Finer grain size increases effects of error and changes influence of environmental predictors on species distribution models

Abstract

Spatial resolution and zoning affect models and predictions of species distribution models. I compared grain sizes of 90m grid cells to ecological units of soil polygons (approximately 209ha composed of discontinuous polygons of 16ha), and then introduced error into samples and examined influence of topographic and soil variables. I used random forests, which is a machine learning classifier, and open access data. Predictions based on 90m grid cells were slightly more accurate than coarser-sized polygons, particularly false positive rates (mean values of 0.11 and 0.16, respectively). The trade-off for accuracy was the number of mapping units required to increase resolution. Probability of presence decreased with resolution. Similarly to grain size comparisons, error affected probability of presence more than accuracy of prediction. Unlike grain size comparisons, the relationship between count of each species (i.e., relative abundance) and area predicted as present was lost with addition of error. Introduction of absences into the modeling sample of presences through plot location error increased probability of presence and introduction of presences into the modeling sample of absences through use of background pseudoabsences decreased probability of presence. Finer resolution amplified the effect of background absences; area predicted for presence was reduced by a factor of 5.4 for grid cells and 1.4 for soil polygons. The choice of fine resolution grid cells or coarser shaped polygons resulted in different models, due to varying influence of topographic variables on models. Use of coarser resolution (tens to hundreds of hectares) may be a worthwhile exchange for greater spatial extent of species distribution models and use of ecologically zoned polygons appeared to avoid the modifiable areal unit problem.