The Contribution of Vegetation and Landscape Configuration for Predicting Environmental Change Impacts on Iberian Birds

Although climate is known to be one of the key factors determining animal species distributions amongst others, projections of global change impacts on their distributions often rely on bioclimatic envelope models. Vegetation structure and landscape configuration are also key determinants of distributions, but they are rarely considered in such assessments. We explore the consequences of using simulated vegetation structure and composition as well as its associated landscape configuration in models projecting global change effects on Iberian bird species distributions. Both present-day and future distributions were modelled for 168 bird species using two ensemble forecasting methods: Random Forests (RF) and Boosted Regression Trees (BRT). For each species, several models were created, differing in the predictor variables used (climate, vegetation, and landscape configuration). Discrimination ability of each model in the present-day was then tested with four commonly used evaluation methods (AUC, TSS, specificity and sensitivity). The different sets of predictor variables yielded similar spatial patterns for well-modelled species, but the future projections diverged for poorly-modelled species. Models using all predictor variables were not significantly better than models fitted with climate variables alone for ca. 50% of the cases. Moreover, models fitted with climate data were always better than models fitted with landscape configuration variables, and vegetation variables were found to correlate with bird species distributions in 26–40% of the cases with BRT, and in 1–18% of the cases with RF. We conclude that improvements from including vegetation and its landscape configuration variables in comparison with climate only variables might not always be as great as expected for future projections of Iberian bird species.

尽管气候被公认为决定动物物种分布的关键因素之一，但全球变化对物种分布影响的预测往往依赖于生物气候包络模型（bioclimatic envelope models）。植被结构与景观配置同样是物种分布的关键决定因子，但此类评估中却极少纳入这两类因素。本研究探讨了在预测全球变化对伊比利亚鸟类物种分布影响的模型中，纳入模拟植被结构、组成及其相关景观配置后的结果变化。本研究针对168种鸟类，采用两种集成预测方法：随机森林（Random Forests, RF）与增强回归树（Boosted Regression Trees, BRT），分别建模其当前与未来的物种分布。针对每个物种，本研究构建了多组模型，各组模型所使用的预测变量（气候、植被、景观配置）各不相同。随后，本研究采用四种常用评估方法（AUC、TSS、特异度、敏感度），测试各模型在当前分布场景下的区分能力。对于建模效果良好的物种，不同预测变量组合得到的空间分布格局较为相似；但对于建模效果欠佳的物种，其未来分布预测结果则存在显著差异。在约50%的案例中，纳入所有预测变量的模型表现并不显著优于仅使用气候变量构建的模型。此外，仅使用气候数据构建的模型始终优于仅使用景观配置变量的模型；在BRT模型中，有26%~40%的案例显示植被变量与鸟类物种分布存在相关性，而在RF模型中这一比例为1%~18%。本研究结论表明，相较于仅使用气候变量的模型，纳入植被及其景观配置变量所能带来的性能提升，在伊比利亚鸟类物种的未来分布预测中未必总能达到预期水平。