US Long-Term Ecological Research Network

Cascade Project at North Temperate Lakes LTER High Frequency Sonde Data from Food Web Resilience Experiment 2008 - 2011

Abstract
High-frequency sonde data collected from the surface waters of two lakes in Upper Peninsula of Michigan during the summers of 2008-2011. The food web of Peter Lake was slowly transformed by gradual additions of Largemouth bass (Micropterus salmoides) while Paul Lake was an unmanipulated reference. Sonde data were used to calculate resilience indicators to evaluate the stability of the food web and to calculate ecosystem metabolism.
Dataset ID
360
Date Range
-
Methods
Data were collected at 5 minute intervals using in-situ automated sensors (sondes). All measurements and samples were collected from a stationary raft over the deepest part of the lake.
Sondes were suspended from floats with probes at a depth of 0.75m below the surface. Sonde sensors were cleaned daily in the field and calibrated monthly following manufacturer guidelines. Peter and Paul lakes were each monitored with two YSI multiparameter sondes (model 6600 V2-4) fitted with optical DO (model 6150), pH (model 6561), optical Chl-a (model 6025), and conductivity-temperature (model 6560) probes. Sensor measurements were made at 0.75 m every 5 min and were calibrated weekly. PAR was measured and the UNDERC meteorology station maintained by the University of Notre Dame or by the North Temperate Lakes Weather Station at Woodruff Airport.
Outliers were replaced by NA. Occasional gaps in the record due to instrument cleaning are NA.
Version Number
1

Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012

Abstract
This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Contact
Core Areas
Creator
Dataset ID
357
Date Range
-
Maintenance
completed
Methods
Fisheries surveys of inland lakes and streams in Wisconsin have been conducted by the Wisconsin Department of Natural Resources (WDNR) professionals and its predecessor the Wisconsin Conservation Department for >70 y. Standard fyke net and boat electrofishing surveys tend to dominate the fisheries surveys and data collected. Most fyke net data on certain species (e.g., Walleye Sander vitreus and Muskellunge Esox masquinongy) originates from annual spring netting surveys following ice-out. These data are used for abundance estimates, mark and recapture surveys for estimating population sizes, and egg-take procedures for the hatcheries. Boat-mounted boom and mini-boom electrofishing surveys became increasingly common in the late 1950s and 1960s. Boat electrofishing surveys have typically been conducted during early summer months (May and June), but some electrofishing survey data are also collected in early spring as part of walleye and muskellunge mark-recapture surveys. Summer fyke netting surveys have been collected more sporadically over time, but were once more commonly used as a panfish survey methodology. Surveys were largely non-standardized. Thus, future users and statistical comparisons utilizing these data should acknowledge the non-standard nature of their collection. More in-depth description of these data can be found in Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Version Number
3

Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012

Abstract
This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Contact
Core Areas
Creator
Dataset ID
356
Date Range
-
Maintenance
completed
Methods
Fisheries surveys of inland lakes and streams in Wisconsin have been conducted by the Wisconsin Department of Natural Resources (WDNR) professionals and its predecessor the Wisconsin Conservation Department for >70 y. Standard fyke net and boat electrofishing surveys tend to dominate the fisheries surveys and data collected. Most fyke net data on certain species (e.g., Walleye Sander vitreus and Muskellunge Esox masquinongy) originates from annual spring netting surveys following ice-out. These data are used for abundance estimates, mark and recapture surveys for estimating population sizes, and egg-take procedures for the hatcheries. Boat-mounted boom and mini-boom electrofishing surveys became increasingly common in the late 1950s and 1960s. Boat electrofishing surveys have typically been conducted during early summer months (May and June), but some electrofishing survey data are also collected in early spring as part of walleye and muskellunge mark-recapture surveys. Summer fyke netting surveys have been collected more sporadically over time, but were once more commonly used as a panfish survey methodology. Surveys were largely non-standardized. Thus, future users and statistical comparisons utilizing these data should acknowledge the non-standard nature of their collection. More in-depth description of these data can be found in Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Version Number
5

Cascade Project at North Temperate Lakes LTER Core Data Process Data 1984 - 2016

Abstract
Data useful for calculating and evaluating primary production processes were collected from 6 lakes from 1984-2016. Chlorophyll a and pheophytin were measured by the same fluorometric method from 1984-2016. In some years chlorophyll and pheophytin were separated into size fractions (total, and a ‘small’ fraction that passed a 35 um mesh screen). Primary production was measured by the 14C method from 1984-1998. Dissolved inorganic carbon for primary production calculation was calculated from Gran alkalinity titration and air-equilibrated pH until 1987 when this method was replaced by gas chromatography. Until 1995 alkaline phosphatase activity was measured as an indicator of phosphorus deficiency.
Core Areas
Dataset ID
354
Date Range
-
Methods
General: Bade, D., J. Houser, and S. Scanga (editors). 1998. Methods of the Cascading Trophic Interactions Project. 5th edition. Center for Limnology, University of Wisconsin-Madison, and Cary Institute of Ecosystem Studies, Millbrook, NY.
Version Number
14

Cascade Project at North Temperate Lakes LTER Core Data Phytoplankton 1984 - 2015

Abstract
Data on epilimnetic phytoplankton from 1984-2015, determined by light microscopy from pooled Van Dorn samples at 100 percent, 50 percent, and 25 percent of surface irradiance. St. Amand (1990) and Cottingham (1996) describe the counting protocols in detail. Samples after 1995 were counted by Phycotech Inc. (http://www.phycotech.com). Sampling Frequency: varies; Number of sites: 5
Dataset ID
353
Date Range
-
Methods
Samples counted prior to 1996 were assigned one taxon name with all taxonomic information. This taxon name was split into distinct columns of genus, species and description for archival as best possible. Samples from 2013-2015 were sent to Phycotech inc. (http://www.phycotech.com/) to be counted.
Version Number
16

Cascade Project at North Temperate Lakes LTER Core Data Carbon 1984 - 2016

Abstract
Data on dissolved organic and inorganic carbon, particulate organic matter, partial pressure of CO2 and absorbance at 440nm. Samples were collected with a Van Dorn sampler. Organic carbon and absorbance samples were collected from the epilimnion, metalimnion, and hypolimnion. Inorganic samples were collected at depths corresponding to 100%, 50%, 25%, 10%, 5%, and 1% of surface irradiance, as well as one sample from the hypolimnion. Samples for the partial pressure of CO2 were collected from two meters above the lake surface (air) and just below the lake surface (water). Sampling frequency: varies; number of sites: 14
Core Areas
Dataset ID
350
Date Range
-
Methods
Detailed field and laboratory protocols can be found in the Cascade Methods Manual, found here: https://cascade.limnology.wisc.edu/public/public_files/methods/CascadeManual1998.pdf
POC, PON and DOC: 1. 100 - 300 ml (Typically ~200mL for PML, 150 metalimnion and 75 – 100 for the hypolimnion) of lake water from each depth was filtered through 153 um mesh to remove large zooplankton. Water was then filtered through a precombusted 25mm GF/F filter (0.7 um pore size) at less than 200 mm Hg pressure. Filters were placed in drying oven at 60 C to dry for at least 48 hours. 20mL of filtered water was stored in a scintillation vial and acidified with 200uL of 2N H2SO4 for DOC analysis. Blank samples for POC and DOC were prepared with deionized water to control for contamination. All samples were sent to the Cary Institute of Ecosystem Studies for analysis.

Version Number
24

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

North Temperate Lakes LTER Processed eddy covariance time series fluxes from tower located on roof of the CFL building oriented toward Lake Mendota 2012 - current

Abstract
We calculated eddy covariance based fluxes of CO2, H2O, heat, and momentum to study lake-atmosphere exchanges since 2012. These data were collected by Ankur Desai from 2012 to present using a CSAT-3 sonic anemometer and LI-7500 gas analyzer located on the roof of the CFL building. A footprint model (Kljun) was used to screen for lake only data.
Contact
Creator
Dataset ID
347
Data Sources
Date Range
-
Methods
Sonic anemometer: Campbell Scientific, Inc. CSAT-3
Gas analyzer: Licor, Inc. LI-7500
We merged data from the CFL Lake Mendota David buoy for air temperature and water temperature (1st level), and also the AOSS rooftop RIG tower for incoming solar radiation. These data were used in the analysis presented in Reed et al (2017) based on gap-filling conducted with REddyProc.
Methodology: Reed, D.R., Dugan, H., Flannery, A., and Desai, A.R., 2017. The carbon sink and source see-saw of a eutrophic deep lake Limnology and Oceanography Letters, #LOL2-17-0040, submitted.
Version Number
5

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
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