Sensitivity of predictive species distribution models to change in grain size
When using species distribution models, grain (resolution) size is a spatial factor that may influence predictive model outcomes. Guisan et. al. (2007) tested the effect of grain size on SDM by comparing model performance of 10 predictive modelling techniques (DIVA-GIS, DOMAIN, GLM, GAM, BRUTO, MARS, BRT, OM-GARP, GDMSS, and MAXENT-T) on presence only data of 50 species in 5 different regions (from Elith et al 2006) and also determined whether affects observed were dependent on the type of region, modelling technique, or organism considered. Model performance at two grain sizes (original and 10-fold) was assessed and prediction success was compared and ranked using Area under ROC curve. Increasing grain size did not affect model performance however it did degrade models on average. Although surprised by the outcome, the somewhat fundamental question reflects realistic issues in SDM. The testing 10 modelling techniques was a well thought out approach to determining factors that apparently aren’t influenced by grain (unless original data lacked predictive power that wouldn’t be influenced by scale anyway). It would be interesting for a follow up paper to test other variables that may be more affected by changes in grain size (sessile organism, species with small home ranges, or factors at the microhabitat level).