Mapping the geographic distribution of non-native aquatic species is a critically important precursor to understanding the anthropogenic and environmental factors that drive freshwater biological invasions. Such efforts are often limited to local scales and/or to a single taxa, missing the opportunity to observe and understand the drivers of macroscale invasion patterns at sub-continental or continental scales. Here we map the distribution of exotic freshwater species richness across the continental United States using publicly accessible species occurrence data (e.g GBIF) and investigate the role of human activity in driving macroscale patterns of aquatic invasion. Using a dasymetric model of human population density and a spatially explicit model of recreational freshwater fishing demand, we analyzed the effect of these metrics of human influence on non-native aquatic species richness at the watershed scale, while controlling for spatial and sampling bias. We also assessed the effects that a temporal mismatch between occurrence data (collected since 1815) and cross-sectional predictors (developed using 2010 data) may have on model fit.
Results/Conclusions
Our results indicated that non-native aquatic species richness exhibits a highly patchy distribution, with hotspots in the Northeast, Great Lakes, Florida, and human population centers on the Pacific coast. These richness patterns are correlated with population density, but are more strongly predicted by patterns of recreational fishing demand. These relationships are strengthened by temporal matching of datasets and are robust to corrections for sampling effort. This suggests that observed patterns are driven by a mechanistic link between recreational activity and aquatic non-native richness, and are not merely the outcome of sampling bias associated with human population density. We are currently expanding this research to investigate the relative power of additional anthropogenic stressors to explain macroscale patterns of aquatic non-native species richness, using data from the EPA’s Stream-Catchment dataset and the EPA’s EnviroAtlas which contains dozens of metrics reflecting human alteration of both terrestrial and aquatic ecosystems.
This abstract has been reviewed and approved by the U.S. Environmental Protection Agency. Its contents do not necessarily reflect the views and policies of the Agency.