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-106 |
EM-122 ![]() |
EM Short Name
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Value of Habitat for Shrimp, Campeche, Mexico | Land-use change and crop-based production, Europe |
EM Full Name
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Value of Habitat for Shrimp, Campeche, Mexico | Land-use change effects on crop-based production, Europe |
EM Source or Collection
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None | EU Biodiversity Action 5 |
EM Source Document ID
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227 | 228 |
Document Author
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Barbier, E. B., and Strand, I. | Haines-Young, R., Potschin, M. and Kienast, F. |
Document Year
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1998 | 2012 |
Document Title
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Valuing mangrove-fishery linkages: A case study of Campeche, Mexico | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript |
EM ID
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EM-106 |
EM-122 ![]() |
Not applicable | Not applicable | |
Contact Name
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E.B. Barbier | Marion Potschin |
Contact Address
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Environment Department, University of York, York YO1 5DD, UK | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom |
Contact Email
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Not reported | marion.potschin@nottingham.ac.uk |
EM ID
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EM-106 |
EM-122 ![]() |
Summary Description
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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: "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 novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." |
Specific Policy or Decision Context Cited
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None identified | None identified |
Biophysical Context
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Gulf of Mexico; mangrove-lagoon system | No additional description provided |
EM Scenario Drivers
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No scenarios presented | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) |
EM ID
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EM-106 |
EM-122 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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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-106 |
EM-122 ![]() |
Document ID for related EM
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None | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 |
EM ID for related EM
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EM-185 | EM-319 | EM-123 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 |
EM Modeling Approach
EM ID
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EM-106 |
EM-122 ![]() |
EM Temporal Extent
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1980-1990 | 1990-2030 |
EM Time Dependence
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time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | future time |
EM Time Continuity
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Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | 6, 10, and 30 |
EM Temporal Grain Size Unit
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Year | Year |
EM ID
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EM-106 |
EM-122 ![]() |
Bounding Type
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Physiographic or Ecological | Geopolitical |
Spatial Extent Name
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Laguna de Terminos Mangrove system | The EU-25 plus Switzerland and Norway |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | >1,000,000 km^2 |
EM ID
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EM-106 |
EM-122 ![]() |
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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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 |
EM ID
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EM-106 |
EM-122 ![]() |
EM Computational Approach
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Analytic | Logic- or rule-based |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-106 |
EM-122 ![]() |
Model Calibration Reported?
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Yes | No |
Model Goodness of Fit Reported?
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Yes | No |
Goodness of Fit (metric| value | unit)
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None |
Model Operational Validation Reported?
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No | No |
Model Uncertainty Analysis Reported?
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Yes | No |
Model Sensitivity Analysis Reported?
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Yes | No |
Model Sensitivity Analysis Include Interactions?
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Unclear | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-106 |
EM-122 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-106 |
EM-122 ![]() |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-106 |
EM-122 ![]() |
Centroid Latitude
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18.61 | 50.53 |
Centroid Longitude
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-91.55 | 7.6 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated |
EM ID
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EM-106 |
EM-122 ![]() |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Mangrove | Not applicable |
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-106 |
EM-122 ![]() |
EM Organismal Scale
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Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-106 |
EM-122 ![]() |
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None Available |
EnviroAtlas URL
EM-106 |
EM-122 ![]() |
GAP Ecological Systems, Big game hunting recreation demand | Hectares of Vegetable Crops |
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-106 |
EM-122 ![]() |
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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-106 |
EM-122 ![]() |
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