Methods

See abstract for detailed description of datasources used. Processed with ArcGIS.

Methods

Yield data was collected at the time of harvest following the 2012 and 2013 growing season using a John Deere 9660 combine equipped with a Greenstar yield monitoring system. Yield data was cleaned by removing any polygons collected during pre-harvest check strips, turn-around at the end of rows, any polygons less than 6 m2, and any polygons where yield exceeded the record reported yield for Dane County WI (327 bu ac-1). Yield was normalized by calculating z-scores (the number of standard deviations away from the mean) within each field and each year. 2012 data was resampled to the 2013 polygon boundaries by taking the mean of all 2012 polygons with their centroid within each 2013 polygon. For each polygon, 2013 interpolated groundwater metrics were extracted using ArcMAP 10.2 software. A full description of this methodology is contained in Zipper et al. (in review).

Methods

We developed a database of water quality interventions by government agencies in the Yahara Watershed. Interventions were included if they were a) publicly funded and implemented, b) land-based, c) implemented within the 5-year period 2007 to 2012, and d) aimed to reduce nutrient (phosphorus and nitrogen) and sediment runoff to surface waters as a primary or secondary goal. Our criteria excluded interventions implemented directly in the water. They also excluded work by non-profit watershed groups and for-profit companies.Interventions were categorized by type of conservation tool: regulation or standard; incentive (grant and cost-share programs); direct management (including public management actions and engineered practices); and acquisition (land conserved through fee simple acquisition or conservation easement). Interventions were next categorized by which government level (or multiple levels) of government were involved in rulemaking and implementation (Table 1). We defined the rulemaking level of government as that which created the standard or wrote the law, and the implementing level of government as that which made field-level decisions, negotiated with landowners, and monitored practices. If the intervention was a grant given to private recipients, the implementing agencies were considered those that supervised grant implementation.We mapped policy interventions in ArcGIS (version 10.1). The goal of mapping was to determine the extent and overlap of interventions throughout the watershed and the agency responsible for establishing and implementing the policies. Public acquisitions of conservation land were mapped and categorized by the government level acquiring the parcel or parcels. Incentive programs – grants and cost-share – were mapped for the parcels where the incentive program was applied from 2007-2012. For federal Farm Bill Natural Resources Conservation Service (NRCS) conservation programs, for instance, the farm parcels of the cost-share recipients were mapped with publicly available data or by matching recipient names with parcel ownership records. Regulatory programs were mapped according to each statutes definition. For example, Wisconsins shoreland zoning ordinances were mapped as the area in the 300 meter buffer around rivers or streams and 1000 meter buffer of lakes or ponds, using the Wisconsin DNR water body base layer. Regulations were represented by specific permit area when permit data were available, such as farms with county winter manure-spreading permits.Regional water quality experts validated the interventions list and map. Reviewers included a regional planner, two municipal administrators, a commissioner on the County Lakes and Watershed Commission, a County water conservationist, a lawyer for an environmental non-profit, and the director of a Wisconsin soil and water conservation organization. Through this process we added several interventions and clarified the mapping rules. Analyses were conducted on 35 of 41 interventions that could be represented spatially through publicly available data. The most significant unmapped intervention was nutrient management planning, for which the County office did not have spatial data. We estimated the percent land area covered by each intervention by subwatershed. The Yahara Watershed was divided into 300 subwatersheds based on a recent modeling effort that delineated 200 subwatersheds in the upper Yahara Watershed (Montgomery Assoc., 2011) and our delineation of 100 comparably-sized subwatersheds based on a Digital Elevation Model in the lower Yahara Watershed. We then calculated the percentage of each subwatershed covered by each of the 35 interventions. The percentage of land covered by every intervention within a subwatershed was then summed to get a cumulative percent coverage. This ranged from 0 to a possible 3,500 for each subwatershed. The total percent intervention coverage is shown in heat maps depicting low to high policy coverage by subwatershed, created in ArcGIS.We categorized subwatersheds as urban or rural in order to compare coverage of interventions. Subwatersheds were classified as urban if the developed land cover classes were 50percent or more of land area, based on 2010 National Land Cover Data, which resulted in 83 urban (28percent) and 217 rural (72percent) subwatersheds. We conducted a Welch 2-sample t-test to determine whether cumulative percent area of interventions differed significantly for urban and rural subwatersheds. The untransformed cumulative percent area data were consistent with assumptions of normality and were not improved by an ArcSin transformation (sometimes used with percentage data), so we report the t-test with untransformed data.We compared intervention locations with total phosphorus yields (kilograms phosphorus per hectare per year) for the 200 subwatersheds modeled for the year 2008 with the Soil and Water Assessment Tool (SWAT). The Montgomery and Associates SWAT model is widely used by policymakers in the watershed. The subwatersheds with the highest nutrient yields are consistent with earlier models and measurements conducted for conservation planning (Lathrop, 2007).A Pearsons product-moment correlation matrix compared interventions with modeled phosphorus yield by subwatershed, calculated in R (version 3.0.1). We correlated phosphorus yields with cumulative percent intervention coverage by municipal, county, state, and federal governments in both their rulemaking and implementation capacities. We also compared the correlation of phosphorus yields with intervention coverage for each type of intervention tool. Interventions were also grouped by whether they targeted agricultural or nonagricultural activities. These correlations give a proxy measure of whether public interventions target areas of concern for watershed nutrient reduction.

Methods

The data set examined was the USGS National Hydrography Dataset (retrieved January 2012, http://nhd.usgs.gov). This combines multiple surveys through time, using the resulting high-resolution USGS topographical maps to delineate aquatic boundaries (Simley and Carswell, 2009). While a variety of resolutions are available, only 1 : 24 000 was used in this analysis. The data set covers the area of all 48 U.S. contiguous states, including Washington D.C. Because they are not completely contained within the U.S., we chose to exclude the Laurentian Great Lakes for this analysis.We extracted all lake, reservoir and pond polygons from the GIS data set. Because the data set does not distinguish between artificial or natural lakes and ponds, no distinction was made for this analysis, and all waterbodies hereafter are collectively referred to as lakes. The elevation component of these polygons was removed using ArcGIS (ESRI ArcGIS v10.1; ESRI, Redlands, CA, U.S.A). For simplicity, all island data were excluded from the results presented here, although we discuss the small resulting bias. Duplicate polygons were identified and removed using the permanent identifier field included with the data. All calculations were completed using the MathWorks Mapping Toolbox (v2010b; MathWorks, Natick, MA, U.S.A), which adds geographical information functionality to MATLAB. A single-point location for each lake was defined as the centroid of the lake boundary polygon.To examine perimeter while considering the issue of a fractal lake perimeter, we estimated perimeter using two complexity-insensitive and repeatable techniques. First, a theoretical minimum perimeter (Pmin) was established by calculating the perimeter of a circle with the same area as each lake using: Pmin = 2(pi(A))^(1/2) where A is the total area of the individual lake (Kalff, 2001). This represents the absolute minimum perimeter, on a flat plane, required to encompass a given area (spherical coordinates could reduce Pmin further, though for the size scale of lakes examined here, the difference is negligible). Second, a resolution-specific perimeter was calculated using a simple yardstick method based on Mandelbrot (1979). With this method, a fixed-length line segment was progressively "walked" along the polygon until reaching the start point. This simulated a perimeter estimate at specific and adjustable mapping resolutions. The yardstick length was varied across a range of values (25–1600 m) to examine the sensitivity of the perimeter estimate to measurement resolution. To compare the sensitivity of perimeter estimate with previous studies, the slope of the relationship between logperimeter and log-yardstick length was calculated using least-squares regression, based on Kent and Wong (1982). Lastly, we calculated the maximum-observed perimeter of each polygon (Pobs) using the full-resolution data and summing the lengths of each polygon segment. Shoreline development factor (SDF) was calculated using the equation: SDF = P / (2((A)pi)^1/2) where A is area and P is perimeter. The perimeter measurement technique used in SDF calculations (Pobs or the yardstick method) is indicated where discussed. Calculations of perimeters, area and centroid were fast for any single polygon, but because of the high number of lakes and high polygon resolution, calculations became computationally intensive. This and other geostatistical calculations were accelerated using a computer cluster. HTCondor software (Thain, Tannenbaum and Livny, 2005) was used to distribute this task.The extents of stream and river shorelines were calculated from the National Hydrography Dataset using the same technique as the fractal-naive perimeter (Pobs). Only features classified with a type of stream/river (USGS Feature no. 460) were included. This feature type includes intermittent, ephemeral and perennial streams and rivers, but excludes features such as underground streams and canals. The shoreline length of small streams, represented in the data set by polyline objects, was estimated as the length of the lines doubled to account for both sides of the stream. The shoreline of larger rivers, which are stored in the data set as polygons, was calculated directly as the observed perimeter of the polygons (Pobs) on the WGS84 datum.Maps of lake abundance (number km^-2), area (per cent cover) and shoreline density (m km^-2) were created by dividing the area of the U.S. into equal-area cells (cell size: 50 km^2). Each lake’s attributes were assigned to a cell based on its unique single-point location, and statistics were calculated for lake density and per cent cover in each cell. Where a lake’s area exceeded that of a single cell, the full lake shape was split into overlapping cells based on the amount of overlapping area. Cumulative distributions of lake number, area and perimeter as a function of lake area were used to evaluate general attributes of the entire U.S. lake population.To aid future work in this area, we have released useful derived data sets on the Web. While the data are freely available from the USGS, the National Hydrography Dataset’s large size makes analyses challenging. To encourage additional research in this area, we have released the extracted perimeter data set. It is available at the data repository hosted by the North Temperate Lakes Long-Term Ecological Research website at http://lter.limnology.wisc.edu

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.

Methods

Heads-up digitized bathymetric contour lines as shapefile line features in ArcGIS over scanned and georeferenced Wisconsin Department of Natural Resources lake survey maps.

Methods

For methods, see: http://www.census.gov/geo/www/tiger/

Methods

For methods, contact the Vilas County Mapping and Land Information Office: 715-479-3655, mapping@co.vilas.wi.us

Methods

For methods, see: http://www.census.gov/geo/www/tiger/