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-319 | EM-450 | EM-941 |
EM Short Name
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Landscape importance for crops, Europe | Redfish and cold water coral (EFH), Norway | Decrease in wave runup, St. Croix, USVI | ESTIMAP - Pollination potential, Iran |
EM Full Name
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Landscape importance for crop-based production, Europe | Linkage between redfish and cold water coral, Norway (essential fish habitat model) | Decrease in wave runup (by reef), St. Croix, USVI | ESTIMAP - Pollination potential, Iran |
EM Source or Collection
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EU Biodiversity Action 5 | None | US EPA | None |
EM Source Document ID
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228 | 259 | 335 | 434 |
Document Author
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Haines-Young, R., Potschin, M. and Kienast, F. | Foley N.S., Kahui V.K., Armstrong C.W., Van Rensburg T.M | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Rahimi, E., Barghjelveh, S., and P. Dong |
Document Year
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2012 | 2010 | 2014 | 2020 |
Document Title
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Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Estimating linkages between redfish and cold water coral on the Norwegian coast | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | 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-319 | EM-450 | EM-941 |
Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Marion Potschin | Naomi S. Foley | Susan H. Yee | Ehsan Rahini |
Contact Address
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Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Dept. of Economics and Management, Univeristy of Tromso, Norway | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran |
Contact Email
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marion.potschin@nottingham.ac.uk | naomifoley@gmail.com | yee.susan@epa.gov | ehsanrahimi666@gmail.com |
EM ID
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EM-99 | EM-319 | EM-450 | 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." | 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 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, storm damage, or coastal inundation during extreme events...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | 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 | Continental slope | No additional description provided | None additional |
EM Scenario Drivers
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No scenarios presented | Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area. | No scenarios presented | N/A |
EM ID
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EM-99 | EM-319 | EM-450 | 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 | Application of existing 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-319 | EM-450 | EM-941 |
Document ID for related EM
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Doc-231 | Doc-228 | Doc-227 | Doc-335 | 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-106 | EM-447 | EM-939 |
EM Modeling Approach
EM ID
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EM-99 | EM-319 | EM-450 | EM-941 |
EM Temporal Extent
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2000 | 1986-2002 | 2006-2007, 2010 | 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 | Not applicable | Not applicable | Not applicable |
EM ID
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EM-99 | EM-319 | EM-450 | 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 | Norwegian Sea (ICES areas I and II) | Coastal zone surrounding St. Croix | Iran |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | >1,000,000 km^2 |
EM ID
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EM-99 | EM-319 | EM-450 | EM-941 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all 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 | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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1 km x 1 km | Not applicable | 10 m x 10 m | ha^2 |
EM ID
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EM-99 | EM-319 | EM-450 | 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-319 | EM-450 | EM-941 |
Model Calibration Reported?
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No | Yes | Yes | No |
Model Goodness of Fit Reported?
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No | Yes | No | 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 | Yes | No |
Model Uncertainty Analysis Reported?
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No | No | 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 | Yes | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-99 | EM-319 | EM-450 | EM-941 |
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None | None |
Comment:Model for Iran - no form preset id for country |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-99 | EM-319 | EM-450 | EM-941 |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-99 | EM-319 | EM-450 | EM-941 |
Centroid Latitude
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50.53 | 70 | 17.73 | 32.29 |
Centroid Longitude
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7.6 | 10 | -64.77 | 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-319 | EM-450 | EM-941 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Open Ocean and Seas | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Not applicable | cold water coral reefs | 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-319 | EM-450 | EM-941 |
EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-99 | EM-319 | EM-450 | 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-319 | EM-450 | 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-319 | EM-450 | EM-941 |
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