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-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
EM Short Name
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Plant species diversity, Central French Alps | Divergence in flowering date, Central French Alps | InVEST carbon storage and sequestration (v3.2.0) | Decrease in erosion (shoreline), St. Croix, USVI | N-SPECT, Sediment and runoff, Isfahan, Iran |
EM Full Name
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Plant species diversity, Central French Alps | Functional divergence in flowering date, Central French Alps | InVEST v3.2.0 Carbon storage and sequestration | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | InVEST | US EPA | None |
EM Source Document ID
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260 | 260 | 315 | 335 | 480 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | The Natural Capital Project | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Khalili, S., Jamali, A.A., Hasanzadeh, M. and Morovvati, A., |
Document Year
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2011 | 2011 | 2015 | 2014 | 2015 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Carbon storage and sequestration - InVEST (v3.2.0) | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran. |
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 |
Comments on Status
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Published journal manuscript | Published journal manuscript | Website | Published journal manuscript | Published journal manuscript |
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | https://coast.noaa.gov/digitalcoast/tools/qnspect.html | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | The Natural Capital Project | Susan H. Yee | Ali Akbar Jamali |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Department of Watershed MGT, Maybod Branch, Islamic Azad University, Maybod, Iran |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | invest@naturalcapitalproject.org | yee.susan@epa.gov | jamaliaa@maybodiau.ac.ir |
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | 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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | Identifying and quantifying the runoff and Sediment yield are the necessary measures in the issues of soil erosion in a watershed. Pelasjan watershed located in West of Isfahan and it is one of the sub basins of Zayanderud which is taken as the study area. In this study the amount of runoff and Sediment yield has been evaluated using the Nonpoint-Source Pollution and Erosion Comparison Tools (N-SPECT) model which is an extension to ArcGIS software. The input layer maps in the GIS environment, including land use, the rain erosion, vegetation, soil erodibility, contour map and watershed boundary map were prepared. By entering the input data and running N-SPECT model, runoff and Sediment yield raster maps of the study area were obtained. To evaluate the model and data comparing, the values obtained from the model and the actual data values of runoff and Sediment yield were converted to the eigenvalues. Special amount of runoff from the model equals 1483 m3/ha/year and the actual runoff is equivalent to 1253 m3/ha/year for 21 water years ,from 1991 to 2012. From the values obtained by the model and the actual data it can be concluded that the model is sufficiently accurate for estimating runoff since the actual runoff value and the value obtained from the model are close to each other and statistically, there is no significant difference between them during this 21 water year. In relation to a Sediment yield, the amount obtained from the model was 7.8 ton/ha/year and the average amount of Sediment yield for 21 water years is 2.1 ton/ha/year, which by comparing with the values obtained for Sediment yield it can be concluded that the model overestimates about three times from the actual amount and there is a significant difference between the real data and data obtained by model so the model has not been very successful in Sediment yield estimating. One of the advantages of this model for estimating runoff and Sediment yield is point to point estimation of runoff and Sediment yield in output maps of the region. This model is particularly recommended for harsh and difficult access regions of the watershed. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None provided |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Not applicable | No additional description provided | Pelasjan watershed, Zagros mountain range |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Optional future scenarios for changed LULC and wood harvest | No scenarios presented | No scenarios presented |
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | Application of existing 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-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | Doc-309 | Doc-335 | Doc-473 |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-349 | EM-447 | EM-448 | EM-1007 | EM-1003 |
EM Modeling Approach
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
EM Temporal Extent
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2007-2009 | 2007-2008 | Not applicable | 2006-2007, 2010 | 1991-2012 |
EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Not applicable | Not applicable |
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Not applicable | Physiographic or ecological | Watershed/Catchment/HUC |
Spatial Extent Name
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Central French Alps | Central French Alps | Not applicable | Coastal zone surrounding St. Croix | Pelasjan watershed |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | Not applicable | 100-1000 km^2 | 1000-10,000 km^2. |
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
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) | spatially distributed (in at least some cases) | spatially lumped (in all 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 | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | application specific | 10 m x 10 m | Not applicable |
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
Model Calibration Reported?
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No | No | Not applicable | Yes | Unclear |
Model Goodness of Fit Reported?
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Yes | Yes | Not applicable | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | No | Not applicable | Yes | Unclear |
Model Uncertainty Analysis Reported?
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No | No | Not applicable | No | Unclear |
Model Sensitivity Analysis Reported?
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No | No | Not applicable | No | Unclear |
Model Sensitivity Analysis Include Interactions?
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Not applicable | 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-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
None | None | None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
Centroid Latitude
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45.05 | 45.05 | -9999 | 17.73 | 32.26 |
Centroid Longitude
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6.4 | 6.4 | -9999 | -64.77 | 50.22 |
Centroid Datum
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WGS84 | WGS84 | Not applicable | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Provided | Not applicable | Estimated | Provided |
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Not applicable | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Terrestrial environments, but not specified for methods | Coral reefs | Desert mountains watershed |
EM Ecological Scale
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Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
EM Organismal Scale
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Community | Community | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
None Available | None Available | 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)
EM-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
None | 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-70 | EM-79 | EM-374 | EM-449 | EM-1017 |
None | None | None |
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None |