Mesozooplankton are a crucial link between primary producers and higher trophic levels and play a vital role in marine food webs, biological carbon pumps, and sustaining fishery resources. However, the global distribution of mesozooplankton biomass and the relevant controlling mechanisms remain elusive. We compared four machine learning algorithms (Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine) to model the spatiotemporal distributions of global mesozooplankton biomass. These algorithms were trained on a compiled dataset of published mesozooplankton biomass observations with corresponding environmental predictors from contemporaneous satellite observations (temperature, chlorophyll, salinity, and mixed layer depth). We found that Random Forest achieved the best predictive accuracy with R2 and RMSE (Root Mean Standard Error) of 0.57 and 0.39, respectively. Also, the global distribution of mesozooplankton biomass predicted by the Random Forest model was more consistent with the observational data than other models. We used the Random Forest model to create a global map of mesozooplankton biomass which serves as a reference for validating process-based ecosystem models. The model outputs confirm that environmental factors, especially surface Chl a, a proxy for prey availability, significantly correlate with the spatiotemporal distribution of mesozooplankton biomass. The scaling relationship between the mesozooplankton biomass and Chl a can be used as an emergent constraint for model validation and development. Moreover, our model predicts that the global total mesozooplankton biomass will decrease by 3% by the end of this century under the “business-as-usual” scenarios, potentially reducing fishery production and carbon sequestration. Our study contributes to predicting global mesozooplankton biomass and provides deep insights into the underlying environmental impacts on the distribution of mesozooplankton biomass.

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使用机器学习对全球中层浮游生物量进行建模


中浮游动物是初级生产者和较高营养级之间的重要纽带，在海洋食物网、生物碳泵和维持渔业资源方面发挥着至关重要的作用。然而，中浮游动物生物量的全球分布和相关控制机制仍然难以捉摸。我们比较了四种机器学习算法 （Boosted Regression Trees、Random Forest、Artificial Neural Network 和 Support Vector Machine） 来模拟全球中浮游动物生物量的时空分布。这些算法是在已发布的中浮游动物生物量观测的编译数据集上训练的，该数据集具有来自同时期卫星观测的相应环境预测因子（温度、叶绿素、盐度和混合层深度）。我们发现随机森林实现了最佳的预测准确性，R2 和 RMSE（均值根标准误差）分别为 0.57 和 0.39。此外，随机森林模型预测的中浮游动物生物量的全球分布与其他模型相比，与观测数据更一致。我们使用随机森林模型创建了中浮游动物生物量的全球地图，作为验证基于过程的生态系统模型的参考。模型输出证实，环境因素，尤其是表面 Chl a，猎物可用性的代理，与中浮游动物生物量的时空分布显着相关。中浮游动物生物量与 Chl a 之间的缩放关系可以作为模型验证和开发的紧急约束。 此外，我们的模型预测，在“一切照旧”的情况下，到本世纪末，全球中浮游动物总生物量将减少 3%，这可能会减少渔业生产和碳封存。我们的研究有助于预测全球中浮游动物生物量，并深入了解环境对中浮游动物生物量分布的潜在影响。