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-65 | EM-195 |
EM-224 
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EM-260 | EM-656 |
EM-735
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EM Short Name
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Green biomass production, Central French Alps | C Sequestration and De-N, Tampa Bay, FL, USA | FORCLIM v2.9, West Cascades, OR, USA | Coral taxa and land development, St.Croix, VI, USA | P8 UCM | C sequestration in grassland restoration, England |
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EM Full Name
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Green biomass production, Central French Alps | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | P8 Urban Catchment model method | Carbon sequestration in grassland diversity restoration, England |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | US EPA | None | None |
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EM Source Document ID
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260 | 186 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
96 |
377 ?Comment:Published to the web. Previously versions prepared for EPA. |
396 |
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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. | Russell, M. and Greening, H. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Walker, W. Jr., and J.D. Walker | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett |
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Document Year
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2011 | 2013 | 2007 | 2011 | 2015 | 2011 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Estimating benefits in a recovering estuary: Tampa Bay, Florida | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | P8 Urban Catchment Model Version 3.5 | Additional carbon sequestration benefits of grassland diversity restoration |
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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 | Not peer reviewed but is published (explain in Comment) | 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 report | Published journal manuscript |
Software and Access
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EM ID
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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| Not applicable | Not applicable | Not applicable | Not applicable | http://www.wwwalker.net/p8/v35/webhelp/splash.htm | Not applicable | |
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Contact Name
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Sandra Lavorel | M. Russell | Richard T. Busing | Leah Oliver | William Walker Jr., PhD | Gerlinde B. De Deyn |
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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 | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | National Health and Environmental Research Effects Laboratory | Concord, Massachusetts | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | Russell.Marc@epamail.epa.gov | rtbusing@aol.com | leah.oliver@epa.gov | bill@wwwalker.net | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com |
EM Description
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EM ID
em.detail.idHelp
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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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., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production 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, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. 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 ecosystem properties. 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 (see Albert et al. 2010)." | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | 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) | Author description: " P8 simulates the generation and transport of stormwater runoff pollutants in urban watersheds. Continuous water-balance and mass-balance calculations are performed on a user-defined drainage system consisting of the following elements: - Watersheds (<= 250 nonpoint source areas) - Devices (<=75 runoff storage/treatment areas or BMP's) - Particles (<= 5 fractions with different settling velocities) - Water Quality Components (<= 10 associated with particles) Simulations are driven by hourly precipitation and daily air temperature time series. Runoff contributions from snowmelt are also simulated. 'P8' abbreviates "Program for Predicting Polluting Particle Passage Thru Pits, Puddles, and Ponds", which more or less captures the basic features and functions of the model. It has been developed for use by engineers and planners in designing and evaluating runoff treatment schemes for existing or proposed urban developments. Design objectives are typically expressed in terms of percentage reduction in suspended solids or other water quality component. Despite its limitations, P8 has been used by state and local regulatory agencies as a consistent framework for evaluating proposed developments. Depending on applications, other models could be either too simple (easily used, but ignoring important factors) or too complex (requiring considerable site-specific data and/or user expertise). P8 attempts to strike a balance to between those extremes. Predicted water quality components include total suspended solids (sum of the individual particle fractions), total phosphorus, total Kjeldahl nitrogen, copper, lead, zinc, and total hydrocarbons. Simulated BMP types include detention ponds (wet, dry, extended), infiltration basins, swales, buffer strips, or other devices with user-specified stage/discharge curves and infiltration rates. A simple water budget algorithm can be used to estimate groundwater storage and stream base flow in watershed-scale applications. Initial calibrations were based upon runoff quality and particle settling velocity data collected under the EPA's Nationwide Urban Runoff Program (Athayede et al., 1983). Calibrations to impervious area runoff parameters for Wisconsin watersheds have been subsequently developed. Inputs are structured in terms which should be familiar to planners and engineers involved in hydrologic evaluation. Several tabular and graphic output formats are provided. " | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" |
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Specific Policy or Decision Context Cited
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None identified | Restoration of seagrass | None Identified | Not applicable | None identified | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | nearshore; <1.5 km offshore; <12 m depth | Urban setting | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. |
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EM Scenario Drivers
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No scenarios presented | Habitat loss or restoration in Tampa Bay Estuary | Two scenarios modelled, forests with and without fire | Not applicable | N/A | Additional benefits due to biodiversity restoration practices |
EM Relationship to Other EMs or Applications
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EM ID
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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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) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs |
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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 | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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Document ID for related EM
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Doc-260 | None | Doc-22 | Doc-23 | None | None | None |
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EM ID for related EM
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | EM-146 | EM-208 | EM-186 | None | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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EM Temporal Extent
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2007-2009 | 1982-2010 | >650 yrs | 2006-2007 | Not applicable | 1990-2007 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Not applicable | Hour | Not applicable |
EM Spatial Extent
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EM ID
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological | Physiographic or Ecological | Not applicable | Other |
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Spatial Extent Name
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Central French Alps | Tampa Bay Estuary | West Cascades, Oregon | St.Croix, U.S. Virgin Islands | Not applicable | Colt Park meadows, Ingleborough National Nature Reserve, northern England |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10-100 km^2 | Not applicable | <1 ha |
Spatial Distribution of Computations
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EM ID
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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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) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all 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 | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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20 m x 20 m | 1 ha | 0.08 ha | Not applicable | Not applicable | 3 m x 3 m |
EM Structure and Computation Approach
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EM ID
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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EM Computational Approach
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Analytic | Analytic | Numeric | Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | stochastic |
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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-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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Model Calibration Reported?
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No | Yes | No | Yes | Yes | Not applicable |
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Model Goodness of Fit Reported?
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Yes | No | No | Yes | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None |
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None | None |
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Model Operational Validation Reported?
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Yes | No | Yes | No | Not applicable | No |
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Model Uncertainty Analysis Reported?
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No | No | No | Yes | Not applicable | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No | Not applicable | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
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| EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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| None |
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None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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Centroid Latitude
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45.05 | 27.95 | 44.24 | 17.75 | Not applicable | 54.2 |
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Centroid Longitude
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6.4 | -82.47 | -122.24 | -64.75 | Not applicable | -2.35 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | NAD83 | Not applicable | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Not applicable | Provided |
Environments and Scales Modeled
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EM ID
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Forests | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subtropical Estuary | Primarily conifer forest | stony coral reef | Urban catchments | fertilized grassland (historically hayed) |
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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 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
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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EM Organismal Scale
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Community | Not applicable | Species | Guild or Assemblage | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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| None Available | None Available |
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None Available | 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.detail.cicesHelp
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| EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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| 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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-65 | EM-195 |
EM-224
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EM-260 | EM-656 |
EM-735
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None |
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None | None |