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-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
EM Short Name
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Coastal protection in Belize | RUM: Valuing fishing quality, Michigan, USA | Fish species richness, St. Croix, USVI | Recreational fishery index, USA | VELMA v. 2.1 contaminant modeling |
EM Full Name
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Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Fish Species Richness, Buck Island, St. Croix , USVI | Recreational fishery index for streams and rivers, USA | VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling |
EM Source or Collection
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InVEST | None | None | US EPA | US EPA |
EM Source Document ID
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350 |
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. |
355 | 414 |
423 ?Comment:Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430. |
Document Author
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Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | McKane |
Document Year
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2016 | 2014 | 2007 | 2021 | None |
Document Title
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The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Valuing recreational fishing quality at rivers and streams | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation |
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 |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report |
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
Not identified in paper | Not applicable | Not applicable | Not applicable | https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355 | |
Contact Name
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Greg Guannel | Richard Melstrom | Simon Pittman | Gregg Lomnicky | Robert B. McKane |
Contact Address
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The Nature Conservancy, Coral Gables, FL. USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | 1305 East-West Highway, Silver Spring, MD 20910, USA | 200 SW 35th St., Corvallis, OR, 97333 | US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 |
Contact Email
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greg.guannel@gmail.com | melstrom@okstate.edu | simon.pittman@noaa.gov | lomnicky.gregg@epa.gov | mckane.bob@epa.gov |
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
Summary Description
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AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | 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: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] | ABSTRACT: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters." |
Specific Policy or Decision Context Cited
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Future rock lobster fisheries management | None identified | None provided | None identified | None identified |
Biophysical Context
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barrier reef and fringing reef in nearshore coastal marine system | stream and river reaches of Michigan | Hard and soft benthic habitat types approximately to the 33m isobath | None | No additional description provided |
EM Scenario Drivers
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Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | targeted sport fish biomass | No scenarios presented | N/A | No scenarios presented |
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only |
New or Pre-existing EM?
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New or revised model | 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-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
Document ID for related EM
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None | None | Doc-355 | None | Doc-430 |
EM ID for related EM
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None | None | EM-590 | EM-699 | None | EM-883 | EM-884 | EM-887 |
EM Modeling Approach
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
EM Temporal Extent
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2005-2013 | 2008-2010 | 2000-2005 | 2013-2014 | Not applicable |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | past time | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | discrete |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Second | Not applicable | Not applicable | Year | Day |
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Not applicable |
Spatial Extent Name
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Coast of Belize | HUCS in Michigan | SW Puerto Rico, | United States | Not applicable |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | Not applicable |
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | volume, for 3-D feature |
Spatial Grain Size
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1 meter | reach in HUC | not reported | stream reach (site) | user defined |
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
EM Computational Approach
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Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
Model Calibration Reported?
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No | No | No | No | Not applicable |
Model Goodness of Fit Reported?
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No | Yes | Yes | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None |
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None | None |
Model Operational Validation Reported?
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No ?Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. |
No | Yes | No | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | Yes | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
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None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
Centroid Latitude
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18.63 | 45.12 | 17.79 | 36.21 | Not applicable |
Centroid Longitude
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-88.22 | 85.18 | -64.62 | -113.76 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Not applicable |
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Rivers and Streams | Near Coastal Marine and Estuarine | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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coral reefs | stream reaches | shallow coral reefs | reach | Terrestrial environment |
EM Ecological Scale
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Ecological scale is finer than that of 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 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
EM ID
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EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
EM Organismal Scale
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Guild or Assemblage | Not applicable | Guild or Assemblage | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
None Available |
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None Available | None Available |
EnviroAtlas URL
EM-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation | The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate | None Available | None Available | None Available |
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-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
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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-542 ![]() |
EM-660 ![]() |
EM-698 | EM-862 | EM-892 |
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None |