Iannella Mattia, Cerasoli Francesco, D'Alessandro Paola, Console Giulia, Biondi Maurizio
Department of Life, Health & Environmental Sciences, University of L'Aquila, L'Aquila, Italy.
PeerJ. 2018 Jun 5;6:e4969. doi: 10.7717/peerj.4969. eCollection 2018.
The pond turtle is an endangered endemic species of Sicily showing a fragmented distribution throughout the main island. In this study, we applied "Ensemble Niche Modelling", combining more classical statistical techniques as Generalized Linear Models and Multivariate Adaptive Regression Splines with machine-learning approaches as Boosted Regression Trees and Maxent, to model the potential distribution of the species under current and future climatic conditions. Moreover, a "gap analysis" performed on both the species' presence sites and the predictions from the Ensemble Models is proposed to integrate outputs from these models, in order to assess the conservation status of this threatened species in the context of biodiversity management. For this aim, four "Representative Concentration Pathways", corresponding to different greenhouse gases emissions trajectories were considered to project the obtained models to both 2050 and 2070. Areas lost, gained or remaining stable for the target species in the projected models were calculated. ' potential distribution resulted to be significantly dependent upon precipitation-linked variables, mainly precipitation of wettest and coldest quarter. Future negative effects for the conservation of this species, because of more unstable precipitation patterns and extreme meteorological events, emerged from our analyses. Further, the sites currently inhabited by are, for more than a half, out of the Protected Areas network, highlighting an inadequate management of the species by the authorities responsible for its protection. Our results, therefore, suggest that in the next future the Sicilian pond turtle will need the utmost attention by the scientific community to avoid the imminent risk of extinction. Finally, the gap analysis performed in GIS environment resulted to be a very informative post-modeling technique, potentially applicable to the management of species at risk and to Protected Areas' planning in many contexts.
池塘龟是西西里岛的一种濒危特有物种，在整个主岛上分布零散。在本研究中，我们应用“集合生态位建模”，将广义线性模型和多元自适应回归样条等更经典的统计技术与提升回归树和最大熵等机器学习方法相结合，来模拟该物种在当前和未来气候条件下的潜在分布。此外，还对该物种的现存地点和集合模型的预测结果进行了“差距分析”，以整合这些模型的输出结果，从而在生物多样性管理的背景下评估这种濒危物种的保护状况。为此，考虑了对应不同温室气体排放轨迹的四条“代表性浓度路径”，以便将所得模型预测到2050年和2070年。计算了预测模型中目标物种失去、获得或保持稳定的区域。潜在分布结果显示显著依赖于与降水相关的变量，主要是最湿润和最寒冷季度的降水量。我们的分析表明，由于降水模式更加不稳定和极端气象事件，该物种的保护未来将受到负面影响。此外，目前该物种栖息的地点有一半以上不在保护区网络内，这凸显了负责其保护的当局对该物种管理不善。因此，我们的结果表明，在不久的将来，西西里池塘龟需要科学界给予最大程度的关注，以避免迫在眉睫的灭绝风险。最后，在地理信息系统环境中进行的差距分析结果表明，这是一种非常有信息量的建模后技术，可能适用于许多情况下的濒危物种管理和保护区规划。
