Implementation of Machine - Learning Models to study Habitat Suitability for Deep-Diving Cetaceans in the Ikarian Basin
Year: 2025
Type: conference-paper
Abstract: The Ikarian Basin, located in the eastern Mediterranean Sea, is home to three deep-diving cetacean species: sperm whales (Physeter macrocephalus), goose-beaked whales (Ziphius cavirostris), and Risso’s dolphins (Grampus griseus). These species, classified as endangered or vulnerable in the Mediterranean by the IUCN, face multiple threats, including overexploitation of fish stocks, habitat degradation, and the effects of climate change. Understanding their biology and ecology - migrations, habitat use, behavioural patterns, and residency dynamics - is essential for the development of effective conservation strategies. Modelling is a key tool for predicting species movements and habitat suitability. This study analyses the habitat suitability of $\mathbf{4 1}$ observations of Grampus griseus, $\mathbf{8 7}$ of Physeter macrocephalus, and 95 of Ziphius cavirostris, using depth, slope, and distance from shore as predictor variables. Using a Random Forest model, species were accurately classified, achieving an out-of-bag (OOB) error rate of $\mathbf{1 0. 7 6 \%}$. Results indicate all environmental variables significantly influence species occurrence. These findings underscore the importance of the Ikarian Basin as a suitable habitat for deepdiving cetaceans, providing a foundation for proposing the area as a potential Important Marine Mammal Area (IMMA). Further research incorporating logged behavioural data and acoustic detections is needed to refine conservation strategies and gain deeper insights into habitat utilization.
Institution: Brunel University of London
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416513737