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-63 |
EM-177 ![]() |
EM-698 | EM-838 |
EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Salmon habitat values, west coast of Canada | Fish species richness, St. Croix, USVI | Eastern meadowlark abundance, Piedmont region, USA |
EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada | Fish Species Richness, Buck Island, St. Croix , USVI | Eastern meadowlark abundance, Piedmont ecoregion, USA |
EM Source or Collection
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US EPA | EnviroAtlas | None | None | None |
EM Source Document ID
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262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
286 | 355 | 405 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Riffel, S., Scognamillo, D., and L. W. Burger |
Document Year
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2013 | 2003 | 2007 | 2008 |
Document Title
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EnviroAtlas - National | Valuing freshwater salmon habitat on the west coast of Canada | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds |
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 on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | |
Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Duncan Knowler | Simon Pittman | Sam Riffell |
Contact Address
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Not reported | School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6 | 1305 East-West Highway, Silver Spring, MD 20910, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA |
Contact Email
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enviroatlas@epa.gov | djk@sfu.ca | simon.pittman@noaa.gov | sriffell@cfr.msstate.edu |
EM ID
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EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "In this paper, we present a framework for valuing benefits for fisheries from protecting areas from degradation, using the example of the Strait of Georgia coho salmon fishery in southern British Columbia, Canada. Our study improves upon previous methods used to value fish habitat in two major respects. First, we use a bioeconomic model of the coho fishery to derive estimates of value that are consistent with economic theory. Second, we estimate the value of changing the quality of fish habitat by using empirical analyses to link fish population dynamics with indices of land use in surrounding watersheds." | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " |
Specific Policy or Decision Context Cited
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None Identified | None identified | None provided | None reported |
Biophysical Context
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No additional description provided | No additional description provided | Hard and soft benthic habitat types approximately to the 33m isobath | Conservation Reserve Program lands left to go fallow |
EM Scenario Drivers
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No scenarios presented | Habitat quality | No scenarios presented | N/A |
EM ID
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EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised 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-63 |
EM-177 ![]() |
EM-698 | EM-838 |
Document ID for related EM
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Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
None | Doc-355 | Doc-405 |
EM ID for related EM
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None | EM-179 | EM-183 | EM-180 | EM-181 | EM-590 | EM-699 | EM-831 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 |
EM Modeling Approach
EM ID
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EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
EM Temporal Extent
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2006-2010 | 1989-1999 | 2000-2005 | 2008 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
Bounding Type
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Geopolitical | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological |
Spatial Extent Name
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counterminous United States | South Thompson watershed | SW Puerto Rico, | Piedmont Ecoregion |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 100,000-1,000,000 km^2 |
EM ID
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EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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irregular | Not applicable | not reported | Not applicable |
EM ID
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EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
EM Computational Approach
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Analytic | Analytic | 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-63 |
EM-177 ![]() |
EM-698 | EM-838 |
Model Calibration Reported?
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No | Yes | No | Yes |
Model Goodness of Fit Reported?
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No | No | Yes | No |
Goodness of Fit (metric| value | unit)
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None | None |
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None |
Model Operational Validation Reported?
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No | No | Yes | No |
Model Uncertainty Analysis Reported?
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No | No | No | No |
Model Sensitivity Analysis Reported?
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No | Yes | Yes | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | No | No | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
Centroid Latitude
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39.5 | 49.29 | 17.79 | 36.23 |
Centroid Longitude
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-98.35 | -123.8 | -64.62 | -81.9 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated |
EM ID
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EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Grasslands |
Specific Environment Type
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Terrestrial | Rivers and streams | shallow coral reefs | grasslands |
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 | 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-63 |
EM-177 ![]() |
EM-698 | EM-838 |
EM Organismal Scale
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Not applicable |
Other (Comment) ?Comment:Coho salmon stock |
Guild or Assemblage | Species |
Taxonomic level and name of organisms or groups identified
EM-63 |
EM-177 ![]() |
EM-698 | EM-838 |
None Available |
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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-63 |
EM-177 ![]() |
EM-698 | EM-838 |
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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-63 |
EM-177 ![]() |
EM-698 | EM-838 |
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