Dealing with spatial autocorrelation in gene flow modeling

Spatial data is common in ecological studies; however, one major problem with spatial data is the presence of the spatial autocorrelation. This phenomenon indicates that data measured at locations relatively close one to each other tend to have more similar values than data measured at locations further apart. Spatial autocorrelation violates the statistical assumption that the analyzed data are independent and identically distributed. This chapter focuses on effects of the spatial autocorrelation when predicting gene flow from Genetically Modified (GM) to non-GM maize fields under real multi-field crop management practices at a regional scale. We present the SCLUS method, an extension of the method CLUS (Blockeel et al., 1998), which learns spatially aware predictive clustering trees (PCTs). The method can consider locally and globally the effects of the spatial autocorrelation as well as can deal with the “ecological fallacy” problem (Robinson, 1950). The chapter concludes with a presentation of an application of this approach for gene flow modeling