Climatic predictors of species distributions neglect biophysiologically meaningful variables

• Published in: Diversity & distributions Vol. 25; no. 8; pp. 1318 - 1333
• Main Authors: Gardner, Alexandra S., Maclean, Ilya M.D., Gaston, Kevin J.
• Format: Journal Article
• Language: English
• Published: Oxford Wiley 01-08-2019; John Wiley & Sons, Inc
• Subjects: Annual rainfall; Biodiversity; BIODIVERSITY RESEARCH; Climate change; Climate models; conservation; Environmental assessment; Moisture content; Physiological responses; Physiology; Plant physiology; Plant species; Predictions; Rainfall; range shifts; Regression analysis; Soil water; Spatial discrimination; Spatial resolution; Species; species distribution models; Variables; Water content
• ISSN: 1366-9516; 1472-4642
• DOI: 10.1111/ddi.12939

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° 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.