Picophytoplankton, the smallest phytoplankton (<3 micron), contribute significantly to primary production in the oligotrophic South China Sea. To improve our ability to predict picophytoplankton abundances in the South China Sea and infer the underlying mechanisms, we compared four machine learning algorithms to estimate the horizontal and vertical distributions of picophytoplankton abundances. The inputs of the algorithms include spatiotemporal (longitude, latitude, sampling depth and date) and environmental variables (sea surface temperature, chlorophyll, and light). The algorithms were fit to a dataset of 2442 samples collected from 2006 to 2012. We find that the Boosted Regression Trees (BRT) gives the best prediction performance with R2 ranging from 77% to 85% for Chl a concentration and abundances of three picophytoplankton groups. The model outputs confirm that temperature and light play important roles in affecting picophytoplankton distribution. Prochlorococcus, Synechococcus, and picoeukaryotes show decreasing preference to oligotrophy. These insights are reflected in the vertical patterns of Chl a and picoeukaryotes that form subsurface maximal layers in summer and spring, contrasting with those of Prochlorococcus and Synechococcus that are most abundant at surface. Our forecasts suggest that, under the “business-as-usual” scenario, total Chl a will decrease but Prochlorococcus abundances will increase significantly to the end of this century. Synechococcus abundances will also increase, but the trend is only significant in coastal waters. Our study has advanced the ability of predicting picophytoplankton abundances in the South China Sea and suggests that BRT is a useful machine learning technique for modelling plankton distribution.

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一种模拟南海微型浮游植物丰度的机器学习方法

微型浮游植物是最小的浮游植物（<3 微米），对贫营养南海的初级生产有重要贡献。为了提高我们预测南海微型浮游植物丰度并推断其潜在机制的能力，我们比较了四种机器学习算法来估计微型浮游植物丰度的水平和垂直分布。算法的输入包括时空（经度、纬度、采样深度和日期）和环境变量（海面温度、叶绿素和光）。该算法适用于从 2006 年到 2012 年收集的 2442 个样本的数据集。我们发现增强回归树 (BRT) 提供了最佳预测性能，对于三个微型浮游植物群的 Chl a 浓度和丰度，R2 范围为 77% 到 85% . 模型输出证实温度和光在影响微型浮游植物分布方面起着重要作用。原绿球藻、聚球藻和微核生物对寡营养的偏好降低。这些见解反映在在夏季和春季形成地下最大层的 Chl a 和微核生物的垂直模式中，与表面最丰富的原绿球藻和聚球藻形成对比。我们的预测表明，在“一切照旧”的情景下，到本世纪末，总 Chl a 将减少，但原绿球藻丰度将显着增加。聚球藻的丰度也将增加，但这一趋势仅在沿海水域显着。