EcoService Models Library (ESML)
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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
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
EM Short Name
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Evoland v3.5 (unbounded growth), Eugene, OR, USA | EnviroAtlas - Restorable wetlands | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Atlantis ecosystem harvest submodel |
EM Full Name
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Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
EM Source or Collection
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Envision | US EPA | EnviroAtlas | None | None |
EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
262 | 358 | 463 |
Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | US EPA Office of Research and Development - National Exposure Research Laboratory | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. |
Document Year
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2008 | 2013 | 2010 | 2011 |
Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | EnviroAtlas - National | 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 | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript |
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
http://evoland.bioe.orst.edu/ | https://www.epa.gov/enviroatlas | http://www.dndc.sr.unh.edu | https://research.csiro.au/atlantis/home/links/ | |
Contact Name
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Michael R. Guzy | EnviroAtlas Team | M. Abdalla | Elizabeth Fulton |
Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. |
Contact Email
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Not reported | enviroatlas@epa.gov | abdallm@tcd.ie | beth.fulton@csiro.au |
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | 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. | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. |
Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None Identified | climate change | None identified |
Biophysical Context
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No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | NA |
EM Scenario Drivers
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Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | fertilization | No scenarios presented |
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
Document ID for related EM
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Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
None | None | Doc-456 | Doc-459 | Doc-461 | Doc-463 |
EM ID for related EM
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EM-12 | EM-369 | None | EM-593 | EM-978 | EM-981 | EM-983 | EM-985 | EM-990 |
EM Modeling Approach
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
EM Temporal Extent
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1990-2050 | 2006-2013 | 1961-1990 | Not applicable |
EM Time Dependence
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time-dependent | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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future time | Not applicable | both | Not applicable |
EM Time Continuity
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discrete | Not applicable | discrete | continuous |
EM Temporal Grain Size Value
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2 | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Day | Not applicable |
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
Bounding Type
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Geopolitical | Geopolitical | Point or points | Not applicable |
Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | conterminous United States | Oak Park Research centre | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 1-10 ha | Not applicable |
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | Not applicable |
Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
Spatial Grain Size
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varies | irregular | Not applicable | Not applicable |
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
EM Computational Approach
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Numeric | Analytic | Numeric | Analytic |
EM Determinism
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stochastic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
Model Calibration Reported?
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Unclear | No | Yes | Not applicable |
Model Goodness of Fit Reported?
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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)
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None | None |
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None |
Model Operational Validation Reported?
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No | No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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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-333 ![]() |
EM-492 | EM-598 | EM-991 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
Centroid Latitude
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44.11 | 39.5 | 52.86 | Not applicable |
Centroid Longitude
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-123.09 | -98.35 | 6.54 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | None provided | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Provided | Not applicable |
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Agroecosystems | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
Specific Environment Type
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Agricultural-urban interface at river junction | Terrestrial | farm pasture | Multiple |
EM Ecological Scale
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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 | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-333 ![]() |
EM-492 | EM-598 | EM-991 |
EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-492 | EM-598 | EM-991 |
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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)
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EM-492 | EM-598 | EM-991 |
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None |
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<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)
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EM-492 | EM-598 | EM-991 |
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None |
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None |