The poleward naturalization of intracontinental alien plants.

Plant introductions outside their native ranges by humans have led to substantial ecological consequences. While we have gained considerable knowledge about intercontinental introductions, the distribution and determinants of intracontinental aliens remain poorly understood. Here, we studied naturalized (i.e., self-sustaining) intracontinental aliens using native and alien floras of 243 mainland regions in North America, South America, Europe, and Australia. We revealed that 4510 plant species had intracontinental origins, accounting for 3.9% of all plant species and 56.7% of all naturalized species in these continents. In North America and Europe, the numbers of intracontinental aliens peaked at mid-latitudes, while the proportion peaked at high latitudes in Europe. Notably, we found predominant poleward naturalization, primarily due to larger native species pools in low-latitudes. Geographic and climatic distances constrained the naturalization of intracontinental aliens in Australia, Europe, and North America, but not in South America. These findings suggest that poleward naturalizations will accelerate, as high latitudes become suitable for more plant species due to climate change.

Machine learning predicts large scale declines in native plant phylogenetic diversity.

Though substantial effort has gone into predicting how global climate change will impact biodiversity patterns, the scarcity of taxon-specific information has hampered the efficacy of these endeavors. Further, most studies analyzing spatiotemporal patterns of biodiversity focus narrowly on species richness. We apply machine learning approaches to a comprehensive vascular plant database for the United States and generate predictive models of regional plant taxonomic and phylogenetic diversity in response to a wide range of environmental variables. We demonstrate differences in predicted patterns and potential drivers of native vs nonnative biodiversity. In particular, native phylogenetic diversity is likely to decrease over the next half century despite increases in species richness. We also identify that patterns of taxonomic diversity can be incongruent with those of phylogenetic diversity. The combination of macro-environmental factors that determine diversity likely varies at continental scales; thus, as climate change alters the combinations of these factors across the landscape, the collective effect on regional diversity will also vary. Our study represents one of the most comprehensive examinations of plant diversity patterns to date and demonstrates that our ability to predict future diversity may benefit tremendously from the application of machine learning.