Quantifying apparent impact and distinguishing impact from invasiveness in multispecies plant invasions

Abstract

The quantification of invader impacts remains a major hurdle to understanding and managing invasions. Here, we demonstrate a method for quantifying the community-level impact of multiple plant invaders by applying Parker et al.'s (1999) equation (impact = range × local abundance × per capita effect or per unit effect) using data from 620 survey plots from 31 grasslands across west-central Montana, USA. In testing for interactive effects of multiple invaders on native plant abundance (percent cover), we found no evidence for invasional meltdown or synergistic interactions for the 25 exotics tested. While much concern exists regarding impact thresholds, we also found little evidence for nonlinear relationships between invader abundance and impacts. These results suggest that management actions that reduce invader abundance should reduce invader impacts monotonically in this system. Eleven of 25 invaders had significant per unit impacts (negative local-scale relationships between invader and native cover). In decomposing the components of impact, we found that local invader abundance had a significant influence on the likelihood of impact, but range (number of plots occupied) did not. This analysis helped to differentiate measures of invasiveness (local abundance and range) from impact to distinguish high-impact invaders from invaders that exhibit negligible impacts, even when widespread. Distinguishing between high- and low-impact invaders should help refine trait-based prediction of problem species. Despite the unique information derived from evaluation of per unit effects of invaders, invasiveness scores based on range and local abundance produced similar rankings to impact scores that incorporated estimates of per unit effects. Hence, information on range and local abundance alone was sufficient to identify problematic plant invaders at the regional scale. In comparing empirical data on invader impacts to the state noxious weed list, we found that the noxious weed list captured 45% of the high-impact invaders but missed 55% and assigned the lowest risk category to the highest-impact invader. While such subjective weed lists help to guide invasive species management, empirical data are needed to develop more comprehensive rankings of ecological impacts. Using weed lists to classify invaders for testing invasion theory is not well supported.