10: Cross-Validation Bias in Presence-Only Spatial Models Applied to Topoclimatic Zoning

Thomas M. Menino Convention & Exhibition Center 

Random cross-validation can overestimate predictive performance in presence-only spatial models when spatial autocorrelation among clustered occurrences is ignored. We quantified this bias using 635 georeferenced occurrences of Carapa guianensis, a key species for Amazonian extractive economies. Random Forest models were fitted with WorldClim predictors under a factorial design combining two feature spaces, topoclimatic variables alone and topoclimatic variables plus geographic coordinates, and four validation schemes: random cross-validation, spatial block, environmental block, and kNNDM. Results showed that the coordinate-augmented model achieved an AUC of 0.96 under random cross-validation, but this value dropped to 0.53–0.62 under environmental and spatial blocking. In addition, a Local Join Count test on residuals detected statistically significant spatial clustering of classification errors (p < 0.05) not identified by global metrics. These results demonstrate that random cross-validation inflated model performance and that spatially structured validation provides more reliable support for topoclimatic zoning.

Spatial Cross-Validation

Spatial Autocorrelation

Spatial Leakage

Species Distrtibuition Models

Random Forest