Expanding our knowledge of winter limnology is critical for managing lakes , reservoirs, and all freshwater resources in a future with shorter winters and less lake ice. In temperate latitudes, we have largely ignored winter as a season that impacts ecological processes, and it is unclear what ramifications the loss of lake ice will have on lake ecosystems. This project will combine long-term observational datasets, high-frequency buoy data, and an experimental approach to understanding the role of light availability in under-ice productivity

Cold-water fish are disappearing from many midwestern lakes as they warm. This loss is due to a combination of de-oxygenation of the deep waters with heating of the surface waters. Together, these climate-driven changes squeeze the depth distribution of fish that require cold, well-oxygenated water, sometimes eliminating their habitat entirely. We will investigate where this combination of factors has likely caused extirpation of cold-water fishes, and where future warming is most likely to eliminate more populations. In addition to hydrodynamic modeling, we are partnering with genomics experts to assess selection on functional genes associated with surviving temperature or oxygen challenges. The goals of this project are to: Manage cold-water lake fishes. Manage fish species of special concern in the state. Guide pre-emptive efforts to prioritize sites for management interventions

The timing of major life cycle events (reproduction, flowering, feeding) is set by seasonal environmental cues in many organisms.  Migratory fish in the Great Lakes are largely spring spawners, and they move into tributary rivers as the water warms in March-June.  There is growing evidence that the timing of these migrations is shifting under climate change, creating ever-earlier migrations.  These changes in timing may profoundly change which species are present in rivers at a given time, potentially unraveling critical ecological linkages during the dynamic spring warming period.  We are analyzing historical data on migration timing of six species across the Great Lakes basin, using Bayesian statistical modeling to maximize power to detect shifts from a patchwork of migration records in space and time