Year of Award
Master of Science (MS)
Department or School/College
College of Forestry and Conservation
Dave Affleck, John Kimball
climate change, conservation planning, GLM, GLMM, historic data, species distribution models, transferability, uncertainty
University of Montana
Species distribution models (SDMs) relate observed locations of a species to climate, and are used for projecting the fate of a species under climate change scenarios. To be useful in a decision-making context, the uncertainty associated with these projections must be known. However, the uncertainty associated with SDM projections is largely ignored, perhaps because many current methods have been shown to produce biased estimates. Failure to account for spatial autocorrelation (SAC) of residual error explains much of this bias. Generalized linear mixed models (GLMM) have the ability to account for SAC through the inclusion of a spatially structured random intercept, interpreted to account for the effect of missing predictors. This framework promises a more realistic representation of parameter and prediction uncertainty. My work assesses the ability of GLMMs and a conventional SDM approach, based on generalized linear models (GLM), to produce accurate projections and estimates of prediction uncertainty. Bayesian methods were used to fit models to historical (1928-1940) observations for 99 woody plant species in California, USA, and assessed using modern "temporally independent" validation data (2000-2005). A set of climatic water balance metrics were calculated to inform the models. GLMMs provided a closer fit to historic data, had fewer significant covariates, were better able to nearly eliminate spatial autocorrelation of residual error, and had larger credible intervals for projections than GLMs. The accuracy of projections was similar between methods but the GLMMs better quantified projection uncertainty. Additionally, the GLMMs produced more conservative estimates of species range size and range size change than the GLMs. I conclude that the GLMM error structure allows for a more realistic characterization of SDM uncertainty. This is critical for conservation applications that rely on robust assessments of projection uncertainty.
Swanson, Alan, "Spatial regression methods capture prediction uncertainty in species distribution model projections through time" (2012). Graduate Student Theses, Dissertations, & Professional Papers. 905.
© Copyright 2012 Alan Swanson