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

Year: 2025

Type: conference-paper

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15237116

Authors: Liu, Meixuan, Benitez-Paez, Fernando, Padget, Oliver, Kishkinev, Dmitry, Demšar, Urška

Institutions: University of St Andrews, University of Liverpool, Keele University

Language: English

Topic: Marine and coastal ecosystems

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Life below water

Open Access status: green

OpenAlex ID: W6930776314

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