US Long-Term Ecological Research Network

Satellite derived secchi disk depth and other lake and landscape characteristics in Wisconsin, USA, 1991 - 2012

Abstract
This data supports the following publication: Rose, K.C., S.R. Greb, M. Diebel, and M.G. Turner. Annual precipitation as a regulator of spatial and temporal drivers of lake water clarity. Ecological Applications. The data uses satellite remotely sensed estimates of Secchi disk depth (Landsat imagery), landscape features, and lake characteristics to understand how and why lakes vary and respond to different drivers through time and space. The data were produced by the authors and their collaborators, as acknowledged in the manuscript. The Secchi disk depth data span the time period 1991-2012.
Contact
Dataset ID
331
Date Range
-
Methods
The complete methods for this manuscript are described in the manuscript: Rose, K.C., S.R. Greb, M. Diebel, and M.G. Turner. Annual precipitation as a regulator of spatial and temporal drivers of lake water clarity. Ecological Applications.
NTL Keyword
Version Number
16

Upper Midwest Great Lakes Region Citizen Secchi Data 1938 - 2012

Abstract
Upper MidwestorGreat Lakes Region Citiizen Secchi Data includes 239,741 citizen Secchi monitoring records (1938 – 2012) from Illinois Volunteer Lake Monitoring Program, Indiana Clean Lakes Program, Iowa Secchi Dip-In Project, Michigan Clean Water Corps, Lakes of Missouri Volunteer Program, Minnesota Citizen Lake Monitoring Program, Ohio Citizen Lake Awareness Program, and Wisconsin Citizen Lake Monitoring Records. Data were obtained from above monitoring groups and merged with the high resolution National Hydrography Dataset based on citizen proved latitudeorlongitude coordinates to verify the location of individual lakes and size of lake (hectare). Code used to estimate annual average Secchi depth (m) provided in metadata. These citizen-collected, publically available Secchi depth measurements were collected to answer two questions: (1) what are the long-term trends in lake water quality across a broad geographic region?; (2) how do trends differ as a function of spatial location, size of lake monitored, and when Secchi records were collected. Data collection and analysis were funded by the National Science Foundation (MSB- 1065786, EF-1065818, EF-1065649), NTL-LTER (DEB-0822700), STRIVE grant 2011-W-FS-7 from the Environmental Protection Agency. GLERL contribution number (1703).
Contact
Dataset ID
300
Date Range
-
LTER Keywords
Maintenance
completed
Metadata Provider
Methods
Secchi monitoring records (1938 – 2012) were obtained from Illinois Volunteer Lake Monitoring Program, Indiana Clean Lakes Program, Iowa Secchi Dip-In Project, Michigan Clean Water Corps, Lakes of Missouri Volunteer Program, Minnesota Citizen Lake Monitoring Program, Ohio Citizen Lake Awareness Program, and Wisconsin Citizen Lake Monitoring Records. Data were obtained from above monitoring groups and merged with the high resolution National Hydrography Dataset (www.nhd.usgs.gov) based on citizen proved latitudeorlongitude coordinates to verify the location of individual lakes and size of lake (hectare). [R] code used to estimate annual average Secchi depth (m) provided in metadata.
NTL Keyword
Short Name
GLR Secchi
Version Number
17

LTREB Chemical and Physical Limnology at Lake Myvatn 2012-current

Abstract
These data are part of a long-term monitoring program at station 33 in the central part of Myvatn that represents the dominant habitat, with benthos consisting of diatomaceous ooze. The program was designed to characterize import benthis and pelagic variables across years as midge populations varied in abundance. Starting in 2012 samples were taken at roughly weekly inervals during June, July, and August, which corresponds to the summer generation of the dominant midge, Tanytarsus gracilentus.
Creator
Dataset ID
287
Date Range
-
Maintenance
Ongoing
Metadata Provider
Methods
Water Profile1. Take Light, DO, pH, Temp profile every 0.5mUse YSI DO probe, pH meter, and Li Cor light meter. Take the light profile from the sunny side of the boat.2. Take Secchi depthLower Secchi disk slowly until you can never see clear boundaries between white and black quarters, record this distance to the surface of the water as lower Secchi disk observation. Then pull the Secchi up until you can always see clear boundaries between white and black quarters, record this distance to the surface as the upper Secchi observation.Benthic Net Primary Production1. Measure light, temperature, percentDO, DO, and pH at 0.5m intervals at the sampling location.2. Take 10 clean/undisturbed cores. Try to get a uniform distance between the sediment and top of tube, so the cores have the same volume of water. Cover in boat with tarp to exclude light.3. Collect water from the shore of the boat and measure temp, percentDO, and DO. Save in bucket.4. Measure light intensity at 0 (out) and 0.5m depth where the cores will be incubated.5. Set up HOBO light recorder on the incubator.6. For each tube, take initial temp, percentDO, and DO. Before taking DO measurement, move the DO probe up and down three times to ensure no DO gradient (but do not disturb sediment). Add, slowly and without bubbling, 10 to 20mL of water (just the amount needed) to the core from bucket (number 3) to ensure no air space, and replace the stopper. Measure the distance from sediment to bottom of stopper to the nearest 0.5cm (column_depth).7. Place cores 1, 3, 5, and 7 in dark chambers (opaque tubes), so there are 4 dark and 6 light treatments.8. Incubate the cores using the metal structure at saturation light intensity if possible (300 mol per meter squared per second at 0.5m depth) for about 3h.9. Before taking DO measurement, move the DO probe up and down three times to ensure no DO gradient (but do not disturb sediment), and then measure percentDO, DO, and temperature in each core.Light controlsOnce a month (June, July, August), on a sunny day, incubate 10 cores for 3h with different light intensities to determine primary productivity under different light intensities and different temperatures. It would be best to do this the day after routine sampling (i.e., when retrieving the benthic sampler) so that the results can be compared to those from the routine sampling. Different light levels are obtained using white mesh bags around the core tubes.Core 1 and 6, lightCore 2 and 7, 2xCore 3 and 8, 4xCore 4 and 9, 8xCore 5 and 10, darkIMPORTANT: After the incubations, measure light intensity inside a core tube covered for the different treatments. This is done by removing the light meter from the metal holder and placing it facing up in a core using zip ties and a blue stopper at the bottom. Then place treatment bags over the top and measure light when holding the core at the level they reach in the incubator; use the marking on the light meter cord to make sure this is standardized for all measurements. This should be done 8 times total (each bag plus twice without bags).Light saturationOnce a month in the summer of 2013, we conducted sediment core incubations with varying amounts of shade cloth applied to the cores. Sediment cores received 0, 2, 4, 8, or 15 layers of shade cloth, with two cores in each treatment. All cores were then incubated in the lake over the same 3hr period at a depth of 0.5m.Sediment Dry Weight and Weight on Combustion1. Remove 0.75cm of sediment from a core into a plastic deli container. This should be done on a fresh core. This is the same sample that is used for chl analysis.2. Subsample 5 to 10mL sediment solution and place in a pre-weighed tin tray in oven at 60C for at least 12 hours. When dry, weigh for dry weight.In 2014, the method for sampling benthic chlorophyll changed. Sediment Dry Weight measurements were taken from these samples as well. Below is the pertinent section from the methods protocols. Processing after the collection of the sample was not changed.Take sediment samples from the 5 cores collected for sediment characteristics. Take 4 syringes of sediment with 10mL syringe (15.3 mm diameter). Take 4-5cm of sediment. Then, remove bottom 2cm and place top 2cm in the film canister.3. Combust at 550C for 4.5 hours. Weigh tray.4. If not analyzing combusted samples immediately, place in drying oven before weighing.
Version Number
15

River Nutrient Uptake and Transport at North Temperate Lakes LTER (2005-2011)

Abstract
These data were collected by Stephen Michael Powers and collaborators for his Ph.d. research, documented in his dissertation: River Nutrient Uptake and Transport Across Extremes in Channel Form and Drainage Characteristics. A major goal of this research was to better understand how ecosystem form and landscape setting dictate aquatic biogeochemical functioning and elemental transport through rivers. To achieve this goal, major and minor ions were measured in both northern and southern Wisconsin streams located in a variety of land use settings. In total, 27 different streams were sampled at 104 different stations (multiple stations per system) from both groundwater and surface water sources. Organic and inorganic carbon and nitrogen pools were also measured in northern and southern Wisconsin streams. The streams that were sampled in northern Wisconsin flow through wetland ecosystems. In sampling such streams, the goal was to better understand how wetland ecosystems influence river nutrient deliveries. There is a large amount of stream chemistry data for Big Spring Creek, WI; where the influence of a small reservoir on solute transportation and transformation was studied in an agricultural watershed. All stream chemistry data is incorporated in a single data file, Water Chemistry 2005-2011. While the data is not included in the dissertation, a sediment core study was also done in the small reservoir and channel of Big Spring (BS) Creek, WI. The results of this study are featured in three data tables: BS Creek Sediment Core Analysis, BS Creek Sediment Core Chemistry, and BS Creek Longitudinal Profile. Finally, two data tables list the geospatial information of sampling sites for stream chemistry and sediment coring in Big Spring Creek. Documentation: Powers, S.M., 2012. River nutrient uptake and transport across extremes in channel form and drainage characteristics. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 140.
Dataset ID
281
Date Range
-
Metadata Provider
Methods
I. Stream chemistry sample collection methods: core-sediment core was taken from the benthic zone of the streamgeopump-geopump used to pump stream water into collection bottlegrab-collection bottle filled with stream water by hand and filtered in the fieldgrabfilter- stream water collected by hand and filtered in field. Unfiltered and filtered samples placed in separate collection bottles.isco- sample collected by use of an ISCO automated samplerpoint- sampled collected by method outlined in patent US8337121sedimentgrab- sediment sample taken in field by hand and placed in collection bottlesyringe- sample collected from stream by syringe and placed in collection bottlesyringe_filter- sample collected from stream by syringe filter. Unfiltered and filtered samples placed in separate collection bottles. II. Stream chemistry analytical methods: All water samples were kept on ice and in the dark following collection, then were either acidified (TN/TP, TDN/TDP) or frozen until analysis (all other analytes).no32_2- This is NO<sub>3-</sub>N which is operationally defined as nitrate nitrogen + nitrite nitrogen. Determined by flow injection analysis on Astoria Pacific Instruments Autoanalyzer (APIA).nh4_n, tn1, tp1, tdn, tdp- All analytes measured by flow injection analysis on Astoria Pacific Instruments Autoanalyzer (APIA).srp- measured colorometrically using the molybdate blue method [APHA 1995] and a Beckman spectrophotometer.doc- measured using a Shimadzu carbon analyzer.doc_qual- the goal in doing this analysis is to determine the source of dissolved organic carbon (doc) measured in a particular riverine ecosystem. This was achieved by UV absorbance which provides an estimate of the aromaticity of the doc in a sample, and by extension, the potential source of the doc.cl, no2, no3, br, and so4- all measured by ion chromatography. See http://www.nemi.gov; method number 4110C. Detection limits for method number 4110C: cl-20&micro;g/l, no2-15&micro;g/l, no3-17&micro;g/l, br-75&micro;g/l, and so4-75&micro;g/l.ysi_cond, do, ph_field, wtemp- all measured by use of a standard YSI meter.tss- measured by standard methods. A thoroughly mixed sample is filtered and dried at 103-105 degreesCelcius. The obtained residue represents the amount of solids suspended in the sample solution. See http://www.nemi.giv; method number D5907.tot_om- measured by standard methods. The residue obtained from the tss procedure is ignited at 550 degreesCelcius and weighed, the difference in weight representing total volatile solids. Total volatile solids represents the portion of the residue that is composed of organic molecules. See http://www.nemi.gov; method number 160.4.turbid- measured by use of a nephelometer. III. Big Spring Sediment Coring Methods A. Field Methods- collecting sediment coresSediment core samples taken with WDNR piston core samplerB. Sediment Analysis- HydrometerDocumentation: Robertson, G.P., Coleman, D.C., Bledsoe, C.S. and Sollins, P., 1999. Standard Soil Methods for Long-Term Ecological Research. Oxford University Press, New York, 462 pp.Hydrometer Analysis- procedure used to determine percent clay:<p style="margin-left:.25in;">1. Dry the sample in a pre-weighed aluminum pan for at least 24 hr at 105 C. Make sure sample is completely dry before weighing.<p style="margin-left:.25in;">2. Weigh the dried sample, then ash for at least 8 hr at 550 C. Make sure to break up any large clumps before ashing.<p style="margin-left:.25in;">3. Weigh the ashed sample, then crush any aggregates with a pestal. Mix sample thoroughly.<p style="margin-left:.25in;">4. Transfer 40g, plus or minus one gram, of the sample into a 500mL wide mouth bottle<p style="margin-left:.25in;">5. Add 10g of sodium hexametaphosphate to the bottle.<p style="margin-left:.25in;">6. Add approx 200mL of deionized water to bottle. Shake vigorously with hand.<p style="margin-left:.25in;">7. Stir samples on shaker table for at least 8 hr at speed 40. Putting them in a box and fastening with bungee cords works best.<p style="margin-left:.25in;">8. Transfer sample to 1L cylinder, making sure to get all of sample out of bottle. Fill cylinder with deionized water up to the 1L mark.<p style="margin-left:.25in;">9. Prepare a blank cylinder by adding 10g of sodium hexametaphosphate and filling to 1L.<p style="margin-left:.25in;">10. Allow all cylinders to equilibrate to room temperature ( approx 30 min).<p style="margin-left:.25in;">11. Starting with the blank cylinder, put stopper into cylinder and shake end-over-end for approx 5 min. Rinse stopper. Repeat this step for all cylinders, rinsing stopper between cylinders.<p style="margin-left:.25in;">12. Record the time that you stopped shaking each cylinder.<p style="margin-left:.25in;">13. At 1.5 hr from time of shaking, record temperature and hydrometer level of the blank cylinder. Then record the 1.5 hr hydrometer level for each successive cylinder.<p style="margin-left:.25in;">14. At 24 hr from time of shaking, record temperature and hydrometer level of the blank cylinder. Then record the 24 hr hydrometer level for each successive cylinder. Sieve Analysis- procedure used to determine quantity of sand and silt<p style="margin-left:.25in;">1. After hydrometer analysis, pour the entire sample into the .063mm sieve. Rinse the sample thoroughly until all the clay is out. Try to break up any clay clumps you see.<p style="margin-left:.25in;">2. Transfer the sample to a pre-weighed and labeled aluminum pan. You will probably need to backwash the sieve to get the entire sample out. You can use a syringe to pull water from the pan if it gets too full. Dry the sample for 48 hours at 50-60C.<p style="margin-left:.25in;">3. Before transferring the dried sample to the sieves, make sure you pre-weigh the sieves and put their weight on the data sheet. You will need to do this before every sample as you might not get all the sample out of the sieves from the previous sample. Stack the sieves in the following order, top to bottom : 4mm, 2mm, 1mm, 0.5mm, 0.25mm, 0.125mm, 0.063mm, and pan. Pour the sample into the top sieve. Place the lid on, located on sieve shaker, and put the stack of sieves into the sieve shaker. Fasten the tie downs. Set shaker for 3 minutes. <p style="margin-left:.25in;">4. Remove stack of sieves from shaker. It&rsquo;s ok to leave the pan behind temporarily as it might be tight. Weigh each sieve and record the weight in the data sheet. If you see any clay clumps, break them up with your fingers and re-shake the stack a little, using hands is okay.<p style="margin-left:.25in;">5. Dump the sample out in the trash and clean the sieve with the brush. At the end of the day it might be necessary to backwash the sieves with water and dry overnight in the oven. <p style="margin-left:.25in;"> Calculations:1. percent clay was determined by the hydrometer analysis- P1.5, P24, X1.5, X24, and m are the variables that were calculated to determine percent clay by the hydrometer analysis.P1.5= ((sample hydrometer reading at 1.5 hours- blank hydrometer reading at 1.5 hours)/ (sample weight)) multiplied by 100.P24= ((sample hydrometer reading at 24 hours- blank hydrometer reading at 24 hours)/ (sample weight)) multiplied by 100X1.5= 1000*(.00019*(-.164* (sample hydrometer reading at 1.5 hours)+16.3)<sup>2</sup> *8100X24=1000*(.00019*(-.164* (sample hydrometer reading at 24 hours)+16.3)<sup>2</sup> *8100m= (P1.5-P24)/(ln(X1.5/X24))percent clay = m * ln(2/X24)) + P24clay (grams) = total weight * ( percent clay/ 100)2. percent Sand and percent Silt were determined based on the results of the sieve analysis which determined the grams of sand and silt.percent sand= total weight * (percent sand/ 100)percent silt= total weight * (percent silt/ 100)3. Othersorganic matter (grams) was calculated in this analysis as dry weight (grams) &ndash; ashed weight (grams)percwnt organic matter was calculated as ((organic matter (grams))/(total dry weight (grams)) multiplied by 100 C. Sediment Chemical Analysis1. SRP/ NaOH-PChemical analysis was done according to the protocol outlined in Pionke and Kunishi (1992). Each sample was first centrifuged and separated into aqueous and sediment fractions. The sediment fraction was then dried. The aqueous fraction was analyzed for soluble reactive phosphorus (srp) by automated colorimetry Nemi Method Number 365.4; see http://www.nemi.gov. NaOH P was then determined by NaOH extractions as described in Pionke and Kunishi (1992). Documentation: Pionke HB, Kunishi HM (1992) Phosphorus status and content of suspended sediment in a Pennsylvania watershed. Soil Sci 153:452&ndash;462.2. NH4 / KCl-NH4 The exact procedure that was used to analyze samples for ammonium is unknown. However, it is known that a KCl extraction was used. The KCl-NH4 was calculated as the concentration of ammonium in milliGramsPerLiter divided by the sediment weight in grams. 3. NO3 / KCl-NO3The exact procedure that was used to analyze samples for nitrate is also unknown. Again, it is known that a KCL extraction was used. The KCl-NO3 was calculated as the concentration of nitrate in milliGramsPerLiter divided by the sediment weight in grams.Note: The same sediment sample was used to measure ammonium and nitrate IV. Big Spring Creek Longitudinal Profile A standard longitudinal stream profile was conducted at Big Spring Creek, WI (wbic=176400) on unknown date(s). It is speculated that the profile was done during the summer of 2005, during which the rest of the data for Big Spring Creek was collected. Measurements for the profile began at the Big Spring Dam site (43.67035,-89.64225), a dam which was subsequently removed. The first (x_dist, y_dist) of (2.296, 5.57) corresponds to the location where the stream crosses Golden Court Road, whereas the second coordinate pair of (-2.615, -36.303) corresponds to the point below the previous Big Spring Creek Dam site. The third (x_dist, y_dist) of (-9.472, 7.681) corresponds to the top of the dam gates and is assigned a distance=0 as it is the starting point.
Version Number
23

Wisconsin Lake Historical Limnological Parameters 1925 - 2009

Abstract
This dataset is a compilation of ten sources of data representing physical and chemical properties of 13,093 Wisconsin lakes. The goal was to compile a comprehensive resource of historical and more recent lake information which would be accessible by querying a single database. Due to the wide temporal extent (1925-2009), methods used for measuring lake parameters in this dataset have varied. A careful look at the available metadata and background information is recommended.Sampling Frequency: variesNumber of sites: 13,093
Contact
Dataset ID
263
Date Range
-
Maintenance
complete
Metadata Provider
Methods
1. Dataset: sr1 - Surface Water Resource Inventory (SWRI) Wisconsin. Temporal coverage: 1960-1980. Original description found in the preface of each Wisconsin Department of Natural Resources (WDNR) SWRI report, published by county.Data manipulation for incorporation into database: Original source of data is WDNR SWRI printed reports. An electronic version (MS Excel spreadsheet) of the data was available (the origin of this spreadsheet was unknown) and was used in preparation of this database. Some discrepancies observed between printed version and electronic version of the dataset: 1) in the printed reports, alkalinity is expressed either as methyl orange or methyl purple; varies from county to county. The electronic format does not contain any metadata or explanation regarding alkalinity. 2) in the printed reports, sometimes max depth provided, sometimes known depth, and sometimes Secchi depth- these values seem to have been transcribed as Secchi depth in the electronic dataset. 3) values of area, conductivity, alkalinity, and depth in electronic format have been rounded up from values in the books. 4) a field in the spreadsheet named "Cl" has no match in books and was not included in the final dataset. 5) color code was not defined in electronic format. It was deciphered and checked against a few lakes from different counties in the printed reports. Final color codes: 1 - Light brown. 2 - Medium brown. 3 - Dark brown. 4 - Clear. 5 - TurbidIssues specific to the electronic format: 13822 records originally. After eliminating all records without WBICs (Water Body Identification Code) or with duplicate WBICs, the dataset reduced to 12638 records with unique WBICs. Of these, 151 records (with area &gt;10 acres) had no or zero data for some chemical parameters. Checked these records using WDNR SWRI reports. Eliminated any record that couldn't be resolved using the books and WDNR WBICs file.. Most records contain both alkalinity and conductivity data, although some do not contain both parameters. Final dataset sr1 has 12383 records2. Dataset: sr2 - Pieter Johnson. Temporal coverage: not specified. Original description: Combination of WDNR Register of Waterbodies (ROW) file, Wisconsin Lakes Book (wilk), and SWRI. Selected lakes with areas &gt;= 10 acres, and lakes in at least 2 of the 3 datasets. Lakes with missing WBIC were not included. Lakes with missing surface area were not included.Data manipulation for incorporation into database: Received original dataset from Jake Vander Zanden (UW-Madison, Center Data manipulation for incorporation into database: Received original dataset from Jake Vander Zanden (UW-Madison, Center for Limnology). The dataset was used in the following publication: Johnson, P.T., J.D. Olden, M.J. Vander Zanden. 2008. Dam invaders: impoundments facilitate biological invasions in freshwaters. Frontiers in Ecology and the Environment 6:357-363. Original dataset contained 5213 records; . Eliminated 8 records without WBIC, legal (TRS) description, and no values for lake characteristics. Note: Many records are repeated from sr1 dataset. Final dataset sr2 has 5205 records.3. Dataset: sr3 - Biocomplexity Project. Temporal coverage: 2001-2004. Original description: Data Set Title: Biocomplexity; Coordinated Field Studies: Chemical Limnology. Investigators: Steve R. Carpenter, Jim Kitchell, Timothy K. Kratz, John J. Magnuson. Contact:NTL LTER Information Manager; Center for Limnology, 680 N Park St, Madison, WI, 53706-1492, USA;(phone) 608-262-2573;(fax) 608-265-2340;(email) infomgr@lter.limnology.wisc.edu; 62 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year)Data manipulation for incorporation into database: Original dataset had 62 records. Replicate samples per lake averaged to single measurements. Two records represented a single lake (Little Rock, North and South basins); these were merged into one record. Final dataset sr3 has 61 records.4. Dataset: sr4 - Landscape Position Project. Temporal coverage: 1998. Original description: Data Set Title: Landscape Position Project: Chemical Limnology. Investigators: Ben Greenfield, Thomas Hrabik, Timothy K. Kratz, David Lewis, Amina Pollard, Karen Wilson. Contact: NTL LTER Information Manager; Center for Limnology, 680 N Park St, Madison, WI, 53706-1492, USA;(phone) 608-890-3446;(fax) 608-265-2340;(email) infomgr@lter.limnology.wisc.edu; Parameters characterizing the chemical limnology and spatial attributes of 51 lakes were surveyed as part of the Landscape Position Project.Data manipulation for incorporation into database: WBICs added. Ward Lake removed from data. Parameters values over multiple sampling events were averaged. Info regarding depth at which samples were taken was not retained. Final dataset sr4 has 50 records.5. Dataset: sr5 - Lillie and Mason. Temporal coverage: 1979. Original description: printed report WI DNR Technical Bulletin no.138. 1983. Limnological characteristics of Wisconsin LakesData manipulation for incorporation into database: Original file containing 667 records received from Paul Garrison (WDNR). 88 records lacked WBICs but 65 of these were assigned using WDNR lakes shapefile, matching names and areas of lakes. Final 23 records without WBICs were removed. Note: Since lake / impoundment classification doesn't seem to match Johnson's dataset (sr2), it was not included. Note from Richard Lathrop (WDNR): total P measurements are probably unreliable due to method used not being sensitive enough. Final dataset sr5 has 644 records.6.Dataset: sr6 - EPA- Eastern Lakes Survey (1984): Temporal coverage: 1984. Original description: Data Set Title: National Surface Water Survey: Eastern Lake Survey-Phase I. The Eastern Lake Survey-Phase I (ELS-I), conducted in the fall of 1984, was the first part of a long-term effort by the U.S. Environmental Protection Agency known as the National Surface Water Survey. It was designed to synoptically quantify the acid-base status of surface waters in the United States in areas expected to exhibit low buffering capacity. The effort was in support of the National Acid Precipitation Assessment Program (NAPAP). The survey involved a three-month field effort in which 1612 probability sample lakes and 186 special interest lakes in the northeast, southeast, and upper Midwest regions of the United States were sampled.Data manipulation for incorporation into database: Original dataset, downloaded from EPA website, has over 100 parameters. Only a small subset of interest was retained. Original documentation for full dataset available is available. Dataset includes 285 Wisconsin lakes. WBICs were assigned using geographic coordinates from dataset. WBIC for one lake could not be determined and was excluded.. Note regarding conductivity parameter: value represents calculated conductivity, as the sum of concentrations of each major cation and anion. It is not a parameter measured in the field or lab. Actual formula used to calculated conductivity was not discovered. Final dataset sr6 has 284 records7. Dataset: sr7 - Environmental Research Lab Duluth (ERLD). Temporal coverage: 1979-1982. Original description: ERLD Lake Survey. Contact(s): NTL LTER Information Manager; Center for Limnology, 680 N Park St, Madison, WI, 53706-1492, USA;(phone) 608-262-2573;(fax) 608-265-2340;(email) infomgr@lter.limnology.wisc.edu; Chemical survey of 832 lakes in Minnesota, Michigan, Wisconsin and Ontario conducted by ERL-Duluth and UMD between 1979 and 1982 for evaluation of trophic state and sensitivity to acid deposition Glass, G.E. and Sorenson, J.A. (1994) USEPA ERLD-UMD acid deposition gradient-susceptibility database. U.S. EPA Environmental Research Laboratory - Duluth and University of Minnesota at Duluth, MN.Data manipulation for incorporation into database: Dataset included 428 Wisconsin records for which WBICs were included. Note: Original dataset had several errors in WBIC assignment: 1179900 was assigned to three different water bodies; correct WBICs are: 1503000, 1502400, 1481100; also 1515800 changed to 1516000. Lake Clara had 5 different stations for most parameters sampled. First station that had values for all parameters was included in final dataset. Final dataset sr7 has 428 records.8. Dataset: sr8 - Birge-Juday Historical Dataset. Temporal coverage: 1925-1941. Original description: Birge-Juday Historical Lake Data. Investigator(s): Edward A. Birge, Chauncy Juday. Contact: NTL LTER Information Manager; Center for Limnology, 680 N Park St, Madison, WI, 53706-1492, USA;(phone) 608-262-2573;(fax) 608-265-2340;(email) infomgr@lter.limnology.wisc.edu; Data collected by Birge, Juday, and collaborators, mostly in north-central Wisconsin, from 1925 through 1941; generally one sample per lake during the summer, but on some lakes, especially around Trout Lake Station, samples were taken on several successive years. Note that not all variables were measured on all lakes (scarce data for nutrients and ions). Documentation: Johnson, M.D. (1984) Documentation and quality assurance of the computer files of historical water chemistry data from the Wisconsin Northern Highland Lake District (the Birge and Juday data).WDNR Technical Report. Number of sites: 608 (generally one sampling point per lake; occasionally, several sampling points per lake on multibasin, large lakes).Data manipulation for incorporation into database: Original dataset downloaded from UW-Madison, Center for Limnology LTER website. Values averaged for lakes with multiple samples. WBICs assigned to 577 lakes via GIS spatial join using site coordinates and WDNR lake shapefile. Note from Johnson, M.D. (1984): the units for alkalinity (fixed CO2) changed from cc/L to mg/L sometime between Aug 1926 and May 1927. 17 entries were originally cc/l. Thus there might be inconsistencies in the alkalinity data. Final dataset sr8 has 577 records.9. Dataset: sr9 - USGS National Water Inventory System (NWIS). Temporal coverage: 1969-2009. Original description: U.S. Geological Survey. This file contains selected water-quality data for stations in the National Water Information System water-quality database (http://nwis.waterdata.usgs.gov/nwis/). Explanation of codes found in this file are followed by the retrieved data. The data you have secured from the USGS NWIS Web database may include data that have not received Director's approval and as such are provisional and subject to revision. The data are released on the condition that neither the USGS nor the United States Government may be held liable for any damages resulting from its authorized or unauthorized use.Data manipulation for incorporation into database: Data downloaded for 240 lakes for the following parameters: calcium, conductivity, alkalinity, pH. Original parameter codes (USGS NWIS schema): p00915 p00095 p00400 p00916 p29801 p39086 p90095. Data are averaged for multiple measurements. WBICs assigned via GIS spatial join using site coordinates and WDNR lake shapefile. Final dataset sr9 has 240 records.10. Dataset: sr10 - WI Department of Natural Resources (WDNR) Temporal coverage: 1969-2009 Original description: available at http://dnr.wi.gov/org/water/swims/Data manipulation for incorporation into database: Original data received from Jennifer Filbert (WDNR). Data were extracted from WDNR Surface Water Integrated Monitoring System (SWIMS) database (http://dnr.wi.gov/org/water/swims/). Lakes represented had one or more of the following parameters: Secchi depth, calcium, conductivity, alkalinity, pH, total P, turbidity,, chlorophyll a. Data were averaged where multiple measurements were available. Final dataset sr10 has 53 records.The Data Source data table contains a summary of the 10 data sources with information on temporal coverage and record counts. It also includes information on the availability of calcium and conductivity data from the data sources.
Short Name
WILIMN1
Version Number
25

North Temperate Lakes LTER: Secchi Disk Depth; Other Auxiliary Base Crew Sample Data 1981 - current

Abstract
Secchi disk depth is measured in the deepest part of each lake for the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout lakes, unnamed lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Fish, Mendota, Monona and Wingra). The disk is circular, 20 cm in diameter, and has alternating black and white quadrants. It is lowered using a calibrated Kevlar rope to minimize stretching. Readings are made on the shaded side of the boat both with and without the aid of a plexiglass viewer. The points at which the disk disappears while being lowered and reappears while being raised are averaged to determine Secchi depth. Auxiliary data include time of day, air temperature, cloud cover, wave height, wind speed and direction and whether the lake was ice covered on the sampledate. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season for the northern lakes. The southern lakes are similar except that sampling occurs monthly during the fall and typically only once during the winter (depending on ice conditions). Number of sites: 11
Dataset ID
31
Date Range
-
LTER Keywords
Maintenance
ongoing
Metadata Provider
Methods
see abstract for secchi disc measurements. Otherwise, these are observations made by the crew while out sampling.
Short Name
NTLPH03
Version Number
29

Lake Wingra Exclosure Experiment at North Temperate Lakes LTER: Secchi Disk Depth 2005 - 2008

Abstract
Starting in late summer 2005, Wisconsin Dept of Natural Resources (WDNR), Dane County, Friends of Lake Wingra (FOLW), and NTL-LTER initiated a 3-year experiment in Lake Wingra to test the response of the native macrophyte community to clearer water produced from a major carp reduction program. This demonstration-scale experiment includes the construction of a 1.0-hectare rectangular carp exclosure with its solid vinyl walls extending from the lake shoreline to a water depth of 2.9 meters. NTL-LTER conducts the routine limnological monitoring of the lake and exclosure and is leading the science evaluation of potential lake restoration activities. The exclosure experiment was terminated in the fall of 2008. The exclosure was removed from Lake Wingra at that time. Sampling is done both within the exclosure and at a control site located nearby in the littoral zone. The sample location within the exclosure is equidistant from the side walls and approximately 75 meters from the shore in a water depth of approximately 2.5 meters. The control site sample location is approximately 75 meters west of the exclosure sample site at the same approximate distance from shore and water depth. Samples are taken at the same time and on the same schedule as the NTL-LTER limnological sampling on Lake Wingra, e.g., biweekly spring through summer, every 4 weeks in the fall, and once during the winter depending on ice conditions. Parameters measured within the exclosure and at the control site include water temperature, dissolved oxygen, secchi depth and chlorophyll-a. Additional parameters measured only within the exclosure include total Kjeldahl nitrogen, nitrate + nitrite nitrogen, ammonia nitrogen, total phosphorus, dissolved reactive phosphorus and dissolved reactive silica. Secchi disk depth is measured within the exclosure and at a nearby control site in the littoral zone . The disk is circular, 20 cm in diameter, and has alternating black and white quadrants. It is lowered using a calibrated Kevlar rope to minimize stretching. Readings are made on the shaded side of the boat without the aid of a plexiglass viewer. The points at which the disk disappears while being lowered and reappears while being raised are averaged to determine Secchi depth. Other parameters measured at depth include water temperature and dissolved oxygen. Auxiliary data include time of day, air temperature, cloud cover, wind speed and direction, and wave height. Sampling Frequency: generally bi-weekly during ice-free season from late March or early April through early September, then every 4 weeks through late November. Number of sites: 2
Core Areas
Dataset ID
189
Date Range
-
LTER Keywords
Maintenance
completed
Metadata Provider
Methods
Secchi disk depth is measured within the exclosure and at a nearby control site in the littoral zone . The disk is circular, 20 cm in diameter, and has alternating black and white quadrants. It is lowered using a calibrated Kevlar rope to minimize stretching. Readings are made on the shaded side of the boat without the aid of a plexiglass viewer. The points at which the disk disappears while being lowered and reappears while being raised are averaged to determine Secchi depth.
Short Name
FOLWEXSE
Version Number
21

Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Coarse Woody Habitat Data 2001 - 2009

Abstract
These data were collected to test for changes in the population dynamics and the food webs of the fish populations of Little Rock and Camp lakes, Vilas County, WI, USA. Little Rock Lake was the site of a whole-lake removal of coarse woody habitat in 2002 and Camp Lake was the site of a whole-lake coarse woody habitat addition in 2004. Sampling began in May of 2001 and ended in August of 2006. Some sampling was repeated from 2007 to 2009. Number of sites: 4. Two lakes with reference and treatment basin in each lake.
Core Areas
Dataset ID
215
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Fish were collected by beach seining, hook and line angling, and minnow traps. Commonly captured species were largemouth bass, bluegill, yellow perch, rock bass, and black crappie. Population Estimates: Chapman-modified continuous Schnabel mark-recapture population estimates were conducted on each basin of Little Rock and Camp lakes annually. Adult population estimates for largemouth bass, yellow perch, rock bass, and black crappie were calculated for Little Rock Lake during 2001-2006. All fish were captured by hook and line angling, minnow traps, and beach seining. Adult population estimates for largemouth bass and bluegill were calculated for Camp Lake during 2002-2006. All fish were captured by hook and line angling and beach seining. Fish Length/Weight Tag data: Length, weight, and mark data was recorded for all fish used to collect diet information. Diet information was collected from up to 15 individuals of each species biweekly May-September using gastric lavage. Diet information was collected from largemouth bass, yellow perch, rock bass, and black crappie in Little Rock Lake from 2001-2005 and 2007 - 2009. Diet information was collected from largemouth bass and yellow perch in Camp Lake from 2002-2005. Fish Length Tag data: Length and mark data was recorded for all fish used to calculate the mark-recapture population estimates. Length and the mark were recorded from all fish captured in Little Rock and Camp lakes from 2001-2006. Length and mark data exists for all fishes collected in Little Rock Lake from 2001-2006 and 2007 - 2009. Fish species from Little Rock include largemouth bass, yellow perch, rock bass, and black crappie. Length and mark data exists for all fishes collected in Camp Lake from 2002-2006. Fish species from Camp Lake include largemouth bass, yellow perch, and bluegill. All fish were captured by beach seining, hook and line angling, and minnow traps. Minnow trap CPUE: Minnow traps were the most effective gear for capturing yellow perch on Little Rock Lake. Standardized minnow trapping was conducted on both basins of Little Rock Lake in 2003-2005. In 2003, 10 minnow traps in each basin were deployed biweekly and picked twice per week. In 2004-2005, 20 minnow traps in each basin were deployed biweekly and picked twice per week. Catch per unit effort was calculated as catch of yellow perch per trap. Age Growth Rates: Growth rates were calculated for a subset of fish collected from Little Rock Lake (2001-2004) and Camp Lake (2002-2005). Back-calculated growth rates from five fish from every 10 mm size increment were examined. In the process, age was determined from scale samples and length at each annulus was back-calculated. Size-specific growth rates were calculated based on the relationship between fish length at age and ln transformed growth rate at age. Back-calculated growth information was assessed from largemouth bass, yellow perch, rock bass, and black crappie in Little Rock Lake. Back-calculated growth information was assessed from largemouth bass and bluegill in Camp Lake.
Short Name
BIOSASS1
Version Number
52

Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Secchi Disk Depth 2001 - 2004

Abstract
Chemical Limnology data collected for Biocomplexity Project; Landscape Context - Coordinated Field Studies Replicate chemical samples were pumped from the surface water (0.5m depth) and secchi depth was recorded at each lake. Temperature/dissolved oxygen profiles were taken throughout the water column at one meter intervals on all lakes. For more detail see the Water Sampling Protocol. Sampling Frequency: During 2001, temperature/dissolved oxygen profiles and secchi depths were taken twice during the stratified summer period. Chemistry samples were only taken once during the 2001 stratified period. From 2002 through 2004, all chemical and physical water samples were taken once during June (or resampled during the stratified period if June samples were bad). All lakes in which color, DIC/DOC, and chlorophyll samples were taken in 2001 were resampled in 2002 due to error in collection and/or analysis. Number of sites: 62 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year).Allequash Lake, Anvil Lake, Arrowhead Lake, Bass Lake, Big Lake, Birch Lake, Ballard Lake, Big Muskellunge Lake, Black Oak Lake, Big Portage Lake, Brandy Lake, Big St Germain Lake, Camp Lake, Crab Lake, Circle Lily, Carpenter Lake, Day Lake, Eagle Lake, Erickson Lake, Escanaba Lake, Found Lake, Indian Lake, Jag Lake, Johnson Lake, Jute Lake, Katinka Lake, Lake Laura, Little Croooked Lake, Little Spider Lake, Little St Germain Lake, Little Crawling Stone Lake, Little John Lake, Lac Du Lune Lake, Little Rock Lake - North, Lost Lake, Little Rock Lake - South, Little Star Lake, Little Arbor Vitae Lake, Lynx Lake, Mccollough Lake, Moon Lake, Morton Lake, Muskellunge Lake, Nebish Lake, Nelson Lake, Otter Lake, Oxbow Lake, Palmer Lake, Pioneer Lake, Pallete Lake, Papoose Lake, Round Lake, Star Lake, Sparkling Lake, Spruce Lake, Stormy Lake, Twin Lake South, Tenderfoot Lake, Towanda Lake, Upper Buckatabon Lake, Vandercook Lake, White Sand Lake, Vilas County, WI, USA
Dataset ID
44
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Lower the Secchi into the water on the shady side of the boat. Lower the disk until you cannot see it; record this depth as the down reading. Raise the disk until you can again see it; record this depth as the up reading.
Short Name
BIOSECH1
Version Number
6

Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Lakes 2001 - 2004

Abstract
The study lakes selected for the &quot;cross-lake comparison&quot; segment of the Biocomplexity Project include 62 lakes located in Vilas County, Wisconsin. The lakes were chosen to represent a range of positions on gradients of both human development and landscape position.Allequash Lake, Anvil Lake, Arrowhead Lake, Bass Lake, Big Lake, Birch Lake, Ballard Lake, Big Muskellunge Lake, Black Oak Lake, Big Portage Lake, Brandy Lake, Big St Germain Lake, Camp Lake, Crab Lake, Circle Lily, Carpenter Lake, Day Lake, Eagle Lake, Erickson Lake, Escanaba Lake, Found Lake, Indian Lake, Jag Lake, Johnson Lake, Jute Lake, Katinka Lake, Lake Laura, Little Croooked Lake, Little Spider Lake, Little St Germain Lake, Little Crawling Stone Lake, Little John Lake, Lac Du Lune Lake, Little Rock Lake - North, Lost Lake, Little Rock Lake - South, Little Star Lake, Little Arbor Vitae Lake, Lynx Lake, Mccollough Lake, Moon Lake, Morton Lake, Muskellunge Lake, Nebish Lake, Nelson Lake, Otter Lake, Oxbow Lake, Palmer Lake, Pioneer Lake, Pallete Lake, Papoose Lake, Round Lake, Star Lake, Sparkling Lake, Spruce Lake, Stormy Lake, Twin Lake South, Tenderfoot Lake, Towanda Lake, Upper Buckatabon Lake, Vandercook Lake, White Sand Lake, Vilas County, WI, USA
Dataset ID
209
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Study Lakes We selected 60 northern temperate lake sites in Vilas County, Wisconsin lake district. Methods for lake choice and sampling are given in greater detail in Marburg et al. (2005) Each lake was sampled once between 2001 and 2004, in June, July, or August (15 different lakes each summer). We chose stratified lakes deeper than 4 m to insure that all the lakes contained a diverse fish community. With two exceptions (chains of lakes), lakes were chosen to be in separate watersheds. Lakes were chosen based on two criteria landscape position, using historical DNR water conductivity data as a proxy of position, and riparian housing development, measured in buildings km-1 shoreline (Marburg et al. 2005). Landscape position refers to the location of a lake along the hydrological gradient. The gradient ranges from the top of a drainage system, where seepage lakes are fed mainly by rainwater, through lakes which receive water from groundwater and have surface outflows, to lakes further down in the drainage system, which receive water from both ground and surface flow (Kratz et al. 1997).Landscape position affects lake water chemistry, because as water flows across the surface and through soil, it picks up carbonates and other ions which increase the waters electrical conductivity (specific conductance, a temperature-independent measure of salinity), alkalinity, and its ability to support algal and macrophyte production. In addition, aspects of lake morphology correlate with landscape position. Most obviously, larger lakes tend to occur lower in drainage systems (Riera et al. 2000).The riparian (near-shore terrestrial) zone around northern Wisconsin lakes is being rapidly developed for use as both summer and permanent housing (Peterson et al., 2003). Concurrent with housing development, humans often directly and indirectly remove logs (Kratz et al. 2002) and aquatic vegetation (Radomski and Goeman 2001) from the littoral zone (near shore shallow water area), resulting in reduced littoral zone complexity. The slowly-decaying logs of fallen trees create physical structure (coarse woody habitat CWH) in the littoral zone of lakes that provides habitat and refuge for aquatic organisms (Christensen et al. 1996). Fish, including plankton-eating species (planktivores), reproduce and develop in shallow water (Becker 1983). Because planktivorous fish affect zooplankton community structure through size-selective predation (Brooks and Dodson 1965), there is the potential for indirect effects of housing development on zooplankton.Lakes ranged in size from 24 to 654 ha. In 2001, 2002 and 2004 we chose lakes from the extreme ends of the conductivity and housing density gradients and in 2003 lakes were chosen to fill in the gap in the middle of the ranges. The study lakes range from oligotrophic to mesotrophic (Kratz et al. 1997 Magnuson et al. 2005).At each lake we sampled zooplankton, water chemistry, riparian and littoral vegetation, fish, crayfish, and macrophytes. Each lake was sampled only once, but given the large number of lakes sampled in this area, we expect to see relationships between variables within lakes and at a landscape scale. A snapshot sampling design maximizes sites that can be visited, and is sufficient for a general characterization of zooplankton communities (Stemberger et al. greater than 001).
Short Name
BIOLAKE1
Version Number
26
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