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-129 ![]() |
EM Short Name
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3-PG, South Australia |
EM Full Name
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3-PG (Physiological Principles Predicting Growth), South Australia |
EM Source or Collection
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None |
EM Source Document ID
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243 |
Document Author
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Crossman, N. D., Bryan, B. A., and Summers, D. M. |
Document Year
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2011 |
Document Title
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Carbon payments and low-cost conservation |
Document Status
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Peer reviewed and published |
Comments on Status
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Published journal manuscript |
EM ID
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EM-129 ![]() |
http://www.csiro.au/products/3PGProductivity#a1 | |
Contact Name
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Anders Siggins |
Contact Address
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Not reported |
Contact Email
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Anders.Siggins@csiro.au |
EM ID
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EM-129 ![]() |
Summary Description
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AUTHOR'S DESCRIPTION: "Carbon trading and its resultant market for carbon offsets are expected to drive investment in the sequestration of carbon through tree plantings (i.e., carbon plantings). Most carbon-planting investment has been in monocultures of trees that offer a rapid return on investment but have relatively little compositional and structural diversity (Bekessy & Wintle 2008; Munro et al. 2009). There are additional benefits available should carbon plantings comprise native species of diverse composition and age that are planted strategically to meet conservation and restoration objectives (hereafter ecological carbon plantings) (Bekessy &Wintle 2008; Dwyer et al. 2009; Bekessy et al. 2010). Ecological carbon plantings may increase availability of resources and refugia for native animals, but they often yield less carbon and are more expensive to establish than monocultures and therefore are less profitable…We used the tree-stand growth model 3-PG (physiological principles predicting growth) (Landsberg & Waring 1997) to simulate annual carbon sequestration under permanent carbon plantings in the part of the study area cleared for agriculture. The 3-PG model calculates total above- and below-ground biomass of a stand after accounting for soil water deficit, atmospheric vapor pressure deficits, and stand age…The 3-PG model was originally parameterized for a generic species, but species-specific parameters have since been calibrated for many commercially valuable trees (Paul et al. 2007) and most recently for mixed species used in permanent ecological restoration plantings (Polglase et al. 2008). We simulated four carbon-planting systems described in Polglase et al. (2008) for which the plants in the systems would grow in our study area. All species were native to areas of Australia with climate similar to that in the study area. We simulated the annual growth of three trees typically grown in monoculture (Eucalyptus globulus, native to Tasmania, constrained to precipitation ≥ 550 mm/year; Eucalyptus camaldulensis, native to the study area, constrained to 350–549 mm/year; Eucalyptus kochii, native to Western Australia, constrained to <350 mm/year). For the simulations of ecological carbon plantings we used a set of trees and shrubs representative of those planted for ecological restoration in temperate southern Australia (species list in England et al. 2006).We assumed the ecological carbon plantings were planted and managed in such a way as to comply with the definition of ecological restoration (Society for Ecological Restoration International Science and PolicyWorking Group 2004)." |
Specific Policy or Decision Context Cited
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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. |
EM Scenario Drivers
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Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings |
EM ID
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EM-129 ![]() |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the average annual biomass flux and carbon sequestration from two types of carbon plantings: 1) Tree-based monocultures of three different species (i.e., monoculture carbon planting) and 2) Diverse plantings of nine different native tree and shrub species (i.e., ecological carbon planting) |
New or Pre-existing EM?
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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-129 ![]() |
Document ID for related EM
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Doc-243 | Doc-246 | Doc-245 |
EM ID for related EM
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None |
EM Modeling Approach
EM ID
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EM-129 ![]() |
EM Temporal Extent
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2009-2050 |
EM Time Dependence
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time-dependent |
EM Time Reference (Future/Past)
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future time |
EM Time Continuity
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discrete |
EM Temporal Grain Size Value
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1 |
EM Temporal Grain Size Unit
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Month |
EM ID
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EM-129 ![]() |
Bounding Type
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Physiographic or Ecological |
Spatial Extent Name
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Agricultural districts of the state of South Australia |
Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 |
EM ID
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EM-129 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature |
Spatial Grain Size
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1 ha x 1 ha |
EM ID
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EM-129 ![]() |
EM Computational Approach
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Numeric |
EM Determinism
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deterministic |
Statistical Estimation of EM
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EM ID
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EM-129 ![]() |
Model Calibration Reported?
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Yes |
Model Goodness of Fit Reported?
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No |
Goodness of Fit (metric| value | unit)
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None |
Model Operational Validation Reported?
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No |
Model Uncertainty Analysis Reported?
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No |
Model Sensitivity Analysis Reported?
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No |
Model Sensitivity Analysis Include Interactions?
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Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-129 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-129 ![]() |
None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-129 ![]() |
Centroid Latitude
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-34.9 |
Centroid Longitude
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138.7 |
Centroid Datum
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WGS84 |
Centroid Coordinates Status
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Estimated |
EM ID
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EM-129 ![]() |
EM Environmental Sub-Class
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Forests | Agroecosystems |
Specific Environment Type
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Agricultural land for annual crops, annual legumes, and grazing of sheep and cows |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-129 ![]() |
EM Organismal Scale
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Species |
Taxonomic level and name of organisms or groups identified
EM-129 ![]() |
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EnviroAtlas URL
EM-129 ![]() |
GAP Ecological Systems, Average Annual Precipitation, Acres of Land Enrolled in the Conservation Reserve Program (CRP) |
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-129 ![]() |
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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-129 ![]() |
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