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-260 | EM-493 |
EM-541 
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EM-889 | EM-964 | EM-1010 |
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EM Short Name
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Green biomass production, Central French Alps | Coral taxa and land development, St.Croix, VI, USA | EnviroAtlas-Carbon sequestered by trees | InVEST fisheries, lobster, South Africa | HWB poor health, Great Lakes waterfront, USA | EcoSim II - method | ORVal, Valuing recreational sites, W. Norwich, UK |
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EM Full Name
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Green biomass production, Central French Alps | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Human well being indicator-poor health, Great Lakes waterfront, USA | EcoSim II - method | ORVal short case study: number 1 valuing recreational sites in West Norwich (version 2.0) |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | EnviroAtlas | i-Tree | InVEST | None | None | None |
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EM Source Document ID
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260 | 96 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
448 | 476 |
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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. | US EPA Office of Research and Development - National Exposure Research Laboratory | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Day, B., & Smith |
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Document Year
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2011 | 2011 | 2013 | 2018 | None | 2000 | None |
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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 | EnviroAtlas - Featured Community | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | ORVal short case study: number 1 valuing recreational sites in West Norwich (version 2.0) |
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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 but unpublished (explain in Comment) | 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 on US EPA EnviroAtlas website | Published journal manuscript | Journal manuscript submitted or in review | Published journal manuscript | Published report |
Software and Access
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EM ID
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EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
| Not applicable | Not applicable | https://www.epa.gov/enviroatlas | https://www.naturalcapitalproject.org/invest/ | Not applicable | https://ecopath.org/downloads/ | https://www.leep.exeter.ac.uk/orval/ | |
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Contact Name
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Sandra Lavorel | Leah Oliver | EnviroAtlas Team | Michelle Ward | Ted Angradi | Carl Walters | Brett Day |
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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 | Not reported | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | None provided |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | leah.oliver@epa.gov | enviroatlas@epa.gov | m.ward@uq.edu.au | tedangradi@gmail.com | c.walters@oceans.ubc.ca | Brett.Day@exeter.ac.uk |
EM Description
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EM ID
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EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | AUTHOR'S DESCRIPTION: "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) | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " | The ORVal Tool is a web application (accessed at https://www.leep.exeter.ac.uk/orval) developed by the Land, Environment, Economics and Policy (LEEP) Institute at the University of Exeter with support from DEFRA. ORVal’s primary purpose is to help quantify the benefits that are derived from accessible outdoor recreation areas in England. Those outdoor recreation areas, or greenspaces, include an array of features such as beaches, parks, nature reserves and country paths This short case study uses ORVal to explore the visits and welfare values that are generated by existing greenspaces in West Norwich. It provides both practical details as to how tasks are performed in ORVal and explanations as to how the numbers reported by the Tool should be interpreted. |
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Specific Policy or Decision Context Cited
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None identified | Not applicable | None identified | Future rock lobster fisheries management | None identified | None | None provided |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | nearshore; <1.5 km offshore; <12 m depth | No additional description provided | No additional description provided | Waterfront districts on south Lake Michigan and south lake Erie | None, Ocean ecosystems | Urban greenspaces in the UK |
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EM Scenario Drivers
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No scenarios presented | Not applicable | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | N/A | N/A | NA |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
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EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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Document ID for related EM
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Doc-260 | None | Doc-345 | None | Doc-422 | None | None |
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EM ID for related EM
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | EM-886 | EM-888 | EM-890 | EM-891 | EM-893 | EM-894 | EM-895 | EM-1055 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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EM Temporal Extent
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2007-2009 | 2006-2007 | 2010-2013 | 1986-2115 | 2022 | Not applicable | 2016-2018 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | Not applicable | both | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | Not applicable |
discrete ?Comment:Modeller dependent |
Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Year | Not applicable | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Other | Geopolitical |
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Spatial Extent Name
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Central French Alps | St.Croix, U.S. Virgin Islands | Durham NC and vicinity | Table Mountain National Park Marine Protected Area | Great Lakes waterfront | Not applicable | West Norwich |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | Not applicable | 1-10 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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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) ?Comment:Census block groups |
spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | map scale, for cartographic feature |
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Spatial Grain Size
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20 m x 20 m | Not applicable | irregular | Not applicable | Not applicable | Not applicable | Not reported |
EM Structure and Computation Approach
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EM ID
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EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic |
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EM Determinism
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deterministic | 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-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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Model Calibration Reported?
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No | Yes | No | No | No | No | Unclear |
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Model Goodness of Fit Reported?
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Yes | Yes | No | No | No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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Yes | No | No |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
No | Not applicable | Unclear |
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Model Uncertainty Analysis Reported?
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No | Yes | No | No | No | Not applicable | Unclear |
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Model Sensitivity Analysis Reported?
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No | No | No | No | Yes | Not applicable | Unclear |
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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 | 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-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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None |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
| None |
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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-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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Centroid Latitude
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45.05 | 17.75 | 35.99 | -34.18 | 42.26 | Not applicable | 52.62 |
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Centroid Longitude
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6.4 | -64.75 | -78.96 | 18.35 | -87.84 | Not applicable | 1.25 |
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Centroid Datum
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WGS84 | NAD83 | None provided | WGS84 | WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Provided | Estimated | Not applicable | Estimated |
Environments and Scales Modeled
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EM ID
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EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Created Greenspace | Atmosphere | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Open Ocean and Seas | Created Greenspace |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | stony coral reef | Urban and vicinity | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Lake Michigan & Lake Erie waterfront | Pelagic | Urban Greenspaces |
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EM Ecological Scale
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
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-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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EM Organismal Scale
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Community | Guild or Assemblage | Not applicable | Individual or population, within a species | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
| None Available |
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None Available |
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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)
em.detail.cicesHelp
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| EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
| None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-65 | EM-260 | EM-493 |
EM-541
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EM-889 | EM-964 | EM-1010 |
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None |
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None |
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None |