Data in Support of Predicting climate change impacts on poikilotherms using physiologically guided species abundance models

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Collection period

1988
2019

Date completed

2022-07-11

Date updated

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Journal Title

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Volume Title

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

Type

Dataset
Observational Data
Survey Data-Quantitative
Other Dataset

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

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Funding information

U.S. Geological Survey Midwest Climate Adaptation Science Center Grant No.G20AC00096

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Suggested citation

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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