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Spatial regression methods capture prediction uncertainty in species distribution model projections through timeAuthor(s): Alan K. Swanson; Solomon Z. Dobrowski; Andrew O. Finley; James H. Thorne; Michael K. Schwartz
Source: Global Ecology and Biogeography. 22: 242-251.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionThe uncertainty associated with species distribution model (SDM) projections is poorly characterized, despite its potential value to decision makers. Error estimates from most modelling techniques have been shown to be biased due to their failure to account for spatial autocorrelation (SAC) of residual error. 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 characterization of parameter and prediction uncertainty. Our aim is to assess the ability of GLMMs and a conventional SDM approach, generalized linear models (GLM), to produce accurate projections and estimates of prediction uncertainty.
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CitationSwanson, Alan K.; Dobrowski, Solomon Z.; Finley, Andrew O.; Thorne, James H.; Schwartz, Michael K. 2013. Spatial regression methods capture prediction uncertainty in species distribution model projections through time. Global Ecology and Biogeography. 22: 242-251.
KeywordsCalifornia, climate change, conservation planning, GLM, GLMM, historic data, species distribution models, transferability, uncertainty
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