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-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
EM Short Name
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ROS (Recreation Opportunity Spectrum), Europe | Retained rainwater, Guánica Bay, Puerto Rico | CRPI, St. Croix, USVI | Aquatic vertebrate IBI for Western streams, USA | HWB indicator-College degree, Great Lakes, USA |
EM Full Name
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ROS (Recreation Opportunity Spectrum), Europe | Retained rainwater, Guánica Bay, Puerto Rico, USA | CRPI (Coral Reef Protection Index, St. Croix, USVI | Development of an aquatic vertebrate index of biotic integrity (IBI) for Western streams, USA | Human well being indicator-College degree, Great Lakes waterfront, USA |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | None | US EPA |
EM Source Document ID
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293 | 338 | 335 | 404 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
Document Author
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Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Pont, D., Hughes, R.M., Whittier, T.R., and S. Schmutz. | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
Document Year
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2014 | 2017 | 2014 | 2009 | None |
Document Title
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Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | A Predictive Index of Biotic Integrity Model for A predictive index of biotic integrity model foraquatic-vertebrate assemblages of Western U.S. Streams | Human well-being and natural capital indictors for Great Lakes waterfront revitalization |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review |
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Maria Luisa Paracchini | Susan H. Yee | Susan H. Yee | Didier Pont | Ted Angradi |
Contact Address
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Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Centre d’E´ tude du Machinisme Agricole et du Genie Rural, des Eaux et Foreˆts (Cemagref), Unit HYAX Hydrobiologie, 3275 Route de Ce´zanne, Le Tholonet, 13612 Aix en Provence, France | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 |
Contact Email
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luisa.paracchini@jrc.ec.europa.eu | yee.susan@epa.gov | yee.susan@epa.gov | didier.pont@cemagref.fr | tedangradi@gmail.com |
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
Summary Description
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ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...An alternative index has been developed specifically for coral reefs, the Coral Reef Protection Index (CRPI), that accounts for the continuity of the reef and distance from shore in addition to reef habitat type (Burke et al., 2008): CRPI = ((Reef type + Reef distribution + Reef distance)/10) x 4 where the scaled magnitude of coastal protection due to each factor ranges from 0 (no protection) to 4 (very high protection; Table 2)." | ABSTRACT: "Because of natural environmental and faunal differences and scientific perspectives, numerous indices of biological integrity (IBIs) have been developed at local, state, and regional scales in the USA. These multiple IBIs, plus different criteria for judging impairment, hinder rigorous national and multistate assessments. Many IBI metrics are calibrated for water body size, but none are calibrated explicitly for other equally important natural variables such as air temperature, channel gradient, or geology. We developed a predictive aquatic-vertebrate IBI model using a total of 871 stream sites (including 162 least-disturbed and 163 most-disturbed sites) sampled as part of the U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program survey of 12 conterminous western U.S. states. The selected IBI metrics (calculated from both fish and aquatic amphibians) were vertebrate species richness, benthic native species richness, assemblage tolerance index, proportion of invertivore–piscivore species, and proportion of lithophilic-reproducing species. Mean model IBI scores differed significantly between least-disturbed and most-disturbed sites as well as among ecoregions. Based on a model IBI impairment criterion of 0.44 (risks of type I and II errors balanced), an estimated 34.7% of stream kilometers in the western USA were deemed impaired, compared with 18% for a set of traditional IBIs. Also, the model IBI usually displayed less variability than the traditional IBIs, presumably because it was better calibrated for natural variability. " | 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. " |
Specific Policy or Decision Context Cited
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None identified | Meeting water demands for agriculture and domestic purposes. | None identified | None reported | None identified |
Biophysical Context
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No additional description provided | No additional descriptions provided | No additional description provided | Wadeable and boatable streams in 12 western USA states | Waterfront districts on south Lake Michigan and south lake Erie |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | not applicable | N/A |
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application View EM Runs | Method + Application |
New or Pre-existing EM?
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Application of existing model | Application of existing 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-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
Document ID for related EM
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Doc-290 | Doc-291 | Doc-289 | None | None | Doc-403 | Doc-422 |
EM ID for related EM
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None | None | None | EM-820 | EM-826 | EM-886 | EM-888 | EM-889 | EM-890 | EM-891 | EM-893 | EM-894 |
EM Modeling Approach
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
EM Temporal Extent
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Not reported | 2006 - 2012 | 2006-2007, 2010 | 2004-2005 | 2022 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary |
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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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Geopolitical |
Spatial Extent Name
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European Union countries | Guanica Bay watershed | Coastal zone surrounding St. Croix | Western 12 states | Great Lakes waterfront |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. |
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
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) ?Comment:871 total sites surveyed for this work |
spatially lumped (in all 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 | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
Spatial Grain Size
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100 m x 100 m | 30 m x 30 m | 10 m x 10 m | stream reach | Not applicable |
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Numeric |
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-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
Model Calibration Reported?
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No | No | Yes | No | No |
Model Goodness of Fit Reported?
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No | No | No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
Model Operational Validation Reported?
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No | No | Yes |
Yes ?Comment:Compared to another journal manuscript IBI scores (Whittier et al) |
No |
Model Uncertainty Analysis Reported?
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No | No | No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No | Yes | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Yes | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
Centroid Latitude
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48.2 | 17.96 | 17.73 | 44.2 | 42.26 |
Centroid Longitude
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16.35 | -67.02 | -64.77 | -113.07 | -87.84 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Estimated |
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Rivers and Streams | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Tundra | Ice and Snow | Atmosphere |
Specific Environment Type
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Not applicable | 13 LULC were used | Coral reefs | wadeable and boatable streams | Lake Michigan & Lake Erie waterfront |
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 corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
EM Organismal Scale
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Not applicable | Not applicable | Community | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
None Available | None Available | None Available |
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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-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
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None |
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-184 | EM-428 | EM-446 |
EM-821 ![]() |
EM-895 |
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None |
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None |