EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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EM Short Name
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Community flowering date, Central French Alps | Coral taxa and land development, St.Croix, VI, USA | Fish species richness, Puerto Rico, USA | Visitation to natural areas, New England, USA | Air quality regulation, Lisbon |
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EM Full Name
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Community weighted mean flowering date, Central French Alps | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Fish species richness, Puerto Rico, USA | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA | Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None | US EPA | None |
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EM Source Document ID
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260 | 96 | 355 | 436 | 454 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin | Matos, P., Vieira, J., Rocha, B., Branquinho, C., & Pinho, P. |
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Document Year
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2011 | 2011 | 2007 | 2020 | 2019 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA | Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
| Not applicable | Not applicable | Not applicable | https://github.com/USEPA/Recreation_Benefits.git | Not applicable | |
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Contact Name
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Sandra Lavorel | Leah Oliver | Simon Pittman | Nathaniel Merrill | Pedro Pinho |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | National Health and Environmental Research Effects Laboratory | 1305 East-West Highway, Silver Spring, MD 20910, USA | Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America, | N/A |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | leah.oliver@epa.gov | simon.pittman@noaa.gov | merrill.nathaniel@epa.gov | ppinho@fc.ul.pt |
EM Description
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | 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: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. " | The UN Sustainable Development Goals states that urban air pollution must be tackled to create more inclusive, safe, resilient and sustainable cities. Urban green infrastructures can mitigate air pollution, but a crucial step to use this knowledge into urban management is to quantify how much air-quality regulation can green spaces provide and to understand how the provision of this ecosystem service is affected by other environmental factors. Considering the insufficient number of air quality monitoring stations in cities to monitor the wide range of natural and anthropic sources of pollution with high spatial resolution, ecological indicators of air quality are an alternative cost-effective tool. The aim of this work was to model the supply of air-quality regulation based on urban green spaces characteristics and other environmental factors. For that, we sampled lichen diversity in the centroids of 42 urban green spaces in Lisbon, Portugal. Species richness was the best biodiversity metric responding to air pollution, considering its simplicity and its significative response to the air pollutants concentration data measured in the existent air quality monitoring stations. Using that metric, we then created a model to estimate the supply of air quality regulation provided by green spaces in all green spaces of Lisbon based on the response to the following environmental drivers: the urban green spaces size and its vegetation density. We also used the unexplained variance of this model to map the background air pollution. Overall, we suggest that management should target the smallest urban green spaces by increasing green space size or tree density. The use of ecological indicators, very flexible in space, allow the understanding and the modeling of the provision of air-quality regulation by urban green spaces, and how urban green spaces can be managed to improve air quality and thus improve human well-being and cities resilience. |
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Specific Policy or Decision Context Cited
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None identified | Not applicable | None provided | None identified | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | nearshore; <1.5 km offshore; <12 m depth | Hard and soft benthic habitat types approximately to the 33m isobath | Natural area water bodies | Green spaces in Lisbon, Portugal |
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EM Scenario Drivers
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No scenarios presented | Not applicable | No scenarios presented | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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Document ID for related EM
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Doc-260 | Doc-269 | None | Doc-355 | None | None |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | EM-698 | EM-699 | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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EM Temporal Extent
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2007-2008 | 2006-2007 | 2000-2005 | 2017 | 2015-2018 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | past time | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological | Point or points | Physiographic or ecological |
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Spatial Extent Name
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Central French Alps | St.Croix, U.S. Virgin Islands | SW Puerto Rico, | Cape Cod | Urban green spaces in Lisbon |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | map scale, for cartographic feature |
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Spatial Grain Size
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20 m x 20 m | Not applicable | not reported | water feature edge (beach) | N/A |
EM Structure and Computation Approach
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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Model Calibration Reported?
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No | Yes | No | Yes | Yes |
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Model Goodness of Fit Reported?
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Yes | Yes | Yes |
Yes ?Comment:Random forest model performance statistics |
Yes |
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Goodness of Fit (metric| value | unit)
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Model Operational Validation Reported?
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No | No | Yes | Yes | No |
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Model Uncertainty Analysis Reported?
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No | Yes | No | Unclear | No |
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Model Sensitivity Analysis Reported?
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No | No | Yes | Yes | Unclear |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No | Unclear | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
| None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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Centroid Latitude
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45.05 | 17.75 | 17.9 | 41.72 | 38.75 |
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Centroid Longitude
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6.4 | -64.75 | 67.11 | -70.29 | 9.8 |
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Centroid Datum
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WGS84 | NAD83 | WGS84 | WGS84 | None provided |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Lakes and Ponds | Near Coastal Marine and Estuarine | Created Greenspace |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | stony coral reef | shallow coral reefs | beaches | Green spaces in Lisbon, Portugal |
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EM Ecological Scale
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
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EM Organismal Scale
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Community | Guild or Assemblage | Guild or Assemblage | Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
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| EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
| None Available |
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None Available |
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EnviroAtlas URL
| EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
| None Available | None Available | None Available | Average Annual Precipitation | Green Space per Capita |
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)
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| EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
| 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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-71 | EM-260 | EM-590 | EM-943 | EM-970 |
| None |
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