EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-93 |
EM-102 
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EM-103 | EM-130 | EM-890 |
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EM Short Name
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Stream nitrogen removal, Mississippi R. basin, USA | Fish species habitat value, Tampa Bay, FL, USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | KINEROS2, River Ravna watershed, Bulgaria | HWB Blood pressure, Great Lakes waterfront, USA |
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EM Full Name
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Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Fish species habitat value, Tampa Bay, FL, USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria | Human well being indicator- Blood pressure, Great Lakes waterfront, USA |
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EM Source or Collection
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US EPA | US EPA | US EPA | EU Biodiversity Action 5 | None |
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EM Source Document ID
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52 | 187 | 275 |
248 ?Comment:Document 277 is also a source document for this EM |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
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Document Author
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Hill, B. and Bolgrien, D. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Nedkov, S., Burkhard, B. | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
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Document Year
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2011 | 2016 | 2014 | 2012 | None |
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Document Title
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Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Human well-being and natural capital indictors for Great Lakes waterfront revitalization |
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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) |
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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 |
Software and Access
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
| Not applicable | Not applicable | Not applicable | http://www.tucson.ars.ag.gov/agwa/ | Not applicable | |
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Contact Name
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Brian Hill | Richard Fulford |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
David C. Goodrich | Ted Angradi |
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Contact Address
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Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 |
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Contact Email
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hill.brian@epa.gov | Fulford.Richard@epa.gov | frazier@nceas.ucsb.edu | agwa@tucson.ars.ag.gov | tedangradi@gmail.com |
EM Description
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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Summary Description
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ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | ABSTRACT: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. The use of the catchment based hydrologic model KINEROS and the GIS AGWA tool provided data about peak rivers’ flows and the capability of different land cover types to “capture” and regulate some parts of the water." AUTHOR'S DESCRIPTION: "KINEROS is a distributed, physically based, event model describing the processes of interception, dynamic infiltration, surface runoff and erosion from watersheds characterized by predominantly overland flow. The watershed is conceptualized as a cascade and the channels, over which the flow is routed in a top–down approach, are using a finite difference solution of the one-dimensional kinematic wave equations (Semmens et al., 2005). Rainfall excess, which leads to runoff, is defined as the difference between precipitation amount and interception and infiltration depth. The rate at which infiltration occurs is not constant but depends on the rainfall rate and the accumulated infiltration amount, or the available moisture condition of the soil. The AGWA tool is a multipurpose hydrologic analysis system addressed to: (1) provide a simple, direct and repeatable method for hydrologic modeling; (2) use basic, attainable GIS data; (3) be compatible with other geospatial basin-based environmental analysis software; and (4) be useful for scenario development and alternative future simulation work at multiple scales (Miller et al., 2002). AGWA provides the functionality to conduct the processes of modeling and assessment for…KINEROS." | 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." |
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Specific Policy or Decision Context Cited
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Not applicable | None identifed | None identified | None identified | None identified |
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Biophysical Context
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Agricultural landuse , 1st-10th order streams | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | Waterfront districts on south Lake Michigan and south lake Erie |
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EM Scenario Drivers
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Not applicable | No scenarios presented | No scenarios presented | No scenarios presented | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | 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 | New or revised 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
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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Document ID for related EM
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Doc-154 | Doc-155 | None | None |
Doc-277 | Doc-294 | Doc-249 | Doc-250 ?Comment:Document 277 is also a source document for this EM |
Doc-422 |
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EM ID for related EM
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None | None | None | EM-132 | EM-133 | EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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EM Temporal Extent
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2000-2008 | 2006-2011 | December 2007 - November 2008 | Not reported | 2022 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not reported | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not reported | Not applicable |
EM Spatial Extent
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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Bounding Type
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Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical |
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Spatial Extent Name
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Upper Mississippi, Ohio and Missouri River sub-basins | Tampa Bay | Yaquina Estuary (intertidal), Oregon, USA | River Ravna watershed | Great Lakes waterfront |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 1000-10,000 km^2. | 1-10 km^2 | 10-100 km^2 | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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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 lumped (in all cases) |
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Spatial Grain Type
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length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (habitat type) | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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1 km | 1 km^2 | 0.87-104.29 ha | 25 m x 25 m | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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Model Calibration Reported?
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No | No | Unclear | Yes | No |
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Model Goodness of Fit Reported?
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No | No | No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | No | No |
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Model Uncertainty Analysis Reported?
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Yes | No | No | No | No |
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Model Sensitivity Analysis Reported?
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Unclear | No | No | No | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
| None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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Centroid Latitude
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36.98 | 27.74 | 44.62 | 42.8 | 42.26 |
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Centroid Longitude
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-89.13 | -82.57 | -124.06 | 24 | -87.84 |
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Centroid Datum
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WGS84 | WGS84 | None provided | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Provided | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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EM Environmental Sub-Class
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Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Not applicable | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Estuarine intertidal | Primarily forested watershed | Lake Michigan & Lake Erie waterfront |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Zone within an ecosystem | 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 and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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EM Organismal Scale
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Not applicable | Species | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
| None Available |
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None Available | 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.detail.cicesHelp
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| EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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None | 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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-93 |
EM-102
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EM-103 | EM-130 | EM-890 |
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None | None |