Water Resources Research Act Program

Details for Project ID 2020IN104AIS

Using Data from a Popular Fishing App to Predict the Spread of Aquatic Invasives and Identify Characteristics of Resistant/Resilient Lakes in the Upper Mississippi River Basin

Institute: Indiana
USGS Grant Number: G21AP10175
Year Established: 2020 Start Date: 2020-01-01 End Date: 2020-01-02
Total Federal Funds: $79,195 Total Non-Federal Funds: $84,490

Principal Investigators: Paul Venturelli

Abstract: Recreational anglers and boaters can be a major vector of the spread of aquatic invasive species (AIS) in the Upper Mississippi Basin (UMRB), but their movement patterns are poorly understood. Recreational fishing apps are a potentially innovative and economical means of obtaining movement data. Anglers that use Fishbrain, the most popular fishing app in the United States, have so far logged nearly 100,000 catches in ~7,000 lakes UMRB since 2011. This proposed project is a 2-year collaboration between Ball State University and the United States Geological Survey (USGS) Nonindigenous Aquatic Species Program that will use big data from Fishbrain and the USGS to identify i) lakes and roads in the UMRB that contribute the most to the spread of AIS (and when), and ii) lake characteristics that infer resistance and resilience to the establishment and impacts of specific AIS in the UMRB. This project will result in information that is essential to the design and optimization of intra- and inter-agency prevention, detection, and monitoring efforts at local and regional scales by all levels of government as well as non-governmental organizations (NGOs) within the UMRB. The project will use Ball State’s new supercomputer to apply cutting-edge network and machine-learning analyses to three sources of big data: Fisbhrain, the USGS National Hydrology Dataset (NHD), and the USGS Nonindigenous Aquatic Species Database. The project will also leverage nearly two years of work at Ball State to analyze low-resolution patterns of angler movement at the national level, and a recent proof-of-concept application of machine learning to lakes in Florida. This project will innovate further to provide detailed and relevant results for the entire UMRB. Dissemination will be via two peer-reviewed publications, presentations to researchers and managers, traditional and social media, and public outreach. It will also include an interactive map that managers and the public can use to explore detailed results and develop appropriate actions.