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-604 | EM-630 |
EM-718 ![]() |
EM-938 |
EM Short Name
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Chinook salmon value (household), Yaquina Bay, OR | WaterWorld v2, Santa Basin, Peru | WESP: Riparian & stream habitat, ID, USA | OpenNSPECT v. 1.2 |
EM Full Name
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Economic value of Chinook salmon per household method, Yaquina Bay, OR | WaterWorld v2, Santa Basin, Peru | WESP: Riparian and stream habitat focus projects, ID, USA | OpenNSPECT v. 1.2 |
EM Source or Collection
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US EPA | None | None | None |
EM Source Document ID
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324 | 368 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
431 |
Document Author
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Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Van Soesbergen, A. and M. Mulligan | Murphy, C. and T. Weekley | Eslinger, David L., H. Jamieson Carter, Matt Pendleton, Shan Burkhalter, Margaret Allen |
Document Year
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2012 | 2018 | 2012 | 2012 |
Document Title
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Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | “OpenNSPECT: The Open-source Nonpoint Source Pollution and Erosion Comparison Tool.” NOAA Office for Coastal Management, Charleston, South Carolina. Accessed (11/2022) at https://coast.noaa.gov/digitalcoast/tools/opennspect.html |
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 journal manuscript | Published report | Webpage |
EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
Not applicable | www.policysupport.org/waterworld | Not applicable | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | |
Contact Name
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Stephen Jordan | Arnout van Soesbergen | Chris Murphy | Not reported |
Contact Address
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U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | NOAA Coastal Services Center, 2234 South Hobson Avenue Charleston, South Carolina 29405-2413 |
Contact Email
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jordan.steve@epa.gov | arnout.van_soesbergen@kcl.ac.uk | chris.murphy@idfg.idaho.gov | Not reported |
EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
Summary Description
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ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | "This open-source version of the Nonpoint Source Pollution and Erosion Comparison Tool is used to investigate potential water quality impacts from climate change and development to other land uses. The downloadable tool is designed to be broadly applicable for coastal and noncoastal areas alike. Tool functions simulate erosion, pollution, and the accumulation from overland flow. OpenNSPECT uses spatial elevation data to calculate flow direction and flow accumulation throughout a watershed. To do this, land cover, precipitation, and soils data are processed to estimate runoff volume at both the local and watershed levels. Coefficients representing the contribution of each land cover class to the expected pollutant load are also applied to land cover data to approximate total pollutant loads. These coefficients are taken from published sources or can be derived from local water quality studies. The output layers display estimates of runoff volume, pollutant loads, pollutant concentration, and total sediment yield. Requires MapWindow GIS v.4.8.8 (open source software)" |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified |
Biophysical Context
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Yaquina Bay estuary | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | restored, enhanced and created wetlands | No additional description provided |
EM Scenario Drivers
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No scenarios presented | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | Sites, function or habitat focus | No scenarios presented |
EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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New or revised model | Application of existing model | 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-604 | EM-630 |
EM-718 ![]() |
EM-938 |
Document ID for related EM
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Doc-324 | None | Doc-390 | None |
EM ID for related EM
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EM-603 | EM-397 | None | EM-706 | EM-729 | EM-730 | EM-734 | EM-743 | EM-749 | EM-750 | EM-756 | EM-757 | EM-758 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-732 | EM-737 | EM-738 | EM-739 | EM-741 | EM-742 | EM-751 | EM-768 | EM-940 |
EM Modeling Approach
EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
EM Temporal Extent
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2003-2008 | 1950-2071 | 2010-2011 | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | both | past time | 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 | Month | Not applicable | Not applicable |
EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Not applicable |
Spatial Extent Name
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Pacific Northwest | Santa Basin | Wetlands in idaho | Not applicable |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | Not applicable |
EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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Not applicable | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
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Not applicable | 1 km2 | Not applicable | 30 m |
EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
EM Computational Approach
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Analytic | * | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
Model Calibration Reported?
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No | No | No | Not applicable |
Model Goodness of Fit Reported?
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No | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None | None |
Model Operational Validation Reported?
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Yes | Yes | No | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
Centroid Latitude
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44.62 | -9.05 | 44.06 | Not applicable |
Centroid Longitude
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-124.02 | -77.81 | -114.69 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Not applicable |
EM ID
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EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | None | Inland Wetlands | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Yaquina Bay estuary and ocean | tropical, coastal to montane | created, restored and enhanced wetlands | Coastal and non-coastal |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Other or unclear (comment) | 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-604 | EM-630 |
EM-718 ![]() |
EM-938 |
EM Organismal Scale
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Other (multiple scales) | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
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None Available | None Available | None Available |
EnviroAtlas URL
EM-604 | EM-630 |
EM-718 ![]() |
EM-938 |
Dasymetric Allocation of Population | Average Annual Precipitation | Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass | 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-604 | EM-630 |
EM-718 ![]() |
EM-938 |
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None |
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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-604 | EM-630 |
EM-718 ![]() |
EM-938 |
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None | None | None |