Abstract
This study combines long-term lake water quality data with statistical modeling to predict coldwater fish habitat conditions from a season-ahead lead. We select a case-study lake in Northern Wisconsin with forty-two years of biological, chemical, and physical data to calculate three summertime oxythermal stress metrics for cisco ( Coregonus artedi ) conditioned on bi-weekly temperature and dissolved oxygen profiles. Significant intra-and-interannual variability are identified in seasonal oxythermal habitat conditions. Springtime global and local climate variables generally indicate a stronger relationship than within lake variables when correlated with summertime oxythermal habitat, suggesting a potentially robust link between climate patterns and lake water quality in Wisconsin. Leveraging both climate and within lake springtime observations, a principal component regression approach was applied to probabilistically predict summertime oxythermal stress metrics. Models showed skillful prediction of multiple oxythermal habitat metrics across summer months of highest stress, with the best fitting model achieving R 2 = 0.42 and RPSS = 0.55. We conclude that characterization and tailored season-ahead prediction of oxythermal habitat conditions may provide prospects for proactive lake management in support of coldwater fish habitat.