Choosing the right climate data for species distribution models

When creating species distributions models, is data based on sort-term variability or long-term averages preferable?

Data resources used via GBIF : 21,500,000 species occurrences
Pine Warbler (Setophaga pinus)

Pine Warbler (Setophaga pinus) by sanguinaria33 via iNaturalist. Photo licensed under CC BY-NC-ND 4.0.

When assessing the potential effects of climate change on species distributions, researchers often create models based on species occurrences and environmental variables. Such models show, for instance, that future climates may lead to species shifting their distributions to higher altitudes and/or elevations. In this study, researchers investigated the role of short-term variability versus long term averages of climate measurements for 320 bird species in the United States. Using more than 21 million GBIF-mediated occurrences, the researchers created sets of models for short- and long-term climate data, respectively. Overall, the two schemes performed well, with short-term models performing slightly better. The study shows that short-term models may produce more accurate results, but data is more difficult to process and complex to analyse, and for predicting general patterns of distribution, models based on long-term climate averages may perform adequately.


Bateman BL, Pidgeon AM, Radeloff VC, Flather CH, VanDerWal J, Akçakaya HR, Thogmartin WE, Albright TP, Vavrus SJ and Heglund PJ (2016) Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data. Ecological Applications. Wiley-Blackwell 26(8): 2720–2731. Available at doi:10.1002/eap.1416.