Dolci David, Peruzzi Lorenzo
Department of Biology, University of Pisa, Via Derna 1, 56126 Pisa, Italy.
Centro Interuniversitario per la Biodiversità Vegetale Big Data-PLANT DATA, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, Via Irnerio 42, 40126 Bologna, Italy.
Biology (Basel). 2022 Aug 14;11(8):1219. doi: 10.3390/biology11081219.
Correlative ecological niche modelling (ENM) is a method widely used to study the geographic distribution of species. In recent decades, it has become a leading approach for evaluating the most likely impacts of changing climate. When used to predict future distributions, ENM applications involve transferring models calibrated with modern environmental data to future conditions, usually derived from Global Climate Models (GCMs). The number of algorithms and software packages available to estimate distributions is quite high. To experimentally assess the effectiveness of correlative ENM temporal projection, we evaluated the transferability of models produced using 12 different algorithms on historical and modern data. In particular, we compared predictions generated using historical data and projected to the modern climate (simulating a "future" condition) with predictions generated using modern distribution and climate data. The models produced with the 12 ENM algorithms were evaluated in geographic (range size and coherence of predictions) and environmental space (Schoener's D index). None of the algorithms shows an overall superior capability to correctly predict future distributions. On the contrary, a few algorithms revealed an inadequate predictive ability. Finally, we provide hints that can be used as guideline to plan further studies based on the adopted general workflow, useful for all studies involving future projections.
相关生态位建模（ENM）是一种广泛用于研究物种地理分布的方法。近几十年来，它已成为评估气候变化最可能影响的主要方法。当用于预测未来分布时，ENM应用涉及将根据现代环境数据校准的模型应用于未来条件，这些条件通常来自全球气候模型（GCM）。可用于估计分布的算法和软件包数量相当多。为了通过实验评估相关ENM时间投影的有效性，我们评估了使用12种不同算法在历史数据和现代数据上生成的模型的可转移性。特别是，我们将使用历史数据生成并投影到现代气候（模拟“未来”条件）的预测与使用现代分布和气候数据生成的预测进行了比较。使用12种ENM算法生成的模型在地理空间（预测的范围大小和一致性）和环境空间（Schoener's D指数）中进行了评估。没有一种算法显示出在正确预测未来分布方面具有总体优越的能力。相反，一些算法显示出预测能力不足。最后，我们提供了一些提示，可作为基于所采用的一般工作流程规划进一步研究的指南，这对所有涉及未来预测的研究都很有用。
