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
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EM ID
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EM-68 | EM-493 | EM-630 |
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EM Short Name
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Fodder crude protein content, Central French Alps | EnviroAtlas-Carbon sequestered by trees | WaterWorld v2, Santa Basin, Peru |
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EM Full Name
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Fodder crude protein content, Central French Alps | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | WaterWorld v2, Santa Basin, Peru |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | EnviroAtlas | i-Tree | None |
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EM Source Document ID
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260 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
368 |
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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. | US EPA Office of Research and Development - National Exposure Research Laboratory | Van Soesbergen, A. and M. Mulligan |
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Document Year
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2011 | 2013 | 2018 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | EnviroAtlas - Featured Community | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru |
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Document Status
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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 on US EPA EnviroAtlas website | Published journal manuscript |
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EM ID
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EM-68 | EM-493 | EM-630 |
| Not applicable | https://www.epa.gov/enviroatlas | www.policysupport.org/waterworld | |
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Contact Name
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Sandra Lavorel | EnviroAtlas Team | Arnout van Soesbergen |
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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 | Not reported | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | enviroatlas@epa.gov | arnout.van_soesbergen@kcl.ac.uk |
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EM ID
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EM-68 | EM-493 | EM-630 |
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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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Fodder crude protein for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on fodder protein content. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | 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 |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | No additional description provided | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. |
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EM Scenario Drivers
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No scenarios presented | 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 |
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EM ID
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EM-68 | EM-493 | EM-630 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) |
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New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing 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-68 | EM-493 | EM-630 |
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Document ID for related EM
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Doc-260 | Doc-269 | Doc-345 | None |
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EM ID for related EM
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EM-65 | EM-66 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None |
EM Modeling Approach
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EM ID
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EM-68 | EM-493 | EM-630 |
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EM Temporal Extent
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2007-2009 | 2010-2013 | 1950-2071 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | both |
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EM Time Continuity
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Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Month |
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EM ID
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EM-68 | EM-493 | EM-630 |
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Bounding Type
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Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC |
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Spatial Extent Name
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Central French Alps | Durham NC and vicinity | Santa Basin |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 |
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EM ID
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EM-68 | EM-493 | EM-630 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Census block groups |
spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
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20 m x 20 m | irregular | 1 km2 |
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EM ID
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EM-68 | EM-493 | EM-630 |
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EM Computational Approach
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Analytic | Numeric | * |
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EM Determinism
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deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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None |
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EM ID
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EM-68 | EM-493 | EM-630 |
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Model Calibration Reported?
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No | No | No |
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Model Goodness of Fit Reported?
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Yes | No | No |
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Goodness of Fit (metric| value | unit)
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None | None |
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Model Operational Validation Reported?
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Yes | No | Yes |
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Model Uncertainty Analysis Reported?
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No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-68 | EM-493 | EM-630 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-68 | EM-493 | EM-630 |
| None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-68 | EM-493 | EM-630 |
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Centroid Latitude
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45.05 | 35.99 | -9.05 |
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Centroid Longitude
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6.4 | -78.96 | -77.81 |
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Centroid Datum
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WGS84 | None provided | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated |
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EM ID
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EM-68 | EM-493 | EM-630 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Created Greenspace | Atmosphere | None |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Urban and vicinity | tropical, coastal to montane |
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EM Ecological Scale
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Other or unclear (comment) |
Scale of differentiation of organisms modeled
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EM ID
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EM-68 | EM-493 | EM-630 |
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EM Organismal Scale
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Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-68 | EM-493 | EM-630 |
| None Available | None Available | None Available |
EnviroAtlas URL
| EM-68 | EM-493 | EM-630 |
| GAP Ecological Systems, Carbon storage by tree biomass (kg/m2) | 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-68 | EM-493 | EM-630 |
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None |
<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-68 | EM-493 | EM-630 |
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None | None |
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