EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
EM Short Name
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Landscape importance for crops, Europe | FORCLIM v2.9, Western OR, USA | EPA H2O, Tampa Bay Region, FL,USA |
EM Full Name
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Landscape importance for crop-based production, Europe | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | EPA H2O, Tampa Bay Region, FL, USA |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA |
EM Source Document ID
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228 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
321 |
Document Author
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Haines-Young, R., Potschin, M. and Kienast, F. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy |
Document Year
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2012 | 2007 | 2015 |
Document Title
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Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | EPA H20 User Manual |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published EPA report |
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
Not applicable | Not applicable | http://www.epa.gov/ged/tbes/EPAH2O | |
Contact Name
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Marion Potschin | Richard T. Busing | Marc J. Russell, Ph.D. |
Contact Address
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Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 |
Contact Email
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marion.potschin@nottingham.ac.uk | rtbusing@aol.com | russell.marc@epa.gov |
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
Summary Description
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ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | 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." Western Oregon forested ecoregions (Omernick classification) were Coastal Volcanics (1d), Mid-coastal Sedimentary (1g), Willamette Valley (3), West Cascade Lowlands (4a), West Cascade Montane (4b), Cascade Crest (4c), East Cascade Ponderosa Pine (9d), and East Cascade Pumice Plateau (9e). | AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . |
Specific Policy or Decision Context Cited
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None identified | None Identified | None reported |
Biophysical Context
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No additional description provided | Coastal to montane, Pacific Northwest US (Oregon) forests. | Not applicable |
EM Scenario Drivers
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No scenarios presented | Two scenarios modelled, forests with and without fire | Land Use, EGS algorithm values, |
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
Method Only, Application of Method or Model Run
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Method + Application |
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 |
New or Pre-existing EM?
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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
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
Document ID for related EM
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Doc-231 | Doc-228 |
Doc-22 | Doc-23 ?Comment:Related document ID 22 provides tree species specific parameters in appendix. |
None |
EM ID for related EM
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EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | EM-146 | EM-208 | EM-224 | None |
EM Modeling Approach
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
EM Temporal Extent
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2000 | >650 yrs | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | past time | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable |
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
Bounding Type
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Geopolitical | Physiographic or ecological |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Spatial Extent Name
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The EU-25 plus Switzerland and Norway | Western Oregon, north of 43.00 N to Washington border | Tampa Bay region |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. |
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
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) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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1 km x 1 km | 0.08 ha | 30m x 30m |
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
EM Computational Approach
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Logic- or rule-based | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
Model Calibration Reported?
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No | No | No |
Model Goodness of Fit Reported?
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No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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Yes | Yes | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-99 |
EM-186 ![]() |
EM-392 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-99 |
EM-186 ![]() |
EM-392 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
Centroid Latitude
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50.53 | 44.66 | 28.05 |
Centroid Longitude
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7.6 | -122.56 | -82.52 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated |
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Forests | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Not applicable | Primarily conifer forest | All terestrial landcover and waterbodies |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-99 |
EM-186 ![]() |
EM-392 |
EM Organismal Scale
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Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-99 |
EM-186 ![]() |
EM-392 |
None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-99 |
EM-186 ![]() |
EM-392 |
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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)
EM-99 |
EM-186 ![]() |
EM-392 |
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None |
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