AI 中文总结
本研究构建混合统计与机器学习堆叠集成模型，结合遥感CHL、SST等数据，预测西赤道印度洋黄鳍金枪鱼月度渔获量，提升了预测性能，揭示卫星数据的预测价值有限。
AI 中文摘要
黄鳍金枪鱼（Thunnus albacares）是印度洋具有重要经济价值的中上层物种，但其月度渔获量的变化难以预测，因为报告的渔获量受海洋生产力、热栖息地、季风季节性、渔场特征及船队行为的影响。本研究开发了一种基于遥感的统计与机器学习堆叠集成模型，用于模拟2003-2024年西赤道印度洋5个渔场的黄鳍金枪鱼月度渔获量，将月度渔业记录与卫星衍生的叶绿素-a浓度（CHL）及海表温度（SST）相结合。采用线性混合模型（LMMs）和广义加性混合模型（GAMM）来表征可解释的当前及滞后环境效应，同时利用随机森林（Random Forest）模型捕捉滞后、滚动、季节性、异常值及空间预测因子间的非线性相互作用；通过2008-2019年新冠疫情前评估期的扩展窗口验证以减少时间泄露。在单个模型中，GAMM取得了对数尺度下的最佳性能，R平方值为0.137，均方根误差（RMSE）为1.284；而RF-E6在千克尺度上实现了最低RMSE。非负岭堆叠集成模型将对数尺度R平方值提升至0.177，RMSE为1.254，平均绝对误差（MAE）为1.000。这些结果表明，卫星衍生的CHL和SST包含对月度渔获量建模有用但并不完整的预测信息。
英文摘要
Yellowfin tuna (Thunnus albacares) is an economically important pelagic species in the Indian Ocean, but its monthly catch variability is difficult to predict because reported catch is influenced by ocean productivity, thermal habitat, monsoon seasonality, fishing-ground characteristics, and fleet behavior. This study develops a remote sensing-based statistical and machine learning stacked ensemble for modeling monthly yellowfin tuna catch across five fishing grounds in the western equatorial Indian Ocean during 2003-2024. Monthly fishery records were integrated with satellite-derived chlorophyll-a concentration (CHL) and sea surface temperature (SST). Linear mixed models (LMMs) and a generalized additive mixed model (GAMM) were used to represent interpretable current and lagged environmental effects, while Random Forest models captured nonlinear interactions among lagged, rolling, seasonal, anomaly, and spatial predictors. Expanding-window validation over the pre-COVID evaluation period from 2008 to 2019 was used to reduce temporal leakage. Among the individual models, the GAMM achieved the best log-scale performance, with an R-squared value of 0.137 and an RMSE of 1.284, while RF-E6 achieved the lowest RMSE on the kilogram scale. The nonnegative Ridge stacked ensemble improved the log-scale R-squared value to 0.177, with an RMSE of 1.254 and an MAE of 1.000. These results show that satellite-derived CHL and SST contain useful but incomplete predictive information for monthly catch modeling.
