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-99 | EM-106 |
EM-345 ![]() |
EM-941 |
EM Short Name
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Landscape importance for crops, Europe | Value of Habitat for Shrimp, Campeche, Mexico | InVEST habitat quality, Puli Township, Taiwan | ESTIMAP - Pollination potential, Iran |
EM Full Name
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Landscape importance for crop-based production, Europe | Value of Habitat for Shrimp, Campeche, Mexico | InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) habitat quality, Puli Township, Taiwan | ESTIMAP - Pollination potential, Iran |
EM Source or Collection
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EU Biodiversity Action 5 | None | InVEST | None |
EM Source Document ID
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228 | 227 | 308 | 434 |
Document Author
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Haines-Young, R., Potschin, M. and Kienast, F. | Barbier, E. B., and Strand, I. | Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T. | Rahimi, E., Barghjelveh, S., and P. Dong |
Document Year
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2012 | 1998 | 2014 | 2020 |
Document Title
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Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Valuing mangrove-fishery linkages: A case study of Campeche, Mexico | Assessing highway's impacts on landscape patterns and ecosystem services: A case study in Puli Township, Taiwan | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) |
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-99 | EM-106 |
EM-345 ![]() |
EM-941 |
Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | |
Contact Name
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Marion Potschin | E.B. Barbier |
Yu-Pin Lin ?Comment:Tel.: +886 2 3366 3467; fax: +866 2 2368 6980 |
Ehsan Rahini |
Contact Address
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Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Environment Department, University of York, York YO1 5DD, UK | Not reported | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran |
Contact Email
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marion.potschin@nottingham.ac.uk | Not reported | yplin@ntu.edu.tw | ehsanrahimi666@gmail.com |
EM ID
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EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
Summary Description
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ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153) | 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. ABSTRACT: "...To assess the effects of different land-use scenarios under various agricultural and environmental conservation policy regimes, this study applies an integrated approach to analyze the effects of Highway 6 construction on Puli Township...A habitat quality assessment using the InVEST model indicates that the conservation of agricultural and forested lands improves habitat quality and preserves rare habitats…" AUTHOR'S DESCRIPTION: "In total, three land-use planning scenarios were simulated based on government policies in Taiwan’s Hillside Protection Act and Regulations on Non-Urban Land Utilization Control. The baseline planning scenario, Scenario A, allows land-use development with-out land-use controls (Appendix Fig. S2), meaning that land-use changes can occur anywhere. Scenario B is based on the Regulations on Non-Urban Land Utilization Control and the maintenance of agricultural areas, such that land-use changes cannot occur in agricultural areas. Scenario C protects agricultural land, hillsides, and naturally forested areas from development...The biodiversity evaluation module in the InVEST model assessed the degree of change in habitat quality and habitat rarity under three scenarios. In the InVEST model, habitat quality is primarily threatened by four factors: the relative impact of each threat; the relative sensitivity of each habitat type to each threat; the distance between habitats and sources of threats; as well as the relative degree to which land is legally protected..." Use of other models in conjunction with this model: Land use data for future scenarios modeled in InVEST were derived from a linear regression model of land use change, and the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model for apportioning those changes to the landscape. | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." |
Specific Policy or Decision Context Cited
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None identified | None identified | Environmental effects of Highway 6 construction on Puli Township, Taiwan | None reported |
Biophysical Context
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No additional description provided | Gulf of Mexico; mangrove-lagoon system | 26% of the land area is categorized as plain and the remaining 74% is categorized as hilly with elevations of 380-700 m. Predominant land classes are forested (47.4%), cultivated (31.8%), and built-up (14.5%). Average annual rainfall is 2120 mm, and average annual temperature is 21°C. The soil in the eastern portion of the basin is primarily clay, and primarily loess elsewhere. | None additional |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Three scenarios; baseline planning (A, without land-use controls), scenario B based on maintenance of agriculture, scenario C protects agriculture, hillsides and naturally forested areas. | N/A |
EM ID
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EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
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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-99 | EM-106 |
EM-345 ![]() |
EM-941 |
Document ID for related EM
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Doc-231 | Doc-228 | None | Doc-278 | Doc-432 |
EM ID for related EM
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EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | EM-185 | EM-319 | EM-143 | EM-939 |
EM Modeling Approach
EM ID
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EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
EM Temporal Extent
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2000 | 1980-1990 | 2010-2025 | 2020 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary |
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 | Year | Not applicable | Not applicable |
EM ID
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EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
Bounding Type
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Geopolitical | Physiographic or Ecological | Geopolitical | Geopolitical |
Spatial Extent Name
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The EU-25 plus Switzerland and Norway | Laguna de Terminos Mangrove system | Puli Township, Nantou County | Iran |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | >1,000,000 km^2 |
EM ID
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EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
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) ?Comment:Varies by inputs, but results are for areas of country |
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 |
Spatial Grain Size
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1 km x 1 km | 1 km x 1 km | 40 m x 40 m | ha^2 |
EM ID
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EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
EM Computational Approach
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Logic- or rule-based | Analytic | 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-99 | EM-106 |
EM-345 ![]() |
EM-941 |
Model Calibration Reported?
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No | Yes | Unclear | No |
Model Goodness of Fit Reported?
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No | Yes | Not applicable | No |
Goodness of Fit (metric| value | unit)
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None |
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None | None |
Model Operational Validation Reported?
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Yes | No | Not applicable | No |
Model Uncertainty Analysis Reported?
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No | Yes | No | No |
Model Sensitivity Analysis Reported?
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No | Yes | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
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Comment:Taiwan |
Comment:Model for Iran - no form preset id for country |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
Centroid Latitude
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50.53 | 18.61 | 23.98 | 32.29 |
Centroid Longitude
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7.6 | -91.55 | 120.96 | 53.68 |
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-99 | EM-106 |
EM-345 ![]() |
EM-941 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Not applicable | Mangrove | Predominantly an agricultural area with associated forest land | terrestrial land types |
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 corresponds to 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-99 | EM-106 |
EM-345 ![]() |
EM-941 |
EM Organismal Scale
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Not applicable | Guild or Assemblage | Community | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-99 | EM-106 |
EM-345 ![]() |
EM-941 |
None Available |
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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-99 | EM-106 |
EM-345 ![]() |
EM-941 |
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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-99 | EM-106 |
EM-345 ![]() |
EM-941 |
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