At least 65 aquatic plant species have been identified as part of a surveillance list of non-native species that pose a threat to biodiversity and ecosystem services in the Laurentian Great Lakes. Early detection of these potentially invasive aquatic plants (IAP) could minimize impacts of novel incursions and facilitate successful eradication. We developed, implemented, and then adaptively refined a probabilistic boat-based sampling design that aimed to maximize the likelihood of detecting novel IAP incursions in large (400+ hectares) Great Lakes coastal areas. Surveys were conducted from 2017 to 2019 at five Great Lakes locations - St Joseph River (MI), Saginaw River (MI), Milwaukee (WI), Cleveland (OH), and the Detroit River (MI). Aquatic plant communities were characterized across the five sites, with a total of 61 aquatic plant species detected. One-fifth of the species detected in our surveys were non-native to the Great Lakes basin. Sample-based species rarefaction curves, constructed from detection data from all surveys combined at each location, show that the estimated sample effort required for high confidence (> 95%) detection of all aquatic plants at a site, including potentially invasive species, varies (< 100 sample units for Detroit River; > 300 sample units for Milwaukee, roughly equivalent to 6 to 18 days sampling effort, respectively). At least 70% of the estimated species pool was detected at each site during initial 3-day surveys. Leveraging information on detection patterns from initial surveys, including depth and species richness strata, improved survey efficiency and completeness at some sites, with detection of at least 80% of the estimated species pool during subsequent surveys. Based on a forest-based classification and regression method, a combination of just five variables explained 70% or more of the variation in observed richness at all sites (depth, fetch, percent littoral, distance to boat ramps and distance to marinas). We discuss how the model outcomes can be used to inform survey design for other Great Lakes coastal areas. The survey design we describe provides a useful template that could be adaptively improved for early detection of IAP in the Great Lakes.

Invasive species surveillance programs can utilize environmental DNA sampling and analysis to provide information on the presence of invasive species. Wider utilization of eDNA techniques for invasive species surveillance may be warranted. This paper covers topics directed towards invasive species managers and eDNA practitioners working at the intersection of eDNA techniques and invasive species surveillance. It provides background information on the utility of eDNA for invasive species management and points to various examples of its use across federal and international programs. It provides information on 1) why an invasive species manager should consider using eDNA, 2) deciding if eDNA can help with the manager's surveillance needs, 3) important components to operational implementation, and 4) a high-level overview of the technical steps necessary for eDNA analysis. The goal of this paper is to assist invasive species managers in deciding if, when, and how to use eDNA for surveillance. If eDNA use is elected, the paper provides guidance on steps to ensure a clear understanding of the strengths and limitation of the methods and how results can be best utilized in the context of invasive species surveillance.

Risk-based prioritization for early detection monitoring is of utmost importance to prevent and mitigate invasive species impacts. The Great Lakes Water Quality Agreement, a binational commitment between the United States and Canada to restore and protect the waters of the Laurentian Great Lakes, identifies aquatic invasive species (AIS) as one of ten priority issues (annexes) that must be addressed to ensure the chemical, physical, and biological integrity of the Great Lakes. The Agreement calls out the need for a comprehensive strategy for detecting and tracking new and potentially invasive species. Yet, with a surface water area of 95, 000 square miles (246, 049 square km) and shoreline length of 10, 210 miles (16, 431 km), the Great Lakes represent a daunting challenge for prioritizing where AIS surveillance activities should occur. Our goal was to develop a spatially-explicit and quantitative approach for identifying the highest risk sites for AIS introduction into the US waters of the Great Lakes based on the cumulative risk of new introductions (including range expansions) from a range of pathways and associated taxa. We estimate "invasion risk" scores for nearly 6,000 sites (9 km x 9 km) across the Great Lakes basin using proxy measures for propagule pressure weighted by the proportion of taxa associated with each proxy variable. Proxy variables include human population, number of ship visits, marina size, number of ponds, and number of natural or artificial aquatic connections. In total, we identify more than 1,800 sites with invasion risk scores >0. A small subset of these 1,800+ sites accounts for a majority of predicted propagule pressure and are therefore logical targets for future surveillance and AIS prevention efforts. Many of the highest risk sites are located in western Lake Erie, southern Lake Michigan, and the St. Clair-Detroit River System.