[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123659-en":3,"doc-seo-123659-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123659,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea","Marine biodiversity in the Mediterranean Sea faces multiple anthropogenic pressures, making conservation planning for cetaceans strategic. Using machine-learning, the study identifies 28 physical and biogeochemical predictors for the abundance of three odontocete species in the Northern Ionian Sea. Sighting data from striped dolphins, common bottlenose dolphins, and Risso’s dolphins (July 2009–October 2021) support habitat models built with Random Forest. Nitrate, phytoplankton carbon biomass, temperature, and salinity are key predictors. Models validated with 2022 sightings confirm good performance, providing evidence to support EU marine spatial planning and conservation measures within the ecosystem-based management framework.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nEnvironmental variablesand machine learning models to predict cetacean abundance in the Central‑eastern Mediterranean Sea  \nRosalia Maglietta1*, Leonardo Saccotelli2, Carmelo Fanizza 3, Vito Telesca4, Giovanni Dimauro 5, Salvatore Causio2, Rita Lecci2, Ivan Federico2, Giovanni Coppini2, Giulia Cipriano6 & Roberto Carlucci6  \nAlthough the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans. The aim of this paper is to understand the habitat requirements of cetaceans, exploiting the advantages of a machine‑learning framework. To this end, 28 physical and biogeochemical variables were identified as environmental predictors related to the abundance of three odontocete species in the Northern Ionian Sea (Central‑eastern Mediterranean Sea). In fact, habitat models were built using sighting data collected for striped dolphins Stenella coeruleoalba, common bottlenose dolphins Tursiopstruncatus, and Risso’s dolphins Grampus griseus between July 2009 and October 2021. Random Forest was a suitable machine learning algorithm for the cetacean abundance estimation. Nitrate, phytoplankton carbon biomass, temperature, and salinity were the most common influential predictors, followed by latitude, 3D‑chlorophyll and density. The habitat models proposed here were validated using sighting data acquired during 2022 in the study area, confirming the good performance of the strategy. This study provides valuable information to support management decisions and conservation measures in the EU marine spatial planning context.  \nThe Marine Strategy Framework Directive (MSFD), Marine Spatial Planning (MSP) and Common Fisheries Policy (CFP) constitute the main policies to maintain the productive, resilient, and good health status (GES) of marine habitats to provide ecosystem services and limit the loss of biodiversity in EU Member States (EEA, 2015). This environmental strategy, although different in terms of achievable objectives, is based on the Ecosystem Based Management approach (EBM), which is assumed to be a holistic and integrated pathway worldwide. In particular, this approach aims to maintain or restore the composition, structure, function, and delivery of services of natural and modified ecosystems to achieve sustainability (Millennium Ecosystem Assessment, 2005) . In this light, knowledge of the spatiotemporal distribution and abundance of target species, as well as the extension of their critical habitats and their overlap with highly impacted areas strongly characterized by anthropogenic pressures, is essential, especially in aquatic ecosystems.  \nAlthough, on a global scale, the Mediterranean Sea is one of the most important hotspots for its richness in marine biodiversity1–3, it has been historically threatened by numerous anthropogenic pressures, such as the presence of commercial maritime and fishing activities, a growing urbanization mostly along coastal zones, and the occurrence of different sources of pollution, from chemical to acoustic4,5. In addition, climate change, the  \n1Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council, via Amendola 122/D-I, 70126 Bari, Italy. 2Ocean Predictions and Applications Division, Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce, Italy. 3Jonian Dolphin Conservation, viale Virgilio 102, 74121 Taranto, Italy. 4School of Engineering, University of Basilicata, viale Ateneo Lucano 10, 85100 Potenza, Italy. 5Department of Computer Science, University of Bari, via Orabona 4, 70125 Bari, Italy. 6Department of Biology, University of Bari, via Orabona 4, 70125 Bari, Italy. *email: [rosalia.maglietta@cnr.it](rosali","cbCaidYsdL0JXIDR","https://ap.wps.com/l/cbCaidYsdL0JXIDR","pdf",2091052,1,14,"English","en",105,"# Environmental context and conservation relevance\n# Study aim and machine-learning framework\n# Environmental predictors and target species\n## Random Forest modeling\n## Influential predictors\n# Validation with independent sighting data\n# Policy and ecosystem-based management background","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To understand habitat requirements of cetaceans by using a machine-learning framework to predict their abundance from environmental variables.\"},{\"question\":\"Which machine-learning algorithm is used for the habitat models?\",\"answer\":\"Random Forest is used as a suitable machine-learning algorithm for cetacean abundance estimation.\"},{\"question\":\"Which environmental variables most influence cetacean abundance?\",\"answer\":\"Nitrate, phytoplankton carbon biomass, temperature, and salinity are identified as the most common influential predictors, followed by latitude, 3D-chlorophyll, and density.\"}]","Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea | 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