Special Issue Article
A climate of uncertainty: accounting for error in climate variables for species distribution models
Article first published online: 18 AUG 2014
© 2014 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of the British Ecological Society.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Methods in Ecology and Evolution
How to Cite
Stoklosa, J., Daly, C., Foster, S. D., Ashcroft, M. B., Warton, D. I. (2014), A climate of uncertainty: accounting for error in climate variables for species distribution models. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12217
- Article first published online: 18 AUG 2014
- Accepted manuscript online: 17 JUN 2014 09:25AM EST
- Manuscript Accepted: 10 JUN 2014
- Manuscript Received: 15 MAY 2014
- Australian Research Council Discovery Project. Grant Numbers: DP0985886, DP130102131
- Future Fellow. Grant Number: FT120100501
- Australian Government's National Environmental Research Program (NERP)
- NERP Marine Biodiversity Hub
- Institute for Marine and Antarctic Studies, University of Tasmania
- CSIRO Wealth from Oceans National Flagship
- Geoscience Australia
- Australian Institute of Marine Science
- Museum Victoria
- Charles Darwin University
- The University of Western Australia
- US National Science Foundation
- climate maps;
- hierarchical statistical models;
- measurement error;
- prediction error;
- Spatial climate variables are routinely used in species distribution models (SDMs) without accounting for the fact that they have been predicted with uncertainty, which can lead to biased estimates, erroneous inference and poor performances when predicting to new settings – for example under climate change scenarios.
- We show how information on uncertainty associated with spatial climate variables can be obtained from climate data models. We then explain different types of uncertainty (i.e. classical and Berkson error) and use two statistical methods that incorporate uncertainty in climate variables into SDMs by means of (i) hierarchical modelling and (ii) simulation–extrapolation.
- We used simulation to study the consequences of failure to account for measurement error. When uncertainty in explanatory variables was not accounted for, we found that coefficient estimates were biased and the SDM had a loss of statistical power. Further, this bias led to biased predictions when projecting change in distribution under climate change scenarios. The proposed errors-in-variables methods were less sensitive to these issues.
- We also fit the proposed models to real data (presence/absence data on the Carolina wren, Thryothorus ludovicianus), as a function of temperature variables.
- The proposed framework allows for many possible extensions and improvements to SDMs. If information on the uncertainty of spatial climate variables is available to researchers, we recommend the following: (i) first identify the type of uncertainty; (ii) consider whether any spatial autocorrelation or independence assumptions are required; and (iii) attempt to incorporate the uncertainty into the SDM through established statistical methods and their extensions.