Invasive species science is heavily geared toward the invasive agent. However, management to protect native species also requires a proactive approach focused on understanding the features affecting community vulnerability to invasion impacts. Vulnerability is likely the result of factors acting across spatial scales, from local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability. We introduce an analytical framework that quantifies the scale-dependent impact of biological invasions from the shape of the native species-area-relationship (SAR). We leverage newly available, biogeographically extensive vegetation data from the US National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of invasion. Results show that a decrease in native richness is consistently associated with invasive species cover, but it is only at relatively high levels of invasion that native richness is compromised. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that are colder and wetter seem to be most vulnerable to losses of native plant species at the local level, while warmer and wetter areas seem most susceptible at the landscape level. We also document how the combined effects of cross-scale factors result in a heterogenous spatial pattern of vulnerability. This pattern cannot be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions.

Code used for the analysis of species-area-relationships (ASR) data. Code used to estimate SAR parameter, and code to analyze SAR parameters as a function of net primary productivity (NPP), human modification index (HMI), invasive ground cover, temperature and precipitation. Analyses and predictions were run in JAGS (Plummer 2003) using the ‘rjags’ package in R (R Core Team 2021).

All data used to run this code are publicly available at:

Plant richness, number of species, and plant cover data (NEON 2020) were downloaded from the NEON data portal application programming interface with the NEON Utilities package (Lunch et al. 2020).

NPP: http://files.ntsg.umt.edu/data/NTSG_Products/MOD17/MODIS_250/modis-250-npp/

HMI: https://doi.org/10.6084/m9.figshare.7283087.v1

Climate: https://www.wordldclim.org/data/index.html  

All data sets were accessed May 2020.

Lunch, C. K., C. M. Laney, and NEON (National Ecological Observatory Network). 2020. neonUtilities: Utilities for Working with NEON Data. R package version 1.3.4. . https://github.com/NEONScience/NEON-utilities.

NEON (National Ecological Observatory Network). 2020. Plant presence and percent cover. DP1.10058.001. https://data.neonscience.org.