A participatory science approach to monitoring and predicting within-lake zebra mussel abundance

Abstract

Zebra mussels (Dreissena polymorpha) are an invasive species that disrupt ecosystems and increase management costs across North America. To monitor their spread and support data-driven management, we developed the Zebra Mussel Safari—a scalable participatory science program. After two pilot years, we engaged 63 participants from 7 Minnesota lakes in 2023 and expanded to 154 participants from 15 lakes in 2024. Volunteers deployed samplers from their docks to measure juvenile settlement, photographed the samplers, and submitted the images online. Automated counts were accurate and efficient, reducing analysis time compared to manual methods. Participant feedback was positive, indicating strong potential for long-term engagement and high-quality data collection at scale. Using data from the program, we explored four Negative Binomial generalized linear models (GLM), varying in sample size, predictor diversity, and cross-validation strategy. Each model search identified a set of high-performing models based on both in-sample fit and out-of-sample predictive performance. Key predictors consistently retained across top models included Secchi depth, number of boat accesses, years since first known infestation, fetch, groundwater calcium, and a lake level suitability score. Across all model searches, predictions were accurate in identifying sites with low or high zebra mussel abundances; however, moderate abundances proved more challenging with consistent overprediction. This work demonstrates the importance of continued monitoring and the potential of public data collection to inform data-driven invasive species management strategies.