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.