Integrating Professional and Citizen Monitoring to Improve Surveillance
This project will develop a modeling framework for integrating professional and citizen-science data, leading to smarter surveillance and improved estimates of AIS distribution that account for imperfect detection and sampling biases. Structured surveys performed by professionals provide high quality data for invasive species surveillance, but the high costs associated with these surveys limit their use.
Citizen scientists play a key and growing role in detecting and monitoring invasive species in Minnesota and elsewhere. For example, volunteers participating in the AIS Detectors' event, Starry Trek, have discovered three of the 13 known starry stonewort populations in Minnesota, and AIS Detectors have performed thousands of hours of surveillance for AIS. Yet, estimating the spatial distribution of invasive species from citizen-science data alone is challenging, because the probability of detecting an invasive species depends on both its abundance and on how sampling effort is allocated—typically in a manner that is spatially heterogeneous and biased towards areas that are easy-to-access. Further, species are not always detected when present (“imperfect detection”). In these cases, it is difficult to estimate occurrence risk, i.e., the probability a target species is truly absent versus present but not yet observed.
1) Develop an integrated modeling framework that leverages the complementary strengths of data collected opportunistically by citizen scientists (widespread coverage in space and time) and structured survey data collected by AIS professionals (known sampling effort, reduced sampling biases);
2) Apply this framework to two high-priority aquatic invasive plant species, Eurasian watermilfoil and starry stonewort. These species differ in their statewide abundance, stages of invasion, biology, and detectability. This framework will lead to improved estimates of the distributions of these species and improved understanding of the factors that influence their occurrence and detectability;
3) Explore ways to improve AIS surveillance more generally (e.g., by strategically allocating sampling effort spatially, temporally, and between structured and unstructured survey efforts).