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
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EM ID
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EM-326 | EM-875 | EM-941 |
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EM Short Name
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ARIES flood regulation, Puget Sound Region, USA | Valuing environmental ed., New York, New York | ESTIMAP - Pollination potential, Iran |
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EM Full Name
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ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | Valuing environmental education, Hudson River Park, New York, New York | ESTIMAP - Pollination potential, Iran |
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EM Source or Collection
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ARIES | None | None |
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EM Source Document ID
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302 | 416 | 434 |
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Document Author
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Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Hutcheson, W. Hoagland, P., and D. Jin | Rahimi, E., Barghjelveh, S., and P. Dong |
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Document Year
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2014 | 2018 | 2020 |
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Document Title
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From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Valuing environmental education as a cultural ecosystem service at Hudson River Park | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-326 | EM-875 | EM-941 |
| http://aries.integratedmodelling.org/ | Not applicable | Not applicable | |
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Contact Name
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Ken Bagstad | Walter Hutcheson | Ehsan Rahini |
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Contact Address
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Geosciences and Environmental Change Science Center, US Geological Survey | New York University, United States | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran |
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Contact Email
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kjbagstad@usgs.gov | wwh235@nyu.edu | ehsanrahimi666@gmail.com |
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EM ID
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EM-326 | EM-875 | EM-941 |
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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." | ABSTRACT: " The Hudson River and its estuary is once again an ecologically, economically, and culturally functional component of New York City’s natural environment. The estuary’s cultural significance may derive largely from environmental education, including marine science programs for the public. These programs are understood as ‘‘cultural” ecosystem services but are rarely evaluated in economic terms. We estimated the economic value of the Hudson River Park’s environmental education programs. We compiled data on visits by schools and summer camps from 32 New York City school districts to the Park during the years 2014 and 2015. A ‘‘travel cost” approach was adapted from the field of environmental economics to estimate the value of education in this context. A small—but conservative—estimate of the Park’s annual education program benefits ranged between $7500 and 25,500, implying an average capitalized value on the order of $0.6 million. Importantly, organizations in districts with high proportions of minority students or English language learners were found to be more likely to participate in the Park’s programs. The results provide an optimistic view of the benefits of environmental education focused on urban estuaries, through which a growing understanding of ecological systems could lead to future environmental improvements. " | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." |
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Specific Policy or Decision Context Cited
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None identified | None identified | None reported |
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Biophysical Context
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No additional description provided | N/A | None additional |
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EM Scenario Drivers
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No scenarios presented | N?A | N/A |
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EM ID
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EM-326 | EM-875 | EM-941 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-326 | EM-875 | EM-941 |
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Document ID for related EM
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Doc-303 | Doc-305 | None | Doc-432 |
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EM ID for related EM
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None | None | EM-939 |
EM Modeling Approach
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EM ID
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EM-326 | EM-875 | EM-941 |
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EM Temporal Extent
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1971-2006 | 2015 | 2020 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable |
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EM ID
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EM-326 | EM-875 | EM-941 |
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Bounding Type
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Physiographic or ecological | Geopolitical | Geopolitical |
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Spatial Extent Name
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Puget Sound Region | Hudson River Park | Iran |
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Spatial Extent Area (Magnitude)
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10,000-100,000 km^2 | 10-100 ha | >1,000,000 km^2 |
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EM ID
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EM-326 | EM-875 | EM-941 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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200m x 200m | Not applicable | ha^2 |
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EM ID
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EM-326 | EM-875 | EM-941 |
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EM Computational Approach
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Analytic | Numeric | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-326 | EM-875 | EM-941 |
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Model Calibration Reported?
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No | No | No |
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Model Goodness of Fit Reported?
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No | No | No |
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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 |
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Model Uncertainty Analysis Reported?
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No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-326 | EM-875 | EM-941 |
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Comment:Model for Iran - no form preset id for country |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-326 | EM-875 | EM-941 |
| None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-326 | EM-875 | EM-941 |
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Centroid Latitude
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48 | 40.73 | 32.29 |
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Centroid Longitude
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-123 | -74.01 | 53.68 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated |
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EM ID
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EM-326 | EM-875 | EM-941 |
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EM Environmental Sub-Class
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Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Terrestrial environment surrounding a large estuary | Park | terrestrial land types |
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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 | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
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EM-326 | EM-875 | EM-941 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-326 | EM-875 | EM-941 |
| None Available | None Available |
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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-875 | EM-941 |
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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-875 | EM-941 |
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