Using Machine Learning Methods to Estimate Sea Surface DMS Concentration for Studying Seabird Movements

Dimethylsulphide (DMS) has been shown to play a vital role in foraging behaviour of seabirds and suggested as a key component of olfactory navigation, yet its spatial distribution remains poorly understood due to limited point-based sampling. This study evaluates five machine learning algorithms and three ensemble models to estimate sea surface DMS concentrations in the North Atlantic using remote-sensing sources. XGBoost and an ensemble model incorporating ridge regression indicate high accuracy and outperform other methods. Also, models reflect that DMS levels are strongly influenced by nitrate and chlorophyll-a, aligning with their chemical properties and increasing the reliability of the model. The proposed approach aims to provide accurate, high-resolution DMS concentration estimates to support future research on avian olfactory navigation.