Sales Lilian Patrícia, Neves Olívia Viana, De Marco Paulo, Loyola Rafael
Programa de Pós-graduação em Ecologia e Evolução, Universidade Federal de Goiás, Goiânia, Goiás, Brazil.
Departamento de Ecologia, Universidade Federal de Goiás, Goiânia, Goiás, Brazil.
PLoS One. 2017 Oct 12;12(10):e0183785. doi: 10.1371/journal.pone.0183785. eCollection 2017.
Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale-patterns of species richness and species vulnerability to climate change-are affected by the inputs used to model and project species distribution.
We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors.
The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses.
From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species.
气候变化可以说是生物多样性保护面临的主要威胁，有多种方法可用于评估其对物种潜在分布的影响。然而，文献中尚未探讨物种分布建模的不同方法在生物地理尺度上对物种丰富度格局的影响程度。在本文中，我们验证了生物地理尺度上物种丰富度格局以及物种对气候变化的脆弱性对气候变化的预期响应是否会受到用于模拟和预测物种分布的输入数据的影响。
我们使用已知会影响物种分布模型（SDMs）结果的以下输入数据的不同组合，对288种脊椎动物（两栖动物、鸟类和哺乳动物）的分布进行了建模，这些物种均为亚马逊盆地特有：1）生物数据类型，2）建模方法，3）温室气体排放情景，4）气候预测。我们使用分层方差分析计算不确定性，其中将这些不同的输入数据视为因素。
最大的变异来源是建模方法。模型性能与数据类型和建模方法相互作用。不同预测中适合气候区域的变异绝对值并不相等，但一些生物学模式仍然一致。所有模型都预测适合物种生存的气候区域将会减少，尤其是两栖动物和灵长类动物。所有模型还表明，目前特有物种丰富度存在从安第斯山脉脚下到亚马逊河下游的东西向梯度。同样，所有模型都预测物种未来将向安第斯山脉高处移动，以及整体物种丰富度的损失。
从方法论的角度来看，我们的工作强调物种分布模型是评估气候变化对生物多样性影响的有用工具。虽然存在不确定性，但在大空间尺度上生物学模式仍然明显。由于建模方法是最大的变异来源，根据研究目标选择合适的统计方法对于估计气候变化对物种分布的影响也至关重要。然而，从保护的角度来看，我们表明亚马逊特有动物群可能容易受到气候变化的影响，因为预计适合气候区域会减少。预计气候驱动的动物运动会朝着安第斯山脉方向进行，安第斯山脉可能成为迁徙物种的气候避难所。
