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-359 ![]() |
EM-940 |
EM Short Name
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InVEST (v1.004) sediment retention, Indonesia | OpenNSPECT v. 1.1, California, U.S. |
EM Full Name
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InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | OpenNSPECT v. 1.1, California, U.S. |
EM Source or Collection
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InVEST | None |
EM Source Document ID
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309 |
433 ?Comment:Additional source for this EM: NOAA, 2012. National Oceanic and Atmospheric Administration. Technical Guide for OpenNSPECT, Version 1.1, p. 44. http://www.csc.noaa.gov/digitalcoast/tools/opennspect. |
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. | Morrison, K. D. and C. A. Kolden |
Document Year
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2014 | 2015 |
Document Title
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Ecosystem services reinforce Sumatran tiger conservation in land use plans | Modeling the impacts of wildfire on runoff and pollutant transport from coastal watersheds to the nearshore environment |
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-359 ![]() |
EM-940 |
https://www.naturalcapitalproject.org/invest/ | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | |
Contact Name
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Nirmal K. Bhagabati | Crystal A. Kolden |
Contact Address
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The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | Not reported |
Contact Email
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nirmal.bhagabati@wwfus.org | ckolden@uidaho. Edu |
EM ID
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EM-359 ![]() |
EM-940 |
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)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...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." | ABSTRACT: "Wildfire is a common disturbance that can significantly alter vegetation in watersheds and affect the rate of sediment and nutrient transport to adjacent nearshore oceanic environments. Changes in runoff resulting from heterogeneous wildfire effects are not well-understood due to both limitations in the field measurement of runoff and temporally-limited spatial data available to parameterize runoff models. We apply replicable, scalable methods for modeling wildfire impacts on sediment and nonpoint source pollutant export into the nearshore environment, and assess relationships between wildfire severity and runoff. Nonpoint source pollutants were modeled using a GIS-based empirical deterministic model parameterized with multi-year land cover data to quantify fire-induced increases in transport to the nearshore environment. Results indicate post-fire concentration increases in phosphorus by 161 percent, sediments by 350 percent and total suspended solids (TSS) by 53 percent above pre-fire years. Higher wildfire severity was associated with the greater increase in exports of pollutants and sediment to the nearshore environment, primarily resulting from the conversion of forest and shrubland to grassland. This suggests that increasing wildfire severity with climate change will increase potential negative impacts to adjacent marine ecosystems. The approach used is replicable and can be utilized to assess the effects of other types of land cover change at landscape scales. It also provides a planning and prioritization framework for management activities associated with wildfire, including suppression, thinning, and post-fire rehabilitation, allowing for quantification of potential negative impacts to the nearshore environment in coastal basins." |
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. | None identified |
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. | Central California coast includes twelve adjacent watersheds covering 87,638 ha and rises steeply from sea level to just below 1800 m within a few km from the coast, and experiences a Mediterranean climate, with fire season typically lasting from June to November. Precipitation is dependent on elevation ranging from 65 cm near the coast to over 130 cm at ridge top. Three ecological zones occur within the study area. These zones are comprised of grasslands, coastal sage scrub, chaparral, oak forests, mixed broadleaf evergreen forest, and coniferous forests. |
EM Scenario Drivers
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Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | No scenarios presented |
EM ID
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EM-359 ![]() |
EM-940 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application |
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-359 ![]() |
EM-940 |
Document ID for related EM
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Doc-338 | Doc-431 |
EM ID for related EM
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EM-435 | EM-938 |
EM Modeling Approach
EM ID
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EM-359 ![]() |
EM-940 |
EM Temporal Extent
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2008-2020 | 2005-2008 |
EM Time Dependence
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time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable |
EM ID
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EM-359 ![]() |
EM-940 |
Bounding Type
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Watershed/Catchment/HUC | Watershed/Catchment/HUC |
Spatial Extent Name
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central Sumatra | Big Sur region, central California |
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-359 ![]() |
EM-940 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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30 m x 30 m | irregular |
EM ID
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EM-359 ![]() |
EM-940 |
EM Computational Approach
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Analytic | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-359 ![]() |
EM-940 |
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 | No |
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-359 ![]() |
EM-940 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-359 ![]() |
EM-940 |
None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-359 ![]() |
EM-940 |
Centroid Latitude
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0 | 35.96 |
Centroid Longitude
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102 | -121.43 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated |
EM ID
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EM-359 ![]() |
EM-940 |
EM Environmental Sub-Class
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Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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104 land use land cover classes | Coastal watersheds |
EM Ecological Scale
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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-359 ![]() |
EM-940 |
EM Organismal Scale
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Community | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-359 ![]() |
EM-940 |
None Available | None Available |
EnviroAtlas URL
EM-359 ![]() |
EM-940 |
The National Hydrography Dataset (NHD), Average Annual Precipitation | Average Annual Precipitation |
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-359 ![]() |
EM-940 |
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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-359 ![]() |
EM-940 |
None | None |