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-326 |
EM-359 ![]() |
EM-368 | EM-417 |
EM Short Name
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ARIES flood regulation, Puget Sound Region, USA | InVEST (v1.004) sediment retention, Indonesia | InVEST - Water Yield (v3.0) | SWAT, Guanica Bay, Puerto Rico, USA |
EM Full Name
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ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | InVEST v3.0 Reservoir Hydropower Projection, aka Water Yield | SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA |
EM Source or Collection
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ARIES | InVEST | InVEST | US EPA |
EM Source Document ID
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302 | 309 | 311 | 334 |
Document Author
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Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | 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. | Natural Capital Project | Hu, W. and Y. Yuan |
Document Year
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2014 | 2014 | 2015 | 2013 |
Document Title
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From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Water Yield: Reservoir Hydropower Production- InVEST (v3.0) | Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Web published | Published EPA report |
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
http://aries.integratedmodelling.org/ | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | |
Contact Name
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Ken Bagstad | Nirmal K. Bhagabati | Natural Capital Project | Yongping Yuan |
Contact Address
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Geosciences and Environmental Change Science Center, US Geological Survey | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | 371 Serra Mall, Stanford University, Stanford, Ca 94305 | USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada |
Contact Email
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kjbagstad@usgs.gov | nirmal.bhagabati@wwfus.org | invest@naturalcapitalproject.org | Yuan.Yongping@epa.gov |
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
Summary Description
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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: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures." | 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)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...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." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. AUTHOR'S DESCRIPTION: "The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape, offering insight into how changes in land use patterns affect annual surface water yield and hydropower production. Modeling the connections between landscape changes and hydrologic processes is not simple. Sophisticated models of these connections and associated processes (such as the WEAP model) are resource and data intensive and require substantial expertise. To accommodate more contexts, for which data are readily available, InVEST maps and models the annual average water yield from a landscape used for hydropower production, rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale. Instead, InVEST calculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production. The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues. The model runs on a gridded map. It estimates the quantity and value of water used for hydropower production from each subwatershed in the area of interest. It has three components, which run sequentially. First, it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration. The model does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a pixel reaches the point of interest via one of these pathways. This model then sums and averages water yield to the subwatershed level. The pixel-scale calculations allow us to represent the heterogeneity of key driving factors in water yield such as soil type, precipitation, vegetation type, etc. However, the theory we are using as the foundation of this set of models was developed at the subwatershed to watershed scale. We are only confident in the interpretation of these models at the subwatershed scale, so all outputs are summed and/or averaged to the subwatershed scale. We do continue to provide pixel-scale representations of some outputs for calibration and model-checking purposes only. These pixel-scale maps are not to be interpreted for understanding of hydrological processes or to inform decision making of any kind. | AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008). |
Specific Policy or Decision Context Cited
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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 |
Biophysical Context
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No additional description provided | 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. | None applicable | Need to fill in |
EM Scenario Drivers
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No scenarios presented | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | N/A | Planting type, fertilizing rate, harvest rate |
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing 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-326 |
EM-359 ![]() |
EM-368 | EM-417 |
Document ID for related EM
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Doc-303 | Doc-305 | Doc-338 | Doc-307 | Doc-280 | Doc-338 | Doc-205 | None |
EM ID for related EM
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None | EM-435 | EM-437 | EM-148 | EM-344 | EM-111 | None |
EM Modeling Approach
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
EM Temporal Extent
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1971-2006 | 2008-2020 | Not applicable | 1981-2004 |
EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | future time |
EM Time Continuity
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Not applicable | Not applicable | discrete | discrete |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Day |
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
Bounding Type
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Physiographic or ecological | Watershed/Catchment/HUC | Not applicable | Watershed/Catchment/HUC |
Spatial Extent Name
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Puget Sound Region | central Sumatra | Not applicable | Guanica Bay, Puerto Rico watersheds |
Spatial Extent Area (Magnitude)
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10,000-100,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 100-1000 km^2 |
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
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) ?Comment:pixel is likely 30m x 30m |
spatially distributed (in at least some cases) |
Spatial Grain Type
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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 |
Spatial Grain Size
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200m x 200m | 30 m x 30 m | Not specified | 30m x 30m |
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
EM Computational Approach
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Analytic | Analytic | Numeric | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
Model Calibration Reported?
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No | No |
Yes ?Comment:Annual Yield can be calibrated with actual yield based up 10 year average input data though this was an "optional" part of the model. Calibrate with total precipitation and potential evapotranspiration. Before the calibration process is commenced, the modelers suggest performing a sensitivity analysis with the observed runoff data to define the parameters that influence model outputs the most. The calibration can then focus on highly sensitive parameters followed by less sensitive ones. |
Yes ?Comment:Used 1981 and 1982 data to calibrate hydrology. |
Model Goodness of Fit Reported?
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No | No | Not applicable |
No ?Comment:Calibration for both the stream flow and Sediment concentration of the mode |
Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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No | No | No |
Yes ?Comment:Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations |
Model Uncertainty Analysis Reported?
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No | No | No | Unclear |
Model Sensitivity Analysis Reported?
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No | No | Not applicable |
Yes ?Comment:Yes for both runoff and sediment |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | No |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
Centroid Latitude
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48 | 0 | -9999 | 18.19 |
Centroid Longitude
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-123 | 102 | -9999 | -66.76 |
Centroid Datum
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WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
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Estimated | Provided | Not applicable | Estimated |
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
EM Environmental Sub-Class
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Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Terrestrial environment surrounding a large estuary | 104 land use land cover classes | Watershed | watershed |
EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
EM Organismal Scale
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Not applicable | Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
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)
EM-326 |
EM-359 ![]() |
EM-368 | EM-417 |
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<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-326 |
EM-359 ![]() |
EM-368 | EM-417 |
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None |
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None |