freshwater systems, and all aspects of associated ecology, environmental aspects of fisheries, and economics. Both theoretical and practical papers are acceptable, including laboratory and field experimental studies relevant to fisheries. Papers on the conservation of exploitable living resources are welcome. Review and Viewpoint articles are also published. As the specified areas inevitably impinge on and interrelate with each other, the approach of the journal is multidisciplinary, and authors are encouraged to emphasise the relevance of their own work to that of other disciplines. The journal is intended for fisheries scientists, biological oceanographers, gear technologists, economists, managers, administrators, policy makers and legislators.