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-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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EM Short Name
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Mangrove development, Tampa Bay, FL, USA | Coral taxa and land development, St.Croix, VI, USA | Savannah Sparrow density, CREP, Iowa, USA | Red-winged blackbird abun, Piedmont region, USA | ARIES Outdoor recreation, Santa Fe, NM | Pollutant dispersion by vegetation barriers |
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EM Full Name
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Mangrove wetland development, Tampa Bay, FL, USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Red-winged blackbird abundance, Piedmont ecoregion, USA | Artificial intelligence for Ecosystem Services (ARIES): Outdoor recreation, Santa Fe, New Mexico | Pollutant dispersion by vegetation barriers |
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EM Source or Collection
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US EPA | US EPA | None | None | None | US EPA |
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EM Source Document ID
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97 | 96 | 372 | 405 | 411 | 435 |
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Document Author
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Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Riffel, S., Scognamillo, D., and L. W. Burger | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Hashad, K. B. Yang, J. T. Steffens, R. W. Baldauf, P. Deshmukh, K. M. Zhang |
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Document Year
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2012 | 2011 | 2010 | 2008 | 2018 | 2021 |
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Document Title
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Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Towards globally customizable ecosystem service models | Parameterizing pollutant dispersion downwind of roadside vegetation barriers |
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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 | Peer reviewed but unpublished (explain in Comment) |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review |
Software and Access
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EM ID
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EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
| Not applicable | Not applicable | Not applicable | Not applicable |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
Not applicable | |
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Contact Name
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Michael Osland | Leah Oliver | David Otis | Sam Riffell | Javier Martinez-Lopez | K. Max Zhang |
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Contact Address
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U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | National Health and Environmental Research Effects Laboratory | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA |
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Contact Email
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mosland@usgs.gov | leah.oliver@epa.gov | dotis@iastate.edu | sriffell@cfr.msstate.edu | javier.martinez@bc3research.org | kz33@cornell.edu |
EM Description
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EM ID
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EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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Summary Description
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ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses." | 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: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Savannah Sparrow (Passerculus sandwichensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SASP density = e^(-1.581362 + 0.0229603 *bbspath + 0.01024* grass3200 + 0.0255867 * hay3200) | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " | ABSTRACT: "Communities living and working in near-road environments are exposed to elevated levels of traffic-related air pollution (TRAP), causing adverse health effects. Roadside vegetation may help reduce TRAP through enhanced deposition and mixing….there are no studies that developed a dispersion model to characterize pollutant concentrations downwind of vegetation barriers. To account for the physical mechanisms, by which the vegetation barrier deposits and disperses pollutants, we propose a multi-region approach that describes the parameters of the standard Gaussian equations in each region. The four regions include the vegetation, a downwind wake, a transition, and a recovery zone. For each region, we fit the relevant Gaussian plume equation parameters as a function of the vegetation properties and the local wind speed. Furthermore, the model captures particle deposition which is a major factor in pollutant reduction by vegetation barriers. We generated data from 75 (CFD)-based simulations, using the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model, to parameterize the Gaussian-based equations. The simulations used reflected a wide range of vegetation barriers, with heights from 2-10 m, and various densities, represented by leaf area index values from 4-11, and evaluated under different urban conditions, represented by wind speeds from 1-5 m/s. The CTAG model has been evaluated against two field measurements to ensure that it can properly represent the vegetation barrier’s pollutant deposition and dispersion. The proposed multi-region Gaussian-based model was evaluated across 9 particle sizes and a tracer gas to assess its capability of capturing deposition. The multi-region model’s normalized mean error (NME) ranged between 0.18-0.3, the fractional bias (FB) ranged between -0.12-0.09, and R2 value ranged from 0.47-0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable range. Even though the multi-region model is parameterized for coniferous trees, our sensitivity study indicates that the parameterized Gaussian-based model can provide useful predictions for hedge/bushes vegetative barriers as well." ADDITIONAL DESCRIPTION: Detailed variable relationships are described in the source document. The VRD associated with the ESML entry provides variables in a simplified form. |
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Specific Policy or Decision Context Cited
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Not applicable | Not applicable | None identified | None reported | None identified | None identified |
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Biophysical Context
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mangrove forest,Salt marsh, estuary, sea level, | nearshore; <1.5 km offshore; <12 m depth | Prairie pothole region of north-central Iowa | Conservation Reserve Program lands left to go fallow | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | Communities living and working in near-road environments |
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EM Scenario Drivers
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Not applicable | Not applicable | No scenarios presented | N/A | N/A | None scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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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 | Method Only |
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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 ?Comment:Models developed by Quamen (2007). |
New or revised model | Application of existing 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-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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Document ID for related EM
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None | None | Doc-372 | Doc-405 | Doc-411 | None |
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EM ID for related EM
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None | None | EM-648 | EM-649 | EM-650 | EM-651 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-846 | EM-847 | EM-855 | EM-856 | EM-858 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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EM Temporal Extent
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1990-2010 | 2006-2007 | 1992-2007 | 2008 | 1981-2015 | Not applicable |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | Not applicable |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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continuous | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Watershed/Catchment/HUC | Not applicable |
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Spatial Extent Name
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Tampa Bay | St.Croix, U.S. Virgin Islands | CREP (Conservation Reserve Enhancement Program) wetland sites | Piedmont Ecoregion | Santa Fe Fireshed | Not applicable |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10-100 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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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 lumped (in all 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 | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) |
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Spatial Grain Size
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m^2 | Not applicable | multiple, individual, irregular shaped sites | Not applicable | 30 m | user defined |
EM Structure and Computation Approach
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EM ID
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EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic | 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-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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Model Calibration Reported?
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No | Yes | Unclear | Yes | Unclear | Yes |
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Model Goodness of Fit Reported?
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No | Yes | No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None |
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None | None | None | None |
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Model Operational Validation Reported?
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No | No | Unclear | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
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Yes | Yes | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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Yes | No | No | Yes | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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No | Not applicable | Not applicable | Unclear | Not applicable | 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-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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Centroid Latitude
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27.8 | 17.75 | 42.62 | 36.23 | 35.86 | Not applicable |
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Centroid Longitude
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-82.4 | -64.75 | -93.84 | -81.9 | -105.76 | Not applicable |
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Centroid Datum
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WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Created Mangrove wetlands | stony coral reef | Grassland buffering inland wetlands set in agricultural land | grasslands | watersheds | Communities living and working in near-road environments |
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EM Ecological Scale
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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 | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of 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-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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EM Organismal Scale
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Not applicable | Guild or Assemblage | Species | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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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)
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| EM-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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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-154 | EM-260 | EM-652 | EM-845 | EM-859 | EM-942 |
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None | None |