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-224 
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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EM Short Name
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FORCLIM v2.9, West Cascades, OR, USA | SIRHI, St. Croix, USVI | EnviroAtlas - Restorable wetlands | N-SPECT land-sea planning submodel | WMOSTsustainable water Danvers-Middleton, MA |
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EM Full Name
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FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | SIRHI (SImplified Reef Health Index), St. Croix, USVI | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | A technical guide to the integrated land-sea planning toolkit | WMOST sustainable water management initiative Danvers-Middleton, MA |
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EM Source or Collection
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US EPA | US EPA | US EPA | EnviroAtlas | None | US EPA |
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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. |
335 | 262 | 473 | 477 |
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Document Author
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Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | US EPA Office of Research and Development - National Exposure Research Laboratory | Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K., | United States EPA |
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Document Year
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2007 | 2014 | 2013 | 2009 | 2013 |
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Document Title
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Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | EnviroAtlas - National | Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit. | Watershed Management Optimization Support Tool (WMOST) v1 User manual |
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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 and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published report | Published EPA report |
Software and Access
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EM ID
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
| Not applicable | Not applicable | https://www.epa.gov/enviroatlas | https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download | https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NHEERL&dirEntryId=262280 | |
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Contact Name
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Richard T. Busing | Susan H. Yee | EnviroAtlas Team |
Patrick Crist ?Comment:No contact information provided |
Naomi Detenbeck |
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Contact Address
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U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | None provided | NHEERL, Atlantic Ecology Division Narragansett, RI 02882 |
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Contact Email
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rtbusing@aol.com | yee.susan@epa.gov | enviroatlas@epa.gov | patrick@planitfwd.com | detenbeck.naomi@epa.gov |
EM Description
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EM ID
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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Summary Description
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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…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | 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...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | The Nonpoint-Source Pollution and Erosion Comparison Tool (N-SPECT) is a screening tool developed to help land use planners and mangers understand the potential impacts of land use change decisions on erosion and water quality. The tool runs as an extension within the ESRI ArcGIS software package. It utilizes digital elevation maps, soils and precipitation information from data sets that are available nationally. However, it also lets users take advantage of local higher resolution and/or more accurate data sets when available. For example, the N-SPECT pollution coefficients used are similar to those in the EPA’s BASINS suite of tools and provide a good starting point for quick comparisons between management scenarios, but the coefficients can still be easily customized as users develop more localized data. The real utility of N-SPECT does not lie in the user’s ability to examine the accuracy of any particular run’s results, but in the comparison of runs between different development (or restoration) scenarios. By allowing users to modify multiple land uses and providing the results of those changes in a GIS environment, N-SPECT enables managers to quickly understand the overall consequences of different land use scenarios. The primary role of N-SPECT in this toolkit is to predict sedimentation and pollution changes from different land use scenarios and identify areas that are key contributors of these inputs. | ABSTRACT: "The Watershed Management Optimization Support Tool (WMOST) is intended to be used as a screening tool as part of an integrated watershed management process such as that described in EPA’s watershed planning handbook (EPA 2008).1 The objective of WMOST is to serve as a public-domain, efficient, and user-friendly tool for local water resources managers and planners to screen a widerange of potential water resources management options across their watershed or jurisdiction for costeffectiveness as well as environmental and economic sustainability (Zoltay et al 2010). Examples of options that could be evaluated with the tool include projects related to stormwater, water supply, wastewater and water-related resources such as Low-Impact Development (LID) and land conservation. The tool is intended to aid in evaluating the environmental and economic costs, benefits, trade-offs and co-benefits of various management options. In addition, the tool is intended to facilitate the evaluation of low impact development (LID) and green infrastructure as alternative or complementary management options in projects proposed for State Revolving Funds (SRF). WMOST is a screening model that is spatially lumped with a daily or monthly time step. The model considers water flows but does not yet consider water quality. The optimization of management options is solved using linear programming. The target user group for WMOST consists of local water resources managers, including municipal water works superintendents and their consultants. This document includes a presentation of a case study appling WMOST to the Danvers-Middleton, MA sustainable water management initiative. |
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Specific Policy or Decision Context Cited
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None Identified | None identified | None Identified | None provided | Not applicable |
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Biophysical Context
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West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | No additional description provided | No additional description provided | Not applicable | None |
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EM Scenario Drivers
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Two scenarios modelled, forests with and without fire | No scenarios presented | No scenarios presented | No scenarios presented |
None ?Comment:Not presented with scenarios, but the model was run with multiple scenarios for costs related to varying instream minimum flows and provided the associated costs for each run. |
EM Relationship to Other EMs or Applications
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EM ID
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method + Application | Method Only | Method + Application |
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New or Pre-existing EM?
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Application of existing model | Application of existing model | New or revised model | New or revised 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
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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Document ID for related EM
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Doc-22 | Doc-23 | None | None | Doc-473 | Doc-477 |
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EM ID for related EM
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EM-146 | EM-208 | EM-186 | None | None | EM-1003 | EM-1005 | EM-1006 | EM-1008 | EM-1017 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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EM Temporal Extent
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>650 yrs | 2006-2007, 2010 | 2006-2013 | Not applicable | Not applicable |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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past time | Not applicable | Not applicable | Not applicable |
Not applicable ?Comment:method description |
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EM Time Continuity
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discrete | Not applicable | Not applicable | other or unclear (comment) | discrete |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Day |
EM Spatial Extent
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EM ID
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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Bounding Type
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Physiographic or ecological | Physiographic or ecological | Geopolitical | Not applicable | Watershed/Catchment/HUC |
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Spatial Extent Name
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West Cascades, Oregon | Coastal zone surrounding St. Croix | conterminous United States | Not applicable | Danvers-Middleton |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | Not applicable | 10-100 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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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) | other or unclear (comment) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
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Spatial Grain Size
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0.08 ha | 10 m x 10 m | irregular | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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EM Computational Approach
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Numeric | Analytic | Analytic | Analytic | 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-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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Model Calibration Reported?
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No | Yes | No | Not applicable | Unclear |
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Model Goodness of Fit Reported?
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No | No | No | Not applicable | Unclear |
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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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Yes | Yes | No | Not applicable | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | Not applicable | Not applicable |
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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-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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None |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
| None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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Centroid Latitude
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44.24 | 17.73 | 39.5 | Not applicable | 42.58 |
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Centroid Longitude
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-122.24 | -64.77 | -98.35 | Not applicable | -70.93 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Not applicable | Estimated |
Environments and Scales Modeled
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EM ID
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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EM Environmental Sub-Class
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Forests | Near Coastal Marine and Estuarine | Agroecosystems | Not applicable | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Primarily conifer forest | Coral reefs | Terrestrial | None | watershed |
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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 corresponds to the Environmental Sub-class | Ecological scale is finer than that of 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-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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EM Organismal Scale
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Species | Guild or Assemblage | Not applicable | Community | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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None Available | 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)
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
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None |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM-224
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EM-418 | EM-492 | EM-1007 | EM-1018 |
| None | None | None | None | None |