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-417 | EM-617 | EM-655 |
EM Short Name
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InVEST (v1.004) sediment retention, Indonesia | SWAT, Guanica Bay, Puerto Rico, USA | RBI Spatial Analysis Method | Hunting recreation, Wisconsin, USA |
EM Full Name
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InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | Hunting recreation, Wisconsin, USA |
EM Source or Collection
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InVEST | US EPA | None | None |
EM Source Document ID
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309 | 334 | 367 | 376 |
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. | Hu, W. and Y. Yuan | Bousquin, J., Mazzotta M., and W. Berry | Qiu, J. and M. G. Turner |
Document Year
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2014 | 2013 | 2017 | 2013 |
Document Title
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Ecosystem services reinforce Sumatran tiger conservation in land use plans | Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | Spatial interactions among ecosystem services in an urbanizing agricultural watershed |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published EPA report | Published EPA report | Published journal manuscript |
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | |
Contact Name
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Nirmal K. Bhagabati | Yongping Yuan | Justin Bousquin | Monica G. Turner |
Contact Address
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The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | Not reported |
Contact Email
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nirmal.bhagabati@wwfus.org | Yuan.Yongping@epa.gov | bousquin.justin@epa.gov | turnermg@wisc.edu |
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
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." | AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008). | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | AUTHOR'S DESCRIPTION (from Supporting Information): "The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility. Similar assumptions were made for this assessment: larger areas and places with more game species would support more hunting, areas closer to large population centers would be used more than remote areas, and proximity to major roads would increase access and use of an area. We first obtained the boundary of public wild areas from Wisconsin DNR and calculated the amount of areas for each management unit. The number of game species (Spe) for each area was derived from Dane County Parks Division (70). We used the same population density (Pop) and road buffer layer (Road) described in the previous forest recreation section. The variables Spe, Pop, and Road were weighted to ranges of 0–40, 0–40, and 0–20, respectively, based on the relative importance of each in determining this service. We estimated overall hunting recreation service for each 30-m grid cell with the following equation: HRSi = Ai Σ(Spei + Popi +Roadi), where HRS is hunting recreation score, A is the area of public wild areas open for hunting/fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28,000) to a range of 0–100, with 0 representing no hunting recreation service and 100 representing highest service. |
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 | None identified | 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. | Need to fill in | wetlands | 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 | Planting type, fertilizing rate, harvest rate | N/A | No scenarios presented |
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application |
New or Pre-existing EM?
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Application of existing model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
Document ID for related EM
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Doc-338 | None | None | None |
EM ID for related EM
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EM-435 | None | None | None |
EM Modeling Approach
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
EM Temporal Extent
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2008-2020 | 1981-2004 | Not applicable | 2000-2006 |
EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable | Not applicable |
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
Bounding Type
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Watershed/Catchment/HUC | Watershed/Catchment/HUC | Not applicable | Watershed/Catchment/HUC |
Spatial Extent Name
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central Sumatra | Guanica Bay, Puerto Rico watersheds | Not applicable | Yahara Watershed, Wisconsin |
Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 100-1000 km^2 | Not applicable | 1000-10,000 km^2. |
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | 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 | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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30 m x 30 m | 30m x 30m | Not reported | 30m x 30m |
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
EM Computational Approach
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Analytic | Numeric | Analytic | Analytic |
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-359 ![]() |
EM-417 | EM-617 | EM-655 |
Model Calibration Reported?
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No |
Yes ?Comment:Used 1981 and 1982 data to calibrate hydrology. |
Not applicable | No |
Model Goodness of Fit Reported?
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No |
No ?Comment:Calibration for both the stream flow and Sediment concentration of the mode |
Not applicable | No |
Goodness of Fit (metric| value | unit)
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None |
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None | None |
Model Operational Validation Reported?
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No |
Yes ?Comment:Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations |
Not applicable | No |
Model Uncertainty Analysis Reported?
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No | Unclear | Not applicable | No |
Model Sensitivity Analysis Reported?
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No |
Yes ?Comment:Yes for both runoff and sediment |
Not applicable | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | No | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
Centroid Latitude
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0 | 18.19 | Not applicable | 43.1 |
Centroid Longitude
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102 | -66.76 | Not applicable | -89.4 |
Centroid Datum
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WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Not applicable | Provided |
EM ID
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EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
EM Environmental Sub-Class
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Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands |
Specific Environment Type
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104 land use land cover classes | watershed | Restored wetlands | Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape |
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 | 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-359 ![]() |
EM-417 | EM-617 | EM-655 |
EM Organismal Scale
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Community | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-359 ![]() |
EM-417 | EM-617 | EM-655 |
None Available | None Available | None Available | 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)
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EM-417 | EM-617 | EM-655 |
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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-417 | EM-617 | EM-655 |
None | None |
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None |