Comparison of native and exotic distribution and richness models across scales reveals essential conservation lessons

Abstract

Comparing native and exotic plant species distribution and richness models can help to reveal the causes of invasive exotic species proliferation and provide recommendations for preserving native‐dominated ecosystems. However, models may have limited applicability if potentially divergent patterns across scales, spatial autocorrelation and correspondence with community‐wide patterns such as species richness are not considered. I modeled the distributions of 20 dominant native and 20 dominant exotic species among and within patches in a heavily‐invaded and threatened ecosystem in western North America, examining the roles of scale and species origin on variable selection, spatial autocorrelation and model accuracy to determine conditions that favour native over exotic dominants, and derive recommendations for effective management. I also compared distribution models with native and exotic species richness models, to determine the extent to which dominant native and exotic species were representative of synoptic community patterns. Predictability was lower for exotic dominants, possibly because they are environmental generalists, and was lower within than among patches. Predictors were generally shared between distribution and richness models; however, species‐specific differences were common within both native and exotic species groups. Predictors for individual species across scales were frequently different and sometimes opposing. Distribution and richness models suggest that management assuming environmental affiliation at one scale may be ineffective at another; that site prioritization to maximize native versus exotic richness may not preserve the habitat of some common native species; and that intensive management to reduce exotics may be difficult due to low predictability and shared affiliations with natives. Comparing native and exotic distribution and richness models at two scales enabled scale‐specific conservation recommendations and elucidated trade‐offs between management for richness and representation that distribution models at an individual scale would not have allowed.