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-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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EM Short Name
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Green biomass production, Central French Alps | Divergence in flowering date, Central French Alps | Coral taxa and land development, St.Croix, VI, USA | SAV occurrence, St. Louis River, MN/WI, USA | Polyscape, Wales | ESTIMAP - Pollination potential, Iran |
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EM Full Name
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Green biomass production, Central French Alps | Functional divergence in flowering date, Central French Alps | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Polyscape, Wales | ESTIMAP - Pollination potential, Iran |
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EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | US EPA | None | None |
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EM Source Document ID
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260 | 260 | 96 | 330 | 379 | 434 |
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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. | 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. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Jackson, B., T. Pagella, F. Sinclair, B. Orellana, A. Henshaw, B. Reynolds, N. Mcintyre, H. Wheater, and A. Eycott | Rahimi, E., Barghjelveh, S., and P. Dong |
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Document Year
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2011 | 2011 | 2011 | 2013 | 2013 | 2020 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | 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 | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Polyscape: A GIS mapping framework providing efficient and spatially explicit landscape-scale valuation of multple ecosystem services | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) |
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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 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 | Published journal manuscript |
Software and Access
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EM ID
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EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
| Not applicable | Not applicable | Not applicable | Not applicable |
https://www.lucitools.org/ ?Comment:The LUCI (Land Utilisation and Capability Indicator) model, is a second-generation extension and software implementation of the Polyscape framework. |
Not applicable | |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | Leah Oliver | Ted R. Angradi | Bethanna Jackson | Ehsan Rahini |
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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 | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | National Health and Environmental Research Effects Laboratory | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | School of Geography, Environment and Earth Sciences, Victoria University of Wellington, PO Box 600, Wellington, New Zealand | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | leah.oliver@epa.gov | angradi.theodore@epa.gov | bethanna.jackson@vuw.ac.nz | ehsanrahimi666@gmail.com |
EM Description
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EM ID
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EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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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)." | 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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date 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: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | ABSTRACT: "This paper introduces a GIS framework (Polyscape) designed to explore spatially explicit synergies and trade-offs amongst ecosystem services to support landscape management (from individual fields through to catchments of ca 10,000 km2 scale). Algorithms are described and results presented from a case study application within an upland Welsh catchment (Pontbren). Polyscape currently includes algorithms to explore the impacts of land cover change on flood risk, habitat connectivity, erosion and associated sediment delivery to receptors, carbon sequestration and agricultural productivity. Algorithms to trade these single-criteria landscape valuations against each other are also provided, identifying where multiple service synergies exist or could be established. Changes in land management can be input to the tool and “traffic light” coded impact maps produced, allowing visualisation of the impact of different decisions. Polyscape hence offers a means for prioritising existing feature preservation and identifying opportunities for landscape change. The basic algorithms can be applied using widely available national scale digital elevation, land use and soil data. Enhanced output is possible where higher resolution data are available..." AUTHOR'S DESCRIPTION: "The framework acts as a screening tool to identify areas where scientific investigation might be valuably directed and/or where a lack of information exists, and allows flexibility and quick visualisation of the impact of different rural land management decisions on a variety of sustainability criteria. Specifically, Polyscape is designed to facilitate: 1. spatially explicit policy implementation; 2. integration of policy implementation across sectors (e.g., water, biodiversity, agriculture and forestry); 3. participation (and learning) by many different stakeholder groups. Importantly, it is designed not as a prescriptive decision making tool, but as a negotiation tool. Algorithms allow identification of ideas of where change might be beneficial – for example where installation of “structures” such as ponds or buffer strips might be considered optimal at a farm scale – but also allows users to trial their own plans and build in their own knowledge/restrictions. The framework aims to highlight areas with maximum potential for improvement, not to place value judgements on which methods (e.g., tillage change, land use change, hard engineering approaches) might be appropriate to realise such potential. Furthermore, the toolbox aims to identify areas of existing high value – e.g., particularly productive cropland, wetlands..." "Our case study site is the 12.5 km2 catchment of the Pontbren in mid-Wales." NOTE: The LUCI (Land Utilisation and Capability Indicator) model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-659. https://esml.epa.gov/detail/em/659 | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." |
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Specific Policy or Decision Context Cited
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None identified | None identified | Not applicable | None identified | Polyscape acts as a screening tool to allow flexibility and visualisation of the impact of different rural land management decisions. | None reported |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | nearshore; <1.5 km offshore; <12 m depth | submerged aquatic vegetation | Elevation ranges between 170 m and 425 m | None additional |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | Initial habitat coverage (1990), and planting additional broadleaved woodland (2001-2007) | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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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 + 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 | 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-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | None | None | Doc-380 | Doc-432 |
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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 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | EM-659 | EM-939 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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EM Temporal Extent
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2007-2009 | 2007-2008 | 2006-2007 | 2010 - 2012 | 1990-2007 | 2020 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | 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-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical |
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Spatial Extent Name
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Central French Alps | Central French Alps | St.Croix, U.S. Virgin Islands | St. Louis River Estuary | Pontbren catchment | Iran |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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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 lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
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Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | Not applicable | 0.07 m^2 to 0.70 m^2 | Not reported | ha^2 |
EM Structure and Computation Approach
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EM ID
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EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic | Numeric |
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EM Determinism
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deterministic | 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-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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Model Calibration Reported?
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No | No | Yes | Yes | No | No |
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Model Goodness of Fit Reported?
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Yes | Yes | Yes | 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 | No | Yes | No | No |
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Model Uncertainty Analysis Reported?
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No | No | Yes | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No | No | 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])
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| EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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None |
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Comment:Model for Iran - no form preset id for country |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
| None | None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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Centroid Latitude
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45.05 | 45.05 | 17.75 | 46.72 | 52.61 | 32.29 |
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Centroid Longitude
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6.4 | 6.4 | -64.75 | -96.13 | -3.3 | 53.68 |
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Centroid Datum
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WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Provided | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | stony coral reef | Freshwater estuarine system | mainly of ‘improved’ pasture, semi-natural, unmanaged moorland, mature woodland, recent tree plantations, and small paved/roofed areas, root crops and open water | terrestrial land types |
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EM Ecological Scale
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Not applicable | Ecological scale is coarser 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 is finer than that of 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-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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EM Organismal Scale
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Community | Community | Guild or Assemblage | Not applicable | Unsure | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
| None Available | None Available |
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None Available | None Available |
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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-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
| None | 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-65 | EM-79 | EM-260 | EM-414 | EM-658 | EM-941 |
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None |
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None |
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