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

Data were collected from two lakes, Sparkling Lake (46.008, -89.701) and Peter Lake (46.253, -89.504), both located in the northern highlands Lake District of Wisconsin and the Upper Peninsula of Michigan over a 10 day period on each lake in July and August of 2007. Refer to Van de Bogert et al. 2011 for limnological characteristics of the study lakes.Measurements of dissolved oxygen and temperature were made every 10 minutes using multiple sondes dispersed horizontally throughout the mixed-layer in the two lakes (n=35 sondes for Sparkling Lake and n=27 sondes for Peter Lake). Dissolved oxygen measurements were corrected for drift.Conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were also measured by a subset of sensors in each lake. Of the 35 sondes in Sparkling Lake, 31 were from YSI Incorporated: 15 of model 600XLM, 14 of model 6920, and 2 of model 6600). The remaining sondes placed in Sparkling Lake were 4 D-Opto sensors, Zebra-Tech, LTD. In Peter Lake, 14 YSI model 6920 and 13 YSI model 600XLM sondes were used.Sampling locations were stratified randomly so that a variety of water depths were represented, however, a higher density of sensors were placed in the littoral rather than pelagic zone. See Van de Bogert et al. 2012 for the thermal (stratification) profile of Sparkling Lake and Peter Lake during the period of observation, and for details on how locations were classified as littoral or pelagic. In Sparkling Lake, 11 sensors were placed within the shallowest zone, 12 in the off-shore littoral, and 6 in each of the remaining two zones, for a total of 23 littoral and 12 pelagic sensors. Similarly, 15 sensors were placed in the two littoral zones, and 12 sensors in the pelagic zone.Sensors were randomly assigned locations within each of the zones using rasterized bathymetric maps of the lakes and a random number generator in Matlab. Within each lake, one pelagic sensor was placed at the deep hole which is used for routine-long term sampling.Note that in Sparkling Lake this corresponds to the location of the long-term monitoring buoy. After locations were determined, sensors were randomly assigned to each location with the exception of the four D-Opto sensor is Sparkling Lake, which are a part of larger monitoring buoys used in the NTL-LTER program. One of these was located near the deep hole of the lake while the other three were assigned to random locations along the north shore, south shore and pelagic regions of the lake. Documentation: Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.

Methods

Ice and snow sampling was conducted weekly from 14 January to 30 March, 2010 on Lake Mendota when the ice was safe to walk on. A Kovacs Mark III core drill, manufactured by Ice Coring and Drilling Service (ICDS), Space Science and Engineering Center (SSEC) UW Madison, was used to collect ice cores. Snow depth was also measured at the locations where ice cores were sampled. All measurements were made in centimeters. Blue ice can be defined as the portion of the ice core that is strictly frozen lake water. White ice can be defined as “snow ice,” which occurs when water rushes through cracks in the ice and soaks the overlying snow, resulting in a mixture of ice and snow that subsequently freezes. Total ice is blue ice + snow ice. Finally, snow depth was calculated as the average of 10 snow depth samples at each sampling location.