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-208 |
EM-598 |
EM-667 |
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EM Short Name
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FORCLIM v2.9, Santiam watershed, OR, USA | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Alewife derived nutrients, Connecticut, USA |
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EM Full Name
em.detail.fullNameHelp
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FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Alewife derived nutrients in stream food web, Connecticut, USA |
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EM Source or Collection
em.detail.emSourceOrCollectionHelp
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US EPA | None | None |
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EM Source Document ID
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23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
358 | 384 |
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Document Author
em.detail.documentAuthorHelp
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Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Walters, A. W., R. T. Barnes, and D. M. Post |
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Document Year
em.detail.documentYearHelp
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2007 | 2010 | 2009 |
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Document Title
em.detail.sourceIdHelp
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Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | 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 | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs |
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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
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
| Not applicable | http://www.dndc.sr.unh.edu | Not applicable | |
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Contact Name
em.detail.contactNameHelp
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Richard T. Busing | M. Abdalla | Annika W. Walters |
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Contact Address
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U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA |
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Contact Email
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rtbusing@aol.com | abdallm@tcd.ie | annika.walters@yale.edu |
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EM ID
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
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Summary Description
em.detail.summaryDescriptionHelp
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ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045. | 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. | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." |
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Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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None identified | climate change | None identified |
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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 | Alewife spawning runs typically occur Mid March - May. |
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EM Scenario Drivers
em.detail.scenarioDriverHelp
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Land Management (3); Climate Change (3) | fertilization | No scenarios presented |
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EM ID
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
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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 ?Comment:Runs differentiated by scenario combination. |
Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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Application of existing model | Application of existing model | New or revised 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-208 |
EM-598 |
EM-667 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-22 | Doc-23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
None | Doc-383 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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EM-146 | EM-186 | EM-224 | EM-593 | EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673 |
EM Modeling Approach
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EM ID
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
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EM Temporal Extent
em.detail.tempExtentHelp
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1990-2050 | 1961-1990 | 1979-2009 |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | both | Not applicable |
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EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Year | Day | Not applicable |
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EM ID
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
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Bounding Type
em.detail.boundingTypeHelp
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Watershed/Catchment/HUC | Point or points | Watershed/Catchment/HUC |
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Spatial Extent Name
em.detail.extentNameHelp
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South Santiam watershed | Oak Park Research centre | Bride Brook |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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100-1000 km^2 | 1-10 ha | 1-10 ha |
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EM ID
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | Not applicable | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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0.08 ha | Not applicable | Not applicable |
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EM ID
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Numeric | Analytic |
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EM Determinism
em.detail.deterStochHelp
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deterministic | 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-208 |
EM-598 |
EM-667 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | Yes |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
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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 |
No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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N/A | 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-208 |
EM-598 |
EM-667 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-208 |
EM-598 |
EM-667 |
| None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
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Centroid Latitude
em.detail.ddLatHelp
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44.24 | 52.86 | 41.32 |
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Centroid Longitude
em.detail.ddLongHelp
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-122.24 | 6.54 | -72.24 |
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Centroid Datum
em.detail.datumHelp
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None provided | None provided | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Provided |
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EM ID
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Forests | Agroecosystems | Rivers and Streams |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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primarily Conifer Forest | farm pasture | Coastal stream |
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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 | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-208 |
EM-598 |
EM-667 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Species | Not applicable | Individual or population, within a species |
Taxonomic level and name of organisms or groups identified
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EM-208 |
EM-598 |
EM-667 |
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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)
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EM-208 |
EM-598 |
EM-667 |
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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)
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EM-208 |
EM-598 |
EM-667 |
| None |
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