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-379 | EM-438 | EM-603 |
EM-660 ![]() |
EM Short Name
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VELMA soil temperature, Oregon, USA | InVESTv3.0 Nutrient retention, Guánica Bay | Chinook salmon value, Yaquina Bay, OR | RUM: Valuing fishing quality, Michigan, USA |
EM Full Name
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VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA |
EM Source or Collection
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US EPA | US EPA | InVEST | US EPA | None |
EM Source Document ID
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317 | 338 | 324 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
Document Author
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Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson |
Document Year
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2013 | 2017 | 2012 | 2014 |
Document Title
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Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Valuing recreational fishing quality at rivers and streams |
Document Status
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Peer reviewed and published | 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 journal manuscript | Published journal manuscript |
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | http://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | |
Contact Name
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Alex Abdelnour | Susan H. Yee | Stephen Jordan | Richard Melstrom |
Contact Address
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Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA |
Contact Email
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abdelnouralex@gmail.com | yee.susan@epa.gov | jordan.steve@epa.gov | melstrom@okstate.edu |
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
Summary Description
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ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model" | 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. | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " |
Specific Policy or Decision Context Cited
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None identified | Improving water quality | None reported | None identified |
Biophysical Context
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Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | No additional description provided | Yaquina Bay estuary | stream and river reaches of Michigan |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A | targeted sport fish biomass |
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
Document ID for related EM
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Doc-13 | Doc-317 | Doc-309 | Doc-205 | None | None |
EM ID for related EM
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EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-363 | EM-112 | EM-604 | EM-397 | None |
EM Modeling Approach
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
EM Temporal Extent
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1969-2008 | 1980 - 2013 | 2003-2008 | 2008-2010 |
EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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future time | other or unclear (comment) | Not applicable | Not applicable |
EM Time Continuity
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discrete | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
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1 | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Day | Year | Not applicable | Not applicable |
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
Bounding Type
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Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC |
Spatial Extent Name
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H. J. Andrews LTER WS10 | Guanica Bay Study Area | Pacific Northwest | HUCS in Michigan |
Spatial Extent Area (Magnitude)
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10-100 ha | 1000-10,000 km^2. | >1,000,000 km^2 | 100,000-1,000,000 km^2 |
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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volume, for 3-D feature | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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30 m x 30 m surface pixel and 2-m depth soil column | 30 m x 30 m | Not applicable | reach in HUC |
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
EM Computational Approach
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Numeric | Numeric | Numeric | 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-379 | EM-438 | EM-603 |
EM-660 ![]() |
Model Calibration Reported?
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No | No | No | No |
Model Goodness of Fit Reported?
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No | No | No | Yes |
Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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No | No |
Yes ?Comment:Compared to a second methodological approach |
No |
Model Uncertainty Analysis Reported?
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No | No | No | No |
Model Sensitivity Analysis Reported?
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No | No | 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-379 | EM-438 | EM-603 |
EM-660 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
None | None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
Centroid Latitude
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44.25 | 17.97 | 44.62 | 45.12 |
Centroid Longitude
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-122.33 | -66.93 | -124.02 | 85.18 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated |
EM ID
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EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
EM Environmental Sub-Class
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Forests | Aquatic Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Created Greenspace | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Rivers and Streams |
Specific Environment Type
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400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | 13 LULC were used | Yaquina Bay | stream reaches |
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-379 | EM-438 | EM-603 |
EM-660 ![]() |
EM Organismal Scale
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Not applicable | Not applicable | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
None Available | None Available |
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EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-379 | EM-438 | EM-603 |
EM-660 ![]() |
None |
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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-379 | EM-438 | EM-603 |
EM-660 ![]() |
None | None | None |
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