Many inland waters across the United States are experiencing warming water temperatures. The impacts of this warming on aquatic ecosystems are significant in many areas, causing problems for fisheries management, as many economically and ecologically important fish species are experiencing range shifts and population declines. Fisheries and natural resource managers need timely and usable data and tools in order to understand and predict changes to lakes and their biota. A previous Northeast CSC-funded project modeled lake temperatures to help state agencies in the Midwest understand trends in walleye and largemouth bass populations and predict lake-specific fish populations under future climate scenarios. These results have been [...]
Summary
Many inland waters across the United States are experiencing warming water temperatures. The impacts of this warming on aquatic ecosystems are significant in many areas, causing problems for fisheries management, as many economically and ecologically important fish species are experiencing range shifts and population declines. Fisheries and natural resource managers need timely and usable data and tools in order to understand and predict changes to lakes and their biota.
A previous Northeast CSC-funded project modeled lake temperatures to help state agencies in the Midwest understand trends in walleye and largemouth bass populations and predict lake-specific fish populations under future climate scenarios. These results have been extremely valuable for decisions and management strategies at the state scale, and this new project will expand these efforts and will focus on lake temperature products at the three spatial scales of most interest to state agency stakeholders: lake, state, and region (for Minnesota, Wisconsin, and Michigan).
The project researchers will use a “hyperscale” modeling approach that will build upon the multi-state modeling framework developed in the earlier project to increase model accuracy for high priority managed lakes. This approach will use all observations of temperature that exist for every study lake in the region and use machine learning techniques to uncover biases in models used for lakes with many observations. The project will generate an improved assessment of aquatic habitat for lake fisheries, and will provide estimates of contemporary thermal habitats to be used by state partners to estimate the distribution and abundance of ecologically and economically important fish species. Deliverables for this project include: 1) hind-casted lake temperature profiles (1979-present), 2) summary outputs from the thermal models, and 3) individually tuned lake models for managers to use for testing and predicting future conditions under different climate change scenarios.
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DayLake_WI_USFS_crop.jpg “Day Lake, Wisconsin - Credit: USFS”
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Purpose
Water temperatures are warming in lakes and streams, resulting in the loss of many native fish. Given clear passage, coldwater stream fishes can take refuge upstream when larger streams become too warm. Likewise, many Midwestern lakes “thermally stratify” resulting in warmer waters on top of deeper, cooler waters. Many of these lakes are connected to threatened streams. To date, assessments of the effects of climate change on fish have mostly ignored lakes, and focused instead on streams. Because surface waters represent a network of habitats, an integrated assessment of stream and lake temperatures under climate change is necessary for decision-making. This work will inform the preservation of lake/stream linkages, prioritization restoration strategies, and stocking efforts for sport fish. This project will employ state-of-the-science methods to model historical thermal habitat for nearly ten thousand lakes.The results of this project will be used by partners and stakeholders to prioritize adaptation and restoration strategies for the region’s freshwater resources. Additionally, these data products will be shared openly in machine-readable formats to spur other innovation and research.
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Technical Summary
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In the face of global change, access to timely, actionable data and information are critical to actively manage natural resources. Our understanding of spatial availability and connectivity of thermal habitats and their influence on species distribution are key to supporting freshwater fish populations as the landscape and climate changes. Past research has separated lake and stream linkages, and no comprehensive lake temperature data profiles exist as they do for streams. In partnership with regional experts and practitioners from Michigan, Minnesota and Wisconsin Departments of Natural Resources, University of Wisconsin Center for Limnology, and informaticists/modelers from U.S. Geological Survey’s Data Science Branch, will leverage and expand upon existing modeling frameworks to produce essential daily hindcasted lake temperature profiles lakes and post-process the results to de-bias the model outputs with machine learning techniques. Our reusable models, tools, and output data products will be widely broadcast and demonstrated via in- person and web-based training sessions to raise awareness, build support and gain feedback, and spur innovation and future research.