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-94 |
EM-541 ![]() |
EM-617 |
EM Short Name
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Reduction in pesticide runoff risk, Europe | InVEST fisheries, lobster, South Africa | RBI Spatial Analysis Method |
EM Full Name
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Reduction in pesticide runoff risk, Europe | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method |
EM Source or Collection
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None | InVEST | None |
EM Source Document ID
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255 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
367 |
Document Author
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Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Bousquin, J., Mazzotta M., and W. Berry |
Document Year
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2012 | 2018 | 2017 |
Document Title
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Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. |
Document Status
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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 EPA report |
EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | |
Contact Name
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Sven Lautenbach | Michelle Ward | Justin Bousquin |
Contact Address
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Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 |
Contact Email
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sven.lautenbach@ufz.de | m.ward@uq.edu.au | bousquin.justin@epa.gov |
EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
Summary Description
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AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " |
Specific Policy or Decision Context Cited
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Future rock lobster fisheries management | None identified |
Biophysical Context
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Not applicable | No additional description provided | wetlands |
EM Scenario Drivers
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No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | N/A |
EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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Application of existing model | Application of existing 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-94 |
EM-541 ![]() |
EM-617 |
Document ID for related EM
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Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
None | None |
EM ID for related EM
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None | None | None |
EM Modeling Approach
EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
EM Temporal Extent
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2000 | 1986-2115 | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable |
EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
Bounding Type
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Geopolitical | Geopolitical | Not applicable |
Spatial Extent Name
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EU-27 | Table Mountain National Park Marine Protected Area | Not applicable |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | Not applicable |
EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
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) |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
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10 km x 10 km | Not applicable | Not reported |
EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
EM Computational Approach
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Analytic | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
Model Calibration Reported?
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No | No | Not applicable |
Model Goodness of Fit Reported?
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No | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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Yes |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Not applicable |
Model Uncertainty Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-94 |
EM-541 ![]() |
EM-617 |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-94 |
EM-541 ![]() |
EM-617 |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
Centroid Latitude
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50.53 | -34.18 | Not applicable |
Centroid Longitude
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7.6 | 18.35 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Provided | Not applicable |
EM ID
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EM-94 |
EM-541 ![]() |
EM-617 |
EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Inland Wetlands |
Specific Environment Type
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Streams and near upstream environments | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Restored wetlands |
EM Ecological Scale
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Ecological scale is coarser 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-94 |
EM-541 ![]() |
EM-617 |
EM Organismal Scale
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Not applicable | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-94 |
EM-541 ![]() |
EM-617 |
None Available |
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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-94 |
EM-541 ![]() |
EM-617 |
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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-94 |
EM-541 ![]() |
EM-617 |
None |
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