Biotic interactions are known to aff ect the composition of species assemblages via several mechanisms, such as competition
and facilitation. However, most spatial models of species richness do not explicitly consider inter-specifi c interactions.
Here, we test whether incorporating biotic interactions into high-resolution models alters predictions of species
richness as hypothesised. We included key biotic variables (cover of three dominant arctic-alpine plant species) into two
methodologically divergent species richness modelling frameworks – stacked species distribution models (SSDM) and
macroecological models (MEM) – for three ecologically and evolutionary distinct taxonomic groups (vascular plants,
bryophytes and lichens). Predictions from models including biotic interactions were compared to the predictions of
models based on climatic and abiotic data only. Including plant – plant interactions consistently and signifi cantly lowered
bias in species richness predictions and increased predictive power for independent evaluation data when compared to
the conventional climatic and abiotic data based models. Improvements in predictions were constant irrespective of the
modelling framework or taxonomic group used. Th e global biodiversity crisis necessitates accurate predictions of how
changes in biotic and abiotic conditions will potentially aff ect species richness patterns. Here, we demonstrate that models
of the spatial distribution of species richness can be improved by incorporating biotic interactions, and thus that these key
predictor factors must be accounted for in biodiversity forecasts.