US Long-Term Ecological Research Network

Lake snow removal experiment phytoplankton community data, under ice, 2019-2021

Abstract
Although it is a historically understudied season, winter is now recognized as a time
of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging
research points to a future of reduced ice cover duration and changing snow conditions that
will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice
conditions, and subsequent changes to under-ice light environment, might impact ecosystem
dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from
a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during
the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from
the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019
season served as a reference year, and snow was not removed from the lake. This dataset
represents phytoplankton community samples (pooled epilimnion and hypolimnion samples
representative of 7 m water column) both under-ice and during some shoulder-season (open
water) dates. Samples were collected into amber bottles and preserved with Lugol's solution
before they were sent to Phycotech Inc. (St. Joseph MI, USA) for phytoplankton taxonomic
identification and quantification.<br/>
Core Areas
Creator
Dataset ID
418
Data Sources
Date Range
-
Methods
Phytoplankton samples were obtained from the epilimnion and hypolimnion by
slowly lowering weighted Tygon tubing through the water column to a depth of 7 m, such
that the tubing was filled with a representative water column sample. Based on the inner
diameter of the tubing, 205 mL of water was pumped from the tubing for the epilimnion
sample. Next, 267 mL of water was pumped from the tubing for the hypolimnion sample. Each
sample was collected into a 250 mL amber bottle that contained 2 mL of Lugol’s solution.
Phytoplankton samples were pooled by sampling date and sent to Phycotech Inc. (St. Joseph
MI, USA) for phytoplankton identification, and concentration and biovolume
quantification.<br/>
Publication Date
Version Number
1

Lake snow removal experiment zooplankton community data, under ice, 2019-2021

Abstract
Although it is a historically understudied season, winter is now recognized as a time
of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging
research points to a future of reduced ice cover duration and changing snow conditions that
will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice
conditions, and subsequent changes to under-ice light environment, might impact ecosystem
dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from
a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during
the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from
the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019
season served as a reference year, and snow was not removed from the lake. This dataset
represents under ice zooplankton community samples (integrated tows at depths of 7 m) and some
shoulder-season (open water) zooplankton community samples. Zooplankton samples were preserved
in 90% ethanol and later processed to determine taxonomic classification at the species-level,
density (individuals / L), and average length (mm).<br/>
Contact
Core Areas
Creator
Dataset ID
414
Date Range
-
Methods
Our study lake, South Sparkling Bog (SSB) (46.003°N, 89.705°W), is a bog lake
located in Vilas County in Northern Wisconsin. South Sparkling Bog is a dystrophic,
dimictic lake with a maximum depth of 8 m, a mean depth of 3.6 m, and a surface area of
0.44 ha. South Sparkling Bog is surrounded by a sphagnum bog mat and has no shoreline
development. During the winters of 2019-2020 and 2020-2021, snow was removed from the
surface of South Sparkling Bog following any snow accumulation event. Removal was
conducted via a snowplow attached to the front of an ARGO all-terrain vehicle and a
snowblower. The winter of 2018-2019 served as a reference year, and snow was not removed
from South Sparkling Bog’s surface. While ice cover persisted, plankton samples were
collected at the deep spot for each lake during on a biweekly-to-monthly basis each
winter. On each sampling date, one integrated zooplankton tow was taken at a depth of 0-7
m using a 56 µm mesh Wisconsin net. All zooplankton samples were collected into glass
sample jars, preserved in 90% ethanol, and saved for laboratory analysis. In the lab,
zooplankton samples were filtered through 53 µm mesh and diluted to a known volume, and
three sub-sample replicates were taken using a 1 mL Hensen-Stempel pipette. Sub-sample
replicates were counted to at least 100 individuals, otherwise the entire sample was
quantified. Sub-sample data was then converted to the known diluted volume and finally
converted to total filtered volume (from the integrated tow sample) to estimate density
(individuals L-1). Zooplankton samples were processed using a Leica M8Z dissecting scope
and Leica imaging software. Replicate subsamples were averaged to estimate total abundance
and density, and average lengths (mm) for each sample taxa were calculated from measures
of the first 30 taxa found within a sample date.<br/>
Publication Date
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

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
Version Number
1

Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format

Abstract
The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Core Areas
Dataset ID
349
Date Range
-
Methods
Protocol available in methods section of: http://msphere.asm.org/content/2/3/e00169-17
Prior to collection, water temperature and dissolved oxygen concentrations are measured using a YSI 550a. The ranges of the epilimnion and hypolimnion are determined based on the location of the thermocline (where temperature/oxygen is changing the fastest). The two layers are collected separately in 1 meter increments using an integrated water column sampler. Water samples are taken back to the lab, shaken thoroughly, and filtered via peristaltic pump through 0.22 micron filters (Pall Supor). Filters are temporarily stored at -20C after collection and then transferred to -80C after transport on dry ice from Trout Lake Station to UW-Madison. Nutrient samples are collected bi-weekly following standard LTER protocols. DNA is extracted from filters using a FASTDNA SpinKit for Soil with minor modifications. (In cases of low yield or specialized sequencing methods, a phenol-chloroform extraction is used instead). The protocol for sequencing and analysis of data varies by year and by sub-project.
Version Number
4

North Temperate Lakes LTER General Lake Model Parameter Set for Lake Mendota, Summer 2016 Calibration

Abstract
The General Lake Model (GLM), an open source, one-dimensional hydrodynamic model, was used to simulate various physical, chemical, and biological variables on Lake Mendota between 15 April 2016 and 11 November 2016. GLM (v.2.1.8) was coupled to the Aquatic EcoDynamics (AED) module library via the Framework for Aquatic Biogeochemical Modeling (FABM). GLM-AED requires four major “scripts” to run the model. First, the glm2.nml file configures lake metadata, meteorological driver data, stream inflow and outflow driver data, and physical response variables. Second, the aed2.nml file configures various biogeochemical modules for the simulation of oxygen, carbon, phosphorus, and nitrogen, among others. Third, aed2_phyto_pars.nml configures all parameters pertaining to phytoplankton dynamics. And fourth, aed2_zoop_pars.nml configures all parameters pertaining to zooplankton dynamics. This dataset contains parameter descriptions and values as they were used to simulate organic carbon and greenhouse gas production on Lake Mendota in summer 2016. Meteorological data and stream files used in this calibration are also included in this dataset. Additional methods and model descriptions can be found in J.A. hart’s Masters Thesis, University of Wisconsin-Madison Center for Limnology, May 2017. Readers are referred to the GLM (Hipsey et al. 2014) and AED (Hipsey et al. 2013) science manuals for further details on model configuration.
Additional Information
completed
Contact
Dataset ID
348
Date Range
-
Methods
This study used the R packages “GLMr” (https://github.com/GLEON/GLMr) and “glmtools” (https://github.com/USGS-R/glmtools) to interact with GLM-AED from the R statistical environment.

Hourly meteorological data were obtained for 2010-2016 from the North American Land Data Assimilation System (NLDAS) and included air temperature, relative humidity, short wave radiation, long wave radiation, wind speed, rain accumulation, and snow accumulation.

Average daily stream discharge and nitrogen and phosphorus loads were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS) water quality database. All streams included in this study are monitored by the USGS. This study used 2014 nutrient loading data over the simulation time period since 2016 nutrient data were not yet available. Only the Yahara River and Pheasant Branch Creek have data on daily nutrient loads; thus, Pheasant Branch nutrient concentrations were also applied to both Dorn Creek and Sixmile Creek, which contribute near equivalent volume to Lake Mendota. Since GLM-AED requires nutrient concentration rather than load, water column concentrations were back calculated using load and discharge.

Daily OC loads (POC and DOC, individually) were estimated for each individual inflow (n=4) from observed weekly measurements of OC concentration (doi: 10.6073/pasta/86094298f5a9085869518372934bf9d7)
using stream-specific linear models (Lathrop et al. 1998). The linear relationship between known log-scaled OC loads and log-scaled discharge for each individual stream was used to predict the OC load for each day in the model simulation time period. Again, water column OC concentrations were back calculated using the load and discharge.

A manual calibration routine was conducted to optimize the goodness-of-fit of various state variables in GLM-AED, especially those known to influence OC and GHG dynamics. We used visual comparison of predictions and observations, as well as a quantitative approach, to assess goodness-of-fit. We calibrated water balance and water temperature to effectively simulate lake level and stratification. We also calibrated DO, Secchi depth, TN, TP, DOC, and CH4. Finally, we calibrated phytoplankton dynamics according to four phytoplankton functional groups, recognizing that phytoplankton contribute substantially to the OC pool.

Version Number
13

Cascade project at North Temperate Lakes LTER - High-resolution spatial analysis of CASCADE lakes during experimental nutrient enrichment 2015 - 2016

Abstract
This dataset contains high-resolution spatio-temporal water quality data from two experimental lakes during a whole-ecosystem experiment. Through gradual nutrient addition, we induced a cyanobacteria bloom in an experimental lake (Peter Lake) while leaving a nearby reference lake (Paul Lake) as a control. Peter and Paul Lakes (Gogebic county, MI USA), were sampled using the FLAMe platform (Crawford et al. 2015) multiple times during the summers of 2015 and 2016. In 2015 nutrient additions to Peter Lake began on 1 June, and ceased on 29 June, Paul Lake was left unmanipulated. In 2016 no nutrients were added to either lake. Measurements were taken using a YSI EXO2 probe and a Garmin echoMap 50s. Sensor- data were collected continuously at 1 Hz and linked via timestamp to create spatially explicit data for each lake.

Crawford, J. T., L. C. Loken, N. J. Casson, C. Smith, A. G. Stone, and L. A. Winslow. 2015. High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology. Environmental Science & Technology 49:442–450.
Contact
Dataset ID
343
Date Range
-
Maintenance
complete
Methods
In two consecutive years, we measured lake-wide spatial patterning of cyanobacteria using the FLAMe platform (Crawford et al. 2015). To evaluate early warning indicators of a critical transition, in the first year we induced a cyanobacteria bloom through nutrient addition in an experimental lake while using a nearby unmanipulated lake as a reference ecosystem (Pace et al. 2017). During the second year, both lakes were left unmanipulated. Proposed detection methods for early warning indicators were compared between the manipulated and reference lakes to test for their ability to accurately detect statistical signals before the cyanobacteria bloom developed.
Peter and Paul Lakes are small, oligotrophic lakes (Peter: 2.5 ha, 6 m, 19.6 m and Paul: 1.7 ha, 3.9 m, 15 m, for surface area, mean, and max depth respectively) located in the Northern Highlands Lake District in the Upper Peninsula of Michigan, USA (89°32’ W, 46°13’ N). These lakes have similar physical and chemical properties and are connected via a culvert with Paul Lake being upstream. Both lakes stratify soon after ice-off and remain stratified usually into November (for extensive lake descriptions, see Carpenter and Kitchell, 1993).
In the first year, Peter Lake was fertilized daily starting on 1 June 2015 (DOY 152) with a nutrient addition of 20 mg N m-2 d-1 and 3 mg P m-2 d-1 (molar N:P of 15:1) through the addition of H3PO4 and NH4NO3 until 29 June (day of year, DOY 180). The decision to stop nutrient additions required meeting four predefined criteria based on temporal changes in phycocyanin and chlorophyll concentrations indicative of early warning behavior of a critical transition to a persistent cyanobacteria bloom state. (Pace et al. 2017). Nutrients uniformly mix within 1-2 days after fertilization based on prior studies (Cole and Pace 1998). No nutrient additions were made to Paul Lake. In the second year (2016), neither lake received nutrient additions.
We mapped the surface water characteristics of both experimental lakes to identify changes in the spatial dynamics of cyanobacteria. In 2015, mapping occurred weekly from 4 June to 15 August (11 sample weeks). In 2016, when neither lake was fertilized, the lakes were mapped three times in early to mid-summer. In both years, mapping occurred between the hours of 07:00 to 12:00 (before the daily nutrient addition). We rotated the order that we sampled the lakes to avoid potential biases due to differences in time of day. Each individual lake sampling event was completed in approximately one hour.
The FLAMe platform maps the spatial pattern of water characteristics. A boat-mounted sampling system continuously pumps surface water from the lake to a series of sensors while geo-referencing each measurement (complete description of the FLAMe platform in Crawford et al. 2015). For this study, the FLAMe was mounted on a small flat-bottomed boat propelled by an electric motor and was outfitted with a YSI EXO2™ multi-parameter sonde (YSI, Yellow Springs, OH, USA). We focused for this study on measures of phycocyanin (a pigment unique to cyanobacteria) and temperature. Phycocyanin florescence was measured using the optical EXO™ Total Algae PC Smart Sensor. The Total Algae PC Smart Sensor was calibrated with a rhodamine solution based on the manufacturer’s recommendations. Phycocyanin concentrations are reported as ug/L; however, these concentrations should be considered as relative because we did not calibrate the sensor to actual phycocyanin nor blue-green algae concentrations. Geographic positions were measured using a Garmin echoMAP™ 50s. Sensor- data were collected continuously at 1 Hz and linked via timestamp to create spatially explicit data for each lake. Each sampling produced approximately 3500 measurements in the manipulated lake and 2000 in the reference lake. The measurements were distributed by following a gridded pattern across the entire lake surface to characterize spatial patterns over the extent of the lake.
Version Number
15

North Temperate Lakes LTER Long-term winter chemical limnology and days since ice-on for primary study lakes 1983 - 2014

Abstract
This data set integrates long-term data sets on winter nutrient chemistry with ice phenology (number of days since ice-on), focusing on the subset of measurements taken during ice cover. Parameters characterizing limnology of 5 primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes, are measured at one station in the deepest part of each lake at the surface, middle, and deep (~1 meter above bottom). These parameters include nitrate-N, ammonium-N, total dissolved phosphorus, dissolved inorganic carbon, water temperature, dissolved oxygen, and pH. Water temperature and dissolved oxygen values are the zonal averages from more complete depth profiles. Sampling Frequency: every 6 weeks during ice-covered season for the northern lakes. Number of sites: 5
Dataset ID
341
Date Range
-
Maintenance
completed
Methods
This is a compilation of three data sets

North Temperate Lakes LTER: Chemical Limnology of Primary Study Lakes: Nutrients, pH and Carbon 1981 - current
https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-chemical-limnology-primary-study-lakes-nutrients-ph-and-carbon-19

North Temperate Lakes LTER: Physical Limnology of Primary Study Lakes 1981 - current
https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-physical-limnology-primary-study-lakes-1981-current

North Temperate Lakes LTER: Ice Duration - Trout Lake Area 1981 - current
https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-ice-duration-trout-lake-area-1981-current
Version Number
6

North Temperate Lakes LTER Estimated winter inputs of stream water and groundwater to primary study lakes 1983 - 2014

Abstract
This data set integrates and summarizes daily surface and groundwater inputs to 5 primary study lakes, using model estimates from a data-driven USGS hydrologic model (Hunt et al. 2013; Hunt and Walker 2017), and ice phenology data (number of days since ice-on). The lakes are Allequash, Big Muskellunge, Crystal, Sparkling, and Trout. Powers et al. (2017) used these data to estimate upper and lower bounds for exogenous chemical inputs to the lakes during winter. For a given lake and winter year, cumulative surface water and groundwater inputs were calculated across the ice cover period. For each lake, this data set reports the mean, maximum, and minimum winter water inputs observed across years, in units of water volume, % of average lake volume, and volume per winter day.Sampling Frequency: 1 per lake, with multiple summary values reported (i.e., mean, min, max). Number of sites: 5Hunt, R.J. et al., 2013. Simulation of Climate - Change effects on streamflow, Lake water budgets, and stream temperature using GSFLOW and SNTEMP, Trout Lake Watershed, Wisconsin. USGS Scientific Investigations Report., pp.2013-5159. Available at: https://www.researchgate.net/publication/258363719_Simulation_of_Climate-Change_Effects_on_Streamflow_Lake_Water_Budgets_and_Stream_Temperature_Using_GSFLOW_and_SNTEMP_Trout_Lake_Watershed_WisconsinHunt, R.J., and Walker, J.F., 2017, GSFLOW groundwater-surface water model 2016 update for the Trout Lake Watershed, Wisconsin: U.S. Geological Survey data release, https://dx.doi.org/10.5066/F7M32SZ2Powers SM, Labou SG, Baulch HM, Hunt RJ, Lottig NR, Hampton SE, Stanley EH. In press (expected 2017). Ice duration drives winter nitrate accumulation in north temperate lakes. Limnology and Oceanography Letters.
Dataset ID
340
Data Sources
Date Range
-
LTER Keywords
Methods
This dataset is a compilations of:
USGS hydrologic model estimates (Hunt et al. in preparation)
and
North Temperate Lakes LTER: Ice Duration - Trout Lake Area 1981 - current
https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-ice-duration-trout-lake-area-1981-current
Version Number
7
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