Journal article
What do we gain from simplicity versus complexity in species distribution models?
C Merow, MJ Smith, TC Edwards, A Guisan, SM Mcmahon, S Normand, W Thuiller, RO Wüest, NE Zimmermann, J Elith
Ecography | Published : 2014
DOI: 10.1111/ecog.00845
Open access
Abstract
Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-envir..
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Awarded by National Science Foundation
Funding Acknowledgements
This study arose from two workshops entitled 'Advancing concepts and models of species range dynamics: understanding and disentangling processes across scales'. Funding was provided by the Danish Council for Independent Research vertical bar Natural Sciences (grant no. 10-085056 to SN). CM acknowledges funding from NSF grant 1046328 and NSF grant 1137366. WT acknowledges support from the European Research Council under the European Community's Seven Framework Programme FP7/2007-2013 Grant Agreement no. 281422 (TEEMBIO). RW acknowledges support from the Swiss National Science Foundation (Synergia Project CRS113-125240, Early Postdoc Mobility Grant PBZHP3_147226). JE acknowledges funding from the Australian Research Council (grant FT0991640). TE states that mention any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.