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-51 ![]() |
EM-327 | EM-964 |
EM Short Name
em.detail.shortNameHelp
?
|
EnviroAtlas-Nat. filtration-water | ARIES sediment regulation, Puget Sound Region, USA | EcoSim II - method |
EM Full Name
em.detail.fullNameHelp
?
|
US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA | ARIES (Artificial Intelligence for Ecosystem Services) Sediment Regulation for Reservoirs, Puget Sound Region, Washington, USA | EcoSim II - method |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
ARIES | None |
EM Source Document ID
|
223 | 302 | 448 |
Document Author
em.detail.documentAuthorHelp
?
|
US EPA Office of Research and Development - National Exposure Research Laboratory | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell |
Document Year
em.detail.documentYearHelp
?
|
2013 | 2014 | 2000 |
Document Title
em.detail.sourceIdHelp
?
|
EnviroAtlas - Featured Community | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II |
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
https://www.epa.gov/enviroatlas | http://aries.integratedmodelling.org/ | https://ecopath.org/downloads/ | |
Contact Name
em.detail.contactNameHelp
?
|
EnviroAtlas Team | Ken Bagstad | Carl Walters |
Contact Address
|
Not reported | Geosciences and Environmental Change Science Center, US Geological Survey | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 |
Contact Email
|
enviroatlas@epa.gov | kjbagstad@usgs.gov | c.walters@oceans.ubc.ca |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model. | 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 mapped sediment regulation as the location of sediment sinks (depositional areas in floodplains), which can absorb sediment transported by hydrologic flows from upstream sources (erosionprone areas) prior to reaching users. In this case the benefit of avoided sedimentation is provided to 29 major reservoirs. Avoided sedimentation helps maintain the ability of reservoirs to provide benefits including hydroelectric power generation, flood control, recreation, and water supply to beneficiaries through the region. Avoided reservoir sedimentation likely helps to protect each of these benefits in different ways, i.e., increased turbidity or the loss of reservoir storage capacity may have a greater impact on some provision of some benefit types than others. For our purposes we ended the modeling and mapping exercise at the reservoirs. Reservoir sedimentation reduces their storage capacity, typically decreasing their ability to provide these benefits without costly dredging. We thus used a probabilistic Bayesian model of soil erosion incorporating vegetation, soils, and rainfall influences and calibrated using regional data from coarser scale and/or RUSLE derived erosion models (Bagstad et al. 2011). We probabilistically modeled sediment deposition in floodplains using data for floodplain vegetation, floodplain width, and stream gradient, which can influence rates of deposition. We calculated the ratio of actual to theoretical sediment regulation using the aggregated sink values upstream of reservoirs in the Puget Sound region, divided by aggregated theoretical sink values for the entire landscape." | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified | None |
Biophysical Context
|
No additional description provided | No additional description provided | None, Ocean ecosystems |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | N/A |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Application of existing model | New or revised 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-51 ![]() |
EM-327 | EM-964 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-198 | Doc-303 | Doc-305 | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-137 | EM-142 | None | EM-1055 |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1999-2010 | 1971-2005 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary. |
time-stationary | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | both |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable |
discrete ?Comment:Modeller dependent |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Day |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Physiographic or ecological | Other |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Durham, NC and vicinity | Puget Sound Region | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
100-1000 km^2 | 10,000-100,000 km^2 | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
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
?
|
irregular | 200m x 200m | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration. |
Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Unclear | Yes | 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
?
|
Unclear | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
Unclear | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
Unclear | No | Not applicable |
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-51 ![]() |
EM-327 | EM-964 |
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-51 ![]() |
EM-327 | EM-964 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
Centroid Latitude
em.detail.ddLatHelp
?
|
35.99 | 48 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
?
|
-78.96 | -123 | Not applicable |
Centroid Datum
em.detail.datumHelp
?
|
None provided | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Created Greenspace | Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Open Ocean and Seas |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Urban areas including streams | Terrestrial environment surrounding a large estuary | Pelagic |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Not applicable | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-327 | EM-964 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Taxonomic level and name of organisms or groups identified
EM-51 ![]() |
EM-327 | EM-964 |
None Available | None Available |
|
EnviroAtlas URL
EM-51 ![]() |
EM-327 | EM-964 |
None Available | GAP Ecological Systems, Average Annual Precipitation, Waterbody area | Big game hunting recreation demand |
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-51 ![]() |
EM-327 | EM-964 |
|
|
|
<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-51 ![]() |
EM-327 | EM-964 |
|
None |
|