Filling in the gaps: modelling native species richness and invasions using spatially incomplete data

Detailed knowledge of patterns of native species richness, an important component of biodiversity, and non-native species invasions is often lacking even though this knowledge is essential to conservation efforts. However, we cannot afford to wait for complete information on the distribution and abundance of native and harmful invasive species. Using information from counties well surveyed for plants across the USA, we developed models to fill data gaps in poorly surveyed areas by estimating the density (number of species km^?2) of native and non-native plant species. Here, we show that native plant species density is non-random, predictable, and is the best predictor of non-native plant species density. We found that eastern agricultural sites and coastal areas are among the most invaded in terms of non-native plant species densities, and that the central USA appears to have the greatest ratio of non-native to native species. These large-scale models could also be applied to smaller spatial scales or other taxa to set priorities for conservation and invasion mitigation, prevention, and control efforts.