No abstract

Potamogeton crispus (curlyleaf pondweed) and Myriophyllum spicatum (Eurasian watermilfoil) are widely thought to competitively displace native macrophytes in North America. However, their perceived competitive superiority has not been comprehensively evaluated. Coexistence theory suggests that invader displacement of native species through competitive exclusion is most likely where high niche overlap results in competition for limiting resources. Thus, evaluation of niche similarity can serve as a starting point for predicting the likelihood of invaders having direct competitive impacts on resident species. Across two environmental gradients structuring macrophyte communities—water depth and light availability—both P. crispus and M. spicatum are thought to occupy broad niches. For a third dimension, phenology, the annual growth cycle of M. spicatum is typical of other species, whereas the winter-ephemeral phenology of P. crispus may impart greater niche differentiation and thus lower risk of native species being competitively excluded. Using an unprecedented dataset comprising 3404 plant surveys from Minnesota collected using a common protocol, we modeled niches of 34 species using a probabilistic niche framework. Across each niche dimension, P. crispus had lower overlap with native species than did M. spicatum; this was driven in particular by its distinct phenology. These results suggest that patterns of dominance seen in P. crispus and M. spicatum have likely arisen through different mechanisms, and that direct competition with native species is less likely for P. crispus than M. spicatum. This research highlights the utility of fine-scale, abundance-based niche models for predicting invader impacts.

Aim Availability of uniformly collected presence, absence, and abundance data remains a key challenge in species distribution modeling (SDM). For invasive species, abundance and impacts are highly variable across landscapes, and quality occurrence and abundance data are critical for predicting locations at high risk for invasion and impacts, respectively. We leverage a large aquatic vegetation dataset comprising point‐level survey data that includes information on the invasive plant Myriophyllum spicatum (Eurasian watermilfoil) to: (a) develop SDMs to predict invasion and impact from environmental variables based on presence–absence, presence‐only, and abundance data, and (b) compare evaluation metrics based on functional and discrimination accuracy for presence–absence and presence‐only SDMs. Location Minnesota, USA. Methods Eurasian watermilfoil presence–absence and abundance information were gathered from 468 surveyed lakes, and 801 unsurveyed lakes were leveraged as pseudoabsences for presence‐only models. A Random Forest algorithm was used to model the distribution and abundance of Eurasian watermilfoil as a function of lake‐specific predictors, both with and without a spatial autocovariate. Occurrence‐based SDMs were evaluated using conventional discrimination accuracy metrics and functional accuracy metrics assessing correlation between predicted suitability and observed abundance. Results Water temperature degree days and maximum lake depth were two leading predictors influencing both invasion risk and abundance, but they were relatively less important for predicting abundance than other water quality measures. Road density was a strong predictor of Eurasian watermilfoil invasion risk but not abundance. Model evaluations highlighted significant differences: Presence–absence models had high functional accuracy despite low discrimination accuracy, whereas presence‐only models showed the opposite pattern. Main conclusion Complementing presence–absence data with abundance information offers a richer understanding of invasive Eurasian watermilfoil's ecological niche and enables evaluation of the model's functional accuracy. Conventional discrimination accuracy measures were misleading when models were developed using pseudoabsences. We thus caution against the overuse of presence‐only models and suggest directing more effort toward systematic monitoring programs that yield high‐quality data.