EcoService Models Library (ESML)
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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-71 | EM-260 |
EM-321 
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EM-414 | EM-590 | EM-947 |
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EM Short Name
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Community flowering date, Central French Alps | Coral taxa and land development, St.Croix, VI, USA | Erosion prevention by vegetation, Portel, Portugal | SAV occurrence, St. Louis River, MN/WI, USA | Fish species richness, Puerto Rico, USA | C-GEM, Lousiana continental shelf, USA |
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EM Full Name
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Community weighted mean flowering date, Central French Alps | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Fish species richness, Puerto Rico, USA | Carbon Generic Estuary Model (C-GEM), Lousiana Continental Shelf, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | EU Biodiversity Action 5 | US EPA | None | US EPA |
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EM Source Document ID
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260 | 96 | 281 | 330 | 355 | 441 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Guerra, C.A., Pinto-Correia, T., Metzger, M.J. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Jarvis, B.M., Lehrter, J.C., Lowe, L.L., Hagy, J.D., Wan, Y., Murrell, M.C., Ko, D.S., Penta, B., and R.W. Gould |
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Document Year
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2011 | 2011 | 2014 | 2013 | 2007 | 2020 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Modeling spatiotemporal patterns of ecosystem metabolism and organic carbon dynamics affecting hypoxia on the Louisiana continental shelf |
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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-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Sandra Lavorel | Leah Oliver | Carlos A. Guerra | Ted R. Angradi | Simon Pittman | Brandon Jarvis |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | National Health and Environmental Research Effects Laboratory | Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal | 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 | 1305 East-West Highway, Silver Spring, MD 20910, USA | 1US EPA, Office of Research and Development, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA Office of Research and Development, U.S. EPA, Gulf Breeze, FL, USA |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | leah.oliver@epa.gov | cguerra@uevora.pt | angradi.theodore@epa.gov | simon.pittman@noaa.gov | jarvis.brandon@epa.gov |
EM Description
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | ABSTRACT: "We present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)." | 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: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT: "The hypoxic zone on the Louisiana Continental Shelf (LCS) forms each summer due to nutrient‐enhanced primary production and seasonal stratification associated with freshwater discharges from the Mississippi/Atchafalaya River Basin (MARB). Recent field studies have identified highly productive shallow nearshore waters as an important component of shelf‐wide carbon production contributing to hypoxia formation. This study applied a three‐dimensional hydrodynamic‐biogeochemical model named CGEM (Coastal Generalized Ecosystem Model) to quantify the spatial and temporal patterns of hypoxia, carbon production, respiration, and transport between nearshore and middle shelf regions where hypoxia is most prevalent. We first demonstrate that our simulations reproduced spatial and temporal patterns of carbon production, respiration, and bottom‐water oxygen gradients compared to field observations. We used multiyear simulations to quantify transport of articulate organic carbon (POC) from nearshore areas where riverine organic matter and phytoplankton carbon production are greatest. The spatial displacement of carbon production and respiration in our simulations was created by westward and offshore POC flux via phytoplankton carbon flux in the surface layer and POC flux in the bottom layer, supporting heterotrophic respiration on the middle shelf where hypoxia is frequently observed. These results support existing studies suggesting the importance of offshore carbon flux to hypoxia formation, particularly on the west shelf where hypoxic conditions are most variable. " |
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Specific Policy or Decision Context Cited
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None identified | Not applicable | None identified | None identified | None provided | None reported |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | nearshore; <1.5 km offshore; <12 m depth | Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure. | submerged aquatic vegetation | Hard and soft benthic habitat types approximately to the 33m isobath | Louisiana coastal continental shelf |
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EM Scenario Drivers
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No scenarios presented | Not applicable | Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area | No scenarios presented | No scenarios presented | Coastal Shelf location |
EM Relationship to Other EMs or Applications
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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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 | 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 | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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Document ID for related EM
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Doc-260 | Doc-269 | None | Doc-282 | Doc-283 | Doc-284 | Doc-285 | None | Doc-355 | None |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | EM-698 | EM-699 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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EM Temporal Extent
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2007-2008 | 2006-2007 | January to December 2003 | 2010 - 2012 | 2000-2005 | 2003-2007 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | Not applicable | Not applicable | past time |
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EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Month | Not applicable | Not applicable | Season |
EM Spatial Extent
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological |
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Spatial Extent Name
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Central French Alps | St.Croix, U.S. Virgin Islands | Portel municipality | St. Louis River Estuary | SW Puerto Rico, | Lousiana continental shelf |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | 10-100 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?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) |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | 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 | Not applicable | 250 m x 250 m | 0.07 m^2 to 0.70 m^2 | not reported | regions of shelf |
EM Structure and Computation Approach
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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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-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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Model Calibration Reported?
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No | Yes | No | Yes | No | Yes |
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Model Goodness of Fit Reported?
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Yes | Yes | No | Yes | Yes | No |
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Goodness of Fit (metric| value | unit)
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None |
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None |
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Model Operational Validation Reported?
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No | No | No | Yes | Yes | Yes |
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Model Uncertainty Analysis Reported?
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No | Yes | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No | Yes | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | No | Unclear |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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None |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
| None |
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None | None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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Centroid Latitude
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45.05 | 17.75 | 38.3 | 46.72 | 17.9 | 30.28 |
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Centroid Longitude
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6.4 | -64.75 | -7.7 | -96.13 | 67.11 | -88.39 |
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Centroid Datum
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WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | stony coral reef | Silvo-pastoral system | Freshwater estuarine system | shallow coral reefs | Louisiana continental shelf |
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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 coarser 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 corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
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EM Organismal Scale
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Community | Guild or Assemblage | Not applicable | Not applicable | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
| None Available |
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None Available | None Available |
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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-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
| None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-71 | EM-260 |
EM-321
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EM-414 | EM-590 | EM-947 |
| None |
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None |
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