Invasive species pose a serious threat to biodiversity and economic security, with acute effects in river ecosystems. Yet despite decades of research, ecology still faces a glaring paradox: many invasion theories are at odds with one another, but each is supported by strong evidence. The paradox exists because most studies are conducted in only one of three key ecological dimensions: scale, space, and time. This project will take a cross-scale approach from small stream segments to whole watersheds across the landscape at numerous time steps. In doing so, this work will use a macrosystems framework to link invasion theories with empirical evidence across all three dimensions to further our understanding of how species invasions happen and how to prevent or manage them. This research will focus on riverine fishes—one of the most diverse yet imperiled groups of organisms in North America. In the process, this project will generate a large-extent, fine-resolution dataset of fish occurrences that can be used for numerous purposes beyond the lifetime of the project. This dataset can be linked to the National Ecological Observatory Network (NEON) and other federally-supported data to infer broad mechanistic patterns and processes. Results from this work will be used to develop an interactive spatial planning tool for predicting future fish invasions across the Mississippi River basin—the largest connected river network in North America. This tool will be shared via numerous communication outlets including webinars, websites, and social media. A diverse group of over 20 graduate and undergraduate students will be trained in the course of the project.
The goal of this project is to understand how natural and human-created dispersal networks (e.g. rivers and roads) interact with invasion drivers (e.g. habitat and species traits) to determine cross-scale invasion patterns. This work will be conducted in the Mississippi River Basin; rivers provide a unique opportunity for studying scale-dependent invasion processes because watersheds create discrete boundaries of fish species distribution. Accordingly, many of the most prolific invaders arrive not from distant continents, but from nearby watersheds. Native status for riverine fishes is thus scale-dependent, which affects how invasions are conceptualized and modeled. Using rich datasets of historical and contemporary fish distribution, this project takes a three-pronged approach: 1) using Bayesian tools to quantify invasion drivers across spatial scales; 2) linking concepts from metacommunity and invasion ecology to investigate invasion network dynamics across space; and 3) predicting trajectories of species invasions through time. The project will integrate information from these three research objectives to forecast future riverine invasions across the landscape.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
