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-652 |
EM-851 ![]() |
EM-960 |
EM Short Name
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Savannah Sparrow density, CREP, Iowa, USA | InVEST Coastal Vulnerability, New York, USA | HAWQS model method |
EM Full Name
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Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | InVEST Coastal Vulnerability, Jamaica Bay, New York, USA | Hydrologic and water quality system (HAWQS) model v.1.1 user's guide methodology |
EM Source or Collection
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None | InVEST | US EPA |
EM Source Document ID
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372 |
410 ?Comment:Sharp R, Tallis H, Ricketts T, Guerry A, Wood S, Chaplin-Kramer R, et al. InVEST User?s Guide. User Guide. Stanford (CA): The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, World Wildlife Fund; 2015. |
445 |
Document Author
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Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Hopper T. and M. S. Meixler | United States Environmental Protection Agency |
Document Year
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2010 | 2016 | 2019 |
Document Title
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Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Modeling coastal vulnerability through space and time | HAWQS 1.0 (Hydrologic and Water Quality System) modeling framework |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published report | Published journal manuscript | Published EPA report |
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
Not applicable | https://naturalcapitalproject.stanford.edu/software/invest-models/coastal-vulnerability | https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/GDOPBA | |
Contact Name
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David Otis | Thomas Hopper | Raghavan Srinivasan |
Contact Address
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U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported | Spatial Sciences Laboratory, Dept. of ecology and conservatin Biology, Texas A&M university |
Contact Email
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dotis@iastate.edu | Tjhop1123@gmail.com | r-srinivasan@tamu.edu |
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
Summary Description
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ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Savannah Sparrow (Passerculus sandwichensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SASP density = e^(-1.581362 + 0.0229603 *bbspath + 0.01024* grass3200 + 0.0255867 * hay3200) | ABSTRACT: "Coastal ecosystems experience a wide range of stressors including wave forces, storm surge, sea-level rise, and anthropogenic modification and are thus vulnerable to erosion. Urban coastal ecosystems are especially important due to the large populations these limited ecosystems serve. However, few studies have addressed the issue of urban coastal vulnerability at the landscape scale with spatial data that are finely resolved. The purpose of this study was to model and map coastal vulnerability and the role of natural habitats in reducing vulnerability in Jamaica Bay, New York, in terms of nine coastal vulnerability metrics (relief, wave exposure, geomorphology, natural habitats, exposure, exposure with no habitat, habitat role, erodible shoreline, and surge) under past (1609), current (2015), and future (2080) scenarios using InVEST 3.2.0. We analyzed vulnerability results both spatially and across all time periods, by stakeholder (ownership) and by distance to damage from Hurricane Sandy. We found significant differences in vulnerability metrics between past, current and future scenarios for all nine metrics except relief and wave exposure…" | Author overview: " The Hydrologic and Water Quality System (HAWQS) is a web-based interactive water quantity and water quality modeling system that employs the internationally-recognized public domain model Soil and Water Assessment Tool (SWAT) as its core modeling engine. HAWQS provides users with: 1) interactive web interfaces and maps and pre-loaded input data; 2) Output data includes tables, charts, graphs, and raw data; 3) A user guide; and 4) Online development, execution, and storage for users modeling projects. HAWQS enables use of SWAT to simulate the effects of management practices based on an extensive array of crops, soils, natural vegetation types, land uses, and climate change scenarios for hydrology and the following water quality parameters: Sediment pathogens, nutrients, biological oxygen demand, dissolved oxygen, pesticides, and water temperature. HAWQS users can select from three watershed scales, or hydrologic unit codes (HUCs)—small (HUC 12), medium (HUC 10), and large (HUC 8)—to run simulations. HAWQS allows for further aggregation and scalability of annual, monthly, and daily estimates of water quality across large geographic areas up to and including the continental United States. The United States Environmental Protection Agency (USEPA) Office of Water (OW) supports and provides project management and funding for HAWQS. The Texas A&M University Spatial Sciences Laboratory and EPA subject matter experts provide ongoing technical support including system design, modeling, and software development. The United States Department of Agriculture (USDA) and Texas A&M University jointly developed SWAT and have actively supported the model for more than 25 years. The system was developed to meet the needs of the USEPA Office of Water. It can also be employed by other Federal Agencies, State and local governments, academics, and contractors. " |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
Biophysical Context
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Prairie pothole region of north-central Iowa | Jamaica Bay, New York, situated on the southern shore of Long Island, and characterized by extensive coastal ecosystems in the central bay juxtaposed with a largely urbanized shoreline containing fragmented and fringing coastal habitat. | N/A |
EM Scenario Drivers
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No scenarios presented | Past (1609), current (2015), and future (2080) scenarios | N/A |
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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Application of existing model ?Comment:Models developed by Quamen (2007). |
Application of existing 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-652 |
EM-851 ![]() |
EM-960 |
Document ID for related EM
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Doc-372 | Doc-408 | None |
EM ID for related EM
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EM-648 | EM-649 | EM-650 | EM-651 | EM-849 | None |
EM Modeling Approach
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
EM Temporal Extent
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1992-2007 | 1609-2080 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time |
EM Time Continuity
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Not applicable | Not applicable |
discrete ?Comment:Time can be in day, month or year increments |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year |
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
Bounding Type
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Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Not applicable |
Spatial Extent Name
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CREP (Conservation Reserve Enhancement Program) wetland sites | Jamaica Bay, Long Island, New York | Not applicable |
Spatial Extent Area (Magnitude)
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1-10 km^2 | 10-100 km^2 | Not applicable |
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
EM Spatial Distribution
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:by coastal segment |
spatially lumped (in all cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | Not applicable |
Spatial Grain Size
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multiple, individual, irregular shaped sites | 80 m | Not applicable |
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
EM Computational Approach
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Analytic | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
Model Calibration Reported?
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Unclear | No | No |
Model Goodness of Fit Reported?
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No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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Unclear | No | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-652 |
EM-851 ![]() |
EM-960 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-652 |
EM-851 ![]() |
EM-960 |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
Centroid Latitude
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42.62 | 40.61 | Not applicable |
Centroid Longitude
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-93.84 | -73.84 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Provided | Not applicable |
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
EM Environmental Sub-Class
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Inland Wetlands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Agroecosystems |
Specific Environment Type
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Grassland buffering inland wetlands set in agricultural land | Coastal | HUCs |
EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | 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
EM ID
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EM-652 |
EM-851 ![]() |
EM-960 |
EM Organismal Scale
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Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-652 |
EM-851 ![]() |
EM-960 |
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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)
EM-652 |
EM-851 ![]() |
EM-960 |
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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-652 |
EM-851 ![]() |
EM-960 |
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None | None |