SEGURADO, P., ARAÚJO, M. B. and KUNIN, W. E. (2006), Consequences of spatial autocorrelation for niche-based models. Journal of Applied Ecology, 43: 433–444. doi: 10.1111/j.1365-2664.2006.01162.x
Spatial autocorrelation is an important bias source in most spatial analysis. Segurado, Araujo and Kunin (2006) examined the bias caused by spatial autocorrelation based on explanatory and predictive power of niche-based species distribution modes. Two kinds of freshwater turtle and two simulated species were used to construct SDM using generalized linear models (GLM), generalized additive models (GAM) and classification tree analysis (CTA). In general, GAM and CTA outperformed GLM, though all of them are vulnerable to the effects of spatial autocorrelation, which leads to an inflation effect up to 90-fold. Efforts for reducing autocorrelation effects included systematical subsampling and inclusion of a contagion term. Subsampling was only partially successful in avoiding inflation effect, whereas the inclusion method fully eliminated or sometime even overcorrected the effect. Based on this study, they recommended to implement techniques and procedures like the null model approach in order to improve niche-based SDM performance. However, their discussion is limited only to univariate modeling. When more then one candidate variable to predict SDM, a more complex assessment needs to be considered. However, since SDM are usually multivariate, their conclusion may still be able to offer informative rules, but to which level autocorrelation will affect SDM, or which model perform better may need further exploration.