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.

中文翻译：

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中东地中海红海龟栖息地适宜性建模：一种使用卫星跟踪数据的机器学习方法


了解海龟物种如何在环境中移动并对环境特征做出反应，对于可持续生态系统管理和有效保护至关重要。本研究通过开发和验证一个多学科框架来调查红海龟 （Caretta caretta） 在亚得里亚海和北爱奥尼亚海（中东地中海）的栖息地适宜性，该框架利用机器学习来研究卫星标签 Argos 卫星标签收集的运动模式。使用随机森林模型分析了来自哥白尼海洋局和 EMODnet 测深仪的 16 个环境变量的卫星跟踪数据，在对红海龟的存在与伪不存在进行分类时获得了 80.9% 的准确率。作为主要发现，海底深度、表面叶绿素 （chl-a） 和混合层深度 （MLD） 被确定为影响这些标本栖息地适宜性的特征。此外，使用 t 检验统计进行评估，发现沿海和中上层位置之间，在不同季节的混合层深度、chl-a、3D-叶绿素、盐度和磷酸盐方面存在统计学上的显着差异。尽管基于有限的标记动物样本，但这项研究表明，红海龟在地中海沿岸和中上层地区的分布模式主要受到与生产力相关的海水特征的影响，因此也与潜在猎物的丰度有关。此外，这个多学科框架提供了一种可复制的方法，可以适应各种物种和地区。