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-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
EM Short Name
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Pollination ES, Central French Alps | Wetland conservation for birds, Midwestern USA | Biological pest control, Uppland Province, Sweden | InVEST (v1.004) Carbon, Indonesia | Wetland shellfish production, Gulf of Mexico, USA | ARIES viewsheds, Puget Sound Region, USA | HexSim v2.4, San Joaquin kit fox, CA, USA | InVESTv3.0 Sed. retention, Guánica Bay, PR, USA | RUM: Valuing fishing quality, Michigan, USA | Alewife derived nutrients, Connecticut, USA | WESP Method | HWB Blood pressure, Great Lakes waterfront, USA | EPA national stormwater calculator tool |
EM Full Name
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Pollination ecosystem service estimated from plant functional traits, Central French Alps | Prioritizing wetland conservation for birds, Midwestern USA | Biological control of agricultural pests by natural predators, Uppland Province, Sweden | InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004) carbon storage and sequestration, Sumatra, Indonesia | Wetland shellfish production, Gulf of Mexico, USA | ARIES (Artificial Intelligence for Ecosystem Services) Scenic viewsheds for homeowners, Puget Sound Region, Washington, USA | HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Sediment Retention, Guánica Bay, Puerto Rico, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Alewife derived nutrients in stream food web, Connecticut, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | Human well being indicator- Blood pressure, Great Lakes waterfront, USA | Environmental Protection Agency National stormwater calculator tool |
EM Source or Collection
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EU Biodiversity Action 5 | None | None | InVEST |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
ARIES | US EPA | US EPA | InVEST | None | None | None | None | US EPA |
EM Source Document ID
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260 | 122 | 299 | 309 | 324 | 302 |
337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
338 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
384 | 390 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
428 ?Comment:This is a tool available on the web for downloading to personal computers. A manual is also available for further documentation of the tool. |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Thogmartin, W. A., Potter, B. A. and Soulliere, G. J. | Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Walters, A. W., R. T. Barnes, and D. M. Post | Adamus, P. R. | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon |
Document Year
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2011 | 2011 | 2014 | 2014 | 2012 | 2014 | 2015 | 2017 | 2014 | 2009 | 2016 | None | 2022 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Bridging the conservation design and delivery gap for wetland bird habitat maintenance and restoration in the midwestern United States | Ecological production functions for biological control services in agricultural landscapes | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Valuing recreational fishing quality at rivers and streams | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | EPA National Stormwater Calculator Web App users guide-Version 3.4.0. |
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 | 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 but unpublished (explain in Comment) | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Journal manuscript submitted or in review | Published EPA report |
EM ID
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | http://aries.integratedmodelling.org/ | http://www.hexsim.net/ | http://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | https://www.epa.gov/water-research/national-stormwatercalculator | |
Contact Name
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Sandra Lavorel | Wayne Thogmartin, USGS | Mattias Jonsson | Nirmal K. Bhagabati | Stephen J. Jordan | Ken Bagstad | Theresa M. Nogeire | Susan H. Yee | Richard Melstrom | Annika W. Walters | Paul R. Adamus | Ted Angradi | Lewis Rossman |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603 | Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | Geosciences and Environmental Change Science Center, US Geological Survey | School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA | 6028 NW Burgundy Dr. Corvallis, OR 97330 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Center for environmental solutions and emergency response, Cincinnati, Ohio |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | wthogmartin@usgs.gov | mattias.jonsson@slu.se | nirmal.bhagabati@wwfus.org | jordan.steve@epa.gov | kjbagstad@usgs.gov | tnogeire@gmail.com | yee.susan@epa.gov | melstrom@okstate.edu | annika.walters@yale.edu | adamus7@comcast.net | tedangradi@gmail.com | n.a. |
EM ID
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
Summary Description
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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 pollination ecosystem service map was a simple sums 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 pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "The U.S. Fish and Wildlife Service’s adoption of Strategic Habitat Conservation is intended to increase the effectiveness and efficiency of conservation delivery by targeting effort in areas where biological benefits are greatest. Conservation funding has not often been allocated in accordance with explicit biological endpoints, and the gap between conservation design (the identification of conservation priority areas) and delivery needs to be bridged to better meet conservation goals for multiple species and landscapes. We introduce a regional prioritization scheme for North American Wetlands Conservation Act funding which explicitly addresses Midwest regional goals for wetland-dependent birds. We developed decision-support maps to guide conservation of breeding and non-breeding wetland bird habitat. This exercise suggested ~55% of the Midwest consists of potential wetland bird habitat, and areas suited for maintenance (protection) were distinguished from those most suited to restoration. Areas with greater maintenance focus were identified for central Minnesota, southeastern Wisconsin, the Upper Mississippi and Illinois rivers, and the shore of western Lake Erie and Saginaw Bay. The shores of Lakes Michigan and Superior accommodated fewer waterbird species overall, but were also important for wetland bird habitat maintenance. Abundant areas suited for wetland restoration occurred in agricultural regions of central Illinois, western Iowa, and northern Indiana and Ohio. Use of this prioritization scheme can increase effectiveness, efficiency, transparency, and credibility to land and water conservation efforts for wetland birds in the Midwestern United States." | ABSTRACT: "We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization." | 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: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... We mapped biomass carbon by assigning carbon values (in ton ha_1) for aboveground, belowground, and dead organic matter to each LULC class based on values from literature, as described in Tallis et al. (2010). We mapped soil carbon separately, as large quantities of carbon are stored in peat soil (Page et al., 2011). We estimated total losses in peat carbon over 50 years into the future scenarios, using reported annual emission rates for specific LULC transitions on peat (Uryu et al., 2008)...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | ABSTRACT: "We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "Within a given viewshed, our models quantified the contribution of viewshed source features such as mountains and water bodies and sinks that detract from view quality, including obstructions or visual blight such as industrial or commercial development. Source, sink, and use locations were linked by a flow model that computed visibility along lines of sight from use locations to scenic viewshed features. The model includes a distance decay function that accounts for changes with distance in the value of views. We then computed the ratio of actual to theoretical provision of scenic views to compare the values accruing to homeowners relative to those for the entire landscape." | ABSTRACT: "...Here, we use an individual-based population model to assess potential population-wide effects of rodenticide exposures on the endangered San Joaquin kit fox (Vulpes macrotis mutica). We estimate likelihood of rodenticide exposure across the species range for each land cover type based on a database of reported pesticide use and literature…" AUTHOR'S DESCRIPTION: "We simulated individual kit foxes across their range using HexSim [33], a computer modeling platform for constructing spatially explicit population models. Our model integrated life history traits, repeated exposures to rodenticides, and spatial data layers describing habitat and locations of likely exposures. We modeled female kit foxes using yearly time steps in which each individual had the potential to disperse, establish a home range, acquire resources from their habitat, reproduce, accumulate rodenticide exposures, and die." "Simulated kit foxes assembled home ranges based on local habitat suitability, with range size inversely related to habitat suitability [34,35]. Kit foxes aimed to acquire a home range with a target score corresponding to the observed 544 ha home range size in the most suitable habitat [26]. Modeled home ranges varied in size from 170 ha to 1000 ha. Kit foxes were assigned to a resource class depending on the quality of the habitat in their acquired home range. The resource class then influenced rates of kit fox survival," "Juveniles and adults without ranges searched for a home range across 30 km2 outside of their natal range, using HexSim’s ‘adaptive’ exploration algorithm [33]." | 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: "…were identified as relevant to stakeholder objectives in the Guanica Bay watershed identified during the 2013 Public Values Forum…Ecological production fuctions were applied to translate LULC measures of ecosystem conditions to supply of ecosystem services…Sediment retention in each watershed depends on geomorphology, climate, vegetation, and management, and was estimated by applying the Universal Soil Loss Equation (USLE) in each HUCH12 sub-watershed using a sediment retention model (InVEST 3.0.0…" | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." | 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: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | "Abstract: EPA’s National Stormwater Calculator (SWC) is a software application tool that estimates the annual amount of rainwater and frequency of runoff from a specific site using green infrastructure as low impact development controls. The SWC is designed for use by anyone interested in reducing runoff from a property, including site developers, landscape architects, urban planners, and homeowners. This User’s guide contains information on the SWC web application. SWC Version 3.4 contains has updated historical meteorological data (from 1970 - 2006 to 1990 - 2019), updated Bureau of Labor Statistics Cost Data (from 2018 to 2020), and the 5.1.015 Stormwater Management Model (SWMM) engine (from 5.1.007). Evaporation was calculated by the Hargreaves method (EPA, 2015), based on historical or future daily temperature data." |
Specific Policy or Decision Context Cited
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None identified | Strategic habitat conservation by USFW for Wetland Conservation Act funding | None identified | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None given |
Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Boreal Hardwood Transition, Eastern Tallgrass Prairie, Prairie Hardwood Transition, Central Hardwoods | Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here. | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Estuarine environments and marsh-land interfaces | No additional description provided | No additional description provided | No additional description provided | stream and river reaches of Michigan | Alewife spawning runs typically occur Mid March - May. | None | Waterfront districts on south Lake Michigan and south lake Erie | Sites up to 12 acres |
EM Scenario Drivers
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No scenarios presented | Conservation efforts for: marsh-wetland breeding birds, regional marsh and open-water for non-breeding birds, mudflat/shallows for birds during non-breeding period. | No scenarios presented | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class | No scenarios presented | targeted sport fish biomass | No scenarios presented | N/A | N/A | Climate change scenarios |
EM ID
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
em.detail.idHelp
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
Document ID for related EM
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Doc-260 | Doc-169 | Doc-170 | Doc-171 | Doc-172 | Doc-173 | Doc-174 | Doc-175 | None | Doc-315 | None | Doc-303 | Doc-305 | Doc-328 | Doc-327 | Doc-2 | Doc-309 | None | Doc-383 | None | Doc-422 | None |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-83 | None | None | EM-374 | EM-604 | EM-603 | None | EM-403 | EM-98 | EM-359 | None | EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673 | EM-718 | EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895 | None |
EM Modeling Approach
EM ID
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
EM Temporal Extent
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Not reported | 2007 | 2009 | 2008-2020 | 1950 - 2050 | 1992-2006 | 60 yr | 1978 - 2013 | 2008-2010 | 1979-2009 | Not applicable | 2022 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Varies by Run | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Not applicable | Geopolitical | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | Upper Mississippi River and Great Lakes Region | Uppland province | central Sumatra | Gulf of Mexico (estuarine and coastal) | Puget Sound Region | San Joaquin Valley, CA | Guanica Bay watershed | HUCS in Michigan | Bride Brook | Not applicable | Great Lakes waterfront | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 1-10 ha | Not applicable | 1000-10,000 km^2. | Not applicable |
EM ID
em.detail.idHelp
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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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) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
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 lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | 1 ha | 25 m x 25 m | 30 m x 30 m | 55.2 km^2 | 200m x 200m | 14 ha | 30 m x 30 m | reach in HUC | Not applicable | not reported | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | No | Yes | No | Unclear | No | No |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
Not applicable | No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | No | No | No | Yes | No | Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None |
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None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | Yes | No | No | No | No | No | No | No | No | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | No | No | No | No | Not applicable | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No |
Yes ?Comment:AUTHOR'S NOTE: "Varying aphid fecundity, overall predator abundances and attack rates affected the biological control effect, but had little influence on the relative differences between landscapes with high and low levels of biological control. The model predictions were more sensitive to changing the predators' landscape relations, but, with few exceptions, did not dramatically alter the overall patterns generated by the model." |
No | No | No | Yes | No | No | No | Not applicable | Yes | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
None | None | None | None |
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None | None | None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 42.05 | 59.52 | 0 | 30.44 | 48 | 36.13 | 17.96 | 45.12 | 41.32 | Not applicable | 42.26 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | -88.6 | 17.9 | 102 | -87.99 | -123 | -120 | -67.02 | 85.18 | -72.24 | Not applicable | -87.84 | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows. | Not reported | Spring-sown cereal croplands and surrounding grassland and non-arable land | 104 land use land cover classes | Submerged aquatic vegetation in estuaries and coastal lagoons | Terrestrial environment surrounding a large estuary | Agricultural region (converted desert) and terrestrial perimeter | None reported | stream reaches | Coastal stream | Wetlands | Lake Michigan & Lake Erie waterfront | Terrrestrial landcover |
EM Ecological Scale
em.detail.ecoScaleHelp
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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 is finer 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 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 is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Species | Individual or population, within a species | Community | Species | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
None Available |
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None Available |
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None Available |
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None Available |
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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)
EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
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None |
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None | 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)
EM-82 | EM-113 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-435 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-937 |
None |
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None |
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None | None |
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None |
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