Climatic predictors of species distributions neglect biophysiologically meaningful variables
Climatic predictors of species distributions neglect biophysiologically meaningful variables
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DOI：
10.1111/ddi.12939
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发表时间：
2019-08-01
影响因子：
4.6
通讯作者：
Gaston, Kevin J.
中科院分区：
文献类型：
--
作者：
Gardner, Alexandra S.;Maclean, Ilya M. D.;Gaston, Kevin J.
目的物种分布模型(SDMS)在预测物种对气候变化的响应方面发挥了关键作用。为了从这些模型中产生可靠和现实的预测，需要使用气候变量来充分捕捉物种对气候的生理反应，从而在气候及其分布之间提供最接近的联系。在这里，我们检查在植物SDMS中使用的气候变量是否与那些已知的直接影响植物生理的气候变量不同。全球定位。方法我们对用于模拟植物物种分布的气候变量进行了广泛、系统的回顾，并与植物生理学文献中确定的重要气候变量进行了比较。我们计算了全球2.5度空间分辨率的前10个SDM和生理变量，并使用主成分分析和多元回归来评估这两个变量集所描述的气候变化之间的相似性。结果我们发现，最常用的SDM变量不能反映最重要的生理变量，主要有两个方面的不同：(A)SDM变量依赖于季节或年降雨量作为植物可用水的简单指标，而忽略了更直接的指标，如土壤水分含量；(B)SDM变量通常是跨季节或跨年平均的，忽略了植物关键生长期内气候事件的重要性。我们在全球范围内发现了它们在空间梯度上的显著差异，并显示了远端变量可能是已知物种对变量做出反应的不太可靠的替代变量。主要结论开发可获得的、精细分辨率的生理变量全球气候表面的需求日益增长。这将提供一种手段，以提高SDMS未来范围预测的可靠性，并支持在不断变化的气候中保护生物多样性的努力。
Aim Species distribution models (SDMs) have played a pivotal role in predicting how species might respond to climate change. To generate reliable and realistic predictions from these models requires the use of climate variables that adequately capture physiological responses of species to climate and therefore provide a proximal link between climate and their distributions. Here, we examine whether the climate variables used in plant SDMs are different from those known to influence directly plant physiology. Location Global. Methods We carry out an extensive, systematic review of the climate variables used to model the distributions of plant species and provide comparison to the climate variables identified as important in the plant physiology literature. We calculate the top 10 SDM and physiology variables at 2.5 degrees spatial resolution for the globe and use principal component analyses and multiple regression to assess similarity between the climatic variation described by both variable sets. Results We find that the most commonly used SDM variables do not reflect the most important physiological variables and differ in two main ways: (a) SDM variables rely on seasonal or annual rainfall as simple proxies of water available to plants and neglect more direct measures such as soil water content; and (b) SDM variables are typically averaged across seasons or years and overlook the importance of climatic events within the critical growth period of plants. We identify notable differences in their spatial gradients globally and show where distal variables may be less reliable proxies for the variables to which species are known to respond. Main conclusions There is a growing need for the development of accessible, fine-resolution global climate surfaces of physiological variables. This would provide a means to improve the reliability of future range predictions from SDMs and support efforts to conserve biodiversity in a changing climate.