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-349 ![]() |
EM-541 ![]() |
EM Short Name
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InVEST (v1.004) Carbon, Indonesia | InVEST fisheries, lobster, South Africa |
EM Full Name
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InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004) carbon storage and sequestration, Sumatra, Indonesia | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa |
EM Source or Collection
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InVEST | InVEST |
EM Source Document ID
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309 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
Document Author
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Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby |
Document Year
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2014 | 2018 |
Document Title
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Ecosystem services reinforce Sumatran tiger conservation in land use plans | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript |
EM ID
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EM-349 ![]() |
EM-541 ![]() |
https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | |
Contact Name
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Nirmal K. Bhagabati | Michelle Ward |
Contact Address
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The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia |
Contact Email
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nirmal.bhagabati@wwfus.org | m.ward@uq.edu.au |
EM ID
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EM-349 ![]() |
EM-541 ![]() |
Summary Description
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Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... We mapped biomass carbon by assigning carbon values (in ton ha_1) for aboveground, belowground, and dead organic matter to each LULC class based on values from literature, as described in Tallis et al. (2010). We mapped soil carbon separately, as large quantities of carbon are stored in peat soil (Page et al., 2011). We estimated total losses in peat carbon over 50 years into the future scenarios, using reported annual emission rates for specific LULC transitions on peat (Uryu et al., 2008)...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." |
Specific Policy or Decision Context Cited
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This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | Future rock lobster fisheries management |
Biophysical Context
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Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | No additional description provided |
EM Scenario Drivers
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Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Fisheries exploitation; fishing vulnerability (of age classes) |
EM ID
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EM-349 ![]() |
EM-541 ![]() |
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 |
New or Pre-existing EM?
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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-349 ![]() |
EM-541 ![]() |
Document ID for related EM
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Doc-315 | None |
EM ID for related EM
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EM-374 | None |
EM Modeling Approach
EM ID
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EM-349 ![]() |
EM-541 ![]() |
EM Temporal Extent
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2008-2020 | 1986-2115 |
EM Time Dependence
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time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | future time |
EM Time Continuity
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Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Year |
EM ID
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EM-349 ![]() |
EM-541 ![]() |
Bounding Type
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Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
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central Sumatra | Table Mountain National Park Marine Protected Area |
Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 100-1000 km^2 |
EM ID
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EM-349 ![]() |
EM-541 ![]() |
EM Spatial Distribution
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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 |
Spatial Grain Size
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30 m x 30 m | Not applicable |
EM ID
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EM-349 ![]() |
EM-541 ![]() |
EM Computational Approach
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Analytic | Numeric |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-349 ![]() |
EM-541 ![]() |
Model Calibration Reported?
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No | No |
Model Goodness of Fit Reported?
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No | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Model Uncertainty Analysis Reported?
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No | No |
Model Sensitivity Analysis Reported?
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No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-349 ![]() |
EM-541 ![]() |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-349 ![]() |
EM-541 ![]() |
None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-349 ![]() |
EM-541 ![]() |
Centroid Latitude
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0 | -34.18 |
Centroid Longitude
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102 | 18.35 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Provided |
EM ID
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EM-349 ![]() |
EM-541 ![]() |
EM Environmental Sub-Class
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Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine |
Specific Environment Type
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104 land use land cover classes | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf |
EM Ecological Scale
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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-349 ![]() |
EM-541 ![]() |
EM Organismal Scale
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Community | Individual or population, within a species |
Taxonomic level and name of organisms or groups identified
EM-349 ![]() |
EM-541 ![]() |
None Available |
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EnviroAtlas URL
EM-349 ![]() |
EM-541 ![]() |
The Watershed Boundary Dataset (WBD) | Big game hunting recreation demand |
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-349 ![]() |
EM-541 ![]() |
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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-349 ![]() |
EM-541 ![]() |
None |
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