Using a Random Forest model to predict the distribution of benthic biomass in the Bering Sea

Marine benthic invertebrates provide a critical resource base for several higher trophic level consumers, such as seabirds and marine mammals. Exploring the distribution and movements of higher level consumers requires maps of benthic resources at appropriately large scales. Logistic constraints render it improbable that a spatially continuous map of benthic biomass can be provided by sampling alone, and predictive modeling offers a valuable alternative to create such maps. Here, we describe how to use an algorithmic model that overcomes many weaknesses of traditional data models to predict benthic biomass at large spatial scales. We use a decision-tree modeling approach (RandomForest) to link benthic biomass to chlorophyll a concentration, sea surface temperature, sea ice cover, depth, distance to coastline, sea bottom temperature and sea bottom salinity, and present a digital map of predicted benthic biomass across the Bering Sea.海洋底栖无脊椎动物（marine benthic invertebrates）是海鸟、海洋哺乳动物等多个更高营养级消费者的关键资源基底。探究更高营养级消费者的分布与移动规律，需基于适用于大空间尺度的底栖资源分布图。 受采样后勤约束限制，仅通过野外采样手段难以获取空间连续的底栖生物量分布图，而预测建模则为这类地图的构建提供了极具价值的替代方案。本文介绍了如何利用一种可克服传统数据模型诸多缺陷的算法模型，实现大空间尺度下的底栖生物量预测。 我们采用决策树建模方法（随机森林，RandomForest），将底栖生物量与叶绿素a浓度、海表温度、海冰覆盖度、水深、距岸距离、海底温度及海底盐度进行关联，并绘制了白令海全域的预测底栖生物量数字地图。