PREDICTING SURFACE SEAWATER DIMETHYLSULFIDE (DMS) CONCENTRATIONS USING A MACHINE LEARNING ALGORITHM (TREENET)

In order to investigate ecological complexities of surface ocean dimethylsulfide (DMS) distribution, a machine learning algorithm (TreeNet) was combined with ArcGIS to create informative predictions of marine DMS concentrations on a global scale. Monthly climatologies of marine DMS concentrations were calculated from 17 environmental predictor variables. We present a use of spatial modeling for predicting DMS concentrations at the sea surface using a machine learning algorithm. Mean squared error (MSE) and r2 values were applied to evaluate model performance among a series of random data subsets extracted from NOAA’s Pacific Marine Environmental Laboratory DMS database. Monthly r2 values ranged from 0.29 to 0.60. Well known areas of high DMS concentrations were closely reproduced. We found that photosynthetically active radiation, solar radiation dose, and standard deviation of sea surface temperature played the most important roles in determining seawater DMS concentrations. These concepts, tools and data layers may be used for further hypothesis testing, and to objectively predict the spatial distribution of ocean compounds. This will enable improved global understanding of ocean biogeochemical systems.为探究表层海洋二甲基硫（dimethylsulfide, DMS）分布的生态复杂性，本研究将机器学习算法TreeNet与ArcGIS相结合，生成了全球尺度下具备参考价值的海洋DMS浓度预测结果。本研究基于17项环境预测变量，计算得到海洋DMS浓度的月尺度气候态数据集。本研究展示了利用机器学习算法开展海表DMS浓度空间预测的研究范式。采用均方误差（mean squared error, MSE）与决定系数（r²），对从美国国家海洋和大气管理局（National Oceanic and Atmospheric Administration, NOAA）太平洋海洋环境实验室DMS数据库中提取的多组随机数据子集的模型性能进行评估。月尺度下的决定系数r²取值范围为0.29至0.60。研究准确复现了已知的高DMS浓度海域分布特征。本研究发现，光合有效辐射（photosynthetically active radiation）、太阳辐射剂量以及海表温度标准差是影响海水DMS浓度的核心驱动因子。上述研究思路、工具与数据图层可用于后续假说验证，并可客观预测海洋化合物的空间分布格局，进而助力提升对全球海洋生物地球化学系统的整体认知水平。