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.

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.

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_3-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)² *8100X24=1000*(.00019*(-.164* (sample hydrometer reading at 24 hours)+16.3)² *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.

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.

Methods

see abstract for secchi disc measurements. Otherwise, these are observations made by the crew while out sampling.

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.

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.

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.

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).