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-79 | EM-469 |
EM-729 ![]() |
EM-887 |
EM Short Name
em.detail.shortNameHelp
?
|
Divergence in flowering date, Central French Alps | Yasso07 - SOC, Loess Plateau, China | WESP: Urban Stormwater Treatment, ID, USA | VELMA v. 2.0 disturbance |
EM Full Name
em.detail.fullNameHelp
?
|
Functional divergence in flowering date, Central French Alps | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | WESP: Urban Stormwater Treament, ID, USA | VELMA (Visualizing Ecosystems for Land Management Assessment) version 2.0 disturbance |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
EU Biodiversity Action 5 | None | None | US EPA |
EM Source Document ID
|
260 | 344 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
366 |
Document Author
em.detail.documentAuthorHelp
?
|
Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Murphy, C. and T. Weekley | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips |
Document Year
em.detail.documentYearHelp
?
|
2011 | 2015 | 2012 | 2014 |
Document Title
em.detail.sourceIdHelp
?
|
Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | VELMA Version 2.0 User Manual and Technical Documentation |
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 report | Published report |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | |
Contact Name
em.detail.contactNameHelp
?
|
Sandra Lavorel | Xing Wu | Chris Murphy | Robert B. McKane |
Contact Address
|
Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Chinese Academy of Sciences, Beijing 100085, China | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | U.S. EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 |
Contact Email
|
sandra.lavorel@ujf-grenoble.fr | xingwu@rceesac.cn | chris.murphy@idfg.idaho.gov | mckane.bob@epa.gov |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
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, 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." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | VELMA – Visualizing Ecosystems for Land Management Assessments - is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas. AUTHOR'S DESCRIPTION: "Understanding how disturbances such as harvest, fire and fertilization affect ecosystem services has been a major motivation in the development of VELMA. For example, how do disturbances such as forest harvest or the application of agronomic fertilizers affect hydrological and biogeochemical processes controlling water quality and quantity, carbon sequestration, production of greenhouse gases, etc.? Abdelnour et al. (2011, 2013) have already demonstrated the use of VELMA v1.0 to simulate the effects of forest clearcutting on ecohydrological processes that regulate a variety of ecosystem services. With the addition of a tissue-specific plant biomass (LSR) simulator and an enhanced GUI, VELMA v2.0 significantly expands the detail, flexibility, and ease of use for simulating disturbance effects. Currently available disturbance models include: - BurnDisturbanceModel, effects of fire. - GrazeDisturbanceModel, effects of grazing. - FertilizeLsrDisturbanceModel, effects of fertilizer applications. - HarvestLsrDisturbanceModel, effects of biomass harvest. Each of these disturbance models specifies where and when a disturbance event will occur. The Burn, Graze and Harvest models have options for specifying how much of each plant tissue and detritus pool (leaves, stems, roots) will be removed and where it goes (offsite and/or to a specified onsite C and N pools). The Fertilize model has options for applying nitrogen as ammonium, nitrate, urea and/or manure." |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None | None identified | None identified |
Biophysical Context
|
Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Agricultural plain, hills, gulleys, forest, grassland, Central China | restored, enhanced and created wetlands | No additional description provided |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | Land use change | Sites, function or habitat focus | No scenarios presented |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | Application of existing model | WESP - Urban Stormwater Treatment | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-260 | Doc-269 | Doc-343 | Doc-342 | Doc-390 | Doc-13 | Doc-317 | Doc-366 | Doc-359 |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-466 | EM-467 | EM-480 | EM-485 | EM-718 | EM-734 | EM-883 | EM-884 | EM-375 | EM-379 | EM-380 | EM-605 | EM-892 |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2007-2008 | 1969-2011 | 2010-2011 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | past time | past time | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | discrete | Not applicable | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | 1 | Not applicable | 1 |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Year | Not applicable | Day |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or Ecological | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Central French Alps | Yangjuangou catchment | Wetlands in idaho | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
20 m x 20 m | 30m x 30m | Not applicable | user defined |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Numeric | Numeric | Numeric |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | Yes | No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Yes |
Yes ?Comment:For the year 2006 and 2011 |
No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
|
|
None | None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | 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-79 | EM-469 |
EM-729 ![]() |
EM-887 |
|
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
Centroid Latitude
em.detail.ddLatHelp
?
|
45.05 | 36.7 | 44.06 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
?
|
6.4 | 109.52 | -114.69 | Not applicable |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Provided | Estimated | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Subalpine terraces, grasslands, and meadows | Loess plain | created, restored and enhanced wetlands | Terrestrial environment sub-classes |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is coarser 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
em.detail.idHelp
?
|
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-79 | EM-469 |
EM-729 ![]() |
EM-887 |
None Available | 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-79 | EM-469 |
EM-729 ![]() |
EM-887 |
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-79 | EM-469 |
EM-729 ![]() |
EM-887 |
None | None | None | None |