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

Dimitris Klaoudatos, Teodora Karagyaurova, Theodoros G. I. Pitropakis, Aikaterini Mari, Dimitris R. Patas, Maria Vidiadaki, Konstantinos Kokkinos
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Abstract

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.
影响地中海濒危海洋物种的因素:利用统计和软计算方法,基于世界自然保护联盟(IUCN)红色名录标准进行分析
地中海是地球上第二大生物多样性热点地区,拥有 700 多种已确认的鱼类物种,但却面临着众多威胁。在《世界自然保护联盟红色名录》评估的 6000 多个分类群中,至少有 20% 面临灭绝威胁。利用科学文献和专家评审的有效数据库,共确定了影响地中海海洋鱼类物种脆弱性的八个关键因素。数据库中包含了 157 种远志鱼类,其威胁程度从最不受关 注到极度濒危不等。名义逻辑曲线确定了物种脆弱性的因子阈值,即成熟年龄、寿命和渐近长度分别为 8.45 岁、36 岁和 221 厘米。二级逐步回归模型确定了影响地中海鱼类物种威胁类别的四个重要因素,即过度捕捞、副渔获物、污染和成熟年龄。利用有监督的机器学习算法进行预测分析,进一步预测地中海海洋鱼类物种的脆弱性,最终建立了一个框架,支持向量机(SVM)和梯度提升(Gradient Boosting)机器学习算法的分类准确率分别为 87.3% 和 86.6%,能够利用有限的信息评估变异程度。
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