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-326 |
EM-359 ![]() |
EM-598 | EM-617 |
EM Short Name
em.detail.shortNameHelp
?
|
ARIES flood regulation, Puget Sound Region, USA | InVEST (v1.004) sediment retention, Indonesia | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method |
EM Full Name
em.detail.fullNameHelp
?
|
ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
ARIES | InVEST | None | None |
EM Source Document ID
|
302 | 309 | 358 | 367 |
Document Author
em.detail.documentAuthorHelp
?
|
Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Bousquin, J., Mazzotta M., and W. Berry |
Document Year
em.detail.documentYearHelp
?
|
2014 | 2014 | 2010 | 2017 |
Document Title
em.detail.sourceIdHelp
?
|
From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. |
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 EPA report |
EM ID
em.detail.idHelp
?
|
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
http://aries.integratedmodelling.org/ | https://www.naturalcapitalproject.org/invest/ | http://www.dndc.sr.unh.edu | Not applicable | |
Contact Name
em.detail.contactNameHelp
?
|
Ken Bagstad | Nirmal K. Bhagabati | M. Abdalla | Justin Bousquin |
Contact Address
|
Geosciences and Environmental Change Science Center, US Geological Survey | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 |
Contact Email
|
kjbagstad@usgs.gov | nirmal.bhagabati@wwfus.org | abdallm@tcd.ie | bousquin.justin@epa.gov |
EM ID
em.detail.idHelp
?
|
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
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." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | climate change | None identified |
Biophysical Context
|
No additional description provided | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | fertilization | N/A |
EM ID
em.detail.idHelp
?
|
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | Application of existing 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-326 |
EM-359 ![]() |
EM-598 | EM-617 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-303 | Doc-305 | Doc-338 | None | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None | EM-435 | EM-593 | None |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1971-2006 | 2008-2020 | 1961-1990 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | both | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Day | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or ecological | Watershed/Catchment/HUC | Point or points | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Puget Sound Region | central Sumatra | Oak Park Research centre | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 1-10 ha | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
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
?
|
200m x 200m | 30 m x 30 m | Not applicable | Not reported |
EM ID
em.detail.idHelp
?
|
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
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-326 |
EM-359 ![]() |
EM-598 | EM-617 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | Yes | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None |
|
None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | Yes | 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-326 |
EM-359 ![]() |
EM-598 | EM-617 |
|
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
Centroid Latitude
em.detail.ddLatHelp
?
|
48 | 0 | 52.86 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
?
|
-123 | 102 | 6.54 | Not applicable |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | None provided | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Provided | Provided | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Agroecosystems | Inland Wetlands |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Terrestrial environment surrounding a large estuary | 104 land use land cover classes | farm pasture | Restored wetlands |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale corresponds to 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-326 |
EM-359 ![]() |
EM-598 | EM-617 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-326 |
EM-359 ![]() |
EM-598 | EM-617 |
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-326 |
EM-359 ![]() |
EM-598 | EM-617 |
|
|
|
|
<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-326 |
EM-359 ![]() |
EM-598 | EM-617 |
|
None |
|
|