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通过动态足迹分析识别容易发生急剧变化的鱼群

Alejandro V Cano¹, Olaf P Jensen², Vasilis Dakos¹

¹Institut des Sciences de l'Évolution de Montpellier, Univ. de Montpellier, CNRS, Institut de recherche pour le développement, Montpellier 34090, France.

Proceedings of the National Academy of Sciences of the United States of America

|August 18, 2025

概括

鱼群可以经历突然的变化,影响渔业管理. 这项研究开发了一种方法来检测面临这种变化风险的人群,有助于制定更为预防性的管理策略.

科学领域:

生态生态学生态生态学
渔业科学渔业科学
时间序列分析时间序列分析

背景情况:

鱼群生物量表现出波动,包括生产率的急剧变化.
整合突然转移预测到库存评估和渔业管理是具有挑战性的,因为预测困难.

研究的目的:

开发一种机制不可知,特定于环境的方法,用于检测容易发生生物质突然转移的鱼群.
利用鱼群时间序列的动态性质来预测突然变化.

主要方法:

分类的鱼群生物质时间序列分为突然和非突然的类别.
使用动态足迹 (变异,自相关) 预测这些分类形状在三个全球数据集.

主要成果:

成功检测到的种群容易发生突然的变化,尽管数据有限,但准确度中等.
在11个大型海洋生态系统地区确定了50种鱼类种群,面临未来突然变化的风险.

结论:

开发的方法为鱼群稳定提供了关键的见解.
确定的有风险种群可以从更为预防性的渔业管理策略中受益.

关键词:

机器学习是机器学习.人口动态人口动态政权的转变政权的转变稳定的稳定性稳定的稳定性

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