Habitat suitability modeling of loggerhead sea turtles in the Central-Eastern Mediterranean Sea: a machine learning approach using satellite tracking data
Understanding how sea turtle species move through the environment and respond to environmental features is fundamental for sustainable ecosystem management and effective conservation. This study investigates the habitat suitability of the loggerhead sea turtle (Caretta caretta) in the Adriatic and N...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1493598/full |
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| author | Rosalia Maglietta Rosalia Maglietta Rocco Caccioppoli Daniele Piazzolla Leonardo Saccotelli Carla Cherubini Carla Cherubini Carla Cherubini Elena Scagnoli Viviana Piermattei Marco Marcelli Marco Marcelli Giuseppe Andrea De Lucia Rita Lecci Salvatore Causio Giovanni Dimauro Francesco De Franco Matteo Scuro Giovanni Coppini |
| author_facet | Rosalia Maglietta Rosalia Maglietta Rocco Caccioppoli Daniele Piazzolla Leonardo Saccotelli Carla Cherubini Carla Cherubini Carla Cherubini Elena Scagnoli Viviana Piermattei Marco Marcelli Marco Marcelli Giuseppe Andrea De Lucia Rita Lecci Salvatore Causio Giovanni Dimauro Francesco De Franco Matteo Scuro Giovanni Coppini |
| author_sort | Rosalia Maglietta |
| collection | DOAJ |
| description | Understanding how sea turtle species move through the environment and respond to environmental features is fundamental for sustainable ecosystem management and effective conservation. This study investigates the habitat suitability of the loggerhead sea turtle (Caretta caretta) in the Adriatic and Northern Ionian Seas (Central-Eastern Mediterranean) by developing and validating a multidisciplinary framework that leverages machine learning to investigate movement patterns collected by satellite tags Argos satellite tags. Satellite tracking data, enriched with sixteen environmental variables from the Copernicus Marine Service and EMODnet-bathymetry, were analyzed using Random Forest models, obtaining an accuracy of 80.9% when classifying presence versus pseudo-absence of loggerhead sea turtles. As main findings, sea bottom depth, surface chlorophyll (chl-a), and mixed layer depth (MLD) were identified as the most influential features in the habitat suitability of these specimens. Moreover, statistically significant differences, evaluated using t-test statistics, were found between coastal and pelagic locations, for the different seasons, in mixed layer depth, chl-a, 3D-clorophyll, salinity and phosphate. Although based on a limited sample of tagged animals, this study demonstrates that the distribution patterns of loggerhead sea turtles in Mediterranean coastal and pelagic areas are primarily influenced by sea water features linked to productivity and, consequently, to potential prey abundance. Additionally, this multidisciplinary framework presents a replicable approach that can be adapted for various species and regions. |
| format | Article |
| id | doaj-art-9a88c9302c1940a8a578bf29aca9b942 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-9a88c9302c1940a8a578bf29aca9b9422024-11-19T10:01:41ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452024-11-011110.3389/fmars.2024.14935981493598Habitat suitability modeling of loggerhead sea turtles in the Central-Eastern Mediterranean Sea: a machine learning approach using satellite tracking dataRosalia Maglietta0Rosalia Maglietta1Rocco Caccioppoli2Daniele Piazzolla3Leonardo Saccotelli4Carla Cherubini5Carla Cherubini6Carla Cherubini7Elena Scagnoli8Viviana Piermattei9Marco Marcelli10Marco Marcelli11Giuseppe Andrea De Lucia12Rita Lecci13Salvatore Causio14Giovanni Dimauro15Francesco De Franco16Matteo Scuro17Giovanni Coppini18Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council, STIIMA-CNR, Bari, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council, STIIMA-CNR, Bari, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyDepartment of Informatics, University of Bari ‘Aldo Moro’, Bari, ItalyLaboratory of Experimental Oceanology end Marine Ecology, Department of Ecological and Biological Sciences (DEB), Università degli Studi della Tuscia, Civitavecchia, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyLaboratory of Experimental Oceanology end Marine Ecology, Department of Ecological and Biological Sciences (DEB), Università degli Studi della Tuscia, Civitavecchia, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyInstitute of Anthropic Impact and Sustainability in Marine Environment, National Research Council (IAS-CNR), Oristano, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyDepartment of Informatics, University of Bari ‘Aldo Moro’, Bari, ItalyConsorzio di gestione di Torre Guaceto, Brindisi, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, ItalyUnderstanding how sea turtle species move through the environment and respond to environmental features is fundamental for sustainable ecosystem management and effective conservation. This study investigates the habitat suitability of the loggerhead sea turtle (Caretta caretta) in the Adriatic and Northern Ionian Seas (Central-Eastern Mediterranean) by developing and validating a multidisciplinary framework that leverages machine learning to investigate movement patterns collected by satellite tags Argos satellite tags. Satellite tracking data, enriched with sixteen environmental variables from the Copernicus Marine Service and EMODnet-bathymetry, were analyzed using Random Forest models, obtaining an accuracy of 80.9% when classifying presence versus pseudo-absence of loggerhead sea turtles. As main findings, sea bottom depth, surface chlorophyll (chl-a), and mixed layer depth (MLD) were identified as the most influential features in the habitat suitability of these specimens. Moreover, statistically significant differences, evaluated using t-test statistics, were found between coastal and pelagic locations, for the different seasons, in mixed layer depth, chl-a, 3D-clorophyll, salinity and phosphate. Although based on a limited sample of tagged animals, this study demonstrates that the distribution patterns of loggerhead sea turtles in Mediterranean coastal and pelagic areas are primarily influenced by sea water features linked to productivity and, consequently, to potential prey abundance. Additionally, this multidisciplinary framework presents a replicable approach that can be adapted for various species and regions.https://www.frontiersin.org/articles/10.3389/fmars.2024.1493598/fullmachine learningrandom forestsatellite tagArgos systemCopernicus marine service (CMS)Caretta caretta |
| spellingShingle | Rosalia Maglietta Rosalia Maglietta Rocco Caccioppoli Daniele Piazzolla Leonardo Saccotelli Carla Cherubini Carla Cherubini Carla Cherubini Elena Scagnoli Viviana Piermattei Marco Marcelli Marco Marcelli Giuseppe Andrea De Lucia Rita Lecci Salvatore Causio Giovanni Dimauro Francesco De Franco Matteo Scuro Giovanni Coppini Habitat suitability modeling of loggerhead sea turtles in the Central-Eastern Mediterranean Sea: a machine learning approach using satellite tracking data Frontiers in Marine Science machine learning random forest satellite tag Argos system Copernicus marine service (CMS) Caretta caretta |
| title | Habitat suitability modeling of loggerhead sea turtles in the Central-Eastern Mediterranean Sea: a machine learning approach using satellite tracking data |
| title_full | Habitat suitability modeling of loggerhead sea turtles in the Central-Eastern Mediterranean Sea: a machine learning approach using satellite tracking data |
| title_fullStr | Habitat suitability modeling of loggerhead sea turtles in the Central-Eastern Mediterranean Sea: a machine learning approach using satellite tracking data |
| title_full_unstemmed | Habitat suitability modeling of loggerhead sea turtles in the Central-Eastern Mediterranean Sea: a machine learning approach using satellite tracking data |
| title_short | Habitat suitability modeling of loggerhead sea turtles in the Central-Eastern Mediterranean Sea: a machine learning approach using satellite tracking data |
| title_sort | habitat suitability modeling of loggerhead sea turtles in the central eastern mediterranean sea a machine learning approach using satellite tracking data |
| topic | machine learning random forest satellite tag Argos system Copernicus marine service (CMS) Caretta caretta |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1493598/full |
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