Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.
University of Potsdam, Maulbeerallee 3, 14469, Potsdam, Germany.
Sci Rep. 2023 Aug 2;13(1):12538. doi: 10.1038/s41598-023-39637-x.
Climate is an important limiting factor of species' niches and it is therefore regularly included in ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values, yet many species experience wide climatic variation over their lifespan and within their geographical range which is unlikely captured by long-term means. Further, depending on their physiology, distinct groups of species cope with climate variability differently. Ectothermic species, which are directly dependent on the thermal environment are expected to show a different response to temporal or spatial variability in temperature than endothermic groups that can decouple their internal temperature from that of their surroundings. Here, we explore the degree to which spatial variability and long-term temporal variability in temperature and precipitation change niche estimates for ectothermic (730 amphibian, 1276 reptile), and endothermic (1961 mammal) species globally. We use three different species distribution modelling (SDM) algorithms to quantify the effect of spatial and temporal climate variability, based on global range maps of all species and climate data from 1979 to 2013. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). The mean performance of SDMs using only climatic means as predictors was TSS = 0.71 and AUC = 0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS = 0.74, mean AUC = 0.92), as does the inclusion of temporal variability (mean TSS = 0.80, mean AUC = 0.94). Including both spatial and temporal variability in SDMs shows the highest scores in AUC and TSS. Accounting for temporal rather than spatial variability in climate improved the SDM prediction especially in ectotherm groups such as amphibians and reptiles, while for endothermic mammals no such improvement was observed. These results indicate that including long term climate interannual climate variability into niche estimations matters most for ectothermic species that cannot decouple their physiology from the surrounding environment as endothermic species can.
气候是物种生态位的一个重要限制因素，因此经常被纳入生态应用，如物种分布模型 (SDM)。气候预测因子通常以长期平均值的形式使用，但许多物种在其生命周期内和地理范围内经历广泛的气候变化，而长期平均值不太可能捕捉到这些变化。此外，根据它们的生理学，不同的物种群体对气候变异性的处理方式也不同。外温动物，即直接依赖于热环境的动物，预计对外温的时空变异性的反应与内温动物不同，内温动物可以将其内部温度与周围环境的温度分离。在这里，我们探索了温度和降水的空间变异性和长期时间变异性在多大程度上改变了全球外温动物（730 种两栖动物，1276 种爬行动物）和内温动物（1961 种哺乳动物）的生态位估计。我们使用三种不同的物种分布模型 (SDM) 算法来量化空间和时间气候变异性的影响，基于所有物种的全球范围图和 1979 年至 2013 年的气候数据。所有 SDM 都经过交叉验证，并使用曲线下面积 (AUC) 和真实技能统计量 (TSS) 来评估其性能。仅使用气候平均值作为预测因子的 SDM 的平均性能 TSS=0.71 和 AUC=0.90。包含空间变异性的 SDM 性能显著提高（平均 TSS=0.74，平均 AUC=0.92），包含时间变异性也是如此（平均 TSS=0.80，平均 AUC=0.94）。在 SDM 中同时包含空间和时间变异性在 AUC 和 TSS 方面得分最高。在 SDM 预测中，考虑到气候的时间变异性而不是空间变异性可以提高预测效果，尤其是对于两栖动物和爬行动物等外温动物，而对于内温哺乳动物则没有观察到这种改善。这些结果表明，对于不能将生理与周围环境分离的外温动物物种而言，将长期气候年际变异性纳入生态位估计比纳入空间变异性更为重要，而对于内温动物物种则不然。
