Methods

Each ecosystem service was quantified and mapped by using empirical estimates and spatially explicit model for the terrestrial landscape of the Yahara Watershed for 2006. Crop production (expected annual crop yield, bu per yr) Crop yield was estimated for the four major crop types (corn, soybean, winter wheat and oats) that account for 98.5 percent of the cultivated land in the watershed by overlaying maps of crop types and soil-specific crop yield estimates. The spatial distribution of each crop was obtained from the 2006 Cropland Data Layer (CDL) from the National Agricultural Statistics Service (NASS) and soil productivity data were extracted from Soil Survey Geographic (SSURGO) database. Crop and soil data were converted to 30 m resolution and the two maps were overlain to estimate crop yield in each cell. For each crop-soil combination, crop area was multiplied by the estimated yield per unit area. Estimates for each crop type were summed to map estimated crop yield for 2006. Pasture production (expected annual forage yield, animal-unit-month per year ) As for crop production, forage yield was estimated by overlaying the distribution of all forage crops (alfalfa, hay and pasture/grass) and soil specific yield estimates. The spatial distribution of each forage crop was also derived from 2006 CDL, and rescaled to 30 m grid prior to calculation. The SSURGO soil productivity layer provided estimates of potential annual yield per unit area for each forage crop. Overlay analyses were performed for each forage-soil combination, as done for crops, and summed to obtain the total expected forage yield in the watershed for 2006. Freshwater supply (annual groundwater recharge, cm per year) . Groundwater recharge was quantified and mapped using the modified Thornthwaite-Mather Soil-Water-Balance (SWB) model. SWB is a deterministic, physically based and quasi three-dimensional model that accounts for precipitation, evaporation, interception, surface runoff, soil moisture storage and snowmelt. Groundwater recharge was calculated on a grid cell basis at a daily step with the following mass balance equation<p align="center">Recharge= (precipitation + snowmelt + inflow) &ndash;<p align="center">(interception + outflow + evapotranspiration) &ndash; delta soil moisture<p align="center"> We ran the model for three years (2004 to 2006) at 30m resolution, with the first two years as spin up of antecedent conditions (e.g. soil moisture and snow cover) that influence groundwater recharge for the focal year of 2006. Carbon storage (metric tonsper ha) We estimated the amount of carbon stored in each 30 m cell in the Yahara Watershed by summing four major carbon pools: aboveground biomass, belowground biomass, soil carbon and deadwood/litter. Our quantification for each pool was based mainly on carbon estimates from the IPCC tier-I approach and other published field studies of carbon density and was estimated by land-use/cover type.Groundwater quality (probability of groundwater nitrate concentration greater than 3.0 mg per liter, unitless 0 to1) Groundwater nitrate data were obtained from Groundwater Retrieve Network (GRN), Wisconsin Department of Natural Resources (DNR). A total of 528 shallow groundwater well (well depth less than the depth from surface to Eau Claire shale) nitrate samples collected in 2006 were used for our study. We performed kriging analysis to interpolate the spatial distribution of the probability of groundwater nitrate concentration greater than 3 mgper liter. We mapped the interpolation results at a 30m spatial resolution using Geostatistical Analyst extension in ArcGIS (ESRI). In this map, areas with lower probability values provided more groundwater quality service, and vice versa. Surface water quality (annual phosphorus loading, kg per hectare). We adapted a spatially explicit, scenario-driven modeling tool, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) to simulate discharge of nonpoint-source phosphorus. A grid cells phosphorus contribution was quantified as a function of water yield index, land use/cover, export coefficient, and downslope retention ability with the following equation:Expx = ALVx * sum of the products from y=x+1 to X for (1-Ey)where ALVx is the adjusted phosphorus export from pixel x , Ey is the filtration efficiency of each downstream pixel y , and X represents phosphorus transport route from where it originated to the downstream water bodies. Filtration efficiency was assigned by cover type: natural vegetation was assigned a high value, semi-natural vegetation an intermediate value, and developed or impervious covers were assigned low values. We ran the model for 2006 and mapped estimated phosphorus loading across the watershed. The ecosystem service of providing high quality surface water was the inverse of phosphorus loading. Therefore, areas with lower phosphorus loading values delivered more surface water quality, and areas with higher phosphorus loading values supplied less surface water quality.Soil retention (annual sediment yield, metric tons per hectare). We quantified annual sediment yield as the (inverse) indicator for soil retention by using the Modified Universal Soil Loss Equation (MUSLE). MUSLE is a storm event based model that estimates sediment yield as a function of runoff factor, soil erodibility, geomorphology, land use/cover and land management. Specifically, a grid cells contribution of sediment for a given storm event is calculated as:Sed= 11.8*(Q*q_p)^0.56 * K * LS * C * Pwhere Sed represents the amount of sediment that is transported downstream network (metric tons), Q is the surface runoff volume (m³), q_pis the peak flow rate (cubic meters per s), K is soil erodibility which is based on organic matter content, soil texture, permeability and profiles, LS is combined slope and steepness factor, and C* P is the product of plant cover and its associated management practice factor. We used the ArcSWAT interface of the Soil and Water Assessment Tool (SWAT) to perform all the simulations. We ran this model at a daily time step from 2004 to 2006, with the first two years as spin up , then mapped total sediment yield for 2006 across the watershed. Similar to surface water quality, the ecosystem service of soil retention was the inverse of sediment yield. In this map, areas with lower sediment yield provided more of this service, and areas with higher sediment yield delivered less. Flood regulation (flooding regulation capacity, unitless, 0 to 100) We used the capacity assessment approach to quantify the flood regulation service based on four hydrological parameters: interception, infiltration, surface runoff and peak flow. We first applied the Kinematic Runoff and Erosion (KINEROS) model to derive estimates of three parameters (infiltration, surface runoff and peak flow) for six sampled sub basins in this watershed. KINEROS is an event-oriented, physically based, distribution model that simulates interception, infiltration, surface runoff and erosion at sub-basin scales. In each simulation, a sub basin was first divided into smaller hydrological units. For the given pre-defined storm event, the model then calculated the amount of infiltration, surface runoff and peak flow for each unit. Second, we classified these estimates into 10 discrete capacity classes with range from 0 to 10 (0 indicates no capacity and 10 indicates the highest capacity) and united units with the same capacity values and overlaid with land cover map. Third, we calculated the distribution of all land use/cover classes within every spatial unit (with a particular capacity). We then assigned each land use/cover a capacity parameter based on its dominance (in percentage) within all capacity classes. As a result, every land use/cover was assigned a 0 to 10 capacity value for infiltration, surface runoff and peak flow. This procedure was repeated for six sub basins, and derived capacity values were averaged by cover type. We applied the same procedure to soil data and derived averaged capacity values for each soil type with the same set of three parameters. In addition, we obtained interceptions from published studies for each land use/cover and standardized to the same 0 to 10 range. Finally, the flood regulation capacity (FRC) for each 30m cell was calculated with the equation below:FRC= for each land use and land cover class the sum of (interception + infilitration + runoff + peakflow) + for each soil class the sum of (infiltration + runoff + peakflow).To simplify interpretation, we rescaled original flood regulation capacity values to a range of 0 to100, with 0 representing the lowest regulation capacity and 100 the highest. Forest recreation (recreation score, unitless, 0 to 100). We quantified the forest recreation service as a function of the amount of forest habitat, recreational opportunities provided, proximity to population center, and accessibility of the area for each 30m grid cell with the equation below:FRSi= Ai * sum of (Oppti + Popi + Roadi)where FRS is forest recreation score, A is the area of forest habitat, Oppt represents the recreation opportunities, Pop is the proximity to population centers, and Road stands for the distance to major roads. To simplify interpretation, we rescaled the original forest recreation score (ranging from 0 to 5200) to a range of 0 to 100, with 0 representing no forest recreation service and 100 representing highest service. Several assumptions were made for this assessment approach. Larger areas and places with more recreational opportunities would provide more recreational service, areas near large population centers would be visited and used more than remote areas, and proximity to major roads would increase access and thus recreational use of an area. Hunting recreation (recreation score, unitless 0 to100) We applied the same procedure used for forest recreation to quantify hunting service. Due to limited access to information regarding private land used for hunting, we only included public lands, mainly state parks, for this assessment. The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility for each 30m grid cell with the following equation:<br />HRSi= Ai * sum of (Spei + Popi + Roadi)where HRS is hunting recreation score, A is the area of public wild areas open for hunting and fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28000) to a range of 0 to100, with 0 representing no hunting recreation service and 100 representing highest service. Similar assumptions were made for this assessment. Larger areas and places with more game species would support more hunting, and areas closer to large population centers would be used more than remote areas. Finally, proximity to major roads would increase access and use of an area.

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

Project: Identifies what part of the project a lake was sampled for.A core landscape position project lake = LPPA core LTER lakes sampled for biology as part of the landscape positon project = LTEROne of the lakes sampled as part of Ben Greenfield MS thesis (2000) = BenA landscape position project lake sampled only for fish = Fish.Lake_order: Lake order is a numerical surrogate for groundwater influx and hydrological position along a drainage network, with the highest number indicating the lake lowest in a watershed. We define lake order as follows: -3 indicates isolated seepage lakes, -2 indicates seepage lakes connected by intermittent streams, -1 indicates seepage lakes connected by a wetland, 0 indicates headwater drainage lakes, and 1 through 4 indicate drainage lakes, with the number indicating the order of the stream that exits the lake (Riera et al. 2000).Area: lake area in acres. Using Arcview coverages, identified in Ben Greenfield MS thesis (2000) as described belowDirect_catchment: area of surrounding catchment feeding directly into lake (square meters). For drainage lakes, delineated starting from the outlet of the immediate upstream lake.Total_catchment: area of surrounding catchment feeding into lake and all lakes upstream of given lake.Max_depth (ft): Using agency published records, listed in Ben Greenfield MS thesis (2000) as described belowPerimeter (m): Lake perimeter. Using Arcview coverages, identified in Ben Greenfield MS thesis (2000)Shoreline_devel: Shoreline development factor, defined in Cole s Limnology textWetlands_250m: Percent wetlands within catchment within buffer strip 250 meters distance from lake. Methods in Ben Greenfield MS thesis (2000Wetlands_500m: Percent wetlands within catchment within buffer strip 500 meters distance from lake. Methods in Ben Greenfield MS thesis (2000Mean_depth (ft): Mean depth, using WDNR data, when available.