Departamento de Ciencias Ecológicas, Universidad de Chile, Las Palmeras 3425, Santiago, Chile.
Ranita de Darwin NGO, Nataniel Cox 152, Santiago, Chile.
Ecol Appl. 2017 Jul;27(5):1633-1645. doi: 10.1002/eap.1556. Epub 2017 Jun 19.
Climate change is a major threat to biodiversity; the development of models that reliably predict its effects on species distributions is a priority for conservation biogeography. Two of the main issues for accurate temporal predictions from Species Distribution Models (SDM) are model extrapolation and unrealistic dispersal scenarios. We assessed the consequences of these issues on the accuracy of climate-driven SDM predictions for the dispersal-limited Darwin's frog Rhinoderma darwinii in South America. We calibrated models using historical data (1950-1975) and projected them across 40 yr to predict distribution under current climatic conditions, assessing predictive accuracy through the area under the ROC curve (AUC) and True Skill Statistics (TSS), contrasting binary model predictions against temporal-independent validation data set (i.e., current presences/absences). To assess the effects of incorporating dispersal processes we compared the predictive accuracy of dispersal constrained models with no dispersal limited SDMs; and to assess the effects of model extrapolation on the predictive accuracy of SDMs, we compared this between extrapolated and no extrapolated areas. The incorporation of dispersal processes enhanced predictive accuracy, mainly due to a decrease in the false presence rate of model predictions, which is consistent with discrimination of suitable but inaccessible habitat. This also had consequences on range size changes over time, which is the most used proxy for extinction risk from climate change. The area of current climatic conditions that was absent in the baseline conditions (i.e., extrapolated areas) represents 39% of the study area, leading to a significant decrease in predictive accuracy of model predictions for those areas. Our results highlight (1) incorporating dispersal processes can improve predictive accuracy of temporal transference of SDMs and reduce uncertainties of extinction risk assessments from global change; (2) as geographical areas subjected to novel climates are expected to arise, they must be reported as they show less accurate predictions under future climate scenarios. Consequently, environmental extrapolation and dispersal processes should be explicitly incorporated to report and reduce uncertainties in temporal predictions of SDMs, respectively. Doing so, we expect to improve the reliability of the information we provide for conservation decision makers under future climate change scenarios.
气候变化是生物多样性的主要威胁；开发能够可靠预测其对物种分布影响的模型是保护生物地理学的当务之急。准确进行物种分布模型（SDM）时间预测的两个主要问题是模型外推和不现实的扩散情景。我们评估了这些问题对南美的扩散受限达尔文蛙 Rhinoderma darwinii 气候驱动 SDM 预测准确性的影响。我们使用历史数据（1950-1975 年）校准模型，并将其投影到 40 年内，以预测当前气候条件下的分布情况，通过 ROC 曲线下面积（AUC）和真实技能统计（TSS）评估预测准确性，将二进制模型预测与时间独立验证数据集（即当前存在/不存在）进行对比。为了评估纳入扩散过程的影响，我们比较了具有扩散限制的 SDM 模型的预测精度与没有扩散限制的 SDM 模型的预测精度；为了评估模型外推对 SDM 预测精度的影响，我们比较了外推和非外推区域之间的预测精度。纳入扩散过程提高了预测精度，主要是因为模型预测的假阳性率降低，这与适宜但不可及的栖息地的区分一致。这也对随时间变化的范围大小变化产生了影响，这是气候变化导致灭绝风险的最常用替代指标。当前气候条件下不存在的基线条件（即外推区域）占研究区域的 39%，导致这些区域模型预测的预测精度显著下降。我们的研究结果表明：1. 纳入扩散过程可以提高 SDM 时间转移的预测精度，并减少全球变化引起的灭绝风险评估的不确定性；2. 由于预计会出现新的气候地理区域，因此必须报告这些区域，因为它们在未来气候情景下的预测准确性较低。因此，环境外推和扩散过程应分别明确纳入以报告和减少 SDM 时间预测的不确定性。这样做，我们期望提高我们在未来气候变化情景下为保护决策者提供的信息的可靠性。
