Factors Influencing Endangered Marine Species in the Mediterranean Sea: An Analysis Based on IUCN Red List Criteria Using Statistical and Soft Computing Methodologies

• Published in: Environments (Basel, Switzerland) Switzerland), 2024-07, Vol.11 (7), p.151
• Main Authors: Klaoudatos, Dimitris, Karagyaurova, Teodora, Pitropakis, Theodoros G. I., Mari, Aikaterini, Patas, Dimitris R., Vidiadaki, Maria, Kokkinos, Konstantinos
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Biodiversity; Biodiversity hot spots; Bycatch; Climate change; Commercial fishing; Cultural heritage; Ecosystems; Endangered & extinct species; Endangered species; Eutrophication; Extinction; Fish; Fisheries management; Fishing; Habitats; Identification and classification; IUCN; Learning algorithms; Machine learning; Marine fish; Marine fishes; Overfishing; Protection and preservation; Regression models; Soft computing; Species extinction; species vulnerability; Statistical analysis; Supervised learning; Support vector machines; Temperature; Threat evaluation; Threatened species; Threats; Wildlife conservation
• ISSN: 2076-3298; 2076-3298
• DOI: 10.3390/environments11070151

The Mediterranean Sea is the second largest biodiversity hotspot on earth, with over 700 identified fish species is facing numerous threats. Of more than 6000 taxa assessed for the IUCN Red List, a minimum of 20% are threatened with extinction. A total of eight key factors that affect vulnerability of marine fish species in the Mediterranean Sea were identified using the scientific literature and expert-reviewed validated databases. A database of 157 teleost fish species with threat status ranging from least concern to critically endangered was compiled. Nominal logistic curves identified the factor thresholds on species vulnerability, namely, age at maturity, longevity, and asymptotic length at 8.45 years, 36 years, and 221 cm, respectively. A second-degree stepwise regression model identified four significant factors affecting the threat category of Mediterranean fish species, namely, overfishing, by-catch, pollution, and age at maturity according to their significance. Predictive analysis using supervised machine learning algorithms was further employed to predict the vulnerability of Mediterranean marine fish species, resulting in the development of a framework with classification accuracy of 87.3% and 86.6% for Support Vector Machine (SVM) and Gradient Boosting machine learning algorithms, respectively, with the ability to assess the degree of variability using limited information.