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-86 | EM-326 |
EM-718 |
|
EM Short Name
em.detail.shortNameHelp
?
|
Area and hotspots of soil retention, South Africa | ARIES flood regulation, Puget Sound Region, USA | WESP: Riparian & stream habitat, ID, USA |
|
EM Full Name
em.detail.fullNameHelp
?
|
Area and hotspots of soil retention, South Africa | ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | WESP: Riparian and stream habitat focus projects, ID, USA |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
None | ARIES | None |
|
EM Source Document ID
|
271 | 302 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
|
Document Author
em.detail.documentAuthorHelp
?
|
Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Murphy, C. and T. Weekley |
|
Document Year
em.detail.documentYearHelp
?
|
2008 | 2014 | 2012 |
|
Document Title
em.detail.sourceIdHelp
?
|
Mapping ecosystem services for planning and management | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. |
|
Document Status
em.detail.statusCategoryHelp
?
|
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 |
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
| Not applicable | http://aries.integratedmodelling.org/ | Not applicable | |
|
Contact Name
em.detail.contactNameHelp
?
|
Benis Egoh | Ken Bagstad | Chris Murphy |
|
Contact Address
|
Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Geosciences and Environmental Change Science Center, US Geological Survey | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID |
|
Contact Email
|
Not reported | kjbagstad@usgs.gov | chris.murphy@idfg.idaho.gov |
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures." | 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. |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified | None identified |
|
Biophysical Context
|
Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | restored, enhanced and created wetlands |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | Sites, function or habitat focus |
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
Doc-303 | Doc-305 | Doc-390 |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-85 | EM-87 | EM-88 | None | EM-706 | EM-729 | EM-730 | EM-734 | EM-743 | EM-749 | EM-750 | EM-756 | EM-757 | EM-758 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-732 | EM-737 | EM-738 | EM-739 | EM-741 | EM-742 | EM-751 | EM-768 |
EM Modeling Approach
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
Not reported | 1971-2006 | 2010-2011 |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | past time |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable |
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
South Africa | Puget Sound Region | Wetlands in idaho |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
>1,000,000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 |
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
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) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
Distributed across catchments with average size of 65,000 ha | 200m x 200m | Not applicable |
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Analytic | Numeric |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | No |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No | No |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | No |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-86 | EM-326 |
EM-718 |
|
|
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-86 | EM-326 |
EM-718 |
| None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
-30 | 48 | 44.06 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
25 | -123 | -114.69 |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Estimated |
|
EM ID
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Not reported | Terrestrial environment surrounding a large estuary | created, restored and enhanced wetlands |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is finer 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
em.detail.idHelp
?
|
EM-86 | EM-326 |
EM-718 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-86 | EM-326 |
EM-718 |
| 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-86 | EM-326 |
EM-718 |
|
|
|
<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-86 | EM-326 |
EM-718 |
| None |
|
None |
Home
Search EMs
My
EMs
Learn about
ESML
Show Criteria
Hide Criteria