Data in Support of Predicting climate change impacts on poikilotherms using physiologically guided species abundance models
2022-07-14
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1988
2019
2019
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2022-07-11
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Title
Data in Support of Predicting climate change impacts on poikilotherms using physiologically guided species abundance models
Published Date
2022-07-14
Author Contact
Hansen, Gretchen J A
ghansen@umn.edu
ghansen@umn.edu
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Observational Data
Survey Data-Quantitative
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Observational Data
Survey Data-Quantitative
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Abstract
Fish catch and effort data for three species caught in gill nets and trap nets between 1988 and 2019 as part of Minnesota Department of Natural Resources (MNDNR) fisheries surveys conducted during the summer and early fall are included from over 1,300 Minnesota lakes. The three fish species included are: bluegill (Lepomis marochirus) a warm-water adapted species, yellow perch (Perca flavescens) a cool-water adapted species, and cisco (Coregonus artedi) a cold-water adapted species. Additional data concerning lake characteristics and surrounding land cover were also included. Mean July lake surface temperature was calculated using simulated daily water temperatures. Watershed land use including agricultural, barren, forest, grass, shrub, urban, and wetland cover, was determined using the 2016 National Land Cover Database. Secchi, a measure of water clarity was calculated from remotely sensed Secchi depth courtesy of Max Glines. Lastly, lake area and maximum depth were obtained from MNDNR public databases.
Description
Fish catch and effort data for three species, along with lake characteristics, and watershed land cover. Additionally, there is mean July surface temperature derived from modeling and remotely sensed Secchi depth. Files include a Readme text file, and the data as a csv.
Referenced by
Wagner, T., E.M. Schliep, J.S. North, H. Kundel, J.K. Ruzich, C.A. Custer, and G.J.A. Hansen. Predicting climate change impacts on poikilotherms using physiologically guided species abundance models. Proceedings of the National Academy of Sciences - PNAS, 120(15), E2214199120.
https://doi.org/10.1073/pnas.2214199120
https://doi.org/10.1073/pnas.2214199120
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U.S. Geological Survey Midwest Climate Adaptation Science Center Grant No.G20AC00096
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Schliep, Erin M; North, Joshua S; Kundel, Holly; Custer, Christopher A; Ruzich, Jenna K; Hansen, Gretchen J A. (2022). Data in Support of Predicting climate change impacts on poikilotherms using physiologically guided species abundance models. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/g1kt-4583.
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