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
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EM ID
em.detail.idHelp
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EM-98 |
EM-127 |
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EM Short Name
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PATCH, western USA | Annual profit - carbon plantings, South Australia |
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EM Full Name
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PATCH (Program to Assist in Tracking Critical Habitat), western USA | Annual profit from carbon plantings, South Australia |
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EM Source or Collection
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US EPA | None |
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EM Source Document ID
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2 | 243 |
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Document Author
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Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Crossman, N. D., Bryan, B. A., and Summers, D. M. |
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Document Year
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2006 | 2011 |
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Document Title
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Defining recovery goals and strategies for endangered species: The wolf as a case study | Carbon payments and low-cost conservation |
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Document Status
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Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript |
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EM ID
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EM-98 |
EM-127 |
| Not applicable | Not applicable | |
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Contact Name
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Carlos Carroll | Neville D. Crossman |
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Contact Address
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Klamath Center for Conservation Research, Orleans, CA 95556 | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia |
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Contact Email
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carlos@cklamathconservation.org | neville.crossman@csiro.au |
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EM ID
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EM-98 |
EM-127 |
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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.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | 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 carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." 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)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially." |
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Specific Policy or Decision Context Cited
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AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | None identified |
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Biophysical Context
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Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | 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. |
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EM Scenario Drivers
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Population growth, road development (density) on public vs private land | Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e |
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EM ID
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EM-98 |
EM-127 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting) |
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New or Pre-existing EM?
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New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
em.detail.idHelp
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EM-98 |
EM-127 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-328 | Doc-337 | Doc-245 | Doc-246 | Doc-247 | Doc-243 |
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EM ID for related EM
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EM-403 | EM-422 | EM-128 | EM-141 |
EM Modeling Approach
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EM ID
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EM-98 |
EM-127 |
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EM Temporal Extent
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2000-2025 | 2009-2050 |
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EM Time Dependence
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time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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future time | future time |
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EM Time Continuity
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discrete | discrete |
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EM Temporal Grain Size Value
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1 | 1 |
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EM Temporal Grain Size Unit
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Year | Year |
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EM ID
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EM-98 |
EM-127 |
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Bounding Type
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Physiographic or ecological | Physiographic or Ecological |
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Spatial Extent Name
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Western United States | Agricultural districts of the state of South Australia |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100,000-1,000,000 km^2 |
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EM ID
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EM-98 |
EM-127 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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504 km^2 | 1 ha x 1 ha |
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EM ID
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EM-98 |
EM-127 |
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EM Computational Approach
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Numeric | Analytic |
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EM Determinism
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stochastic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-98 |
EM-127 |
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Model Calibration Reported?
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Unclear | No |
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Model Goodness of Fit Reported?
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No | No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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Model Operational Validation Reported?
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No | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Unclear | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-98 |
EM-127 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-98 |
EM-127 |
| None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-98 |
EM-127 |
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Centroid Latitude
em.detail.ddLatHelp
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39.88 | -34.9 |
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Centroid Longitude
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-113.81 | 138.7 |
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Centroid Datum
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WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated |
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EM ID
em.detail.idHelp
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EM-98 |
EM-127 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems |
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Specific Environment Type
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Not reported | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-98 |
EM-127 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Species | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
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EM-98 |
EM-127 |
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EnviroAtlas URL
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EM-98 |
EM-127 |
| Dasymetric Allocation of Population | Carbon Storage by Tree Biomass |
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-98 |
EM-127 |
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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-98 |
EM-127 |
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None |
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