EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-51 ![]() |
EM-80 |
EM-125 ![]() |
EM-148 ![]() |
EM-345 ![]() |
EM-367 |
EM-375 ![]() |
EM-617 |
EM-661 ![]() |
EM-685 | EM-706 |
EM-735 ![]() |
EM-948 ![]() |
EM Short Name
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EnviroAtlas-Nat. filtration-water | Agronomic ES and plant traits, Central French Alps | Land-use change and recreation, Europe | InVEST - Water provision, Francoli River, Spain | InVEST habitat quality, Puli Township, Taiwan | InVEST Coastal Blue Carbon | VELMA hydro, Oregon, USA | RBI Spatial Analysis Method | Alwife phosphorus flux in lakes, Connecticut, USA | Visitor value lost to a beach closure, MA, USA | WESP Method | C sequestration in grassland restoration, England | Global forest stock, biomass and carbon downscaled |
EM Full Name
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US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA | Agronomic ecosystem service estimated from plant functional traits, Central French Alps | Land-use change effects on recreation, Europe | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) habitat quality, Puli Township, Taiwan | InVEST v3.0 Coastal Blue Carbon | VELMA (visualizing ecosystems for land management assessments) hydro, Oregon, USA | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Visitor value lost to a beach closure, Barnstable, Massachusetts, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | Carbon sequestration in grassland diversity restoration, England | Global forest growing stock, biomass and carbon downscaled map |
EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
EU Biodiversity Action 5 | EU Biodiversity Action 5 | InVEST | InVEST | InVEST | US EPA | None | None | US EPA | None | None | None |
EM Source Document ID
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223 | 260 | 228 | 280 | 308 | 310 | 13 | 367 | 383 | 386 | 390 | 396 | 442 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Haines-Young, R., Potschin, M. and Kienast, F. | Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T. | Natural Capital Project | Abdelnour, A., Stieglitz, M., Pan, F. and McKane, R. B. | Bousquin, J., Mazzotta M., and W. Berry | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Adamus, P. R. | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett | Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner |
Document Year
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2013 | 2011 | 2012 | 2013 | 2014 | 2014 | 2011 | 2017 | 2010 | 2018 | 2016 | 2011 | 2008 |
Document Title
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EnviroAtlas - Featured Community | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | Assessing highway's impacts on landscape patterns and ecosystem services: A case study in Puli Township, Taiwan | Blue Carbon model - InVEST (v3.0) | Catchment hydrological responses to forest harvest amount and spatial pattern | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Additional carbon sequestration benefits of grassland diversity restoration | A global forest growing stock, biomass and carbon map based on FAO statistics |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | other | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript |
EM ID
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EM-51 ![]() |
EM-80 |
EM-125 ![]() |
EM-148 ![]() |
EM-345 ![]() |
EM-367 |
EM-375 ![]() |
EM-617 |
EM-661 ![]() |
EM-685 | EM-706 |
EM-735 ![]() |
EM-948 ![]() |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | Not applicable | |
Contact Name
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EnviroAtlas Team | Sandra Lavorel | Marion Potschin | Montse Marquès |
Yu-Pin Lin ?Comment:Tel.: +886 2 3366 3467; fax: +866 2 2368 6980 |
Gregg Verutes | A. Abdelnour | Justin Bousquin | Derek C. West | Kate K, Mulvaney | Paul R. Adamus | Gerlinde B. De Deyn | Georg Kindermann |
Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | Not reported | Stanford University | Dept. of Civil and Environmental Engineering, Goergia Institute of Technology, Atlanta, GA 30332-0335, USA | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | Not reported | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands | International Institute for Applied Systems Analysis, Laxenburg, Austria |
Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | marion.potschin@nottingham.ac.uk | montserrat.marques@fundacio.urv.cat | yplin@ntu.edu.tw | gverutes@stanford.edu | abdelnouralex@gmail.com | bousquin.justin@epa.gov | derek.west@yale.edu | Mulvaney.Kate@EPA.gov | adamus7@comcast.net | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | kinder(at)iiasa.ac.at |
EM ID
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EM-51 ![]() |
EM-80 |
EM-125 ![]() |
EM-148 ![]() |
EM-345 ![]() |
EM-367 |
EM-375 ![]() |
EM-617 |
EM-661 ![]() |
EM-685 | EM-706 |
EM-735 ![]() |
EM-948 ![]() |
Summary Description
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The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model. | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Recreation); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...To assess the effects of different land-use scenarios under various agricultural and environmental conservation policy regimes, this study applies an integrated approach to analyze the effects of Highway 6 construction on Puli Township...A habitat quality assessment using the InVEST model indicates that the conservation of agricultural and forested lands improves habitat quality and preserves rare habitats…" AUTHOR'S DESCRIPTION: "In total, three land-use planning scenarios were simulated based on government policies in Taiwan’s Hillside Protection Act and Regulations on Non-Urban Land Utilization Control. The baseline planning scenario, Scenario A, allows land-use development with-out land-use controls (Appendix Fig. S2), meaning that land-use changes can occur anywhere. Scenario B is based on the Regulations on Non-Urban Land Utilization Control and the maintenance of agricultural areas, such that land-use changes cannot occur in agricultural areas. Scenario C protects agricultural land, hillsides, and naturally forested areas from development...The biodiversity evaluation module in the InVEST model assessed the degree of change in habitat quality and habitat rarity under three scenarios. In the InVEST model, habitat quality is primarily threatened by four factors: the relative impact of each threat; the relative sensitivity of each habitat type to each threat; the distance between habitats and sources of threats; as well as the relative degree to which land is legally protected..." Use of other models in conjunction with this model: Land use data for future scenarios modeled in InVEST were derived from a linear regression model of land use change, and the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model for apportioning those changes to the landscape. | Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. | AUTHOR'S DESCRIPTION: "VELMA uses a distributed soil column framework to simulate the movement of water and nutrients (NH4, NO3, DON, DOC) within the soil, between the soil and the vegetation, and between the soil surface and vegetation to the atmosphere. The soil column model consists of three coupled submodels: (1) a hydrological model that simulates vertical and lateral movement of water within soil, losses of water from soil and vegetation to the atmosphere, and the growth and ablation of the seasonal snowpack, (2) a soil temperature model that simulates daily soil layer temperatures from surface air temperature and snow depth, and (3) a plant-soil model that simulates C and N dynamics. (Note: for the purposes of this paper we describe only the hydrologic aspects of the model.) Each soil column consists of n soil layers. Soil water balance is solved for each layer (equations (A1)–(A6)). We employ a simple logistic function that is based on the degree of saturation to capture the breakthrough characteristics of soil water drainage (equations (A7)–(A9)). Evapotranspiration increases exponentially with increasing soil water storage and asymptotically approaches the potential evapotranspiration rate (PET) as water storage reaches saturation [Davies and Allen, 1973; Federer, 1979, 1982; Spittlehouse and Black, 1981] (equation (A12)). PET is estimated using a simple temperature-based method [Hamon, 1963] (equation (A13)). An evapotranspiration recovery function is used to account for the effects of changes in stand-level transpiration rates during succession, e.g., after fire or harvest (equation (B2)). Snowmelt is estimated using the degree-day approach [Rango and Martinec, 1995] and accounts for the effects of rain on snow [Harr, 1981] (equation (A10)). [15] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water (Figures A1 and A2). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow is modeled using a simple logistic function multiplied by a factor to account for the local topographic slope angle (equation (A16))… The model is forced with daily temperature and precipitation. Daily observed streamflow data is used to calibrate and validate simulated discharge." "Model calibration is needed to accurately capture the pre- and postharvest dynamics at WS10. This model calibration consists of two simulations: an old-growth simulation for the period 1969-1974 and a post-harvest simulation for the period 1975-2008." Two additional sets of VELMA simulations examining changes in streamflow are presented in the paper, but not included here. Twenty simulations were conducted varying the location across the watershed of a 20% har | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "While it might be assumed that the economic value of a beach day and the value of a lost beach day would be symmetric, they are not quite the same in our analysis. This is because the town has many fixed costs for beach management, including staff, facility maintenance, and other amenities. These fixed costs are offset by the daily parking fees charged to out-of-town visitors and the various beach stickers available for town residents. Assuming the town does not make a profit and just breaks even on beach parking fees in relation to the costs incurred to provide the services, the net economic value of a day without a closure (benefits less costs) would simply be the consumer surplus for the public. However, this amount is different than the net economic value lost due to a beach closure, which includes the lost consumer surplus as well as the lost revenue to the town. This revenue is money the town would have collected to cover costs and therefore is considered a loss (negative producer surplus). Therefore, a beach day affected by a closure is valued as a loss of consumer surplus plus lost parking revenue…" Equation 3, page 19, provides the resulting formula for the value lost from a beach closure. | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" | ABSTRACT: "Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www. iiasa.ac.at/Research/FOR/. " |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | Environmental effects of Highway 6 construction on Puli Township, Taiwan | None identified | None identified | None identified | Restoration and management of diadromous fish runs in coastal New England | Economic value of protecting coastal beach water quality from contamination caused closures. | None identified | None identified | None identified |
Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | Mediteranean coastal mountains | 26% of the land area is categorized as plain and the remaining 74% is categorized as hilly with elevations of 380-700 m. Predominant land classes are forested (47.4%), cultivated (31.8%), and built-up (14.5%). Average annual rainfall is 2120 mm, and average annual temperature is 21°C. The soil in the eastern portion of the basin is primarily clay, and primarily loess elsewhere. | Land use land class; habitat type | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. Mean annual precipitation is 2300 mm and falls primarily as rain between October and April. Total rainfall during June– September averages 200 mm. Snow rarely persists longer than a couple of weeks and usually melts within 1 to 2 days. Average annual streamflow is 1600 mm, which is approximately 70% of annual precipitation. Soils are of the Frissel series, classified as Typic Dystrochrepts with fine loamy to loamy-skeletal texture that are generally deep and well drained. These soils quickly transmit subsurface water to the stream. Prior to the 1975 100% clearcut, WS10 was a 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). The dominant vegetation of WS10 today is a 35 year old mixed Douglasfir and western hemlock stand. | wetlands | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | Four separate beaches within the community of Barnstable | None | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030) | IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | Three scenarios; baseline planning (A, without land-use controls), scenario B based on maintenance of agriculture, scenario C protects agriculture, hillsides and naturally forested areas. | Land use land cover changes; habitat disturbance | Stand age; old-growth (pre-harvest), and harvested (postharvest) | N/A | current and historical run size | No scenarios presented | N/A | Additional benefits due to biodiversity restoration practices | No scenarios presented |
EM ID
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EM-51 ![]() |
EM-80 |
EM-125 ![]() |
EM-148 ![]() |
EM-345 ![]() |
EM-367 |
EM-375 ![]() |
EM-617 |
EM-661 ![]() |
EM-685 | EM-706 |
EM-735 ![]() |
EM-948 ![]() |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-51 ![]() |
EM-80 |
EM-125 ![]() |
EM-148 ![]() |
EM-345 ![]() |
EM-367 |
EM-375 ![]() |
EM-617 |
EM-661 ![]() |
EM-685 | EM-706 |
EM-735 ![]() |
EM-948 ![]() |
Document ID for related EM
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Doc-198 | Doc-260 | Doc-270 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 | Doc-307 | Doc-311 | Doc-338 | Doc-205 | Doc-278 | None | Doc-317 | None | None | Doc-386 | Doc-387 | None | None | None |
EM ID for related EM
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EM-137 | EM-142 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-81 | EM-82 | EM-83 | EM-122 | EM-123 | EM-124 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | EM-344 | EM-368 | EM-437 | EM-111 | EM-143 | None | EM-379 | EM-380 | EM-605 | EM-884 | EM-883 | EM-887 | None | EM-667 | EM-672 | EM-674 | EM-673 | EM-682 | EM-684 | EM-683 | EM-686 | EM-718 | None | None |
EM Modeling Approach
EM ID
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EM-51 ![]() |
EM-80 |
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EM-345 ![]() |
EM-367 |
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EM-685 | EM-706 |
EM-735 ![]() |
EM-948 ![]() |
EM Temporal Extent
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1999-2010 | Not reported | 1990-2030 | 1971-2100 | 2010-2025 | Not applicable | 1969-2008 | Not applicable | 1960"s and early 2000's | July 1, 2011 to June 31, 2016 | Not applicable | 1990-2007 | 1999-2005 |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary. |
time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable |
EM ID
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EM-80 |
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EM-685 | EM-706 |
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EM-948 ![]() |
Bounding Type
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Geopolitical | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Not applicable | Watershed/Catchment/HUC | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Other | No location (no locational reference given) |
Spatial Extent Name
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Durham, NC and vicinity | Central French Alps | The EU-25 plus Switzerland and Norway | Francoli River | Puli Township, Nantou County | Not applicable | H. J. Andrews LTER WS10 | Not applicable | Bride Lake and Linsley Pond | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Not applicable | Colt Park meadows, Ingleborough National Nature Reserve, northern England | Global |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | Not applicable | 10-100 ha | Not applicable | 10-100 ha | 10-100 ha | Not applicable | <1 ha | >1,000,000 km^2 |
EM ID
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EM-51 ![]() |
EM-80 |
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EM-345 ![]() |
EM-367 |
EM-375 ![]() |
EM-617 |
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EM-685 | EM-706 |
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EM-948 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | volume, for 3-D feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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irregular | 20 m x 20 m | 1 km x 1 km | 30m x 30m | 40 m x 40 m | user-specified | 30 m x 30 m surface pixel and 2-m depth soil column | Not reported | Not applicable | by beach site | not reported | 3 m x 3 m | 0.5 x 0.5 degrees |
EM ID
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EM-367 |
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EM-685 | EM-706 |
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EM-948 ![]() |
EM Computational Approach
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Analytic ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration. |
Analytic | Logic- or rule-based | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-685 | EM-706 |
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EM-948 ![]() |
Model Calibration Reported?
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Unclear | No | No | No | Unclear | Not applicable | Yes | Not applicable | Yes | Yes | Not applicable | Not applicable | No |
Model Goodness of Fit Reported?
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No | No | No | No | Not applicable | Not applicable | Yes | Not applicable | No | No | Not applicable | Not applicable |
Yes ?Comment:For the 0.5 grid level equation where the country forest level is missing. |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None |
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None | None | None | None | None |
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Model Operational Validation Reported?
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Unclear | No | No |
Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
Not applicable | Not applicable | No | Not applicable | No | No | No | No | Yes |
Model Uncertainty Analysis Reported?
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Unclear | No | No | No | No | Not applicable | No | Not applicable | No | No | Not applicable | No | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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Unclear | No | No | No | No | Not applicable | No | Not applicable | Yes |
No ?Comment:n/a |
Not applicable | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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Comment:Taiwan |
None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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None | None | None | None | None | None | None | None | None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-51 ![]() |
EM-80 |
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EM-148 ![]() |
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EM-367 |
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EM-685 | EM-706 |
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Centroid Latitude
em.detail.ddLatHelp
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35.99 | 45.05 | 50.53 | 41.26 | 23.98 | -9999 | 44.15 | Not applicable | 41.33 | 41.64 | Not applicable | 54.2 | 44.51 |
Centroid Longitude
em.detail.ddLongHelp
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-78.96 | 6.4 | 7.6 | 1.18 | 120.96 | -9999 | -122.2 | Not applicable | -72.24 | -70.29 | Not applicable | -2.35 | -123.51 |
Centroid Datum
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None provided | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Provided | Estimated | Estimated | Estimated | Not applicable | Provided | Not applicable | Estimated | Estimated | Not applicable | Provided | Estimated |
EM ID
em.detail.idHelp
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EM-51 ![]() |
EM-80 |
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EM-148 ![]() |
EM-345 ![]() |
EM-367 |
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EM-617 |
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EM-685 | EM-706 |
EM-735 ![]() |
EM-948 ![]() |
EM Environmental Sub-Class
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Rivers and Streams | Created Greenspace | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Inland Wetlands | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Forests | Inland Wetlands | Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Forests |
Specific Environment Type
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Urban areas including streams | Subalpine terraces, grasslands, and meadows. | Not applicable | Coastal mountains | Predominantly an agricultural area with associated forest land | user specified | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Restored wetlands | Coastal lakes and ponds and associated streams | Saltwater beach | Wetlands | fertilized grassland (historically hayed) | Forests |
EM Ecological Scale
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Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM Organismal Scale
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Not applicable | Community | Not applicable | Not applicable | Community | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Community | Not applicable |
Taxonomic level and name of organisms or groups identified
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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None |
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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None | None |