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
em.detail.idHelp
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EM-59 |
EM-598 |
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EM Short Name
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EnviroAtlas-Air pollutant removal | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland |
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EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | DeNitrification-DeComposition simulation of N2O flux Ireland |
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EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
None |
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EM Source Document ID
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223 | 358 |
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Document Author
em.detail.documentAuthorHelp
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US EPA Office of Research and Development - National Exposure Research Laboratory | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. |
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Document Year
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2013 | 2010 |
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Document Title
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EnviroAtlas - Featured Community | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture |
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Document Status
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Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript |
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
| https://www.epa.gov/enviroatlas | http://www.dndc.sr.unh.edu | |
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Contact Name
em.detail.contactNameHelp
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EnviroAtlas Team | M. Abdalla |
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Contact Address
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Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland |
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Contact Email
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enviroatlas@epa.gov | abdallm@tcd.ie |
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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Summary Description
em.detail.summaryDescriptionHelp
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. |
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Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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None identified | climate change |
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Biophysical Context
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No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C |
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EM Scenario Drivers
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No scenarios presented | fertilization |
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application (multiple runs exist) View EM Runs | Method + Application |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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Application of existing 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
em.detail.idHelp
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EM-59 |
EM-598 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-345 | None |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | EM-593 |
EM Modeling Approach
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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EM Temporal Extent
em.detail.tempExtentHelp
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2008-2010 | 1961-1990 |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-dependent |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | both |
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EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | discrete |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | 1 |
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EM Temporal Grain Size Unit
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Hour | Day |
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Point or points |
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Spatial Extent Name
em.detail.extentNameHelp
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Durham NC and vicinity | Oak Park Research centre |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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100-1000 km^2 | 1-10 ha |
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
spatially lumped (in all cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | Not applicable |
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Numeric |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | Yes |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-59 |
EM-598 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-59 |
EM-598 |
| None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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Centroid Latitude
em.detail.ddLatHelp
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35.99 | 52.86 |
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Centroid Longitude
em.detail.ddLongHelp
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-78.96 | 6.54 |
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Centroid Datum
em.detail.datumHelp
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None provided | None provided |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided |
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Created Greenspace | Atmosphere | Agroecosystems |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | farm pasture |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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 |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-59 |
EM-598 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-59 |
EM-598 |
| None Available | None Available |
EnviroAtlas URL
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EM-59 |
EM-598 |
| Average Annual Precipitation | GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day) |
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)
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EM-59 |
EM-598 |
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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)
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EM-59 |
EM-598 |
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