Methods

See Soranno, P.A., Bissell, E.G., Cheruvelil, K.S., Christel, S.T., Collins, S.M., Fergus, C.E., Filstrup, C.T., Lapierre, J.F., Lottig, N.R., Oliver, S.K., Scott, C.E., Smith, N.J., Stopyak, S., Yuan, S., Bremigan, M.T., Downing, J.A., Gries, C., Henry, E.N., Skaff, N.K., Stanley, E.H., Stow, C.A., Tan, P.-N., Wagner, T., and Webster, K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4 for details on how the observed values were obtained.
See Collins et al. Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales, Submitted to Ecological Applications, for details on data filtering and relationships between nutrient chemistry and landscape characteristics

Methods

Limnological water chemistry samples were collected through individual monitoring programs carried out or overseen by state agencies. Water chemistry analyses were performed using standard methods by individual labs. Methods for integrating the disparate state datasets are described in detail in Soranno et al. 2015. The data for each lake were selected from the most recent 10 yr period available – 2002-2011. The data were first aggregated within a year by taking the median (as well as the mean) of the values if there were more than one sample per year. Then, the data were aggregated for each lake across years by taking the median (and mean) of the annual medians (or means). We calculated the number of years for which there are data, which is included in the dataset, as well as the standard deviation. The geographic data was calculated using the LAGOS GIS toolbox, https://soranno.github.io/LAGOS_GIS_Toolbox/ using GIS datasets available at the US national scale (see the above citation for further details on data sources).

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

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

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

Methods

For methods, contact the Dane County Land Information Office: 608-266-439, lio.web.mail@co.dane.wi.us

Methods

Riparian buffer strips within the Lake Mendota watershed, created by buffering the Lake Mendota feature in the Wisconsin Department of Natural Resources waterbody layer.

Methods

Integrated U.S. Census and USGS National Land Cover Data to map the Federal Register definition of WUI (Federal Register 66:751, 2001).

Methods

Land use/land cover was interpreted from historical aerial photographs for selected watersheds in Dane County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.

Methods

Land use/land cover was interpreted from historical aerial photographs for selected watersheds in Dane County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.