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 Short Name
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ROS (Recreation Opportunity Spectrum), Europe | Urban Temperature, Baltimore, MD, USA |
EM Full Name
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ROS (Recreation Opportunity Spectrum), Europe | Urban Air Temperature Change, Baltimore, MD, USA |
EM Source or Collection
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EU Biodiversity Action 5 | i-Tree | USDA Forest Service |
EM Source Document ID
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293 | 217 |
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. |
Document Year
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2014 | 2016 |
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 |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript |
EM ID
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EM-184 | EM-306 |
Not applicable | Not applicable | |
Contact Name
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Maria Luisa Paracchini | Gordon M. Heisler |
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 |
Contact Email
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luisa.paracchini@jrc.ec.europa.eu | gheisler@fs.fed.us |
EM ID
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EM-184 | EM-306 |
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." |
Specific Policy or Decision Context Cited
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None identified | None identified |
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. |
EM Scenario Drivers
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No scenarios presented | No scenarios presented |
EM ID
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EM-184 | EM-306 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
New or Pre-existing EM?
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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-184 | EM-306 |
Document ID for related EM
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Doc-290 | Doc-291 | Doc-289 | Doc-220 | Doc-219 | Doc-218 |
EM ID for related EM
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None | None |
EM Modeling Approach
EM ID
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EM-184 | EM-306 |
EM Temporal Extent
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Not reported | May 5-Sept 30 2006 |
EM Time Dependence
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time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | future time |
EM Time Continuity
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Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Hour |
EM ID
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EM-184 | EM-306 |
Bounding Type
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Geopolitical | Geopolitical |
Spatial Extent Name
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European Union countries | Baltimore, MD |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 |
EM ID
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EM-184 | EM-306 |
EM Spatial Distribution
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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 |
Spatial Grain Size
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100 m x 100 m | 10m x 10m |
EM ID
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EM-184 | EM-306 |
EM Computational Approach
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Analytic | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-184 | EM-306 |
Model Calibration Reported?
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No | Yes |
Model Goodness of Fit Reported?
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No | Yes |
Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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No | No |
Model Uncertainty Analysis Reported?
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No | No |
Model Sensitivity Analysis Reported?
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No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-184 | EM-306 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-184 | EM-306 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-184 | EM-306 |
Centroid Latitude
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48.2 | 39.28 |
Centroid Longitude
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16.35 | -76.62 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated |
EM ID
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EM-184 | EM-306 |
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 |
Specific Environment Type
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Not applicable | Urban landscape and surrounding area |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-184 | EM-306 |
EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-184 | EM-306 |
None Available | None Available |
EnviroAtlas URL
EM-184 | EM-306 |
Dasymetric Allocation of Population, GAP Ecological Systems, Ecosystem Markets: Imperiled Species and Habitats | Average Annual Precipitation, Percent Impervious Area |
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 |
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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 |
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