Spatial autocorrelation reduces model precision and predictive power in deforestation analyses

Generalized linear models are often used to identify covariates of landscape processes and to model land-use change. Generalized linear models however, overlook the spatial component of land-use data, and its effects on statistical inference. Spatial autocorrelation may artificially reduce variance in observations, and inflate the effect size of covariates. To uncover the consequences of overlooking this spatial component, we tested both spatially explicit and non-spatial models of deforestation for Colombia. Parameter estimates, analyses of residual spatial autocorrelation, and Bayesian posterior predictive checks were used to compare model performance. Significant residual correlation showed that non-spatial models failed to adequately explain the spatial structure of the data. Posterior predictive checks revealed that spatially explicit models had strong predictive power for the entire range of the response variable and only failed to predict outliers, in contrast with non-spatial models, which lacked predictive power for all response values. The predictive power of non-spatial models was especially low in regions away from Colombia's center, where about half the observations were clustered. While all analyses consistently identified a core of important covariates of deforestation rates, predictive modeling requires parameter estimates informed by the spatial structure of the data. To inform increasingly important forest and carbon sequestration policy, land-use models must account for spatial autocorrelation.