US Long-Term Ecological Research Network

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

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/>
Core Areas
Dataset ID
Date Range
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

Snow Manipulation Greenhouse Gas Measurements at South Sparkling and Trout Bog

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
Data Sources
Date Range
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
Version Number

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

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
Date Range
This is a compilation of three data sets

North Temperate Lakes LTER: Chemical Limnology of Primary Study Lakes: Nutrients, pH and Carbon 1981 - current

North Temperate Lakes LTER: Physical Limnology of Primary Study Lakes 1981 - current

North Temperate Lakes LTER: Ice Duration - Trout Lake Area 1981 - current
Version Number

Chloride and sulfate concentrations in 1918 Marsh, Madison, WI, 2012-2016

Beginning in September 2014 bi-weekly chloride and sulfate concentrations at a suite of sampling locations in and around 1918 Marsh, a small wetland on the University of Wisconsin-Madison campus from 2012-2016. Collection is ongoing at a lesser intensity. Water temperature, water depth, and ice thickness are provided for each sampling event. Since fall 2014 water was collected via syringes, and filtered through a 25 mm 0.45um GMF filter into plastic scintillation vials in the field. All samples were analyzed on an ion chromatograph (Dionex ICS 2100) using an electro-chemical suppressor. Prior to September 2014 samples were taken by dipping the opening of a plastic sample bottle below the surface and the sample was filtered in the Laboratory prior to chemical measurements. Samples were largely from the winter season and taken at ca. 3-week intervals. In these early samples dissolved oxygen was also measured with a meter in the field.
Additional Information
Title of grant: LTER: Comparative Study of a Suite of Lakes in Wisconsin
Principle Investigator: Emily Stanley, Center for Limnology, University of Wisconsin - Madison
Granting agency: National Science Foundation under Cooperative Agreement
Grant identification number: #DEB-1440297
Dataset ID
Date Range
All samples were collected at the surface of the marsh. If ice was present, a hole was first made with an ice drill or an ice chisel (spud). Since fall 2014 water was collected via syringes, and filtered through a 25 mm 0.45um GMF filter into plastic scintillation vials in the field. All samples were stored at 4 ºC and analyzed at the University of Wisconsin’s Center for Limnology on an ion chromatograph (Dionex ICS 2100) using an electro-chemical suppressor. For each sampling event, water temperature, water depth, ice thickness, and snow depth were recorded. Water depth was measured from the water surface to the marsh bottom. Water depth was measured from the water surface to the marsh bottom. Depth measurements are prone to error owing to soft bottom sediments and benthic macrophytes. Ice thickness was measured with a meter stick after using an ice drill or ice chisel (spud) to open a hole in the lake ice. The meter stick was affixed with a horizontal bar that was used to locate the bottom of the ice. Water Temperature was measured with a Taylor household thermometer. Prior to September 2014 samples were taken by dipping the opening of a plastic sample bottle below the surface and the sample was filtered in the Laboratory prior to chemical measurements. Samples were largely from the winter season and taken at ca. 3-week intervals. In these early samples dissolved oxygen was also measured with a meter in the field.
Short Name
1918 Anions
Version Number

Lake Mendota water temperature secchi depth snow depth ice thickness and meterological conditions 1894 - 2007

Data for water temperature at different depth and different frequencies assembled from various sources by Dale Roberson. A table with additional parameters collected at the same time is also provided for dates when available. These parameters are weather observations, secchi depth, snow and ice depths.
Dataset ID
Date Range
Data were assembled from different collectors, names are given in metadata. Measurements were conducted by hand.
NTL Keyword
Version Number

Lake ice seasonality over the past 320 to 570 years

Lake and river ice seasonality (dates of ice freeze and breakup) responds sensitively to climatic change and variability. We analyzed climate-related changes using direct human observations of ice freeze dates (1443–2014) for Lake Suwa, Japan, and of ice breakup dates (1693–2013) for Torne River, Finland. We found a rich array of changes in ice seasonality of two inland waters from geographically distant regions: namely a shift towards later ice formation for Suwa and earlier spring melt for Torne, increasing frequencies of years with warm extremes, changing inter-annual variability, waning of dominant inter-decadal quasi-periodic dynamics, and stronger correlations of ice seasonality with atmospheric CO2 concentration and air temperature after the start of the Industrial Revolution. Although local factors, including human population growth, land use change, and water management influence Suwa and Torne, the general patterns of ice seasonality are similar for both systems, suggesting that global processes including climate change and variability are driving the long-term changes in ice seasonality.
Dataset ID
Date Range
Please see Sharma, S. et al. Direct observations of ice seasonality reveal changes in climate over the past 320–570 years. Sci. Rep. 6, 25061; doi: 10.1038/srep25061 (2016).
Version Number

Lake Mendota at North Temperate Lakes LTER: Snow and Ice Depth 2009-2010

Ice core data collected by Yi-Fang (Yvonne) Hsieh and collaborators for her PhD project, "Modeling Ice Cover and Water Temperature of Lake Mendota."; Part of the project was the development of a 3D hydrodynamic-ice model that simulated both temporal and spatial distributions of ice cover on Lake Mendota for the winter 2009-2010. The parameters from these ice core data were used as model inputs to run model simulations. Parameters measured include: blue ice, white ice, snow depth, and total ice. On February 13, 2009, ice cores were taken on Lake Mendota at four different stations. From January 14, 2010 through March 3, 2010 ice cores were taken on Lake Mendota at 31 different stations. In addition, ice cores were taken on other Yahara Lakes during February of 2009: Lake Kegonsa (4 stations_February 6), Lake Waubesa (4 stations_February 7), Lake Wingra (2 stations_February 8), and Lake Monona (4 stations_February 8). Only total ice measurements are reported for 2009. Included in this data set are the ice core data, and geospatial information for ice coring stations. Documentation: Hsieh, Y.-F., 2012a. Modeling ice cover and water temperature of Lake Mendota. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 157.
Dataset ID
Date Range
Metadata Provider
Ice and snow sampling was conducted weekly from 14 January to 30 March, 2010 on Lake Mendota when the ice was safe to walk on. A Kovacs Mark III core drill, manufactured by Ice Coring and Drilling Service (ICDS), Space Science and Engineering Center (SSEC) UW Madison, was used to collect ice cores. Snow depth was also measured at the locations where ice cores were sampled. All measurements were made in centimeters. Blue ice can be defined as the portion of the ice core that is strictly frozen lake water. White ice can be defined as &ldquo;snow ice,&rdquo; which occurs when water rushes through cracks in the ice and soaks the overlying snow, resulting in a mixture of ice and snow that subsequently freezes. Total ice is blue ice + snow ice. Finally, snow depth was calculated as the average of 10 snow depth samples at each sampling location.
Version Number

Ice Phenology Workshop at Lake Erken, Sweden


Ice Phenology Workshop: Comparing change in Scandinavia and the Great Lakes region

Long term observations of freeze and breakup dates for lakes and rivers across the northern hemisphere show consistent and widespread changes in ice phenology3. Recent analyses have focused on understanding geographic patterns in such changes4,5, and their consequences for aquatic communities6. Two regions of the world have a sufficient quantitiy of long term ice phenology records to allow for a detailed regional approach to understanding their patterns: the Laurentian Great Lakes region and Scandinavia. In both regions, ice phenology has changed more rapidly in warmer locations4,7; however, the influence of other meteorological variables (e.g., timing of snowfall, snow depth, and cloud cover) is not well understood. Overlain on this complexity is the combined impact of long term climate change and decadal scale oscillations.

We propose a workshop to begin a comparative investigation of ice phenology changes in Scandinavia and the Laurentian Great Lakes region. Ice phenology has been an active area of research for scientists at the NTL LTER site and its analogs in Sweden, Finland, and Switzerland. Using spatial analysis and time series techniques, we will investigate relationships between ice phenology, meteorological variables (snow and solar radiation), and large scale climate drivers (SOI, NAO, PDO, etc.). The contribution of individual lake characteristics (depth, surface area, and elevation) to these relationships will also be explored. The results will allow us to identify lake characteristics and geographic locations that are sensitive to climatically-induced changes in ice phenology. The workshop will also provide an opportunity to discuss available biological time series (zooplankton, fish recruitment, etc.) that may be compared with ice phenology, further develop aquatic research within the ILTER network and strengthen ties with long term ecological research sites in Sweden (potential ILTER sites).

This workshop is proposed for October 2007 at the Lake Erken field station of Uppsala University, Sweden with Barbara Benson, John Magnuson, Olaf Jensen (Ph.D. candidate) attending from the University of Wisconsin and, at a minimum, Gesa Weyhenmeyer (Uppsala University, Sweden), David Livingstone (Swiss Federal Inst. of Environmental Sci. and Tech.) and Johanna Korhonen (Finnish Environment Institute) attending as European partners.

3Magnuson, J.J. et al. 2000. Science 289:1743-1746; 4Jensen, O.P. et al. Limnology & Oceanography In review; 5Magnuson, J.J. et al. 2005. Verh. Internat. Verein. Limnol. 29:521-527; 6Weyhenmeyer, G. 2001. Ambio 30:565-571; 7 Weyhenmeyer, G. et al. 2004. Geophysical Research Letters 31:L07203.


April 1

  • Arrivals
  • 6pm     Dinner

April 2

  • 7:30am Breakfast
  • 8:30am Welcome, logistics (Thorsten, Barbara)
  • Presentations on current research (related to ice) (15 min each)
  • Discuss goals for the week
  1. Go over available data, make accessible to everyone at workshop
  2. List of possible paper titles and data needs for each
  3. Preliminary list of people working on each topic in (2) and leader for each
  • 12-1pm Lunch
  • 1-5pm    Divide into smaller groups (~3 people each) focused around topics
    • Outline tasks associated with each topic (data compilation, analyses, figure generation, writing, etc
    • Kaffe Pause
    • Repeat small groups
  • 6pm Dinner

April 3

  • 7:30am  Breakfast    
  • 8:30am  Reports from groups
    • Analysis, outlining, writing, literature search, additional discussion
    • Possible submission of session proposal for ASLO (Jan 2009)
  • 12-1pm Lunch
  • 1-5pm   Analysis, outlining, writing, literature search, additional discussion
  • 6pm Dinner

April 4

  • 7:30am Breakfast     
  • 8:30am Reports from groups (with outlines, key findings, and results!)
  • 12-1pm Lunch
  • 1-5pm    Divide into topic groups and discuss plans for finishing manuscripts
    • Review proposal for ASLO meeting session
    • Discuss plans for future workshops, collaborations, etc.    
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