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-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
EM Short Name
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Area and hotspots of soil retention, South Africa | Envision, Puget Sound, WA, USA | Chinook salmon value (household), Yaquina Bay, OR | WESP: Riparian & stream habitat, ID, USA |
EM Full Name
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Area and hotspots of soil retention, South Africa | Envision, Puget Sound, WA, USA | Economic value of Chinook salmon per household method, Yaquina Bay, OR | WESP: Riparian and stream habitat focus projects, ID, USA |
EM Source or Collection
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None | Envision | US EPA | None |
EM Source Document ID
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271 |
313 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
324 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Bolte, J. and Vache, K. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Murphy, C. and T. Weekley |
Document Year
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2008 | 2010 | 2012 | 2012 |
Document Title
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Mapping ecosystem services for planning and management | Envisioning Puget Sound Alternative Futures: PSNERP Final Report | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. |
Document Status
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Peer reviewed and published | Documentation is peer-reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published report | Published journal manuscript | Published report |
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
Not applicable | http://envision.bioe.orst.edu | Not applicable | Not applicable | |
Contact Name
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Benis Egoh |
John Bolte ?Comment:Phone# 541-737-2041 |
Stephen Jordan | Chris Murphy |
Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Oregon State University, Dept. of Biological & Ecological Engineering, 116C Gilmore Hall, Corvallis, OR 97333 | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID |
Contact Email
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Not reported | boltej@engr.orst.edu | jordan.steve@epa.gov | chris.murphy@idfg.idaho.gov |
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
Summary Description
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AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | SUMMARY: "...the Puget Sound Nearshore Ecosystem Restoration Project, completed an analysis of alternative future regional trajectories of landscape change for the Puget Sound region. This effort developed three scenarios of change: 1) Status Quo, reflecting a continuation of current trends in the region, 2) Managed Growth, reflecting the adoption of an aggressive set of land use management policies focusing on protecting and restoring ecosystem function and concentrating growth within Urban Growth Areas (UGA) and near regional growth centers, and 3) Unmanaged Growth, reflecting a relaxation of land use restrictions with limited protection of ecosystem functions. Analyses assumed a fixed population growth rate across all three scenarios, defined by the Washington Office of Financial Management county level growth estimates. Scenarios were generated using a spatially- and temporally-explicit alternative futures analysis model, Envision, previously developed by Oregon State University researchers. The model accepts as input a vector-based representation of the landscape and associated datasets describing relevant landscape characteristics, descriptors of various processes influencing landscape change, and a set of policies, or decision alternatives, which reflect scenario-specific land management alternatives. The model generates 1) a set of spatial coverages (maps) reflecting scenario outcomes of a variety of landscape variables, most notably land use/land cover, shoreline modifications, and population projections, and 2) a set of summary statistics describing landscape change variables summarized across spatial reporting units. Analyses were run on each of such sub-basins in the Puget Sound, and aggregated to providing Sound-wide results. This information is being used by PSNERP to project future impairment of ecosystem functions, goods, and services. The Puget Sound Nearshore Ecosystem project data also provide inputs to calculate aspects of future nearshore process degradation. Impairment and degradation are primary factors being used to define future conditions for the PSNERP General Investigation Study." AUTHOR'S DESCRIPTION: "In this report, we document the application of an alternative futures analysis framework that incorporates these capabilities to the analysis of alternative future trajectories in the Puget Sound region. This framework, Envision (Bolte et al, 2007; Hulse et al. 2008) is a spatially and temporally explicit, standards-based, open source toolset specifically designed to facilitate alternative futures analyses. It employs a multiagent-based modeling approach that contains a robust capability for defining alternative management strategies and scenarios, incorporating a variety of landscape change processes, and creating maps of alternative landscape trajectories, expressed though a variety of metrics defined in an application-specific way." ABOUT ENVISION (ENVISION WEBSITE): "Central to Envision, and conceived at the s | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified |
Biophysical Context
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Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | Yaquina Bay estuary | restored, enhanced and created wetlands |
EM Scenario Drivers
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No scenarios presented | Alternative future land management strategies (status quo, managed growth, unmanaged growth) | No scenarios presented | Sites, function or habitat focus |
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
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 (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
Document ID for related EM
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Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
Doc-314 | Doc-47 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
Doc-324 | Doc-390 |
EM ID for related EM
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EM-85 | EM-87 | EM-88 | EM-12 | EM-333 | EM-603 | EM-397 | EM-706 | EM-729 | EM-730 | EM-734 | EM-743 | EM-749 | EM-750 | EM-756 | EM-757 | EM-758 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-732 | EM-737 | EM-738 | EM-739 | EM-741 | EM-742 | EM-751 | EM-768 |
EM Modeling Approach
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
EM Temporal Extent
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Not reported | 2000-2060 | 2003-2008 | 2010-2011 |
EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | past time |
EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable | Not applicable |
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
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South Africa | Puget Sound watershed | Pacific Northwest | Wetlands in idaho |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 |
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | Irregular | Not applicable | Not applicable |
Spatial Grain Size
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Distributed across catchments with average size of 65,000 ha | Varies | Not applicable | Not applicable |
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
EM Computational Approach
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Analytic | Numeric | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
Model Calibration Reported?
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No | Unclear | No | No |
Model Goodness of Fit Reported?
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No | Not applicable | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None | None |
Model Operational Validation Reported?
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No | Not applicable | Yes | No |
Model Uncertainty Analysis Reported?
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No | Not applicable | No | No |
Model Sensitivity Analysis Reported?
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No | Not applicable | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
Centroid Latitude
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-30 | 47.58 | 44.62 | 44.06 |
Centroid Longitude
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25 | -122.32 | -124.02 | -114.69 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated |
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands |
Specific Environment Type
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Not reported | Pacific NW US region, coastal to montane, urban to rural | Yaquina Bay estuary and ocean | created, restored and enhanced wetlands |
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 is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
EM Organismal Scale
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Not applicable | Not applicable | Other (multiple scales) | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
None Available | 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-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
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None |
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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-86 |
EM-369 ![]() |
EM-604 |
EM-718 ![]() |
None | None |
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None |