Fish abundance training data in support of: Climate-driven declines in abundance across thermal guilds in fish communities of 11,000 temperate lakes

Abstract

Anticipating and planning for changes in biological communities due to climate warming presents numerous challenges, particularly in projecting how species abundance relationships will respond to future thermal conditions. In this study, we use regional fisheries-independent catch data to train a novel physiologically guided model that predicts fish abundances under warming scenarios in over 11,000 lakes across the Midwestern U.S. The dataset includes catch-per-effort data for eight sport fish species (cisco, northern pike, walleye, black crappie, yellow perch, smallmouth bass, largemouth bass, and bluegill sunfish) from 6,805 lakes, 46,287 surveys, and spanning 81 years (1940–2023) across seven states. We selected survey gear types for each state and species based on agency recommendations and survey documentation to ensure accurate representation of relative abundance. Rigorous data screening was performed to eliminate anomalies that could bias abundance estimates. Each survey location is linked to National Hydrography Dataset (NHD) identifiers, enabling integration with landscape-level environmental covariates. These data were used in a companion study to inform a joint species physiologically guided abundance model to project future species abundances across the region.