Methods

Methods are described in Wilkinson et al. 2018 (Ecological Monographs 88:188-203) and Pace et al. 2017 (Proceedings of the National Academy of Sciences USA 114: 352-357). These publications including supplements should be consulted for details.
In Paul, Peter and Tuesday lakes two sondes were deployed at 0.75 meters near lake center. One sonde was a Hydrolab (model DS5X) with temperature, oxygen, pH, phycocyanin, and chlorophyll a sensors. One sonde was a Yellow Springs Instruments (YSI) 6600-V2-4 with temperature, dissolved oxygen, pH, phycocyanin, and chlorophyll a sensors. Measurements were made every five minutes. Brief gaps in the data record due to calibration or sensor malfunction were interpolated using a bivariate autoregressive state-space model with the MARSS package in R version 3.9 to create a continuous daily time series.

Methods

Methods are described in Pace and Cole 2002 (https://doi.org/10.4319/lo.2002.47.2.0333). Surface water samples for the analysis of pH, dissolved organic carbon (DOC), chlorophyll a , total phosphorus color were collected by dipping a sample bottle. The total phosphorus (TP) samples were stored in a separate acid-washed bottle. Samples were collected monthly from May through August from a set of 20 lakes for the years 1995-2000. A subset of fifteen lakes were sampled in the same way from 2001-2003. Samples were stored in a cooler and returned the lab for processing within a few hours.

Methods

Data were collected at 5 minute intervals using in-situ automated sensors (sondes). All measurements and samples were collected from a stationary raft over the deepest part of the lake.
Sondes were suspended from floats with probes at a depth of 0.75m below the surface. Sonde sensors were cleaned daily in the field and calibrated monthly following manufacturer guidelines. Peter and Paul lakes were each monitored with two YSI multiparameter sondes (model 6600 V2-4) fitted with optical DO (model 6150), pH (model 6561), optical Chl-a (model 6025), and conductivity-temperature (model 6560) probes. Sensor measurements were made at 0.75 m every 5 min and were calibrated weekly. PAR was measured and the UNDERC meteorology station maintained by the University of Notre Dame or by the North Temperate Lakes Weather Station at Woodruff Airport.
Outliers were replaced by NA. Occasional gaps in the record due to instrument cleaning are NA.

Methods

Rate of bubble releaseWe deployed 30 inverted funnel-style bubble traps (Molongoski and Klug, 1980; Baulch et al., 2011) on Allequash Creek on 31 May 2013 to measure volumetric bubble release rates. Fifteen traps were placed in two sandy sediment sections and 15 were placed in muck sediments in the wetland portion of the creek (number 8–22), which sits in-between the two sandy sections. Site 1 was the most downstream sampling site (water flows from East to West). Traps were sampled approximately every other day after 1 June 2013 until 31 October 2013 (we omitted the first samples collected 24 h following trap installation; total of 65 sample events per trap). Our sampling design allowed us to assess both the spatial and temporal variability in ebullition along Allequash Creek and how ebullition related to potential controlling factors such as sediment composition, atmospheric pressure, groundwater CH4, and organic matter content (discussed further below). To characterize our ebullition time series from Allequash Creek in the larger context of the NHLD, we installed an additional 12 traps on three additional creeks (Mann Creek, Stevenson Creek, and North Creek in the Trout Lake drainage; three per site in an even mix of sand and muck sediments) and the headwater spring ponds that drain into Allequash Creek on 23 June 2013 which we sampled approximately every week for the remainder of the study. Bubble traps had a bottom surface area of appr. 503 cm2 which narrowed at the top into a graduated (1 mL resolution) syringe and 3-way stopcock. Traps were attached to steel poles that were pounded into the substrate. Traps were almost completely submerged and contained no headspace at deployment. Water depth below traps averaged 55.7 cm, but we were unable to place traps in locations where water depth was shallower than 15 cm. Water velocity during baseflow at the traps averaged 0.06 m s -1 (range = 0.003–0.23 m s -1). We sampled traps by carefully approaching them either by boat (muck sites) or by wading (sandy sites) to avoid induced ebullition. Volume of accumulated gas in the trap was based on the graduated syringe, and volumes less than 1 mL were recorded as zero. Traps were reset between sampling events by refilling them completely with water to eliminate all headspace. To assess the hypothesis that declines in atmospheric pressure are related to increased bubble release (Mattson and Likens, 1990; Comas et al., 2011), we compared a 15 min resolution atmospheric pressure time series recorded using a Vaisala BAROCAP barometer deployed near trap number 7 with a subset of the bubble release time series.

Methods

Sampling DesignSampling of gas partial pressures, and gas transfer velocities was performed weekly at five stream sites (Mann Creek, Allequash Lower Creek, Allequash Middle, Stevenson Creek, North Creek) that drain into Trout Lake (Figure 1), one of the regions larger lakes. Sampling began in May 2012 and continued through September 2012. These data were used to establish variability in gas transfer rates for the basin, and to investigate spatiotemporal patterns. To test for the effects of upstream lakes, sampling at 30 additional longitudinal transect sites along 6 streams (5 sites per stream; Figure 1) was conducted approximately every 3 weeks beginning in May 2012. Transect streams were chosen based on the presence or absence of lakes in the upstream watershed. Streams with lakes (Lost, White Sand, Aurora) were sampled starting at the approximate lake outlet (site selection based on aerial photographs), and along a 2000 m transect (0m, 250m, 500m, 1000m, 2000m). Upstream lake chemistry (epilimnion) was also sampled during late July or early August 2012 at the lake center to allow for a direct comparison with streams. Streams without upstream lakes (Stella, Mud, and North) were sampled at an arbitrary upstream location (0m) and followed the same sampling progression as streams with lakes. We analyzed stream chemistry and stream morphology data from a regional stream survey (Lottig and others 2011) which we use to scale fluxes to the NHLD (Figure 1). We also studied groundwater CO2 and CH4 patterns along a hillslope transect at Allequash Creek during 2001. In 2002, we monitored hourly CO2 and O2 dynamics at the four WEBB sites to assess the role of ecosystem metabolism.

Methods

I. Study System Lake Mývatn, Iceland (65°36 N, 17°0′ W) is a large (38 km²) shallow (4 m max depth) lake divided into two large basins that function mostly as independent hydrologic bodies (Ólafsson 1979). The number of non-biting midge (Diptera: Chironomidae) larvae on the lake bottom is high, but variable: midge production between 1972-74 ranged from 14-100 g ash-free dw m^-2 yr^-1, averaging 28 g dw m^-2 yr^-1 (Lindegaard and Jónasson 1979). The midge assemblage is mostly comprised of two species (> 90% of total individuals), Chironomus islandicus (Kieffer) and Tanytarsus gracilentus (Holmgren) that feed as larvae in the sediment in silken tubes by scraping diatoms, algae, and detritus off the lake bottom (Lindegaard and Jónasson 1979). At maturity (May-August) midge pupae float to the lake surface, emerge as adults, and fly to land, forming large mating swarms around the lake (Einarsson et al. 2004, Gratton et al. 2008). On land, midges are consumed by terrestrial predators (Dreyer et al. 2012, Gratton et al. 2008), or enter the detrital pool upon death (Gratton et al. 2008, Hoekman et al. 2012). Midge populations naturally cycle with 5-8 year periodicity, with abundances fluctuating by 3-4 orders of magnitude (Einarsson et al. 2002, Ives et al. 2008). II. Midge Infall Measurement We deployed eleven transects of passive, lethal aerial infall traps arrayed at variable distances from Lake Mývatn to estimate relative midge abundance on shore during the summers 2008-2011. Each transect was perpendicular to the lake edge, with traps located at approximately 5, 50, 150, and 500 m (where possible) from shore for a total of 31 traps around the lake. Sampling locations were recorded using GPS and precise distances from the lake were calculated within a geographic information system. Traps consisted of a single 1000 mL clear plastic cup (0.0095 m² opening) affixed 1 m above the ground on a stake and filled with 300-500 mL of a 1:1 mixture of water and ethylene glycol and a trace amount of unscented detergent to capture, kill, and preserve insects landing on the surface of the liquid (Gratton et al. 2008, Dreyer et al. 2012). Midges and other insects were emptied from the traps weekly and the traps were reset immediately, thus collections span the entirety of each summer. III. Identification, Counts, and Conversions Midges were counted and identified to morphospecies, small and large. The midge (Diptera,Chrionomidae) assemblage at Mývatn is dominated by two species, Chironomus islandicus (Kieffer)(large, 1.1 mg dw) and Tanytarsus gracilentus (Holmgren)(small, 0.1 mg dw), together comprising 90 percent of total midge abundance (Lindegaard and Jonasson 1979). First, the midges collected in the infall traps were spread out in trays, and counted if there were only a few. Some midges were only identified to the family level of Simuliidae, and other arthropods were counted and categorized as the group, others. Arthropods only identified to the family level Simuliidae or classified as others were not dually counted as Chironomus islandicus or Tanytarsus gracilentus . If there were many midges, generally if there were hundreds to thousands, in an infall trap, subsamples were taken. Subsampling was done using plastic rings that were dropped into the tray. The rings were relatively small compared to the tray, about 2 percent of the area of a tray was represented in a ring. The area inside a ring and the total area of the trays were also measured. Note that different sized rings and trays were used in subsample analysis. These are as follows, trays, small (area of 731 square centimeters), “large1” (area of 1862.40 square centimeters), and large2 (area of 1247 square centimeters). Rings, standard ring (diameter of 7.30 centimeters, subsample area is 41.85 square centimeters) and small ring (diameter of 6.5 centimeters, subsample area is 33.18 square centimeters). A small ring was only used to subsample trays classified as type “large2.”The fraction subsampled was then calculated depending on the size of the tray and ring used for the subsample analysis. If the entire tray was counted and no subsampling was done then the fraction subsampled was assigned a value of 1.0. If subsampling was done the fraction subsampled was calculated as the number of subsamples taken multiplied by the fraction of the tray that a subsample ring area covers (number of subsamples multiplied by (ring area divided by tray area)). Note that this is dependent on the tray and ring used for subsample analysis. Finally, the number of midges in an infall trap accounting for subsampling was calculated as the raw count of midges divided by the fraction subsampled (raw count divided by fraction subsampled).Other metrics such as total insects in meters squared per day, and total insect biomass in grams per meter squared day can be calculated with these data. In addition to the estimated average individual midge masses in grams, For 2008 through 2010 average midge masses were calculated as, Tanytarsus equal to .0001104 grams, Chironomus equal to .0010837 grams. For 2011 average midge masses were, Tanytarsus equal to .000182 grams, Chironomus equal to .001268 grams.

Methods

I. Study System Lake Mývatn, Iceland (65°36 N, 17°0′ W) is a large (38 km²) shallow (4 m max depth) lake divided into two large basins that function mostly as independent hydrologic bodies (Ólafsson 1979). The number of non-biting midge (Diptera: Chironomidae) larvae on the lake bottom is high, but variable: midge production between 1972-74 ranged from 14-100 g ash-free dw m^-2 yr^-1, averaging 28 g dw m^-2 yr^-1 (Lindegaard and Jónasson 1979). The midge assemblage is mostly comprised of two species (> 90% of total individuals), Chironomus islandicus (Kieffer) and Tanytarsus gracilentus (Holmgren) that feed as larvae in the sediment in silken tubes by scraping diatoms, algae, and detritus off the lake bottom (Lindegaard and Jónasson 1979). At maturity (May-August) midge pupae float to the lake surface, emerge as adults, and fly to land, forming large mating swarms around the lake (Einarsson et al. 2004, Gratton et al. 2008). On land, midges are consumed by terrestrial predators (Dreyer et al. 2012, Gratton et al. 2008), or enter the detrital pool upon death (Gratton et al. 2008, Hoekman et al. 2012). Midge populations naturally cycle with 5-8 year periodicity, with abundances fluctuating by 3-4 orders of magnitude (Einarsson et al. 2002, Ives et al. 2008). II. Midge Emergence Measurement We used submerged conical traps to estimate midge emergence from Lake Mývatn. Traps were constructed of 2 mm clear polycarbonate plastic (Laird Plastics, Madison, WI) formed into a cone with large-diameter opening of 46 cm (0.17 m²). The tops of the cones were open to a diameter of 10 cm, with a clear jar affixed at the apex. The trap was weighted to approximately neutral buoyancy, with the jar at the top containing air to allow mature midges to emerge. Traps were suspended with a nylon line ~1 m below the surface of the lake from an anchored buoy. For sampling, traps were raised to the surface and rapidly inverted, preventing midges from escaping. Jars and traps were thoroughly rinsed with lake water to collect all trapped midges, including unmetamorphosed larvae and pupae, and scrubbed before being returned to the lake to prevent growth of epiphytic algae and colonization by midges. We assume that the emergence traps collect all potentially emerging midges from the sampling area, though it is likely an underestimate, since some midges initially captured could fall out of the trap. Thus, our results should be considered a conservative estimate of potential midge emergence from the surface of the lake.We sampled midge emergence throughout the south basin of Lake Mývatn. Emergence was sampled at six sites in 2008 and 2011 and ten sites in 2009 and 2010, with locations relocated using GPS and natural sightlines. Each site had two traps within 5 m of each other that were monitored during midge activity, approximately from the last week of May to the first week of August. Midge emergence outside of this time frame is extremely low (Lindegaard & Jónasson 1979) and we assume it to be zero. Traps were checked weekly during periods of high emergence (initial and final 2-3 weeks of the study), and bi-weekly during low emergence periods in the middle of the study (July). III. Identification, Counts, and Conversions Midges were counted and identified to morphospecies, small and large. The midge (Diptera,Chrionomidae) assemblage at Mývatn is dominated by two species, Chironomus islandicus (Kieffer)(large, 1.1 mg dw) and Tanytarsus gracilentus (Holmgren)(small, 0.1 mg dw), together comprising 90 percent of total midge abundance (Lindegaard and Jonasson 1979). First, the midges collected in the infall traps were spread out in trays, and counted if there were only a few. Some midges were only identified to the family level of Simuliidae, and other arthropods were counted and categorized as the group, others. Arthropods only identified to the family level Simuliidae or classified as others were not dually counted as Chironomus islandicus or Tanytarsus gracilentus . If there were many midges, generally if there were hundreds to thousands, in an infall trap, subsamples were taken. Subsampling was done using plastic rings that were dropped into the tray. The rings were relatively small compared to the tray, about 2 percent of the area of a tray was represented in a ring. The area inside a ring and the total area of the trays were also measured. Note that different sized rings and trays were used in subsample analysis. These are as follows, trays, small (area of 731 square centimeters), “large1” (area of 1862.40 square centimeters), and large2 (area of 1247 square centimeters). Rings, standard ring (diameter of 7.30 centimeters, subsample area is 41.85 square centimeters) and small ring (diameter of 6.5 centimeters, subsample area is 33.18 square centimeters). A small ring was only used to subsample trays classified as type “large2.”The fraction subsampled was then calculated depending on the size of the tray and ring used for the subsample analysis. If the entire tray was counted and no subsampling was done then the fraction subsampled was assigned a value of 1.0. If subsampling was done the fraction subsampled was calculated as the number of subsamples taken multiplied by the fraction of the tray that a subsample ring area covers (number of subsamples multiplied by (ring area divided by tray area)). Note that this is dependent on the tray and ring used for subsample analysis. Finally, the number of midges in an infall trap accounting for subsampling was calculated as the raw count of midges divided by the fraction subsampled (raw count divided by fraction subsampled).Other metrics such as total insects in meters squared per day, and total insect biomass in grams per meter squared day can be calculated with these data. In addition to the estimated average individual midge masses in grams, For 2008 through 2010 average midge masses were calculated as, Tanytarsus equal to .0001104 grams, Chironomus equal to .0010837 grams. For 2011 average midge masses were, Tanytarsus equal to .000182 grams, Chironomus equal to .001268 grams.

Methods

Overview: Hard copy historical plat maps of Dane County in four year intervals from 1962to 2005 were digitized and converted to a GIS format using a process known as rectification, wherebycontrol points are set such that a point placed on the scanned image takes on the coordinates of thepoint chosen from the earliest GIS dataset, which for Dane County is from 2005. After a number ofcontrol points are set, the map is assigned the coordinates of the 2005 GIS dataset. In this way,the scanned plat map is now an image file with a distinct spatial location. Since the scanned platmaps do not have any attributes associated with the parcels, the third step is to assign attributesby working backwards from the 2005 GIS dataset. This process begins by making a copy of the 2005 GISdataset, then overlaying this new layer with the rectified scanned image. A subdivision choice isidentified where the parcel lines on the GIS layer are not in agreement with the scanned plat maps.The last step is to modify the copy of the 2005 GIS layer so that it matches the underlying plat map- in effect creating a historical GIS layer corresponding to the year of the plat map. When thelines that delineate a parcel appear in the GIS file but not the plat map, the multiple smallparcels in the 2005 GIS layer are merged together to represent the pre-subdivision parcel. Thisprocess is repeated for each historical year that plat maps are available. In the end, each timeperiod-1974 through 2000 in 4 year intervals-has a GIS file with all of the spatial attributes ofthe parcels.Land Atlas - Plat Books: The Land Atlas plat books were obtained for Dane County from theMadison Public Library, Stoughton Public Library and Robinson Map Library. With these materials onloan the pages were scanned at 150ppi in grayscale format; this process took place at the RobinsonMap Library. Once scanned, these images were georeferenced based on the 2000 digital parcel map.This process of rectification was done in Russell Labs using ESRI ArcMap 9.3. Control points such asroad intersections, were chosen to accurately georeference the 1997 scanned parcel map (1973 wasdone in this way as well). This process was done using a specific ArcGIS tool(View/Toolbars/Georeferencing). For the other years the scanned images were georeferenced based offthe four corners of the 1997 georeferenced scanned images. Georeferencing off the 1997 rectifiedimage allows for easier and quicker rectification but also facilitated detection of differencesbetween the scanned plats. The scanned image of the land ownership could be turned on and off foreasy comparison to the previous time set; these differences are the changes which were made on thedigital ownership map. We scanned and digitized the following years:Scanned plats: 1958, 1962, 1968, 1973, 1978, 1981, 1985, 1989, 1993, 1997, 2001, 2005Digitized plats: 1962, 1968, 1973, 1978, 1981, 1985, 1989, 1993, 1997, 2001, 2005Prepping the parcel Map: Digital parcel shapefiles for the years 2000 and 2005 wereprovided by the Dane County Land Information Office(http://www.countyofdane.com/lio/metadata/Parcels.htm) and were used as the starting reference.These datasets needed to be prepared for use. Many single parcels were represented by multiplecontiguous polygons. These were dissolved. (Multi-part, or non-contiguous polygons were notdissolved.) Here is the process to dissolve by NAME_CONT (contact name): Many polygons do not have acontact name. The majority of Madison and other towns do not have NAME_CONT, but most large parcelsdo. In order not to dissolve all of the parcels for which NAME_CONT is blank we did the following:Open the digital parcel shapefile and go to Selection/Select by Attributes. In this window choose thecorrect layer, chose method create new selection , scroll and double click NAME_CONT, then in thebottom both make sure it says [ "NAME_CONT" <>; ] (without brackets). This will select allpolygons which do not have an empty Name Contact attribute (empty value). From those polygonsselected they were aggregated based on the Name Contact field (parcels with the same NameContact were combined), where borders were contiguous. To do this the dissolve tool in DataManagement Tool/Generalization/Dissolve was used. Dissolve on field NAME_CONT and enter everyother field into the statistical fields menu. This was done without the multipart feature optionchecked, resulting in parcels only being combined when they share border. Keep these dissolvedpolygons highlighted. Once the dissolve process is complete use select by attributes tool again butthis time choose method of Add to Current Selection and say [ "NAME_CONT" = ]. This will provide adigital layer of polygons aggregated by name as well as nameless polygons to be manuallymanipulated.Parcel Map Manipulation: The goal from here was to, as accurately as possible, recreate adigital replica of the scanned parcel map, and aggregate up parcels with the same owner. This goalof replication is in regards to the linework as opposed to the owner name or any other informationin order to accurately capture the correct area as parcel size changed. This process of movingboundaries was independent of merging parcels. If individual scanned parcel boundaries are differentfrom the overlayed digital parcel shapefile, then the digital parcel linework must be changed. Asthis project utilizes both parcel shape and area, the parcels must be accurate. When mergingparcels, parcels with the same owner name, same owner connected on the plat map with an arrow, sameowner but separated by a road, or same owner and share a same point (two lots share a single pointat the corner) were merged to create a multi-part feature. Parcels with the same owner separated byanother parcel of a different owner with no points touching where not merged. This process ofreverse digitization was done using ArcMap. The already dissolved shapefile was copied to create onefile that was a historical record and one file to be edited to become the previous year (the nextyear back in time). With the digital parcel shapefile loaded, the rectified scanned plat maps werethen added. Once open, turn on the Editor Toolbar and Start Editing . The tools to use are thesketch tool and the merge tool. Quick keys where used (editor tool bar\customize) to speed thisprocess. To edit, zoom to a comfortable level (1:12,000) and slowly move across the townships in apattern which allows no areas to be missed (easiest to go township by township). When polygonsneeded to be reconstructed (the process of redrawing the parcel boundary linework), this was doneusing the sketch tool with either the create new polygon option or cut polygon option in theeditor toolbar. Using the sketch tool, with area highlighted, you can redraw the boundaries bycutting the polygons. Areas can be merged then recut to depict the underlying parcel map. If, forexample, a new development has gone in, many small parcels can be merged together to create a bigparcel, and then that large parcel can be broken into the parcels that were originally combined toform the subdivision. We can do this because the names in the attribute are not being preserved.This is a key note: THE OWNER NAME IS NOT A VARIABLE WE ARE CREATING, PRESERVING, OR OTHERWISEREPRESENTING. Once you merge the parcels, they will only maintain one of the names (and which nameis maintained is pretty much random). After the entire county is complete, go through again to checkthe new parcel shapefile, there will be mistakes. Snake through, going across the bottom one row ofsquares at a time. Examples of mistakes include primarily multi-part features that were exploded tochange one part, where the other parts would need to be re-merged. Another common correction arosebecause we typically worked on one township at a time, whereas ownership often crossed townships, soduring this second pass, we corrected cross-township ownership at the edges of the two scannedparcel maps. Finally, some roads which had been built into parcels (driveways) needed to be removedand these were not always caught during the first pass. Once the second run through is complete copythis shapefile so that it also has a back up.

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.