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-184 | EM-306 | EM-651 | EM-682 |
EM Short Name
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ROS (Recreation Opportunity Spectrum), Europe | Urban Temperature, Baltimore, MD, USA | Dickcissel density, CREP, Iowa, USA | WTP for a beach day, Massachusetts, USA |
EM Full Name
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ROS (Recreation Opportunity Spectrum), Europe | Urban Air Temperature Change, Baltimore, MD, USA | Dickcissel population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Willingness to pay (WTP) for a beach day, Barnstable, Massachusetts, USA |
EM Source or Collection
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EU Biodiversity Action 5 | i-Tree | USDA Forest Service | None | US EPA |
EM Source Document ID
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293 | 217 | 372 | 386 |
Document Author
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Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | 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 | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta |
Document Year
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2014 | 2016 | 2010 | 2018 |
Document Title
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Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts |
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 | Published journal manuscript |
EM ID
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EM-184 | EM-306 | EM-651 | EM-682 |
Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Maria Luisa Paracchini | Gordon M. Heisler | David Otis | Kate K, Mulvaney |
Contact Address
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Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported |
Contact Email
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luisa.paracchini@jrc.ec.europa.eu | gheisler@fs.fed.us | dotis@iastate.edu | Mulvaney.Kate@EPA.gov |
EM ID
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EM-184 | EM-306 | EM-651 | EM-682 |
Summary Description
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ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | 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 Dickcissel (Spiza americana)... 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: DICK density = 1-1/1+e^(-6.811334 + 1.889878 * bbspath) * e^(-1.831015 + 0.0312571 * hay400) | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "We used existing studies in a meta-analysis to estimate appropriate benefit transfer values of consumer surplus per beach visit for Barnstable. The studies we include in the model are for beaches across the United States, allowing the metaregression model to be more broadly applicable to other beaches and for values to be adjusted based on appropriate site attributes...To identify relevant studies, we selected 25 studies of beach use and swimming from the Recreation Use Values Database (RUVD), where consumer surplus values are presented as value per day in 2016 dollars...We added beach length and history of closures to contextualize the model for our application by proxying water quality and site quality." Equation 1, page 11, provides the meta-regression. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | Economic value of protecting coastal beach water quality from contamination caused closures. |
Biophysical Context
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No additional description provided | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | Prairie pothole region of north-central Iowa | Four separate beaches within the community of Barnstable |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented |
EM ID
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EM-184 | EM-306 | EM-651 | EM-682 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
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-184 | EM-306 | EM-651 | EM-682 |
Document ID for related EM
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Doc-290 | Doc-291 | Doc-289 | Doc-220 | Doc-219 | Doc-218 | Doc-372 | Doc-386 | Doc-387 |
EM ID for related EM
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None | None | EM-652 | EM-650 | EM-649 | EM-648 | EM-684 | EM-685 | EM-683 | EM-686 |
EM Modeling Approach
EM ID
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EM-184 | EM-306 | EM-651 | EM-682 |
EM Temporal Extent
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Not reported | May 5-Sept 30 2006 | 1992-2007 | July 1, 2011 to June 31, 2016 |
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 | Hour | Not applicable | Not applicable |
EM ID
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EM-184 | EM-306 | EM-651 | EM-682 |
Bounding Type
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Geopolitical | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological |
Spatial Extent Name
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European Union countries | Baltimore, MD | CREP (Conservation Reserve Enhancement Program) wetland sites | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 1-10 km^2 | 10-100 ha |
EM ID
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EM-184 | EM-306 | EM-651 | EM-682 |
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 | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) |
Spatial Grain Size
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100 m x 100 m | 10m x 10m | multiple, individual, irregular shaped sites | by beach site |
EM ID
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EM-184 | EM-306 | EM-651 | EM-682 |
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-184 | EM-306 | EM-651 | EM-682 |
Model Calibration Reported?
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No | Yes | Unclear | Yes |
Model Goodness of Fit Reported?
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No | Yes | No | Yes |
Goodness of Fit (metric| value | unit)
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None |
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None |
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Model Operational Validation Reported?
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No | No | Unclear | No |
Model Uncertainty Analysis Reported?
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No | No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
Yes ?Comment:p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables. |
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-184 | EM-306 | EM-651 | EM-682 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-184 | EM-306 | EM-651 | EM-682 |
None | None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-184 | EM-306 | EM-651 | EM-682 |
Centroid Latitude
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48.2 | 39.28 | 42.62 | 41.64 |
Centroid Longitude
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16.35 | -76.62 | -93.84 | -70.29 |
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-184 | EM-306 | EM-651 | EM-682 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Inland Wetlands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine |
Specific Environment Type
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Not applicable | Urban landscape and surrounding area | Grassland buffering inland wetlands set in agricultural land | Saltwater beach |
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 corresponds to 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-184 | EM-306 | EM-651 | EM-682 |
EM Organismal Scale
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Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-184 | EM-306 | EM-651 | EM-682 |
None Available | None Available |
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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-184 | EM-306 | EM-651 | EM-682 |
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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-184 | EM-306 | EM-651 | EM-682 |
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