North Temperate Lakes
US Long-Term Ecological Research Network

Modeled Organic Carbon, Dissolved Oxygen, and Secchi for six Wisconsin Lakes, 1995-2014

Abstract
This data package contains model output data, driving data, and supplemental information for a two-layer modeling study that investigated organic carbon and oxygen dynamics within six Wisconsin lakes over a twenty-year period (1995-2014). The six lakes are Lake Mendota, Lake Monona, Trout Lake, Allequash Lake, Big Muskellunge Lake, and Sparkling Lake. The model output includes daily predictions of six state variables: labile particulate organic carbon, recalcitrant particulate organic carbon, labile dissolved organic carbon, recalcitrant dissolved organic carbon, dissolved oxygen, and Secchi depth. The output also includes daily predictions of physical and metabolism fluxes that were used in the prediction of the state variables. This data package also contains model driving data for each lake and other supplemental information that was calculated during the modeling runs.<br/>
Core Areas
Creator
Dataset ID
421
Date Range
-
Methods
Data included in this package include output, driving data, and supplemental calculated information for a modeling study.<br/>
NTL Themes
Version Number
1

Cascade Project at North Temperate Lakes LTER – Daily Bloom Data for Whole Lake Experiments 2011 - 2019

Abstract
Daily measurements of algal bloom variables (chlorophyll, phycocyanin
fluorescence, dissolved oxygen, and pH) from the surface waters of Paul, Peter, and
Tuesday lakes from mid-May to early September for the years 2011 to 2019, excluding
2012 and 2017. In some years, Peter (2013-2015, 2019) and Tuesday (2013-2015) lakes
had inorganic nitrogen and phosphorus added to them daily to cause algal blooms
while Paul Lake served as an unmanipulated reference.<br/>
Core Areas
Dataset ID
413
Data Sources
Date Range
-
Methods
Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>
NTL Themes
Version Number
1

Cascade Project at North Temperate Lakes LTER – High-resolution Spatial Data for Whole Lake Experiments 2018 - 2019

Abstract
Spatial measurements of water quality from Peter and Paul lakes in 2018 and 2019.
In 2019, inorganic nitrogen and phosphorus were added to Peter Lake daily to cause
an algal bloom while Paul Lake was an unmanipulated reference lake. In 2018, both
lakes were sampled 1 time per week, while in 2019 lakes were sampled three times per
week. Measurements were taken using the FLAMe sampling platform (Crawford et al.
2015, Environmental Science and Technology 49:442-450), which was driven in a grid
pattern and recorded GPS coordinates and water measurements at 1Hz to create high
resolution spatial maps.<br/>
Dataset ID
412
Data Sources
Date Range
-
Methods
Two lakes were studied for two years to test for spatial early warning
statistics (EWS) prior to an experimentally induced algal bloom. In 2018,
both Peter and Paul lakes were unmanipulated and spatial measurements of
each lake were taken weekly from June 6th to August 21st to establish
baseline conditions and EWS values. In 2019, nutrients were added to Peter
Lake while Paul Lake remained an unmanipulated reference lake. Both lakes
were measured three times per week from May 29th to September 4th. More
details on nutrient additions (loading rates, N:P ratios) are provided in
Buelo et al. 2022 (Ecological Applications, link below). <br/>Two lakes were studied for two years to test for spatial early warning
statistics (EWS) prior to an experimentally induced algal bloom. In 2018,
both Peter and Paul lakes were unmanipulated and spatial measurements of
each lake were taken weekly from June 6th to August 21st to establish
baseline conditions and EWS values. In 2019, nutrients were added to Peter
Lake while Paul Lake remained an unmanipulated reference lake. Both lakes
were measured three times per week from May 29th to September 4th. More
details on nutrient additions (loading rates, N:P ratios) are provided in
Buelo et al. 2022 (Ecological Applications, link below). <br/>Two lakes were studied for two years to test for spatial early warning
statistics (EWS) prior to an experimentally induced algal bloom. In 2018,
both Peter and Paul lakes were unmanipulated and spatial measurements of
each lake were taken weekly from June 6th to August 21st to establish
baseline conditions and EWS values. In 2019, nutrients were added to Peter
Lake while Paul Lake remained an unmanipulated reference lake. Both lakes
were measured three times per week from May 29th to September 4th. More
details on nutrient additions (loading rates, N:P ratios) are provided in
Buelo et al. 2022 (Ecological Applications, link below). <br/>Two lakes were studied for two years to test for spatial early warning
statistics (EWS) prior to an experimentally induced algal bloom. In 2018,
both Peter and Paul lakes were unmanipulated and spatial measurements of
each lake were taken weekly from June 6th to August 21st to establish
baseline conditions and EWS values. In 2019, nutrients were added to Peter
Lake while Paul Lake remained an unmanipulated reference lake. Both lakes
were measured three times per week from May 29th to September 4th. More
details on nutrient additions (loading rates, N:P ratios) are provided in
Buelo et al. 2022 (Ecological Applications, link below). <br/>Two lakes were studied for two years to test for spatial early warning
statistics (EWS) prior to an experimentally induced algal bloom. In 2018,
both Peter and Paul lakes were unmanipulated and spatial measurements of
each lake were taken weekly from June 6th to August 21st to establish
baseline conditions and EWS values. In 2019, nutrients were added to Peter
Lake while Paul Lake remained an unmanipulated reference lake. Both lakes
were measured three times per week from May 29th to September 4th. More
details on nutrient additions (loading rates, N:P ratios) are provided in
Buelo et al. 2022 (Ecological Applications, link below). <br/>
NTL Themes
Version Number
1

Snow Manipulation Greenhouse Gas Measurements at South Sparkling and Trout Bog
2020-2021

Abstract
To investigate the effect of a winter with decreased snow cover on greenhouse gas
emissions, we experimentally removed snowfall from a small dystrophic lake in
northern Wisconsin. As a comparative study, we were able to explore the role of
light in under-ice gas dynamics and spring emissions in dimictic lakes. This dataset
contains greenhouse gas and temperature/dissolved oxygen profile data collected on
South Sparkling and Trout Bog during the winter of 2020 through the winter of 2021.
Data were collected between 09 January 2020 and 13 April 2021 in the deep hole of
both bogs. Dissolved greenhouse gas concentrations of carbon dioxide and methane
were measured using the headspace equilibrium method.<br/>
Dataset ID
405
Data Sources
Date Range
-
Methods
Dissolved gas samples were collected at 0.5, 3, 5 and 7 m using the
headspace method. From January to March 2020, water at each discrete depth
was pumped directly into the bottom of a 1-L Nalgene bottle and flushed with
at least three times the volume before being capped with a rubber stopper.
60 mL of ambient air was added while 60 mL of sample water was removed from
the bottle and equilibrated by shaking for 90 seconds. From May 2020
onwards, water was pumped into a closed bottle system, and using syringe,
105mL of water was extracted and 35mL of ambient air was added. The
headspace was then equilibrated for 2 minutes by shaking and 10 mL of
equilibrated gas sample was then removed from the bottle and injected into a
5.9 mL Labco Exetainer vial that had been previously vacuumed. While in the
field, samples were stored in pouches within a survival suit to prevent
extreme temperature change. We analyzed the gas samples for CO2 and CH4 with
a gas chromatograph (GC-2014; Shimadzu Scientific Instruments) equipped with
a methanizer and flame ionization detector. Greenhouse gas concentrations
were calculated according to Henry’s law and corrected by measured ambient
air.<br/>
Version Number
1

Chloride Concentrations, Conductivity, and Water Temperature Data from Lake Mendota and Lake Monona Madison, WI: December 2019 – April 2021

Abstract
Conductivity and chloride were measured for 2 years in Lake Mendota and Lake Monona in Madison, WI. Conductivity was continuously measured (every 30 minutes) on under-ice buoys in the eplimnia (1-2m below the surface) and hypolimnia (1m off the bottom of the lake) of the lakes. Depth-discrete chloride grab samples were collected from the lakes quarterly. Profile sampling in Mendota, which is approximately 25 m deep, occurred every 5m from 0-20m and at 23.5m. Profile sampling in Monona, which is approximately 21m deep, occurred every 4m from 0-20m. This data was needed for a master’s research thesis with the goal of identifying the lakes' mixing dynamics and how salinization may impact them.<br/>
Core Areas
Dataset ID
403
Data Sources
Date Range
-
Methods
Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>
Version Number
1

Spatially Distributed Lake Mendota EXO Multi-Parameter Sonde Measurements Summer 2019

Abstract
This data was collected over 9 sampling trips from June to August 2019. 35 grid boxes were generated over Lake Mendota. Before each sampling effort, sample point locations were randomized within each grid box. Surface measurements were taken with an EXO multi-parameter sonde at the 35 locations throughout Lake Mendota during each sampling trip. Measurements include temperature, conductivity, chlorophyll, phycocyanin, turbidity, dissolved organic material, ODO, pH, and pressure.
Core Areas
Dataset ID
388
Date Range
-
Maintenance
ongoing
Methods
Conducted weekly data sampling (9 boat trips in June-August 2019) using an EXO multi-parameter sonde to collect temperature, conductivity, chlorophyll (ug/L), phycocyanin (ug/L), turbidity, dissolved organic material, ODO, pH, and pressure at 35 locations based on GPS guided stratified random sampling. 35 grid boxes were generated over Lake Mendota using qGIS. Point locations within each grid box were randomized before each sampling effort. At each point, variables were recorded continuously with the EXO sonde for a two-minute period. Continuous data was overaged over the two-minute period for each sample point.
Publication Date
Related Sites
Version Number
1

North Temperate Lakes LTER Regional Survey water temperature DO 2015 - current

Abstract
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry.
Water temperature and dissolved oxygen profiles were taken on sampling days.
Contact
Dataset ID
382
Date Range
-
Maintenance
ongoing
Methods
water temperature and dissolved oxygen were measured at 1 meter intervals with a opto sonde
Version Number
1

North Temperate Lakes LTER Regional Survey Water Chemistry 2015 - current

Abstract
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry. The regional lakes survey in 2015 followed the standard LTER protocol for standard water chemistry and biology. Samples were taken as close to solar noon as possible. Seven lakes had replicates performed, which were chosen at random.
Contact
Dataset ID
380
Date Range
-
Maintenance
ongoing
Methods
Inorganic and organic carbon
Inorganic carbon is analyzed by phosphoric acid addition on a Shimadzu TOC-V-csh Total Organic Carbon Analyzer.
Organic carbon is analyzed by combustion, on a Shimadzu TOC-V-csh Total Organic Carbon Analyzer.
Version Number
2

Wisconsin creel dataset as well as predictor variables for lakes from 1990 to 2017 to estimate statewide recreational fisheries harvest

Abstract
Recreational fisheries have high economic worth, valued at $190B globally. An important, but underappreciated, secondary value of recreational catch is its role as a source of food. This contribution is poorly understood due to difficulty in estimating recreational harvest at spatial scales beyond an individual system, as traditionally estimated from angler creel surveys. Here, we address this gap using a 28-year creel survey of ~300 Wisconsin inland lakes. We develop a statistical model of recreational harvest for individual lakes and then scale-up to unsurveyed lakes (3769 lakes; 73% of statewide lake surface area) to generate a statewide estimate of recreational lake harvest of ~4200 t and an estimated annual angler consumption rate of ~3 kg, nearly double estimated United States per capita freshwater fish consumption. Recreational fishing harvest makes significant contributions to human diets, is critical for discussions on food security, and the multiple ecosystem services of freshwater systems.
Contact
Core Areas
Dataset ID
379
Date Range
-
Maintenance
completed
Methods
The state of Wisconsin is comprised of about 15,000 inland lakes ranging from 0.5 to 53,394 ha (WDNR 2009). Most lakes occur in the northern and eastern part of the state as a result of glaciation. about 3,620 lakes are greater than 20 ha and together comprise about 93% of the state's inland lake surface area (Wisconsin Department of Natural Resources 2009). Wisconsin lakes constitute a wide range of physical and biological characteristics. Wisconsin inland lakes support valuable recreational fisheries for a variety of species, including Walleye (Sander vitreus), Northern Pike (Esox lucius), Muskellunge (Esox masquinongy), Yellow Perch (Perca flavescens), Largemouth Bass (Micropterus salmoides), Smallmouth Bass (Micropterus dolomieu), Lake Sturgeon (Acipenser fulvescens), and a variety of sunfish species (Lepomis spp.).
Related Sites
Version Number
2

North Temperate Lakes LTER Regional Survey Water Color Scans 2015 - current

Abstract
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry. Color is measured in water samples that are filtered in the field through 0.45 um nucleopore membrane filters. A spectrophotometer is used to quantify color in the lab as absorbance (unitless) at 1 nm intervals between the wavelengths of 200 and 800 nm. Absorbance data are considered suspect for values greater than 2.
Dataset ID
377
Date Range
-
LTER Keywords
Methods
We collect water samples for color at the deepest part of the lakes. The samples are surface water, filtered in the field through 0.45u polycarbonate membrane filters. We run a wavelength scan from 800 to 200nm, using a 5cm rectangular quartz cell in a Beckman Coulter Model DU800 spectrophotometer. Any samples that display absorbance values above 2AU are run again from 400 to 200nm using a 1cm quartz cuvette. Inititally the full range of wavelengths were run again and two values may be found in the database even if the original measurement with the large cuvette did not exceed 2AU. The user should discard values above 2AU and use values from the smaller cuvette instead. All values are given as measurements at the path lenth of the employed cuvette and need to be devided by the cuvette length for a comparable value at a pathlength of 1 cm.

The single beam Beckman Coulter DU800 spec is blanked first on a sample of DI water. Additional blank values are from a scan run on DI after that blanking as a check and are reported alongside the scans but are not subtracted from the scan values.
Version Number
3
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National Science Foundation | LTER Network | Center for Limnology        

This material is based upon work supported by the National Science Foundation under Cooperative Agreement #DEB-2025982, NTL LTER (ROR: 04gq8q482). Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.