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-88 |
EM-148 ![]() |
EM-195 | EM-939 |
EM Short Name
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Area and hotspots of carbon storage, South Africa | InVEST - Water provision, Francoli River, Spain | C Sequestration and De-N, Tampa Bay, FL, USA | ESTIMAP- Recreation, Europe |
EM Full Name
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Area and hotspots of carbon storage, South Africa | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | ESTIMAP- Recreation, Europe |
EM Source or Collection
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None | InVEST | US EPA | None |
EM Source Document ID
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271 | 280 | 186 | 432 |
Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | Russell, M. and Greening, H. | Zulian, G., Parrachini, M.L., Maes, J., |
Document Year
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2008 | 2013 | 2013 | 2013 |
Document Title
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Mapping ecosystem services for planning and management | The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | Estimating benefits in a recovering estuary: Tampa Bay, Florida | ESTIMAP: Ecosystem services mapping at the European scale |
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 report |
EM ID
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EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | N.A. | |
Contact Name
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Benis Egoh | Montse Marquès | M. Russell | Grazia Zulian |
Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy |
Contact Email
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Not reported | montserrat.marques@fundacio.urv.cat | Russell.Marc@epamail.epa.gov | grazia.zulian@jrc.ec.europa.e |
EM ID
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EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
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…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot." | 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: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | AUTHOR Descriptions: "ESTIMAP consists of a set of separate components, each of which can be run separately. The models have been all framed in the ecosystem services cascade model [4] which connects ecosystem structure and functioning to human well-being through the flow of ecosystem services. At present, three modules are operational and described in further detail in this report: pollination, recreation and coastal protectionPeople can benefit from the opportunities provided by nature for recreational activities if they are able to reach them. The Recreation Opportunity spectrum was chosen as a method to map different degrees of service available according to their proximity to the people. Remoteness and proximity have been addressed in the second step of the analysis, in order to assess how the benefit (recreation) can be delivered to people. The proxy that has been identified couples information on both variables and has been mapped by classifying the EU into zones of proximity versus remoteness. From the ROS perspective this part takes into account remoteness and to some extent expected social experience. Distance from roads and residential areas have been used as inputs. The information on the road network is provided by the TeleAtlas database, and covers all paved roads in Europe. Gravel roads have been discarded to ease the processing. Residential areas are extracted from CORINE land cover classes “continuous urban fabric” and “discontinuous urban fabric”, therefore, all urban patches larger than 25 ha are considered in the mapping. In the current exercise there was the necessity to adapt overseas experiences to the peculiarities of the European continent, especially considering that the EU does not contain large wilderness areas like other continents " |
Specific Policy or Decision Context Cited
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None identified | None identified | Restoration of seagrass | None |
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. | Mediteranean coastal mountains | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | Continential Scale |
EM Scenario Drivers
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No scenarios presented | IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | Habitat loss or restoration in Tampa Bay Estuary | N.A. |
EM ID
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EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
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 Only |
New or Pre-existing EM?
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New or revised 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-88 |
EM-148 ![]() |
EM-195 | EM-939 |
Document ID for related EM
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Doc-271 | Doc-307 | Doc-311 | Doc-338 | Doc-205 | None | None |
EM ID for related EM
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EM-85 | EM-86 | EM-87 | EM-344 | EM-368 | EM-437 | EM-111 | None | EM-941 |
EM Modeling Approach
EM ID
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EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
EM Temporal Extent
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Not reported | 1971-2100 | 1982-2010 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | Not applicable |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Physiographic or Ecological | No location (no locational reference given) |
Spatial Extent Name
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South Africa | Francoli River | Tampa Bay Estuary | Not applicable |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 |
EM ID
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EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
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) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | 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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Distributed across catchments with average size of 65,000 ha | 30m x 30m | 1 ha | Pixel size |
EM ID
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EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
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-88 |
EM-148 ![]() |
EM-195 | EM-939 |
Model Calibration Reported?
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No | No | Yes | No |
Model Goodness of Fit Reported?
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No | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None | None |
Model Operational Validation Reported?
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No |
Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
No | Unclear |
Model Uncertainty Analysis Reported?
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No | No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
None | None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
Centroid Latitude
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-30 | 41.26 | 27.95 | Not applicable |
Centroid Longitude
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25 | 1.18 | -82.47 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Not applicable |
EM ID
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EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Not applicable | Coastal mountains | Subtropical Estuary | Not applicable |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to 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-88 |
EM-148 ![]() |
EM-195 | EM-939 |
EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-88 |
EM-148 ![]() |
EM-195 | EM-939 |
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-88 |
EM-148 ![]() |
EM-195 | EM-939 |
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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-88 |
EM-148 ![]() |
EM-195 | EM-939 |
None |
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None |