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-367 | EM-699 |
EM Short Name
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InVEST Coastal Blue Carbon | Fish species richness, St. John, USVI, USA |
EM Full Name
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InVEST v3.0 Coastal Blue Carbon | Fish species richness, St. John, USVI, USA |
EM Source or Collection
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InVEST | None |
EM Source Document ID
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310 | 355 |
Document Author
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Natural Capital Project | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco |
Document Year
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2014 | 2007 |
Document Title
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Blue Carbon model - InVEST (v3.0) | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean |
Document Status
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Documented, not peer reviewed | Peer reviewed and published |
Comments on Status
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other | Published journal manuscript |
EM ID
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EM-367 | EM-699 |
http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | Not applicable | |
Contact Name
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Gregg Verutes | Simon Pittman |
Contact Address
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Stanford University | 1305 East-West Highway, Silver Spring, MD 20910, USA |
Contact Email
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gverutes@stanford.edu | simon.pittman@noaa.gov |
EM ID
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EM-367 | EM-699 |
Summary Description
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Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." |
Specific Policy or Decision Context Cited
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None identified | None provided |
Biophysical Context
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Land use land class; habitat type | Hard and soft benthic habitat types approximately to the 33m isobath |
EM Scenario Drivers
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Land use land cover changes; habitat disturbance | No scenarios presented |
EM ID
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EM-367 | EM-699 |
Method Only, Application of Method or Model Run
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Method Only | Method + Application |
New or Pre-existing EM?
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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-367 | EM-699 |
Document ID for related EM
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None | Doc-355 |
EM ID for related EM
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None | EM-590 | EM-698 |
EM Modeling Approach
EM ID
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EM-367 | EM-699 |
EM Temporal Extent
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Not applicable | 2000-2005 |
EM Time Dependence
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time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable |
EM Time Continuity
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discrete | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable |
EM ID
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EM-367 | EM-699 |
Bounding Type
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Not applicable | Physiographic or ecological |
Spatial Extent Name
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Not applicable | SW Puerto Rico, |
Spatial Extent Area (Magnitude)
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Not applicable | 100-1000 km^2 |
EM ID
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EM-367 | EM-699 |
EM Spatial Distribution
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spatially distributed (in at least some 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 |
Spatial Grain Size
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user-specified | not reported |
EM ID
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EM-367 | EM-699 |
EM Computational Approach
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Analytic | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-367 | EM-699 |
Model Calibration Reported?
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Not applicable | No |
Model Goodness of Fit Reported?
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Not applicable | Yes |
Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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Not applicable | Yes |
Model Uncertainty Analysis Reported?
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Not applicable | No |
Model Sensitivity Analysis Reported?
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Not applicable | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | No |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-367 | EM-699 |
None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-367 | EM-699 |
None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-367 | EM-699 |
Centroid Latitude
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-9999 | 17.79 |
Centroid Longitude
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-9999 | -64.62 |
Centroid Datum
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Not applicable | WGS84 |
Centroid Coordinates Status
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Not applicable | Estimated |
EM ID
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EM-367 | EM-699 |
EM Environmental Sub-Class
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Inland Wetlands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine |
Specific Environment Type
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user specified | shallow coral reefs |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-367 | EM-699 |
EM Organismal Scale
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Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-367 | EM-699 |
None Available |
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EnviroAtlas URL
EM-367 | EM-699 |
Carbon Storage by Tree Biomass | None Available |
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-367 | EM-699 |
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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-367 | EM-699 |
None |
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