Development and evaluation of species distribution models for fourteen native central U.S. fish species
Environmental change has and will continue to adversely influence aquatic communities. Efforts to model impacts of environmental change on fisheries have largely focused on cold water, commercial, and recreationally valued species, even though warm water, non-game species have important roles in ecosystem services and processes. We developed species distribution models for fourteen warm water fish species native to the central United States and evaluated environmental drivers and predictive performance. We used an ensemble model approach produced by combining forecasts of five single-model techniques. Response plots and variable importance calculations were used to evaluate the influence of individual variables. The predictive performance of the ensemble models was assessed using area under the curve of the receiver operating characteristic plot (AUC). AUC values indicate ensemble models performed better than single-model types, suggesting ensemble models are more reliable and applicable for management purposes than single models. Most models were influenced by a mix of climate, land use, and geophysical variables; however, climate variables were the dominant environmental drivers across models. Given the high sensitivity of models to climate and land use, we expect future climate and land use changes to influence distributions.
KeywordsEnsemble model Fish distributions Model performance Warm water fishes Range projections
The authors thank the state natural resource agencies that shared fish data with us to make this project feasible: Arkansas Department of Environmental Quality, Illinois Department of Natural Resources, Kansas Department of Wildlife and Parks, Minnesota Department of Natural Resources, Minnesota Pollution Control Agency, Missouri Department of Conservation, Missouri Department of Natural Resources, Nebraska Game and Fish Commission, North Dakota Department of Health, North Dakota Game, Fish and Parks, and Wisconsin Department of Natural Resources. We also thank Dr. Justin Schoof for providing historical climate data.
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