US Long-Term Ecological Research Network

Microbial Observatory at North Temperate Lakes LTER Spatial and temporal cyanobacterial population dynamics in Lake Mendota 2009 - 2011

Abstract
Toxic cyanobacterial blooms threaten freshwaters worldwide but have proven difficult to predict because the mechanisms of bloom formation and toxin production are unknown, especially on weekly time scales. Water quality management continues to focus on aggregated metrics, such as chlorophyll and total nutrients, which may not be sufficient to explain complex community changes and functions such as toxin production. For example, nitrogen (N) speciation and cycling play an important role, on daily time scales, in shaping cyanobacterial communities because declining N has been shown to select for N fixers. In addition, subsequent N pulses from N<sub>2</sub> fixation may stimulate and sustain toxic cyanobacterial growth. Herein, we describe how rapid early summer declines in N followed by bursts of N fixation have shaped cyanobacterial communities in a eutrophic lake (Lake Mendota, Wisconsin, USA), possibly driving toxic <em>Microcystis</em> blooms throughout the growing season. On weekly time scales in 2010 and *2011, we monitored the cyanobacterial community in a eutrophic lake using the phycocyanin intergenic spacer (PC-IGS) region to determine population dynamics. In parallel, we measured microcystin concentrations, N<sub>2</sub> fixation rates, and potential environmental drivers that contribute to structuring the community.
Core Areas
Dataset ID
299
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Field sample collection and processingAt each location, temperature, dissolved oxygen (DO), and pH were collected at 1 m increments from the surface to the maximum depth (YSI 556MPS). Photic zone depth was defined at 1percent of photosynthetically active radiation (PAR) as measured using a PAR sensor (LiCor 192SA). Integrated photic zone samples were then collected using a weighted 2-inch diameter polypropylene tube. Samples for DNA, nutrients, toxins, and pigment analyses were collected in acid-washed, sterile bottles, (rinsed three times with in situ water before collection) and stored on ice until further processing.Once transported back to the lab, samples were immediately processed. For dissolved reactive phosphorus (DRP), total dissolved phosphorus (TDP), total dissolved nitrogen (TDN), nitrate, and nitrite, 100 mL of water was filtered through a Whatman glass fiber filter (GForF) and frozen at -20 degreeC. For TP and total nitrogen (TN), HCl was added to 100mL of sample to a final concentration of 0.1percent and stored at -20 degreeC. Ammonium samples were immediately measured to avoid oxidation during freezing. Chlorophyll-a and phycocyanin samples were collected onto GForF filters and stored in black tubes at -20 degreeC. For community analysis (DNA), samples were filtered onto 0.2 &micro;m polyethersulfone membrane filters (Supor-200; Pall Corporation) and frozen at -20 degreeC until extraction. 20 mL of unfiltered water was preserved in formalin (3percent final concentration) and stored at room temperature in the dark for microscopy. An additional 50 mL of unfiltered water was stored at -20 degreeC for toxin analysis.Analytical measurementsDRP was measured by the ascorbic acid-molybdenum blue method 4500 P E (Greenberg et al. 1992). Ammonium was measured spectrophotometrically (Solórzano 1969). Nitrate and nitrite were measured individually using high-performance liquid chromatography (HPLC) (Flowers et al. 2009). TPorTDP and TNorTDN were digested as previously described (White et al. 2010), prior to analysis as for DRP and nitrate. For TDN and TN, the resulting solution was oxidized completely to nitrate and was measured via HPLC as above. Nitrate, nitrite, and ammonium were summed and reported as dissolved inorganic nitrogen (DIN).Phycocyanin was extracted in 20 mM sodium acetate buffer (pH 5.5) following three freeze-thaw cycles at -20 &ordm;C and on ice, respectively. The extract was centrifuged and then measured spectrophotometrically at 620 nm with correction at 650 nm (Demarsac and Houmard 1988). Chlorophyll-a (Chl-a) was extracted overnight at -20 &ordm;C in 90percent acetone and then measured spectrophotometrically with acid correction (Tett et al. 1975).For toxin analysis, whole water samples were lyophilized, resuspended in 5percent acetic acid, separated by solid phase extraction (SPE; Bond Elut C18 column, Varian), and eluted in 50percent methanol as previously described (Harada et al. 1988). Microcystin (MC) variants of leucine (L), arginine (R), and tyrosine (Y) were detected and quantified at the Wisconsin State Lab of Hygiene (SLOH) using liquid chromatography electrospray ionization tandem mass spectroscopy (API 3200, MSorMS) after separation by HPLC (Eaglesham et al. 1999). We report only MCLR concentrations since MCYR and MCRR were near the limit of quantification for the sampling period (0.01 &micro;g L-1).In situ N2 fixation measurementsN2 fixation rates were measured, with some modifications, following the acetylene reduction assay (Stewart et al. 1967). A fresh batch of acetylene was generated each day before sampling by combing 1 g of calcium carbide (Sigma Aldrich 270296) with 100 mL ddH2O. Following sample collection, 1 L of water was concentrated by gentle filtration onto a 47 mm GForF filter in the field. The filter was then gently washed into a 25 mL serum bottle using the lake water filtrate (final volumes 10 mL aqueous, 15 mL gas). Samples were spiked with 1 mL of acetylene gas and incubated in situ for two hours. The assay was terminated with 5percent final concentration trichloracetic acid and serum bottles were transported back to the lab. For each sampling period, rates were controlled and corrected for using a series of the following incubated acetylene blanks: 1) 1 mL of acetylene in filtrate alone, 2) 1 mL of acetylene in a killed sample, and 3) 1 mL of acetylene in ddH2O. Ethylene formed was measured by a gas chromatograph (GC; Shimadzu GC-8A) equipped with a flame ionization detector (FID), Porapak N column (80or100 mesh, 1or8OD x 6), and integrator (Hewlett Packard 3396) with N2 as the carrier gas (25 mL min-1 flow rate). Molar N2 fixation rates were estimated using a 1:4 ratio of N2 fixed to ethylene formed (Jensen and Cox 1983). All N2 fixation values are reported as integrated photic zone rates of &micro;g N L-1 hr-1.DNA extraction and processing of PC-IGS fragmentDNA was extracted from frozen filters using a xanthogenate-phenol-chloroform protocol previously described (Miller and McMahon 2011). For amplification of the phycocyanin intergenic spacer (PC-IGS) region, we used primers PCalphaR (5-CCAGTACCACCAGCAACTAA-3) and PCbetaF (5-GGCTGCTTGTTTACGCGACA-3, 6-FAM-labelled) and PCR conditions that were previously described (Neilan et al. 1995). Briefly, each 50 &micro;l reaction mixture contained 5 &micro;l of 10X buffer (Promega, Madison, WI), 2.5 &micro;l of dNTPs (5 mM), 2 &micro;l of forward and reverse primers (10 &micro;M), 2 &micro;l of template DNA, and 0.5 &micro;l of Taq DNA polymerase (5 U &micro;l-1). Following precipitation with ammonium acetate and isopropanol, the DNA pellet was resuspended in ddH2O and digested for 2 hrs at 37 &ordm;C using the MspI restriction enzyme, BSA, and Buffer B (Promega, Madison, WI). The digested product was precipitated and then resuspended in 20 &micro;L of ddH2O. 2 &micro;L of final product was combined with 10 &micro;L of formamide and 0.4 &micro;L of a custom carboxy-x-rhodamine (ROX) size standard (BioVentures, Inc).Cyanobacterial PC-IGS community fingerprinting and cell countsWe analyzed the cyanobacterial community using an automated phycocyanin intergenic spacer analysis (APISA) similar to the automated ribosomal intergenic spacer analysis (ARISA) previously described (Yannarell et al. 2003). Briefly, this cyanobacterial-specific analysis exploits the variable PC-IGS region of the phycocyanin operon (Neilan et al. 1995). Following MspI digestion, the variable lengths of the PC-IGS fragment can be used to identify subgenus level taxonomic units of the larger cyanobacterial community (Miller and McMahon 2011). The MspI fragments were sized using denaturing capillary electrophoresis (ABI 3730xl DNA Analyzer; University of Wisconsin Biotechnology Center (UWBC)). For each sample, triplicate electropherogram profiles were analyzed using GeneMarker&reg; (SoftGenetics) software v 1.5. In addition, a script developed in the R Statistics Environment was used to distinguish potential peaks from baseline noise (Jones and McMahon 2009, Jones et al. 2012). Relative abundance data output from this script were created using the relative proportion of fluorescence each peak height contributed per sample. Aligned, overlapping peaks were binned into subgenus taxonomic units (Miller and McMahon 2011). These taxa were named based on the genus and base pair length of the PC-IGS fragment identified (e.g. For Mic215, Mic = Microcystis and 215 = 215 base pair fragment). Fragment lengths were matched to an in silico digested database of PC-IGS sequences using the Phylogenetic Assignment Tool (https:ororsecure.limnology.wisc.eduortrflpor).The NTL-LTER program collects biweekly phytoplankton samples between April and September for cell counts and detailed descriptions of the field and laboratory protocols are available online at http:ororlter.limnology.wisc.edu. When indicated, biomass has been converted to mg L-1 using the biovolume calculated during the cell count process and assuming a density equivalent to water.
Version Number
22

Historical Plat Maps of Dane County Digitized and Converted to GIS (1962-2005)

Abstract
We constructed a time-series spatial dataset of parcel boundaries for the period 1962-2005, in roughly 4-year intervals, by digitizing historical plat maps for Dane County and combining them with the 2005 GIS digital parcel dataset. The resulting datasets enable the consistent tracking of subdivision and development for all parcels over a given time frame. The process involved 1) dissolving and merging the 2005 digital Dane County parcel dataset based on contiguity and name, 2) further merging 2005 parcels based on the hard copy 2005 Plat book, and then 3) the reverse chronological merging of parcels to reconstruct previous years, at 4-year intervals, based on historical plat books. Additional land use information such as 1) whether a structure was actually constructed (using the companion digitized aerial photo dataset), 2) cover crop, and 3) permeable surface area, can be added to these datasets at a later date.
Dataset ID
291
Date Range
-
Maintenance
Completed
Metadata Provider
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 [ &quot;NAME_CONT&quot; &lt;&gt;; ] (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 [ &quot;NAME_CONT&quot; = ]. 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.
Purpose
<p>Our purpose was to forecast detailed empirical distributions of the spatial pattern of land-use and ecosystem change and to test hypotheses about how economic variables affect land development in the Yahara watershed.</p>
Quality Assurance
<p>Accuracy was double check by visually comparing against corresponding plat book twice.</p>
Short Name
Historical Plat Maps of Dane County
Version Number
14

WSC 2006 Spatial interactions among ecosystem services in the Yahara Watershed

Abstract
Understanding spatial distributions, synergies and tradeoffs of multiple ecosystem services (benefits people derive from ecosystems) remains challenging. We analyzed the supply of 10 ecosystem services for 2006 across a large urbanizing agricultural watershed in the Upper Midwest of the United States, and asked: (i) Where are areas of high and low supply of individual ecosystem services, and are these areas spatially concordant across services? (ii) Where on the landscape are the strongest tradeoffs and synergies among ecosystem services located? (iii) For ecosystem service pairs that experience tradeoffs, what distinguishes locations that are win win exceptions from other locations? Spatial patterns of high supply for multiple ecosystem services often were not coincident locations where six or more services were produced at high levels (upper 20th percentile) occupied only 3.3 percent of the landscape. Most relationships among ecosystem services were synergies, but tradeoffs occurred between crop production and water quality. Ecosystem services related to water quality and quantity separated into three different groups, indicating that management to sustain freshwater services along with other ecosystem services will not be simple. Despite overall tradeoffs between crop production and water quality, some locations were positive for both, suggesting that tradeoffs are not inevitable everywhere and might be ameliorated in some locations. Overall, we found that different areas of the landscape supplied different suites of ecosystem services, and their lack of spatial concordance suggests the importance of managing over large areas to sustain multiple ecosystem services. <u>Documentation</u>: Refer to the supporting information of the follwing paper for full details on data sources, methods and accuracy assessment: Qiu, Jiangxiao, and Monica G. Turner. &quot;Spatial interactions among ecosystem services in an urbanizing agricultural watershed.&quot; <em>Proceedings of the National Academy of Sciences</em> 110.29 (2013): 12149-12154.
Contact
Dataset ID
290
Date Range
-
Maintenance
completed
Metadata Provider
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 tons<sup> </sup>per 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 mg<sup> </sup>per 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<sub>p</sub>)<sup>0.56</sup> * K * LS * C * Pwhere Sed represents the amount of sediment that is transported downstream network (metric tons), Q is the surface runoff volume (m<sup>3</sup>), q<sub>p </sub>is 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.
Short Name
Ecosystem services in the Yahara Watershed
Version Number
20

LTREB Biological Limnology at Lake Myvatn 2012-current

Abstract
These data are part of a long-term monitoring program in the central part of Myvatn that represents the dominant habitat, with benthos consisting of diatomaceous ooze. The program was designed to characterize import benthis and pelagic variables across years as midge populations varied in abundance. Starting in 2012 samples were taken at roughly weekly inervals during June, July, and August, which corresponds to the summer generation of the dominant midge,<em>Tanytarsus gracilentus</em>.
Creator
Dataset ID
296
Date Range
-
Maintenance
Ongoing
Metadata Provider
Methods
Benthic Chlorophyll Field sampling (5 samples) (2012, 2013)1. Take 5 cores from the lake2. Cut the first 0.75 cm (1 chip) of the core with the extruder and place in deli container. Label with date and core number.3. Place deli containers into opaque container (cooler) and return to lab. This is the same sample that is used for the organic matter analysis.In 2014, the method for sampling benthic chlorophyll changed. The calculation of chlorophyll was changed to reflect the different area sampled. Below is the pertinent section from the methods protocols. Processing after the collection of the sample was not changed.Take sediment samples from the 5 cores collected for sediment characteristics. Take 4 syringes of sediment with 10mL syringe (15.96mm diameter). Take 4-5cm of sediment. Then, remove bottom 2cm and place top 2cm in the film canister.Filtering1. Measure volume of material in deli container with 60mL syringe and record.2. Homogenize and take 1mL sample with micropipette. The tip on the micropipette should be cut to avoid clogging with diatoms. Place the 1mL sample in a labeled film canister. Freeze sample at negative 20 degrees Celsius unless starting methanol extraction immediately.3. Add 20mL methanol. This methanol can be kept cool in the fridge, although then you will need a second bottle of methanol for the fluorometer. Shake for 5 sec.4. After 6-18 hours, shake container for 5 sec.Fluorometer1. Allow the film canisters to sit at room temperature for approximately 15 min to avoid excessive condensation on the glass tubes. Shake tubes for 5 sec after removing from fridge but then be careful to let them settle before removing sample.2. Record the sample information for all of the film canisters on the data sheet.3. Add 4mL of sample to a 13x100mL glass tube.4. Insert the sample into the fluorometer and record the reading in the Fluor Before Acid column. The sample reading should be close to one of the secondary solid standards (42ug/L or 230ug/L), if not, dilute the sample to within 25 per cent of the secondary solid standards (30-54ug/L or 180-280ug/L). It is a good idea to quickly check 2mL of a sample that is suspected to be too high to get an idea if other samples may need to be diluted. If possible, read the samples undiluted.5. If a sample needs to be diluted, use a 1000 microLiter pipette and add 2mL of methanol to a tube followed by 2mL of undiluted sample. Gently invert the tube twice and clean the bottom with a paper towel before inserting it into the fluorometer. If the sample is still outside of the ranges above, combine 1 mL of undiluted sample with 3 mL of methanol. Be sure to record the dilution information on the data sheet.6. Acidify the sample by adding 120microLiters of 0.1 N HCl (30microLiters for every one mL of sample). Then gently invert the sample and wait 90 seconds (we used 60 seconds in 2012, the protocol said 90) before putting the sample into the fluorometer and recording the reading in the Fluor After Acid column. Be sure to have acid in each tube for exactly the same amount of time. This means doing one tube at a time or spacing them 30-60 seconds apart.7. Double check the results and redo samples, which have suspicious numbers. Make sure that the after-acidification values make sense when compared to the before acidification value (the before acid/after acid ratio should be approximately the same for all samples).Clean up1. Methanol can be disposed of down the drain as long as at least 50 times as much water is flushed.2. Rinse the film canisters and lids well with tap water and scrub them out with a bottle brush making sure to remove any remaining filter paper. Give a final rinse with distilled water. Pelagic Chlorophyll Field sampling (5 samples)1. Take 2 samples at each of three depths, 1, 2, and 3m with Arni&rsquo;s zooplankton trap. For the 1m sample, drop the trap to the top of the chain. Each trap contains about 2.5L of water when full. 2. Empty into bucket by opening the bottom flap with your hand.3. Take bucket to lab.Filtering1. Filter 1L water from integrated water sample (or until the filter is clogged) through the 47 mm GF/F filter. The pressure used during filtering should be low ( less than 5 mm Hg) to prevent cell breakage. Filtering and handling of filters should be performed under dimmed lighting.2. Remove the filter with forceps, fold it in half (pigment side in), and put it in the film canister. Take care to not touch the pigments with the forceps.3. Add 20mL methanol. This methanol can be kept cool in the fridge, although then you will need a second bottle of methanol for the fluorometer. Shake for 5 sec. and place in fridge.4. After 6-18 hours, shake container for 5 sec.5. Analyze sample in fluorometer after 24 hours.Fluorometer1. Allow the film canisters to sit at room temperature for approximately 15 min to avoid excessive condensation on the glass tubes. Shake tubes for 5 sec after removing from fridge but then be careful to let them settle before removing sample.2. Record the sample information for all of the film canisters on the data sheet.3. Add 4mL of sample to a 13x100mL glass tube.4. Insert the sample into the fluorometer and record the reading in the Fluor Before Acid column. The sample reading should be close to one of the secondary solid standards (42ug/L or 230ug/L), if not, dilute the sample to within 25 percent of the secondary solid standards (30-54ug/L or 180-280ug/L). It is a good idea to quickly check 2mL of a sample that is suspected to be too high to get an idea if other samples may need to be diluted. If possible, read the samples undiluted.5. If a sample needs to be diluted, use a 1000uL pipette and add 2mL of methanol to a tube followed by 2mL of undiluted sample. Gently invert the tube twice and clean the bottom with a paper towel before inserting it into the fluorometer. If the sample is still outside of the ranges above, combine 1 mL of undiluted sample with 3 mL of methanol. Be sure to record the dilution information on the data sheet.6. Acidify the sample by adding 120 microLiters of 0.1 N HCl (30 microLiters for every one mL of sample). Then gently invert the sample and wait 90 seconds (we used 60 seconds in 2012, the protocol said 90) before putting the sample into the fluorometer and recording the reading in the Fluor After Acid column. Be sure to have acid in each tube for exactly the same amount of time. This means doing one tube at a time or spacing them 30-60 seconds apart.7. Double check the results and redo samples, which have suspicious numbers. Make sure that the after-acidification values make sense when compared to the before acidification value (the before acid/after acid ratio should be approximately the same for all samples).Clean up1. Methanol can be disposed of down the drain as long as at least 50 times as much water is flushed.2. Rinse the film canisters and lids well with tap water and scrub them out with a bottle brush making sure to remove any remaining filter paper. Give a final rinse with distilled water. Pelagic Zooplankton Counts Field samplingUse Arni&rsquo;s zooplankton trap (modified Schindler) to take 2 samples at each of 1, 2, and 3m (6 total). For the 1m sample, drop the trap to the top of the chain. Each trap contains about 2.5L of water when full. Integrate samples in bucket and bring back to lab for further processing.Sample preparation in lab1. Sieve integrated plankton tows through 63&micro;m mesh and record volume of full sample2. Collect in Nalgene bottles and make total volume to 50mL3. Add 8 drops of lugol to fix zooplankton.4. Label bottle with sample date, benthic or pelagic zooplankton, and total volume sieved. Samples can be stored in the fridge until time of countingCounting1. Remove sample from fridge2. Sieve sample with 63 micro meter mesh over lab sink to remove Lugol&rsquo;s solution (which vaporizes under light)3. Suspend sample in water in sieve and flush from the back with squirt bottle into counting tray4. Homogenize sample with forceps or plastic pipette with tip cut off5. Identify (see zooplankton identification guide) using backlit microscope and count with multiple-tally counter. i. Set magnification so that you can see both top and bottom walls of the tray. ii. Change focus depth to check for floating zooplankton that must be counted as well.6. Pipette sample back into Nalgene bottle, add water to 50mL, add 8 drops Lugol&rsquo;s solution, and return to fridgeSubsamplingIf homogenized original sample contains more than 500 individuals in the first line of counting tray, you may subsample under the following procedure.1. Return original sample to Nalgene bottle and add water to 50mL2. Homogenize sample by swirling Nalgene bottle3. Collect 10mL of zooplankton sample with Hensen-Stempel pipette4. Empty contents of Hensen-Stempel pipette into large Bogorov tray5. Homogenize sample in tray with forceps or plastic pipette with tip cut off6. Identify (see zooplankton identification guide) using backlit microscope and count with multiple-tally counter. i. Set magnification so that you can see both top and bottom walls of the tray. ii. Change focus depth to check for floating zooplankton that must be counted, too! 7. Pipette sample back into Nalgene bottle, add water to 50mL, add 8 drops Lugol&rsquo;s solution, and return to fridge Benthic Microcrustacean Counts Field samplingLeave benthic zooplankton sampler for 24h. Benthic sampler consists of 10 inverted jars with funnel traps in metal grid with 4 feet. Set up on bench using feet (on side) to get a uniform height of the collection jars (lip of jar = 5cm above frame). Upon collection, pull sampler STRAIGHT up, remove jars, homogenize in bucket and bring back to lab. Move the boat slightly to avoid placing sampler directly over cored sediment.Sample preparation in lab1. Sieve integrated samples through 63 micrometer mesh and record volume of full sample2. Collect in Nalgene bottles and make total volume to 50mL3. Add 8 drops of lugol to fix zooplankton.4. Label bottle with sample date, benthic or pelagic zooplankton, and total volume sieved. Samples can be stored in the fridge until time of countingCounting1. Remove sample from fridge2. Sieve sample with 63 micrometer mesh over lab sink to remove Lugol&rsquo;s solution (which vaporizes under light)3. Suspend sample in water in sieve and flush from the back with squirt bottle into counting tray4. Homogenize sample with forceps or plastic pipette with tip cut off5. Identify (see zooplankton identification guide) using backlit microscope and count with multiple-tally counter. i. Set magnification so that you can see both top and bottom walls of the tray. ii. Change focus depth to check for floating zooplankton that must be counted, too!6. Pipette sample back into Nalgene bottle, add water to 50mL, add 8 drops Lugol&rsquo;s solution, and return to fridgeSubsamplingIf homogenized original sample contains more than 500 individuals in the first line of counting tray, you may subsample under the following procedure.1. Return original sample to Nalgene bottle and add water to 50mL2. Homogenize sample by swirling Nalgene bottle3. Collect 10mL of zooplankton sample with Hensen-Stempel pipette4. Empty contents of Hensen-Stempel pipette into large Bogorov tray5. Homogenize sample in tray with forceps or plastic pipette with tip cut off6. Identify (see zooplankton identification guide) using backlit microscope and count with multiple-tally counter. i. Set magnification so that you can see both top and bottom walls of the tray. ii. Change focus depth to check for floating zooplankton that must be counted, too! 7. Pipette sample back into Nalgene bottle, add water to 50mL, add 8 drops Lugol&rsquo;s solution, and return to fridge Chironomid Counts (2012, 2013) For first instar chironomids in top 1.5cm of sediment only (5 samples)1. Use sink hose to sieve sediment through 63 micrometer mesh. You may use moderate pressure to break up tubes.2. Back flush sieve contents into small deli container.3. Return label to deli cup (sticking to underside of lid works well).For later instar chironomids in the section 1.5-11.5cm (5 samples)4. Sieve with 125 micrometer mesh in the field.5. Sieve through 125micrometer mesh again in lab to reduce volume of sample.6. Transfer sample to deli container or pitfall counting tray.For all chironomid samples7. Under dissecting scope, pick through sieved contents for midge larvae. You may have to open tubes with forceps in order to check for larvae inside.8. Remove larvae with forceps while counting, and place into a vial containing 70 percent ethanol. Larvae will eventually be sorted into taxonomic groups (see key). You may sort them into taxonomic groups as you pick the larvae, or you can identify the larvae while measuring head capsules if chironomid densities are low (under 50 individuals per taxanomic group).9. For a random sample of up to 50 individuals of each taxonomic group, measure head capsule, see Chironomid size (head capsule width).10. Archive samples from each sampling date together in a single 20mL glass vial with screw cap in 70 percent ethanol and label with sample contents , Chir, sample date, lake ID, station ID, and number of cores. Chironomid Cound (2014) In 2014, the method for sampling chironomid larvae changed starting with the sample on 2014-06-27; the variable &quot;top_bottom&quot; is coded as a 2. In contrast to previous measurements, the top and bottom core samples were combined and then subsampled. Below is the pertinent section of the protocols.Chironomid samples should be counted within 24 hours of collection. This ensures that larvae are as active and easily identified as possible, and also prevents predatory chironomids from consuming other larvae. Samples should be refrigerated upon returning from the field.<strong>For first instar chironomids in top 1.5cm of sediment only (5 samples)</strong>1. Use sink hose to sieve sediment through 63&micro;m mesh. You may use moderate pressure to break up tubes.2. Back flush sieve contents using a water bottle into small deli container.3. Return label to deli cup (sticking to underside of lid works well).<strong>For larger instar chironomids in the section 1.5-11.5cm (5 samples)</strong>4. Sieve with 125&micro;m mesh in the field.5. Sieve through 125&micro;m mesh again in lab to reduce volume of sample and break up tubes.6. Transfer sample to deli container with the appropriate label.<strong>Subsample if necessary</strong>If necessary, subsample with the following protocol.a. Combine top and bottom samples from each core (1-5) in midge sample splitter.b. Homogenize sample thoroughly, collect one half in deli container, and label container with core number and &ldquo;1/2&rdquo;c. If necessary, split the half that remains in the sampler into quarters, and collect each in deli containers labeled with core number, &ldquo;1/4&rdquo;, and replicate 1 or 2d. Store all deli containers in fridge until counted, and save until all counting is complete&quot; Chironomid Size (head capsule width) 1. Obtain picked samples preserved in ethanol and empty onto petri dish.2. Sort larvae by family groups, arranging in same orientation for easy measurment.3. Set magnification to 20, diopter, x 50 times4. Take measurments for up to 50 or more individuals of each taxa. Round to nearest optical micrometer unit.5. Fill out data sheet for number of larvae in each taxa, Chironomid measurements for each taxa, date of sample, station sample was taken from, which core the sample came from, who picked the core, and your name as the measurer.6. Enter data into shared sheetSee &quot;Chironomid Counts&quot; for changes in sampling chironomid larvae in 2014.
Version Number
17

Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Zooplankton Presence/Absence 2001 - 2004

Abstract
Zooplankton samples were taken at approximately the deepest part of 58 lakes included in the &quot;cross-lake comparison&quot; segment of the Biocomplexity Project. The samples were from years 2001 through 2004. The study lakes are located in Vilas County, Wisconsin and were chosen to represent a range of positions on gradients of both human development and landscape position. Zooplankton samples were analyzed for planktonic crustacean and insect species. Number of sites: 58 Sampling Frequency: each site sampled once
Core Areas
Dataset ID
208
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Wisconsin Net samplesLower the Wisconsin net to the bottom sample depth ( top of the net should be one meter above the bottom). Pull it up slowly at a rate of about 3 seconds per meter. A slow haul prevents the net from pushing water and plankton away from the mouth of the net. To drain the cup swirl it until the water level is below the lower mesh window, then pour contents into the sample jar. Avoid inverting the cup while swirling, as you will lose the sample into the net. Rinse the inside of the cup with 95percent ETOH several times adding the rinse to the sample jar. Wait until the chemistry crew member is finished taking Temp or D.O. profile before taking the Wisconsin net sample, so as not to stir up the sediments. Take replicate sample.
Short Name
BIOZOOP1
Version Number
7

Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Riparian Plots 2001 - 2004

Abstract
Living and dead trees and abiotic and anthropogenic characteristics of the shoreline were surveyed at 488 sites around lakes in Vilas County. These data were collected as part of the &quot;cross-lake comparison&quot; segment of the Biocomplexity Project (Landscape Context - Coordinated Field Studies). The study explored the links between terrestrial and aquatic systems across a gradient of residential development and lake landscape position. Specifically, this project attempted to relate the abundance of coarse wood in the littoral zone with abiotic, biotic and anthropogenic features of the adjacent shore. At each of the 488 sites, three 100 sq m plots, extending from the shoreline 10 m inland, were sampled. Additional plots farther inland were sampled at some sites. At each plot the survey team recorded the general appearance of the plot, measured all trees at least 5 cm dbh, measured and described downed wood and snags at least 10 cm in diameter, and recorded any overhanging trees. Saplings (at least 30 cm tall, but less than 5 cm dbh) were counted in two 5m x 5m plots per site. Sampling Frequency: each site sampled once Number of sites: 488 sites on 61 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year; eight sites per lake).Allequash Lake, Anvil Lake, Arrowhead Lake, Bass Lake, Big Lake, Birch Lake, Ballard Lake, Big Muskellunge Lake, Black Oak Lake, Big Portage Lake, Brandy Lake, Big St Germain Lake, Camp Lake, Crab Lake, Circle Lily, Carpenter Lake, Day Lake, Eagle Lake, Erickson Lake, Escanaba Lake, Found Lake, Indian Lake, Jag Lake, Johnson Lake, Jute Lake, Katinka Lake, Lake Laura, Little Croooked Lake, Little Spider Lake, Little St Germain Lake, Little Crawling Stone Lake, Little John Lake, Lac Du Lune Lake, Little Rock Lake - North, Lost Lake, Little Rock Lake - South, Little Star Lake, Little Arbor Vitae Lake, Lynx Lake, Mccollough Lake, Moon Lake, Morton Lake, Muskellunge Lake, Nebish Lake, Nelson Lake, Otter Lake, Oxbow Lake, Palmer Lake, Pioneer Lake, Pallete Lake, Papoose Lake, Round Lake, Star Lake, Sparkling Lake, Spruce Lake, Stormy Lake, Twin Lake South, Tenderfoot Lake, Towanda Lake, Upper Buckatabon Lake, Vandercook Lake, White Sand Lake, Vilas County, WI, USA
Dataset ID
126
Date Range
-
LTER Keywords
Maintenance
completed
Metadata Provider
Methods
Riparian samplingPREPARATIONDatasheet packets:Each lake has 8 survey sites.One packet per site:3 10m x 10m riparian zone plot data sheets1 Sapling plot or General Site Info data sheetFor 2 of the 8 sites, packets will need to include 2 riparian subzone data sheets.Weather can be highly variable. Data sheets should be printed on write in rain paper.Survey site selections:8 Sites per lake will be selected using GIS software.Subzones: To look at the effects of wind, sun, and fetch; select 2 of the 8 sites for additional subzone surveys. One site must be located in the NW quarter of the lake and the other in the SE. Within each of these 2 chosen sites, randomly select a 10m x 10m subzone plot in zone 2 and another 10m x 10m subzone plot in zone 3. (See figure 1).Sapling plots: At each site, two 5m x 5m sapling plots should be randomly selected within plots A, C, andoror E (Refer to figure 3).EQUIPMENT LISTClipboard, data sheet packets, lake and site maps, pencils, watch, compass, 50m measuring tapes, Diameter tapes (fabric and combination tapes), flagging, GPS unit,Oars, cushions and vests, motor, gas. Appropriate rain gear and boots.FIELD DATA COLLECTIONRecord the lake name, site number, plot number, date, observers, start and stop time.Collect a GPS point at the start of each of the 8 survey sites (plot A).timesIf the site has to be relocated due to denied permissions, mark new location on lake maps.Prepare Survey Plots:Each site is 30m x 50m in size. Five 10mx10m plots along shoreline are the zone 1 survey plots. Subzones are located in Zones 2 and 3. Plots should never overlap.Set up plots (A, C, E)Facing the selected site location (looking from the water towards shore), plot A is on the left, C and E are to the right of A respectively.Mark the sites starting point (with a flag and a GPS point). Using a meter tape to place flags at 10m increments along the shorelines ordinary high water mark (0m, 10m, 20m, 30m, 40m, 50m).For each 10x10 plot, determine the shoreline aspect, then use a compass and meter tape to place corner flags back 10 meters from shore so that each plot is square.Record the slope and aspect (perpendicular to shore) for the start of plots A, C, and E. This will represent the hills steepness and direction.Recording Data:General site info:Site information must be recorded for all 5 plots (A, B, C, D, and E)Record ownership (public or private).List the number of docks and buildings &ndash;count them only once if they cross into 2 plots.Presenceorabsence information &ndash; Using the list provided, check anything that is present, or list it as other. Record what is dominant. There are 2 parts to the General site info list:Qualitative assessment of habitat (forest stands, herbaceous, wetlands, etc).Human development andoror disturbance.FOR PLOTS A, C, and E:Live Trees:Record the species and diameter at breast height (DBH) for every living tree that is larger or equal to 5cm DBH (other woody plants having a greater than or equal to 5cm DBH should also be recorded).Diameter at breast height: Since trees are swelled at the base, measurements are made 4.5 feet (1.37 meters) above the ground in order to give an average diameter estimate.Trees on plot edge: Sometimes trees will be questionable as to whether they are in or out of the plot. Good rule of thumb is a 50percent cut off. If the tree is more than 50percent within the plot, count it. Do not count 1 tree in more than one plot!Standing snags: A snag is a (or part of a) dead standing tree taller than 1.37 meters (DBH). If a snag is greater than or equal to 10cm DBH then record type (snag), type of break (natural, un-natural, beaver), species (if known), DBH, and branchiness (0-3).Stumps: A stump is dead tree cut or broken off below 1.37 meters (DBH). Record stumps that are greater than or equal to 10cm in diameter. Take the diameter at the base of the stump but above the root mass. Record type (stump), type of break (natural, un-natural, beaver), species (if known), and diameter at base. Branchiness is assumed to be 0.Coarse Woody Debris (CWD) in Riparian zone:For this study, CWD is considered any logs greater than or equal to 10cm in diameter and greater than or equal to 150cm in length.Record type (log) and type of break (natural, un-natural, beaver, unknown). Record the species type (species, conifer, hardwood, or unknown), the diameter at base, and log length from base to longest branch tip.Record Branchiness (0-3). Where 0 is no branches, 1 is few, 2 is moderate, and 3 is many branches.Record Decay (0-5). Where 0 is a live tree touching the ground at two or more points, 1 is recent downwood (e.g. lacking litter or moss cover), 2 is downwood with litterorhumus or moss cover; bark sound, 3 is bark sloughing from wood; wood still sound, 4 is downwood mostly barkless; staubs loosening; wood beginning to decay; logs becoming oval and in contact with the ground along most of their length, and 5 is decay advanced; pieces of wood blocky and softened; logs becoming elliptically compressed. timestimes NOTE: paper birch retains its bark long after the wood has rotted, score logs of this species by the softness of the wood, not the presenceorabsence of bark. timestimesAdditional parameters:If a log extends out of a plot, record its entire length and measure diameter at the base regardless of whether the base is inside or outside of the plot.If a log crosses into more than one plot, record the entire length and measure diameter at the base, but record log only in the plot where the base is (if the base is outside of the site, then record in the plot closest to the base).Paper birch: often are broken into many small parts. If segments are still in line (no more than ~5 cm separating them), then you can count breaks as a single log.Logs that extend over the water are measured only from the base to the shoreline and listed in notes as measured to water.For each site, Two 5m x 5m sapling plots are randomly selected in plots A, C, andoror E. Use the numbering scheme depicted in graphic.Use compass and meter tape to setup and mark square plots using the original plot aspect.For each sapling plot, count and record all tree saplings greater than 30 centimeters in height but having less than a 5 cm DBH.Subzones:Subzone plot data are recorded the same as plot data.Refer to figure 1 to set up random subplots at 2 of the 8 sites at a lake. Use compass and meter tape to setup and mark square subplots. Use the original plot aspect when possible.For each square 10m x 10m subplot (one in zone 2 and one in zone 3) record slope and aspect.Record all live trees that have greater than or equal to 5cm DBH. Record all stumps greater than or equal to 10cm DBH and snags greater than or equal to 10cm diameter at base. Record logs greater than or equal to 10cm in diameter and greater than or equal to 150cm in length.
Short Name
BIORPLOT
Version Number
9

Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Secchi Disk Depth 2001 - 2004

Abstract
Chemical Limnology data collected for Biocomplexity Project; Landscape Context - Coordinated Field Studies Replicate chemical samples were pumped from the surface water (0.5m depth) and secchi depth was recorded at each lake. Temperature/dissolved oxygen profiles were taken throughout the water column at one meter intervals on all lakes. For more detail see the Water Sampling Protocol. Sampling Frequency: During 2001, temperature/dissolved oxygen profiles and secchi depths were taken twice during the stratified summer period. Chemistry samples were only taken once during the 2001 stratified period. From 2002 through 2004, all chemical and physical water samples were taken once during June (or resampled during the stratified period if June samples were bad). All lakes in which color, DIC/DOC, and chlorophyll samples were taken in 2001 were resampled in 2002 due to error in collection and/or analysis. Number of sites: 62 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year).Allequash Lake, Anvil Lake, Arrowhead Lake, Bass Lake, Big Lake, Birch Lake, Ballard Lake, Big Muskellunge Lake, Black Oak Lake, Big Portage Lake, Brandy Lake, Big St Germain Lake, Camp Lake, Crab Lake, Circle Lily, Carpenter Lake, Day Lake, Eagle Lake, Erickson Lake, Escanaba Lake, Found Lake, Indian Lake, Jag Lake, Johnson Lake, Jute Lake, Katinka Lake, Lake Laura, Little Croooked Lake, Little Spider Lake, Little St Germain Lake, Little Crawling Stone Lake, Little John Lake, Lac Du Lune Lake, Little Rock Lake - North, Lost Lake, Little Rock Lake - South, Little Star Lake, Little Arbor Vitae Lake, Lynx Lake, Mccollough Lake, Moon Lake, Morton Lake, Muskellunge Lake, Nebish Lake, Nelson Lake, Otter Lake, Oxbow Lake, Palmer Lake, Pioneer Lake, Pallete Lake, Papoose Lake, Round Lake, Star Lake, Sparkling Lake, Spruce Lake, Stormy Lake, Twin Lake South, Tenderfoot Lake, Towanda Lake, Upper Buckatabon Lake, Vandercook Lake, White Sand Lake, Vilas County, WI, USA
Dataset ID
44
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Lower the Secchi into the water on the shady side of the boat. Lower the disk until you cannot see it; record this depth as the down reading. Raise the disk until you can again see it; record this depth as the up reading.
Short Name
BIOSECH1
Version Number
6

Biocomplexity at North Temperate Lakes LTER: Coordinated Field Studies: Riparian Littoral Sites 2001 - 2004

Abstract
General descriptive data for sites sampled as part of the &quot;cross-lake comparison&quot; segment of the Biocomplexity Project (Landscape Context - Coordinated Field Studies). The goal of the study was to explore the links between terrestrial and aquatic systems across a gradient of residential development and lake landscape position. Specifically, this project attempted to relate the abundance of Coarse Wood in the littoral zone with abiotic, biotic and anthropogenic features of the adjacent shoreline. Sampling Frequency: each site sampled once Number of sites: 488 sites on 61 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year; eight sites per lake).
Dataset ID
124
Date Range
-
LTER Keywords
DOI
10.6073/pasta/81a92a387657882c77ac51d8a18caf6c
Maintenance
completed
Metadata Provider
Methods
Study Lakes We selected 60 northern temperate lake sites in Vilas County, Wisconsin lake district. Methods for lake choice and sampling are given in greater detail in Marburg et al. (2005) Each lake was sampled once between 2001 and 2004, in June, July, or August (15 different lakes each summer). We chose stratified lakes deeper than 4 m to insure that all the lakes contained a diverse fish community. With two exceptions (chains of lakes), lakes were chosen to be in separate watersheds. Lakes were chosen based on two criteria landscape position, using historical DNR water conductivity data as a proxy of position, and riparian housing development, measured in buildings km-1 shoreline (Marburg et al. 2005). Landscape position refers to the location of a lake along the hydrological gradient. The gradient ranges from the top of a drainage system, where seepage lakes are fed mainly by rainwater, through lakes which receive water from groundwater and have surface outflows, to lakes further down in the drainage system, which receive water from both ground and surface flow (Kratz et al. 1997).Landscape position affects lake water chemistry, because as water flows across the surface and through soil, it picks up carbonates and other ions which increase the waters electrical conductivity (specific conductance, a temperature-independent measure of salinity), alkalinity, and its ability to support algal and macrophyte production. In addition, aspects of lake morphology correlate with landscape position. Most obviously, larger lakes tend to occur lower in drainage systems (Riera et al. 2000).The riparian (near-shore terrestrial) zone around northern Wisconsin lakes is being rapidly developed for use as both summer and permanent housing (Peterson et al., 2003). Concurrent with housing development, humans often directly and indirectly remove logs (Kratz et al. 2002) and aquatic vegetation (Radomski and Goeman 2001) from the littoral zone (near shore shallow water area), resulting in reduced littoral zone complexity. The slowly-decaying logs of fallen trees create physical structure (coarse woody habitat CWH) in the littoral zone of lakes that provides habitat and refuge for aquatic organisms (Christensen et al. 1996). Fish, including plankton-eating species (planktivores), reproduce and develop in shallow water (Becker 1983). Because planktivorous fish affect zooplankton community structure through size-selective predation (Brooks and Dodson 1965), there is the potential for indirect effects of housing development on zooplankton.Lakes ranged in size from 24 to 654 ha. In 2001, 2002 and 2004 we chose lakes from the extreme ends of the conductivity and housing density gradients and in 2003 lakes were chosen to fill in the gap in the middle of the ranges. The study lakes range from oligotrophic to mesotrophic (Kratz et al. 1997 Magnuson et al. 2005).At each lake we sampled zooplankton, water chemistry, riparian and littoral vegetation, fish, crayfish, and macrophytes. Each lake was sampled only once, but given the large number of lakes sampled in this area, we expect to see relationships between variables within lakes and at a landscape scale. A snapshot sampling design maximizes sites that can be visited, and is sufficient for a general characterization of zooplankton communities (Stemberger et al. greater than 001).
Version Number
8

Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Littoral Plots 2001 - 2004

Abstract
In 2001 - 2004 the abundance of coarse wood and other aspects of the physical structure of the littoral zone were surveyed along transects that followed the 0.5 m depth contour at 488 sites in Vilas County. These data were collected as part of the &quot;cross-lake comparison&quot; segment of the Biocomplexity Project (Landscape Context - Coordinated Field Studies). The study explored the links between terrestrial and aquatic systems across a gradient of residential development and lake landscape position. Specifically, this project attempted to relate the abundance of Coarse Wood in the littoral zone with abiotic, biotic and anthropogenic features of the adjacent shoreline. Each of the 488 sites was a 50 m stretch of shoreline. The transects started and ended at the beginning and end of the site; the length of each transect, therefore, varied. Logs which were at least 150 cm in length were counted; more detailed descriptions were taken of logs at least 10 cm in diameter and 150 cm long. Information on littoral and shoreline substrate was also collected. Sampling Frequency: each site sampled once Number of sites: 488 sites on 61 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year; eight sites per lake).
Dataset ID
125
Date Range
-
Maintenance
completed
Metadata Provider
Methods
In 2001 - 2004 littoral habitat, fish and macrophyte surveys were performed at eight sites within each of the 55 lakes. The sites were chosen by randomly selecting two points per compass quadrant of each lake. Each year littoral habitat surveys were conducted in June, fish surveys in July and macrophyte surveys in August.Littoral habitat (substrate and coarse woody habitat) was measured along a 50 m transect parallel to shore along the 0.5 meter depth contour at each site. The two Littoral CWH variables (number of logs km-1 greater than 5 cm diameter, and number greater than 10 cm) were transformed by log of (1+number) to normalize the variables.
Short Name
BIOLPLOT
Version Number
7
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