Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea
Investigating environmental drivers of cetacean feeding behaviour is essential for effective marine resource management, especially in the Mediterranean Sea, a biodiversity hotspot heavily impacted by human activities and climate change. This study realized a pioneer assessment of feeding activity r...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000755 |
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| author | Carla Cherubini Giulia Cipriano Leonardo Saccotelli Giovanni Dimauro Giovanni Coppini Roberto Carlucci Carmelo Fanizza Rosalia Maglietta |
| author_facet | Carla Cherubini Giulia Cipriano Leonardo Saccotelli Giovanni Dimauro Giovanni Coppini Roberto Carlucci Carmelo Fanizza Rosalia Maglietta |
| author_sort | Carla Cherubini |
| collection | DOAJ |
| description | Investigating environmental drivers of cetacean feeding behaviour is essential for effective marine resource management, especially in the Mediterranean Sea, a biodiversity hotspot heavily impacted by human activities and climate change. This study realized a pioneer assessment of feeding activity related to the marine environment for three cetacean species - striped dolphin, common bottlenose dolphin, and Risso's dolphin - in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean) using an innovative Machine Learning (ML) approach. Behavioural data from April 2016 to October 2023, coupled with 20 environmental variables from Copernicus Marine Service and EMODnet-bathymetry datasets, were used to build Cetacean Feeding Models (CFMs) for the target species using Random Forest and RUSBoost algorithms. Multiple subsets of environmental predictors—physiographic, physical, inorganic, and bio-chemical—were employed to develop and evaluate ML models tailored to feeding prediction. Risso's dolphin resulted to be the best modelled species, with the bio-chemical model based on the RUSBoost algorithm achieving a Balanced Classification Rate (BCR) of 94 %, primarily influenced by 3D chlorophyll-a concentrations, a close proxy for prey availability. The second-best model was the physical one for the common bottlenose dolphin with a BCR of 72 %, influenced by salinity, currents speed, and temperature. These differences in predictive performance might reflect the distinct trophic niches of the studied odontocetes. Finally, simulated predictive maps of Risso's dolphin feeding habitats for summer months were realized in the Gulf of Taranto, providing actionable insights for conservation and sustainable management. The developed CFMs enhance understanding of cetacean feeding preferences and offer a versatile framework for integrating behavioural processes into species distribution models to inform area-based conservation measures, with significant potential for application across other Mediterranean areas. |
| format | Article |
| id | doaj-art-4c6b2896a750416f9c855b4722655fcb |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-4c6b2896a750416f9c855b4722655fcb2025-02-12T05:30:47ZengElsevierEcological Informatics1574-95412025-05-0186103066Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean SeaCarla Cherubini0Giulia Cipriano1Leonardo Saccotelli2Giovanni Dimauro3Giovanni Coppini4Roberto Carlucci5Carmelo Fanizza6Rosalia Maglietta7Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, CNR-STIIMA, Bari, Italy; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy; CMCC Foundation - Euro-Mediterranean Centre on Climate Change, Lecce, ItalyDepartment of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, Bari, ItalyCMCC Foundation - Euro-Mediterranean Centre on Climate Change, Lecce, ItalyDepartment of Computer Science, University of Bari, Bari, ItalyCMCC Foundation - Euro-Mediterranean Centre on Climate Change, Lecce, ItalyDepartment of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, Bari, ItalyJonian Dolphin Conservation, Taranto, ItalyInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, CNR-STIIMA, Bari, Italy; CMCC Foundation - Euro-Mediterranean Centre on Climate Change, Lecce, Italy; Corresponding author at: Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, CNR-STIIMA, Bari, Italy.Investigating environmental drivers of cetacean feeding behaviour is essential for effective marine resource management, especially in the Mediterranean Sea, a biodiversity hotspot heavily impacted by human activities and climate change. This study realized a pioneer assessment of feeding activity related to the marine environment for three cetacean species - striped dolphin, common bottlenose dolphin, and Risso's dolphin - in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean) using an innovative Machine Learning (ML) approach. Behavioural data from April 2016 to October 2023, coupled with 20 environmental variables from Copernicus Marine Service and EMODnet-bathymetry datasets, were used to build Cetacean Feeding Models (CFMs) for the target species using Random Forest and RUSBoost algorithms. Multiple subsets of environmental predictors—physiographic, physical, inorganic, and bio-chemical—were employed to develop and evaluate ML models tailored to feeding prediction. Risso's dolphin resulted to be the best modelled species, with the bio-chemical model based on the RUSBoost algorithm achieving a Balanced Classification Rate (BCR) of 94 %, primarily influenced by 3D chlorophyll-a concentrations, a close proxy for prey availability. The second-best model was the physical one for the common bottlenose dolphin with a BCR of 72 %, influenced by salinity, currents speed, and temperature. These differences in predictive performance might reflect the distinct trophic niches of the studied odontocetes. Finally, simulated predictive maps of Risso's dolphin feeding habitats for summer months were realized in the Gulf of Taranto, providing actionable insights for conservation and sustainable management. The developed CFMs enhance understanding of cetacean feeding preferences and offer a versatile framework for integrating behavioural processes into species distribution models to inform area-based conservation measures, with significant potential for application across other Mediterranean areas.http://www.sciencedirect.com/science/article/pii/S1574954125000755Machine learningRandom ForestSpecies distribution modelsCetacean conservationFeeding behaviourBehavioural science |
| spellingShingle | Carla Cherubini Giulia Cipriano Leonardo Saccotelli Giovanni Dimauro Giovanni Coppini Roberto Carlucci Carmelo Fanizza Rosalia Maglietta Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea Ecological Informatics Machine learning Random Forest Species distribution models Cetacean conservation Feeding behaviour Behavioural science |
| title | Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea |
| title_full | Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea |
| title_fullStr | Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea |
| title_full_unstemmed | Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea |
| title_short | Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea |
| title_sort | cetacean feeding modelling using machine learning a case study of the central eastern mediterranean sea |
| topic | Machine learning Random Forest Species distribution models Cetacean conservation Feeding behaviour Behavioural science |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000755 |
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