EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
EM Short Name
em.detail.shortNameHelp
?
|
Green biomass production, Central French Alps | Community flowering date, Central French Alps | ESTIMAP - Pollination potential, Iran | CAESAR landscape evolution model |
EM Full Name
em.detail.fullNameHelp
?
|
Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | ESTIMAP - Pollination potential, Iran | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | None |
EM Source Document ID
|
260 | 260 | 434 | 468 |
Document Author
em.detail.documentAuthorHelp
?
|
Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Rahimi, E., Barghjelveh, S., and P. Dong | Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J. |
Document Year
em.detail.documentYearHelp
?
|
2011 | 2011 | 2020 | 2007 |
Document Title
em.detail.sourceIdHelp
?
|
Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
Not applicable | Not applicable | Not applicable | http://www.coulthard.org.uk/ | |
Contact Name
em.detail.contactNameHelp
?
|
Sandra Lavorel | Sandra Lavorel | Ehsan Rahini | Marco J. Van De Wiel |
Contact Address
|
Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | Department of Geography, University of Western Ontario, London, Ontario, Canada |
Contact Email
|
sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | ehsanrahimi666@gmail.com | mvandew3@uwo.ca |
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
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 (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | 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: "Community-weighted mean date of flowering onset 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." | 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." | We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK. |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified | None reported | None identified |
Biophysical Context
|
Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | None additional | River Teifi, Lampeter, Wales |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | N/A | Varying flow velocities and durations |
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | New or revised 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
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-260 | Doc-260 | Doc-269 | Doc-432 | Doc-467 |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-939 | EM-997 |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2007-2009 | 2007-2008 | 2020 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable | continuous |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Central French Alps | Central French Alps | Iran | River Teifi |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. |
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some 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 |
spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
20 m x 20 m | 20 m x 20 m | ha^2 | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Analytic | Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Yes | Yes | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
|
|
None | None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Yes | No | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 | EM-71 | EM-941 | EM-998 |
|
|
Comment:Model for Iran - no form preset id for country |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-71 | EM-941 | EM-998 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
Centroid Latitude
em.detail.ddLatHelp
?
|
45.05 | 45.05 | 32.29 | 52.04 |
Centroid Longitude
em.detail.ddLongHelp
?
|
6.4 | 6.4 | 53.68 | -4.39 |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Provided | Estimated | Estimated |
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Grasslands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | terrestrial land types | River |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Not applicable | Not applicable | 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
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-941 | EM-998 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-71 | EM-941 | EM-998 |
None Available | None Available |
|
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-65 | EM-71 | EM-941 | EM-998 |
None | None |
|
|
<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-65 | EM-71 | EM-941 | EM-998 |
|
None |
|
|