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NLDAS model: Projected shifts in fish species dominance in Wisconsin lakes under climate change

Metadata Updated: July 6, 2024

Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines in walleye and increases in largemouth bass populations have raised questions regarding the future trajectories and appropriate management actions for these important species. We developed a thermodynamic model of water temperatures driven by downscaled climate data and lake specific characteristics to estimate daily water temperature profiles for 2148 lakes in Wisconsin, USA under contemporary (1989-2014) and future (2040-2064 and 2065-2089) conditions. We correlated contemporary walleye recruitment success and largemouth bass relative abundance to modeled water temperature, lake morphometry, and lake productivity, and projected lake specific changes in each species under future climate conditions. Walleye recruitment success was negatively related and largemouth bass abundance was positively related to water temperature degree days. Both species exhibited a threshold response at the same degree day value, albeit in opposite directions. Degree days were predicted to increase in the future, although the magnitude of increase varied among lakes, time periods, and global circulation models (GCMs). Under future conditions, we predicted a loss of walleye recruitment in 30-70% of lakes, and an increase to high largemouth bass relative abundance in 17-55% of additional lakes. The percentage of lakes with abundant largemouth bass and failed walleye recruitment was predicted to increase from 59% in contemporary conditions to 86% of lakes by mid-century and to 91% of lakes by late century, based on median projections across GCMs. Conversely, the number of lakes with successful walleye recruitment and low largemouth bass abundance was predicted to decline from 8.5% of lakes in contemporary conditions to only 38 1% of lakes in both future periods. Importantly, we identify nearly 100 resilient lakes predicted to continue to support walleye recruitment. Management resources could target preserving these resilient walleye populations. This data set contains the following parameters: year, WBDY_WBIC, days_12_28, height_12_28, vol_12_28, days_10.6_11.2, height_10.6_11.2, vol_10.6_11.2, days_18.2_28.2, height_18.2_28.2, vol_18.2_28.2, days_18_22, height_18_22, vol_18_22, days_19.3_23.3, height_19.3_23.3, vol_19.3_23.3, days_19_23, height_19_23, vol_19_23, days_20.6_23.2, height_20.6_23.2, vol_20.6_23.2, days_20_30, height_20_30, vol_20_30, days_21_100, days_22_23, height_22_23, vol_22_23, days_23_31, height_23_31, vol_23_31, days_25_29, height_25_29, vol_25_29, days_26.2_32, height_26.2_32, vol_26.2_32, days_26_28, height_26_28, vol_26_28, days_26_30, height_26_30, vol_26_30, days_28_29, height_28_29, vol_28_29, days_28_32, height_28_32, vol_28_32, days_29_100, height_29_100, vol_29_100, days_30_31, height_30_31, vol_30_31, durStrat, winter_dur_0-4, spring_days_in_10.5_15.5, mean_surf_jul, mean_surf_JAS, peak_temp, post_ice_warm_rate, SthermoD_mean, dateOver21, dateOver18, , dateOver8.9, SmetaTopD_mean, SmetaBotD_mean, coef_var_30_60, coef_var_0_30, mean_epi_hypo_ratio, mean_epi_vol, mean_hyp_vol, simulation_length_days, volume_mean_m_3, volume_sum_m_3_day, GDD_wtr_10c, GDD_wtr_5c, optic_hab_8_64, thermal_hab_11_25, optic_thermal_hab, optic_hab_8_64_surf, thermal_hab_11_25_surf, optic_thermal_hab_surf calculated for 2148 lakes

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date May 31, 2023
Metadata Updated Date July 6, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date May 31, 2023
Metadata Updated Date July 6, 2024
Publisher U.S. Geological Survey
Maintainer
@Id http://datainventory.doi.gov/id/dataset/85622ee391af232ea107aab3e7a0a66c
Identifier USGS:57a37e26e4b006cb455692d5
Data Last Modified 20200820
Category geospatial
Public Access Level public
Bureau Code 010:12
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 682b90d0-9737-4ece-a039-698756cd8ac1
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -92.814,42.489,-86.937,46.853
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash 2f8c4b8cbc31d2dcc46d5fcfbe5881510e42ae883b82f4475dc70ce1372b23ee
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -92.814, 42.489, -92.814, 46.853, -86.937, 46.853, -86.937, 42.489, -92.814, 42.489}

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