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Trees provide ecosystem services that improve the environment and human health. The magnitude of these improvements may be related to tree diversity within green spaces, yet spatially explicit diversity data necessary to investigate such associations are often missing. Here, we evaluate two methods to model tree diversity at genus level based on environmental covariates and presence point data. We aimed to identify the drivers and suitable methods for urban and rural tree diversity models in the heterogeneous region of Flanders, Belgium. We stratified our research area into dominantly rural and dominantly urban areas and developed distribution models for 13 tree genera for both strata as well as for the area as a whole. Occurrence data were obtained from an open-access presence-only database of validated observations of vascular plants. These occurrence data were combined with environmental covariates in MaxEnt models. Tree diversity was modelled by adding up the individual species distribution models. Models in the dominantly rural areas were driven by soil characteristics (soil texture and drainage class). Models in the dominantly urban areas were driven by environmental covariates explaining urban heterogeneity. Nevertheless, the stratification into urban and rural did not contribute to a higher model quality. Generic tree diversity estimates were better when presences derived from distribution models were simply added up (binary stacking, True Positive Rate of 0.903). The application of macro-ecological constraints resulted in an underestimation of generic tree diversity (probability stacking, True Positive Rate of 0.533). We conclude that summing presences derived from species distribution models (binary stacking) is a suitable approach to increase knowledge on regional diversity.
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