Temporally stacked bee forage species distribution modeling for flower abundance mapping

Predicting spatial distribution of flowering forage availability is critical for guiding migratory beekeeping decisions.Species distribution modelling (SDM) is widely used to predict the geographic distribution or species ranges. Stacked distributions of multiple species (S-SDM) have been used in predicting species richness or assemblages. Here, we present a method for stacking SDMs based on a temporal element, the flowering phenology of melliferous flora species. First, we used presence-only data for thirty key forage species used for honey production in Western Australia, combined with environmental variables for predicting the geographic distribution of species, using MaxEnt software. The output distribution grids were then stacked based on monthly flowering times of each species to develop grids representing the richness of flowering species by grid cell. While designed for modelling flowering forage availability for a migratory beekeeping system, the approach can be used for predicting temporal forage availability for a range of different fauna How to use temporally stacked species distribution modelling for generic distribution of flowering availability using presence-only data.A procedure for developing flowering richness and availability grids.

Automated summary

Predicting the spatial distribution of flowering forage availability is crucial for guiding decisions in migratory beekeeping. Species distribution modelling (SDM) is commonly used in predicting geographic distribution or species ranges. In this study, a method is presented for stacking SDMs based on the temporal element of flowering phenology for melliferous flora species.