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-80 | EM-133 |
EM-735 ![]() |
EM Short Name
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Agronomic ES and plant traits, Central French Alps | Flood regulation supply-demand, Etropole, Bulgaria | C sequestration in grassland restoration, England |
EM Full Name
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Agronomic ecosystem service estimated from plant functional traits, Central French Alps | Flood regulation supply vs. demand, Municipality of Etropole, Bulgaria | Carbon sequestration in grassland diversity restoration, England |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | None |
EM Source Document ID
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260 | 248 | 396 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Nedkov, S., Burkhard, B. | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett |
Document Year
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2011 | 2012 | 2011 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Additional carbon sequestration benefits of grassland diversity restoration |
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 journal manuscript |
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Stoyan Nedkov | Gerlinde B. De Deyn |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | snedkov@abv.bg | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com |
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. Maps of demands for flood regulating ecosystem services in the study region were compiled based on a digital elevation model, land use information and accessibility data. Finally, the flood regulating ecosystem service supply and demand data were merged in order to produce a map showing regional supply-demand balances.The flood regulation ecosystem service demand map shows that areas of low or no relevant demands far exceed the areas of high and very high demands, which comprise only 0.6% of the municipality’s area. According to the flood regulation supply-demand balance map, areas of high relevant demands are located in places of low relevant supply capacities" AUTHOR'S DESCRIPTION: "A similar relative scale ranging from 0 to 5 was applied to assess the demands for flood regulation. A 0-value indicates that there is no relevant demand for flood regulation and 5 would indicate the highest demand for flood regulation within the case study region. Values of 2, 3 and 4 represent respective intermediate demands. The calculations were based on the assumption that the most vulnerable areas would have the highest demand for flood regulation. The vulnerability, defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard” (UN/ISDR, 2009), has different dimensions (e.g. social, economic, environmental, institutional). The most vulnerable places in the case study area were defined by using different sources of demographic, statistical, topographic and economic data (Nikolova et al., 2009). These areas will have the highest (5-value) demand for flood regulation…For analyzing source and sink dynamics and to identify flows of ecosystem services, the information in the matrixes and in the maps of ecosystem service supply and demand can be merged (Burkhard et al., 2012). As the landscapes’ flood regulation supply and demand are not analyzed and modeled in the same units it is not possible to calculate the balance between them quantitatively. Using the relative scale (0–5) it becomes possible to compare them and to calculate supply-demand budgets. Although this does not providea clear indication of whether there is excess supply or demand, the resulting map shows where areas of qualitatively high demand correspond with low supply and vice versa." | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Additional benefits due to biodiversity restoration practices |
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | 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 | 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-80 | EM-133 |
EM-735 ![]() |
Document ID for related EM
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Doc-260 | Doc-270 | Doc-248 | None |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-81 | EM-82 | EM-83 | EM-130 | EM-132 | None |
EM Modeling Approach
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
EM Temporal Extent
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Not reported | Not reported | 1990-2007 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable |
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
Bounding Type
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Physiographic or Ecological | Geopolitical | Other |
Spatial Extent Name
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Central French Alps | Municipality of Etropole | Colt Park meadows, Ingleborough National Nature Reserve, northern England |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | <1 ha |
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Distributed by land cover and soil type polygons |
spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
Spatial Grain Size
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20 m x 20 m | Distributed by irregular land cover and soil type polygons | 3 m x 3 m |
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
EM Computational Approach
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Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | stochastic |
Statistical Estimation of EM
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EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
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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No | No | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
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-80 | EM-133 |
EM-735 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-80 | EM-133 |
EM-735 ![]() |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
Centroid Latitude
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45.05 | 42.8 | 54.2 |
Centroid Longitude
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6.4 | 24 | -2.35 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Provided |
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Rivers and Streams | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Grasslands |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Mountainous flood-prone region | fertilized grassland (historically hayed) |
EM Ecological Scale
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Ecological scale is coarser 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 |
Scale of differentiation of organisms modeled
EM ID
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EM-80 | EM-133 |
EM-735 ![]() |
EM Organismal Scale
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Community | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
EM-80 | EM-133 |
EM-735 ![]() |
None Available | None Available | None Available |
EnviroAtlas URL
EM-80 | EM-133 |
EM-735 ![]() |
GAP Ecological Systems, Carbon storage by tree biomass (kg/m2) | None Available | None Available |
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-80 | EM-133 |
EM-735 ![]() |
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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-80 | EM-133 |
EM-735 ![]() |
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None |