Project period: 1 25 24-8 31 25PROJECT title: evaluating the effects of climate change on lake erie fishery recruitmentthe research project aims to evaluate the impact of climate change on fish…

The University of Toledo received a $120,214 Cooperative Agreement award from the U.S. Geological Survey (CFDA 15.808 - USGS Research and Data Collection program) to evaluate the impact of climate change on fish populations in Lake Erie. The research project aims to develop Bayesian hierarchical models analyzing long-term data on weather, water conditions, and fish recruitment in Lake Erie. Key deliverables include scientific publications, a PhD dissertation chapter, and a GitHub repository with data and code. The research findings will support regional and state governments in developing effective fishery management strategies for the Great Lakes in response to climate change impacts. The methodological work on Bayesian modeling and extreme value analysis will also benefit the broader climate science research community.

Project period: 1 25 24-8 31 25PROJECT title: evaluating the effects of climate change on lake erie fishery recruitmentthe research project aims to evaluate the impact of climate change on fish populations in lake erie using the bayesian hierarchical modeling approach. Researchers will analyze long-term data encompassing weather, water conditions, and fish populations to develop a comprehensive understanding of how climate-related factors, such as temperature, tributary discharge, and lake erie winter ICE cover, influence fish recruitmenta critical aspect for maintaining sustainable fisheries. To achieve this, researchers will develop a modeling framework applying various bayesian hierarchical models, allowing them to disentangle long-term trends and seasonal oscillations. Specific research activities include collecting long-term data on climate change (tributary discharge, lake erie surface water temperature, daily ICE cover, and wave exposure) and lake erie fish recruitment (catches of age-0 and age-1 fish in trawl surveys), exploring appropriate model formulations describing typical seasonal patterns, developing bayesian hierarchical models for both biotic (annual temporal scale) and abiotic (mostly daily temporal scale) data, and exploring patterns of change in significant events and their impact on fish recruitment in lake erie. The project will deliver a comprehensive report detailing the data collection process, a technical report and manuscript outlining the model development process, manuscripts examining the effects of climate change on lake erie fisheries, a chapter in a PHD dissertation focusing on the climate change impact on abiotic factors, a methodological manuscript describing the analysis of extreme values, and a github repository housing all data and computer code. The research will provide scientific understanding of the climate change effect on great lakes fisheries, supporting regional (the great lakes region) and state governments in their effort to develop effective long-term fishery management strategies. The methodological research in bayesian hierarchical modeling and extreme value statistics will benefit the climate-related scientific community at large.