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Model selection and information theory in geographical ecology
Author(s) -
DinizFilho José Alexandre Felizola,
Rangel Thiago Fernando L. V. B.,
Bini Luis Mauricio
Publication year - 2008
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1111/j.1466-8238.2008.00395.x
Subject(s) - akaike information criterion , spatial analysis , information criteria , model selection , statistics , ordinary least squares , autocorrelation , econometrics , collinearity , selection (genetic algorithm) , ecology , null model , autoregressive model , mathematics , spatial ecology , computer science , biology , artificial intelligence
Aim Although parameter estimates are not as affected by spatial autocorrelation as Type I errors, the change from classical null hypothesis significance testing to model selection under an information theoretic approach does not completely avoid problems caused by spatial autocorrelation. Here we briefly review the model selection approach based on the Akaike information criterion (AIC) and present a new routine for Spatial Analysis in Macroecology (SAM) software that helps establishing minimum adequate models in the presence of spatial autocorrelation. Innovation We illustrate how a model selection approach based on the AIC can be used in geographical data by modelling patterns of mammal species in South America represented in a grid system ( n = 383) with 2° of resolution, as a function of five environmental explanatory variables, performing an exhaustive search of minimum adequate models considering three regression methods: non‐spatial ordinary least squares (OLS), spatial eigenvector mapping and the autoregressive (lagged‐response) model. The models selected by spatial methods included a smaller number of explanatory variables than the one selected by OLS, and minimum adequate models contain different explanatory variables, although model averaging revealed a similar rank of explanatory variables. Main conclusions We stress that the AIC is sensitive to the presence of spatial autocorrelation, generating unstable and overfitted minimum adequate models to describe macroecological data based on non‐spatial OLS regression. Alternative regression techniques provided different minimum adequate models and have different uncertainty levels. Despite this, the averaged model based on Akaike weights generates consistent and robust results across different methods and may be the best approach for understanding of macroecological patterns.