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-126 | EM-185 |
EM-333 ![]() |
EM-376 |
EM Short Name
em.detail.shortNameHelp
?
|
Annual profit from agriculture, South Australia | Blue crabs and SAV, Chesapeake Bay, USA | Evoland v3.5 (unbounded growth), Eugene, OR, USA | MIMES: For Massachusetts Ocean (v1.0) |
EM Full Name
em.detail.fullNameHelp
?
|
Annual profit from agriculture, South Australia | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
None | None | Envision | US EPA |
EM Source Document ID
|
243 |
292 ?Comment:Conference paper |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
316 |
Document Author
em.detail.documentAuthorHelp
?
|
Crossman, N. D., Bryan, B. A., and Summers, D. M. | Mykoniatis, N. and Ready, R. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Altman, I., R.Boumans, J. Roman, L. Kaufman |
Document Year
em.detail.documentYearHelp
?
|
2011 | 2013 | 2008 | 2012 |
Document Title
em.detail.sourceIdHelp
?
|
Carbon payments and low-cost conservation | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) |
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Not formally documented | Peer reviewed and published | Documented, not peer reviewed |
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Conference proceedings | Published journal manuscript | Published report |
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | http://www.afordablefutures.com/orientation-to-what-we-do | |
Contact Name
em.detail.contactNameHelp
?
|
Neville D. Crossman | Nikolaos Mykoniatis | Michael R. Guzy | Irit Altman |
Contact Address
|
CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | Oregon State University, Dept. of Biological and Ecological Engineering | Boston University, Portland, Maine |
Contact Email
|
neville.crossman@csiro.au | Not reported | Not reported | iritaltman@bu.edu |
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns from agriculture and from carbon plantings." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010). In this context, opportunity cost is usually expressed as the profit from agricultural production…We based our calculations of agricultural profit on Bryan et al. (2009), who calculated profit at full equity (i.e., economic return to land, capital, and management, exclusive of financial debt). We calculated an annual profit at full equity (PFEc) layer for each commodity (c) in the set of agricultural commodities (C), where C is wheat, field peas, beef cattle, or sheep." | ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | **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." | AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors. |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | Not applicable | 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 |
Biophysical Context
|
Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | Submerged Aquatic Vegetation (SAV), eelgrass | No additional description provided | No additional description provided |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | Essential or Facultative habitat | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented |
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | 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-126 | EM-185 |
EM-333 ![]() |
EM-376 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-244 | Doc-227 |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None | EM-106 | EM-12 | EM-369 | None |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2002-2008 | 1993-2011 | 1990-2050 | 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 | future time | future time |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | discrete | discrete | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | 1 | 2 | 1 |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Year | Year | Year |
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or Ecological | Physiographic or ecological | Geopolitical | Physiographic or ecological |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Agricultural districts of the state of South Australia | Chesapeake Bay | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Massachusetts Ocean |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. |
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
1 ha | Not applicable | varies | 1 km x1 km |
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Analytic | Numeric | Numeric |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | stochastic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | Yes | Unclear | No |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | Yes | No | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | Yes | No | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | Yes | No | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | Yes | No | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Yes | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
|
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
None |
|
None |
|
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
Centroid Latitude
em.detail.ddLatHelp
?
|
-34.9 | 36.99 | 44.11 | 41.72 |
Centroid Longitude
em.detail.ddLongHelp
?
|
138.7 | -75.95 | -123.09 | -69.87 |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Estimated | Estimated |
EM ID
em.detail.idHelp
?
|
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | None | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Near Coastal Marine and Estuarine |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Yes | Agricultural-urban interface at river junction | None identified |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is finer than that of the Environmental Sub-class | Yes | 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-126 | EM-185 |
EM-333 ![]() |
EM-376 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Guild or Assemblage | Yes | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-126 | EM-185 |
EM-333 ![]() |
EM-376 |
|
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-126 | EM-185 |
EM-333 ![]() |
EM-376 |
|
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-126 | EM-185 |
EM-333 ![]() |
EM-376 |
None | None |
|
|