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-321 
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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EM Short Name
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Erosion prevention by vegetation, Portel, Portugal | InVEST Coastal Blue Carbon | EcoAIM v.1.0 APG, MD | Fish species richness, St. John, USVI, USA | EPA Stormwater Manamgement Model | Atlantis ecosystem biology submodel |
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EM Full Name
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Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal | InVEST v3.0 Coastal Blue Carbon | EcoAIM v.1.0, Aberdeen Proving Ground, MD | Fish species richness, St. John, USVI, USA | Storm Water Management Model User's Manual Version 5.2 | Calibrating process-based marine ecosystem models: An example case using Atlantis |
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EM Source or Collection
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EU Biodiversity Action 5 | InVEST | None | None | US EPA | None |
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EM Source Document ID
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281 | 310 | 374 | 355 | 452 | 459 |
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Document Author
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Guerra, C.A., Pinto-Correia, T., Metzger, M.J. | Natural Capital Project | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Rossman, L. A., M., Simon | Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E. |
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Document Year
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2014 | 2014 | 2014 | 2007 | 2022 | 2019 |
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Document Title
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Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy | Blue Carbon model - InVEST (v3.0) | Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Storm Water Management Model User's Manual Version 5.2 | Calibrating process-based marine ecosystem models: An example case using Atlantis |
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Document Status
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Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | other | Published report | Published journal manuscript | Published EPA report | Published journal manuscript |
Software and Access
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EM ID
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
| Not applicable | http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | Not applicable | Not applicable | https://www.epa.gov/water-research/storm-water-management-model-swmm | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
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Contact Name
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Carlos A. Guerra | Gregg Verutes | Pieter Booth | Simon Pittman | David Burden | Heidi R. Pethybridge |
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Contact Address
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Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal | Stanford University | Exponent Inc., Bellevue WA | 1305 East-West Highway, Silver Spring, MD 20910, USA | U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198 | CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia |
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Contact Email
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cguerra@uevora.pt | gverutes@stanford.edu | pbooth@ramboll.com | simon.pittman@noaa.gov | burden.david@epa.gov | Heidi.Pethybridge@csiro.au |
EM Description
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EM ID
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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Summary Description
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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)." | Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. | [ABSTRACT: "This report describes the demonstration of the EcoAIM decision support framework and GIS-based tool. EcoAIM identifies and quantifies the ecosystem services provided by the natural resources at the Aberdeen Proving Ground (APG). A structured stakeholder process determined the mission and non-mission priorities at the site, elicited the natural resource management decision process, identified the stakeholders and their roles, and determine the ecosystem services of priority that impact missions and vice versa. The EcoAIM tool was customized to quantify in a geospatial context, five ecosystem services – vista aesthetics, landscape aesthetics, recreational opportunities, habitat provisioning for biodiversity and nutrient sequestration. The demonstration included a Baseline conditions quantification of ecosystem services and the effects of a land use change in the Enhanced Use Lease parcel in cantonment area (Scenario 1). Biodiversity results ranged widely and average scores decreased by 10% after Scenario 1. Landscape aesthetics scores increased by 10% after Scenario 1. Final scores did not change for recreation or nutrient sequestration because scores were outside the boundaries of the baseline condition. User feedback after the demonstration indicated positive reviews of EcoAIM as being useful and usable for land use decisions and particularly for use as a communication tool. " | 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." |
EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?Comment:The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual. |
Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers. |
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Specific Policy or Decision Context Cited
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None identified | None identified | None reported | None provided | NA | N/A |
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Biophysical Context
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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. | Land use land class; habitat type | Chesapeake bay coastal plain, elev. 60ft. | Hard and soft benthic habitat types approximately to the 33m isobath | NA | Marine ecosystem |
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EM Scenario Drivers
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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 | Land use land cover changes; habitat disturbance | N/A | No scenarios presented | NA | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method Only | Method Only |
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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 | Application of existing 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-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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Document ID for related EM
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Doc-282 | Doc-283 | Doc-284 | Doc-285 | None | None | Doc-355 | None | Doc-456 |
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EM ID for related EM
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None | None | None | EM-590 | EM-698 | EM-971 | EM-978 | EM-983 | EM-985 | EM-990 | EM-991 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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EM Temporal Extent
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January to December 2003 | Not applicable | 2014 | 2000-2005 | Not applicable | Not applicable |
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EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | both | Not applicable |
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EM Time Continuity
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discrete | discrete | Not applicable | Not applicable | continuous | continuous |
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EM Temporal Grain Size Value
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1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Month | Year | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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Bounding Type
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Geopolitical | Not applicable | Geopolitical | Physiographic or ecological | No location (no locational reference given) | Not applicable |
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Spatial Extent Name
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Portel municipality | Not applicable | Aberdeen Proving Ground | SW Puerto Rico, | Not applicable | Not applicable |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | Not applicable | 100-1000 km^2 | 100-1000 km^2 | Not applicable | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:500m x 500m is also used for some computations. The evaluation does include some riparian buffers which are linear features along streams. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | Not applicable |
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Spatial Grain Type
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area, for pixel or radial feature | volume, for 3-D feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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250 m x 250 m | user-specified | 100m x 100m | not reported | mm | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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EM Computational Approach
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Analytic | Analytic | Numeric | Analytic | Analytic | Analytic |
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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-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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Model Calibration Reported?
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No | Not applicable |
No ?Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) |
No | Not applicable | Yes |
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Model Goodness of Fit Reported?
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No | Not applicable | Not applicable | Yes | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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None | None |
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Model Operational Validation Reported?
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No | Not applicable | No | Yes | Not applicable | Not applicable |
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Model Uncertainty Analysis Reported?
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No | Not applicable | No | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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No | Not applicable |
Unclear ?Comment:Just cannot tell, but no mention of sensitivity was made. |
Yes | Not applicable | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | No | 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-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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None |
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None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
| None | None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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Centroid Latitude
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38.3 | -9999 | 39.46 | 17.79 | Not applicable | Not applicable |
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Centroid Longitude
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-7.7 | -9999 | 76.12 | -64.62 | Not applicable | Not applicable |
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Centroid Datum
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WGS84 | Not applicable | WGS84 | WGS84 | Not applicable | Not applicable |
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Centroid Coordinates Status
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Estimated | Not applicable | Estimated | Estimated | Not applicable | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland | Inland Wetlands | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
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Specific Environment Type
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Silvo-pastoral system | user specified | Coastal Plain | shallow coral reefs | User-defined catchments | Multiple |
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EM Ecological Scale
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Other or unclear (comment) | 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-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
| None Available | None Available | None Available |
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None Available | None Available |
EnviroAtlas URL
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EM-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
| Average Annual Precipitation | Carbon Storage by Tree Biomass | GAP Ecological Systems, Percent IUCN Status II, Ecosystem Markets: Imperiled Species and Habitats, Total Annual Reduced Nitrogen Deposition | None Available | None Available | None Available |
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-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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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-321
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EM-367 | EM-647 | EM-699 | EM-968 | EM-981 |
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None |
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