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-79 | EM-1017 |
EM Short Name
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Divergence in flowering date, Central French Alps | N-SPECT, Sediment and runoff, Isfahan, Iran |
EM Full Name
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Functional divergence in flowering date, Central French Alps | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran |
EM Source or Collection
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EU Biodiversity Action 5 | None |
EM Source Document ID
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260 | 480 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Khalili, S., Jamali, A.A., Hasanzadeh, M. and Morovvati, A., |
Document Year
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2011 | 2015 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran. |
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-79 | EM-1017 |
Not applicable | https://coast.noaa.gov/digitalcoast/tools/qnspect.html | |
Contact Name
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Sandra Lavorel | Ali Akbar Jamali |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Watershed MGT, Maybod Branch, Islamic Azad University, Maybod, Iran |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | jamaliaa@maybodiau.ac.ir |
EM ID
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EM-79 | EM-1017 |
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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | Identifying and quantifying the runoff and Sediment yield are the necessary measures in the issues of soil erosion in a watershed. Pelasjan watershed located in West of Isfahan and it is one of the sub basins of Zayanderud which is taken as the study area. In this study the amount of runoff and Sediment yield has been evaluated using the Nonpoint-Source Pollution and Erosion Comparison Tools (N-SPECT) model which is an extension to ArcGIS software. The input layer maps in the GIS environment, including land use, the rain erosion, vegetation, soil erodibility, contour map and watershed boundary map were prepared. By entering the input data and running N-SPECT model, runoff and Sediment yield raster maps of the study area were obtained. To evaluate the model and data comparing, the values obtained from the model and the actual data values of runoff and Sediment yield were converted to the eigenvalues. Special amount of runoff from the model equals 1483 m3/ha/year and the actual runoff is equivalent to 1253 m3/ha/year for 21 water years ,from 1991 to 2012. From the values obtained by the model and the actual data it can be concluded that the model is sufficiently accurate for estimating runoff since the actual runoff value and the value obtained from the model are close to each other and statistically, there is no significant difference between them during this 21 water year. In relation to a Sediment yield, the amount obtained from the model was 7.8 ton/ha/year and the average amount of Sediment yield for 21 water years is 2.1 ton/ha/year, which by comparing with the values obtained for Sediment yield it can be concluded that the model overestimates about three times from the actual amount and there is a significant difference between the real data and data obtained by model so the model has not been very successful in Sediment yield estimating. One of the advantages of this model for estimating runoff and Sediment yield is point to point estimation of runoff and Sediment yield in output maps of the region. This model is particularly recommended for harsh and difficult access regions of the watershed. |
Specific Policy or Decision Context Cited
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None identified | None provided |
Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Pelasjan watershed, Zagros mountain range |
EM Scenario Drivers
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No scenarios presented | No scenarios presented |
EM ID
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EM-79 | EM-1017 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised 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-79 | EM-1017 |
Document ID for related EM
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Doc-260 | Doc-269 | Doc-473 |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-1007 | EM-1003 |
EM Modeling Approach
EM ID
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EM-79 | EM-1017 |
EM Temporal Extent
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2007-2008 | 1991-2012 |
EM Time Dependence
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time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable |
EM ID
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EM-79 | EM-1017 |
Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC |
Spatial Extent Name
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Central French Alps | Pelasjan watershed |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 1000-10,000 km^2. |
EM ID
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EM-79 | EM-1017 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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20 m x 20 m | Not applicable |
EM ID
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EM-79 | EM-1017 |
EM Computational Approach
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Analytic | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-79 | EM-1017 |
Model Calibration Reported?
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No | Unclear |
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 | Unclear |
Model Uncertainty Analysis Reported?
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No | Unclear |
Model Sensitivity Analysis Reported?
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No | Unclear |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-79 | EM-1017 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-79 | EM-1017 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-79 | EM-1017 |
Centroid Latitude
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45.05 | 32.26 |
Centroid Longitude
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6.4 | 50.22 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Provided |
EM ID
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EM-79 | EM-1017 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Desert mountains watershed |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-79 | EM-1017 |
EM Organismal Scale
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Community | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-79 | EM-1017 |
None Available | None Available |
EnviroAtlas URL
EM-79 | EM-1017 |
None Available | Average Annual Precipitation |
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-79 | EM-1017 |
None |
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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-79 | EM-1017 |
None | None |