US Long-Term Ecological Research Network

North Temperate Lakes LTER: Northern Wisconsin boater survey, 2011 - 2012

Abstract
Understanding public perceptions of the importance of environmental issues is crucial for gauging support for management activities. This survey assess the importance boaters placed on 16 water issues in a lake-rich region of northern Wisconsin.<br/>Understanding public perceptions of the importance of environmental issues is crucial for gauging support for management activities. This survey assess the importance boaters placed on 16 water issues in a lake-rich region of northern Wisconsin.<br/>Understanding public perceptions of the importance of environmental issues is crucial for gauging support for management activities. This survey assess the importance boaters placed on 16 water issues in a lake-rich region of northern Wisconsin.<br/>
Dataset ID
404
Date Range
-
LTER Keywords
Methods
Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>Boaters were recruited into a yearlong trip diary program at the landings of lakes in Vilas and Oneida Counties (Wisconsin) between Memorial Day weekend and Labor Day, 2011.<br/>
Version Number
1

WSC 2007 - 2012 Yahara Watershed surface water quality policies and practices created and implemented by public agencies

Abstract
This dataset was created June 2012 - August 2013 to contribute to research under the Water Sustainability and Climate project. Interventions collected are those land-based policies and practices written and implemented by public agencies. Policies were implemented in Wisconsin&#39;s Yahara Watershed the period 2007-2012. They aim to improve surface water quality through nutrient (phosphorus and nitrogen) and sediment reduction. Interventions included in the mapping must have spatially-explicit, publicly available data through personal communication or website.
Contact
Dataset ID
309
Data Sources
Date Range
-
Maintenance
complete
Metadata Provider
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 &ndash; grants and cost-share &ndash; 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.
Version Number
16

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

North Temperate Lakes LTER: Northern Wisconsin Lake Resident Survey 2005 and 2008

Abstract
The purpose of this survey was to understand what lake characteristics people value most, what activities they enjoy most, and what they expect for the future of northern Wisconsin lakes. Questions covered aspects such as the property search process individuals went through leading up to their purchase of lakeshore property in Vilas County WI, what activities the individual&#39;s household participate in on lakes in Vilas County WI, their attitude about the future of their lake, their perception of the current state of their lake, and lake qualities they would like improved on their lake. Demographics of the respondents and background information about their lake were also collected. Two types of surveying methodologies were used for this survey, one being an internet-based survey, while the other was a mail survey. Surveys were conducted in 2005 and repeated in 2008.
Dataset ID
275
Date Range
-
LTER Keywords
Maintenance
completed
Metadata Provider
Methods
These surveys were conducted via web and mail. Full survey text: http://lter.limnology.wisc.edu/sites/default/files/ntl/pdf/Lakeshore%20survey%202008.pdf
Short Name
NWLRSURV12
Version Number
16

North Temperate Lakes LTER: Boat Traffic Through Yahara Locks 1976 - 2011

Abstract
One of the dominant uses of the Madison area lakes is for boating. In order to develop a long term data set on the temporal fluctuations and trends in such activity, the LTER project has obtained records of boat traffic that passes through the locks at the head of the Yahara River on its stretch between Lake Mendota and Lake Monona. This data was gathered by the Dane County Department of Public Works as part of the County's ongoing monitoring of its own facilities and their use. This data set will be augmented as the Department of Public Works makes updates available. Sampling Frequency: tallied daily April through October; exact starting dates vary each year Number of sites: 1
Contact
Creator
Dataset ID
15
Date Range
-
Maintenance
ongoing
Metadata Provider
Methods
This data has been and will continue to be gathered by the Dane County Department of Public Works as part of the County s ongoing monitoring of its own facilities and their use.
Short Name
NTLLC01
Version Number
24

North Temperate Lakes LTER: Lake District Revenues 1992 - 1996

Abstract
Lake District revenues record one of the primary means of action available to lake districts. These districts are a form of special purpose district created by the state of Wisconsin in the early 1970s to address the specific problems of lake mangement not being addressed by existing governmental capacities. These districts are by statute granted the authority to levy property taxes, special assessments, special charges and delinquent charges against property owners within their geographic boundaries. This fund-raising authority gives to lake districts a capacity not only to raise substantial funds but to do so consistently over time, thereby alloting to them a practical capacity not readily available to other groups (such as lake associations) involved in lake management. Data are from years 1992 - 1996. Sampling Frequency: annually Number of sites: 190
Dataset ID
16
Data Sources
Date Range
-
LTER Keywords
Maintenance
completed
Metadata Provider
Methods
public records
Short Name
NTLLD01
Version Number
9

North Temperate Lakes LTER: Manure Managment in Urbanizing Settings 2003 - 2004

Abstract
The management of manure in urbanizing settings is a critical issue in the Lake Mendota watershed. The primary focus of this project was to examine the difficulties faced by livestock operations when managing manure on field systems that are fragmented by development. A survey regarding manure management was sent to Dane County, WI farms within the Lake Mendota watershed. The survey was conducted in two phases; March to May 2003 and March to May 2004. This dataset and accompanying survey entitled &quot;Manure Management on the Urban Fringe&quot; is available for users wishing to ascertain animal feeding operation size and management patterns in the Lake Mendota watershed. The data also include the distance from animal feeding operations to nearest urban centers via Euclidean (crow flies) and Road Network distances. Results suggest that exurban developments exert a strong influence on manure management routines of livestock producers. This influence is very local. Farmers in an urbanizing setting were more likely to encounter problems during manure hauling when the fields they were accessing were in close proximity to urban developments, regardless of their proximity to the urban core. The distances and times required to haul manure between the farm and the most distant field increased in the last five years. Land rental rates steadily increased at the same time that lease lengths shortened. Cash grain land tends to be sparse as livestock producers compete with developers for tracts on which to distribute manure. Manure brokering is a possible strategy to monitor land availability and coordinate manure placement between farms Cabot, P. E., S. K. Bowen, and P. J. Nowak. 2004. Manure management in urbanizing settings. Journal of Soil and Water Conservation 59:235-243. The survey &quot;Manure Management on the Urban Fringe&quot; was developed with assistance from Roger Schmidt and Charmaine Tryon-Petith with the Integrated Crop and Pest Management Program, University of Wisconsin-Madison. Number of sites: 83 farms
Dataset ID
114
Date Range
-
LTER Keywords
Maintenance
completed
Metadata Provider
Methods
<a href="/sites/default/files/ntl/pdf/manure_survey.pdf" target="_blank">Survey Instrument</a>
Short Name
MANMGT1
Version Number
5

North Temperate Lakes LTER: Residential Lakeshore Property Sales in Vilas County 1997 - 2004

Abstract
Sales of residential shoreline property parcels in Vilas County, WI, USA for the period January 1997 througt Dec 2004. This dataset includes sales of over 2000 parcels on 234 lakes. In addtion to the sale price, other information collected include assessed value of the land, assessed value of improvements, length of lake frontage and total size of the parcel.
Dataset ID
111
Date Range
-
LTER Keywords
Maintenance
completed
Metadata Provider
Methods
extracted from the tax assessors database
Short Name
NTLPSAL1
Version Number
7

North Temperate Lakes LTER: Vilas County Property Tax Records 1997 - 2004

Abstract
Each year, the county government in Vilas County, WI, assembles data on each parcel of property in the county for various governmental administrative purposes. These records serve the register of deeds, the department of taxation, and other county departments, as well as private citizens or businesses, such as realtors, who seek information on properties in the county. These records include assessments of the value of land and built improvements, their location, the name and mailing address of the owner, and other data. LTER has begun to collect these data each year from the county as a means of monitoring owners, and the social identities of owners (whether they are state agencies, private individuals, corporations, non-profit/conservation organizations, etc.). These data have already proved useful as a sampling frame for social science surveys. This data set includes property tax records for Vilas County for the years 1997, 1998, 2001, 2003, and 2004. Sampling Frequency: annually Number of sites: 15 Townships of Vilas County, WI, USA
Dataset ID
39
Maintenance
complete
Metadata Provider
Methods
see abstract for method description
Short Name
NTLPR01
Version Number
7

North Temperate Lakes LTER: Northern Wisconsin Lake Resident Survey 2005

Abstract
The purpose of this 2005 survey was to understand what lake characteristics people value most, what activities they enjoy most, and what they expect for the future of northern Wisconsin lakes. Questions covered aspects such as the property search process individuals went through leading up to their purchase of lakeshore property in Vilas County WI, what activities the individual&#39;s household participate in on lakes in Vilas County WI, their attitude about the future of their lake, their perception of the current state of their lake, and lake qualities they would like improved on their lake. Demographics of the respondents and background information about their lake were also collected. Two types of surveying methodologies were used for this survey, one being an internet-based survey, while the other was a mail survey. The dataset includes 1554 observations. <a href="/sites/default/files/ntl/pdf/lakeresident_survey_summary.pdf" target="_blank">Summary of results.</a>
Dataset ID
210
Date Range
-
LTER Keywords
Maintenance
completed
Metadata Provider
Methods
<a href="/sites/default/files/ntl/pdf/nwlr_survey.pdf" target="_blank">Survey Instrument</a>
Short Name
NWLRSURV1
Version Number
21
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