LEVERAGING SEASON-AHEAD FORECASTS OF LAKE WATER QUALITY TO PREDICT FISH STRESS AND VULNERABILITY

Recent advances in subseasonal-to-seasonal scale predictions of climate and hydrology variables provide prospects for sectoral management; however, little attention has been devoted to prediction of water quality factors that directly or indirectly impact aquatic habitat conditions. Concurrently, significant effort has been aimed at advancing routine lake monitoring and assembly of local scale datasets of water quality and aquatic abundance. Presently there is a gap in pairing predictions with lake-specific data to understand the ability of models to predict water quality and habitat conditions and subsequently inform preseason management opportunities. Sufficient summertime oxythermal habitat for fish is closely tied to water quality parameters, most notably water temperature, dissolved oxygen, and nutrient levels. Cold-water fish are at high risk from a changing climate, given the direct reduction of habitat availability under warming temperatures. In response, direct prediction (e.g. fish stress) and indirect prediction (e.g. air and water temperature, nutrient levels, DO, oxythermal stress indices, and combinations) models were developed, using global and local hydro-climate features as predictors. Existing long-term datasets monitoring Wisconsin lakes were leveraged to assess a variety of metrics to predict oxythermal stress conditions for cold-water fish species on a season-ahead scale. Modeling and predicting changes at this time scale can inform actionable resource decisions, and compliment short-term and long-term climate change adaptation strategies developed by Departments of Natural Resources and other organizations. Future research includes evaluating anticipatory management strategies, particularly for predictions of extreme conditions, including fish kills, and understanding how lake water conditions and predictability may evolve under a changing climate.

Primary Presenter: Emma Blackford, University of Wisconsin-Madison (eblackford@wisc.edu)

Authors:

Emma Blackford, University of Wisconsin-Madison  (eblackford@wisc.edu)

Paul Block, University of Wisconsin-Madison  (paul.block@wisc.edu)