16 research outputs found
Climatic predictors of species distributions neglect biophysiologically meaningful variables
This is the final version. Available on open access from Wiley via the DOI in this record.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 ten SDM and physiology
variables at 2.5 degree 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: (i) 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 (ii) 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