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-126 | EM-185 | EM-376 |
EM-718 ![]() |
EM Short Name
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Annual profit from agriculture, South Australia | Blue crabs and SAV, Chesapeake Bay, USA | MIMES: For Massachusetts Ocean (v1.0) | WESP: Riparian & stream habitat, ID, USA |
EM Full Name
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Annual profit from agriculture, South Australia | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | WESP: Riparian and stream habitat focus projects, ID, USA |
EM Source or Collection
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None | None | US EPA | None |
EM Source Document ID
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243 |
292 ?Comment:Conference paper |
316 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
Document Author
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Crossman, N. D., Bryan, B. A., and Summers, D. M. | Mykoniatis, N. and Ready, R. | Altman, I., R.Boumans, J. Roman, L. Kaufman | Murphy, C. and T. Weekley |
Document Year
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2011 | 2013 | 2012 | 2012 |
Document Title
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Carbon payments and low-cost conservation | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. |
Document Status
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Peer reviewed and published | Not formally documented | Documented, not peer reviewed | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Conference proceedings | Published report | Published report |
EM ID
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
Not applicable | Not applicable | http://www.afordablefutures.com/orientation-to-what-we-do | Not applicable | |
Contact Name
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Neville D. Crossman | Nikolaos Mykoniatis | Irit Altman | Chris Murphy |
Contact Address
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CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | Boston University, Portland, Maine | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID |
Contact Email
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neville.crossman@csiro.au | Not reported | iritaltman@bu.edu | chris.murphy@idfg.idaho.gov |
EM ID
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
Summary Description
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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." | 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. | 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
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None identified | Not applicable | None identified | None identified |
Biophysical Context
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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 | restored, enhanced and created wetlands |
EM Scenario Drivers
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No scenarios presented | Essential or Facultative habitat | No scenarios presented | Sites, function or habitat focus |
EM ID
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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New or revised model | Application of existing 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
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
Document ID for related EM
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Doc-244 | Doc-227 | None | Doc-390 |
EM ID for related EM
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None | EM-106 | 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
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
EM Temporal Extent
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2002-2008 | 1993-2011 | Not applicable | 2010-2011 |
EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | past time | future time | past time |
EM Time Continuity
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Not applicable | discrete | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Year | Year | Not applicable |
EM ID
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
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Agricultural districts of the state of South Australia | Chesapeake Bay | Massachusetts Ocean | Wetlands in idaho |
Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 |
EM ID
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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1 ha | Not applicable | 1 km x1 km | Not applicable |
EM ID
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
EM Computational Approach
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Analytic | Analytic | Numeric | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
Model Calibration Reported?
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No | Yes | No | No |
Model Goodness of Fit Reported?
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No | Yes | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None | None |
Model Operational Validation Reported?
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No | Yes | No | No |
Model Uncertainty Analysis Reported?
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No | Yes | No | No |
Model Sensitivity Analysis Reported?
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No | Yes | No | No |
Model Sensitivity Analysis Include Interactions?
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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-376 |
EM-718 ![]() |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
Centroid Latitude
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-34.9 | 36.99 | 41.72 | 44.06 |
Centroid Longitude
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138.7 | -75.95 | -69.87 | -114.69 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated |
EM ID
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
EM Environmental Sub-Class
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Agroecosystems | None | Near Coastal Marine and Estuarine | Inland Wetlands |
Specific Environment Type
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Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Yes | None identified | created, restored and enhanced wetlands |
EM Ecological Scale
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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
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EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
EM Organismal Scale
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Guild or Assemblage | Yes | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
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None Available |
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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-376 |
EM-718 ![]() |
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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)
EM-126 | EM-185 | EM-376 |
EM-718 ![]() |
None | None |
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None |