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-319 | EM-941 |
EM Short Name
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Landscape importance for crops, Europe | Value of Habitat for Shrimp, Campeche, Mexico | Redfish and cold water coral (EFH), Norway | 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 | Linkage between redfish and cold water coral, Norway (essential fish habitat model) | ESTIMAP - Pollination potential, Iran |
EM Source or Collection
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EU Biodiversity Action 5 | None | None | None |
EM Source Document ID
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228 | 227 | 259 | 434 |
Document Author
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Haines-Young, R., Potschin, M. and Kienast, F. | Barbier, E. B., and Strand, I. | Foley N.S., Kahui V.K., Armstrong C.W., Van Rensburg T.M | Rahimi, E., Barghjelveh, S., and P. Dong |
Document Year
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2012 | 1998 | 2010 | 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 | Estimating linkages between redfish and cold water coral on the Norwegian coast | 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-319 | EM-941 |
Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Marion Potschin | E.B. Barbier | Naomi S. Foley | 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 | Dept. of Economics and Management, Univeristy of Tromso, Norway | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran |
Contact Email
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marion.potschin@nottingham.ac.uk | Not reported | naomifoley@gmail.com | ehsanrahimi666@gmail.com |
EM ID
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EM-99 | EM-106 | EM-319 | 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) | ABSTRACT: "…This paper applies the production function approach to estimate the link between cold water corals and redfish in Norway. Both the carrying capacity and growth rate of redfish are found to be functions of cold water coral habitat and thus cold water corals can be considered an essential fish habitat…The essential habitat model shows the best fit to the data…" AUTHOR'S DESCRIPTION: "…the EFH model presented by Barbier and Strand (1998), in which the habitat is considered essential to the stock; i.e., if the habitat declines to zero the fish stock will perish…based on the Gordon-Schaefer model, which is a single-species biomass model, where effort is the control variable and fish stock is the state variable. In the case of habitat-fisheries interactions, such as in our case, a second state variable is introduced, the habitat (CWC)…Scientists have stimated that 30-50% of CWC habitat has been damaged (Fossa, Mortensen, and Furevik 2002. Working within these bounds, we empirically estimate the relationship between CWC as a habitat and a fish stock..." | 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 | None identified | None reported |
Biophysical Context
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No additional description provided | Gulf of Mexico; mangrove-lagoon system | Continental slope | None additional |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area. | N/A |
EM ID
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EM-99 | EM-106 | EM-319 | EM-941 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | 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-319 | EM-941 |
Document ID for related EM
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Doc-231 | Doc-228 | None | Doc-227 | 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-106 | EM-939 |
EM Modeling Approach
EM ID
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EM-99 | EM-106 | EM-319 | EM-941 |
EM Temporal Extent
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2000 | 1980-1990 | 1986-2002 | 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-319 | EM-941 |
Bounding Type
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Geopolitical | Physiographic or Ecological | Physiographic or ecological | Geopolitical |
Spatial Extent Name
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The EU-25 plus Switzerland and Norway | Laguna de Terminos Mangrove system | Norwegian Sea (ICES areas I and II) | Iran |
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-99 | EM-106 | EM-319 | EM-941 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all 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 | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
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1 km x 1 km | 1 km x 1 km | Not applicable | ha^2 |
EM ID
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EM-99 | EM-106 | EM-319 | 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-319 | EM-941 |
Model Calibration Reported?
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No | Yes | Yes | No |
Model Goodness of Fit Reported?
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No | Yes | Yes | No |
Goodness of Fit (metric| value | unit)
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None |
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None |
Model Operational Validation Reported?
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Yes | No | No | No |
Model Uncertainty Analysis Reported?
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No | Yes | No | No |
Model Sensitivity Analysis Reported?
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No | Yes | Yes | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Unclear | Yes | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-99 | EM-106 | EM-319 | EM-941 |
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None |
Comment:Model for Iran - no form preset id for country |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-99 | EM-106 | EM-319 | EM-941 |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-99 | EM-106 | EM-319 | EM-941 |
Centroid Latitude
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50.53 | 18.61 | 70 | 32.29 |
Centroid Longitude
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7.6 | -91.55 | 10 | 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-319 | EM-941 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Open Ocean and Seas | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Not applicable | Mangrove | cold water coral reefs | 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 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-99 | EM-106 | EM-319 | EM-941 |
EM Organismal Scale
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Not applicable | Guild or Assemblage | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-99 | EM-106 | EM-319 | EM-941 |
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-319 | 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-319 | EM-941 |
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