EcoService Models Library (ESML)
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Compare EMs
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.
EM Identity and Description
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
EM Short Name
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Area & hotspots of soil accumulation, South Africa | Coral taxa and land development, St.Croix, VI, USA | Beach visitation, Barnstable, MA, USA | HWB poor health, Great Lakes waterfront, USA | EcoSim II - method | ORVal, Valuing recreational sites, W. Norwich, UK |
EM Full Name
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Area and hotspots of soil accumulation, South Africa | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Beach visitation, Barnstable, Massachusetts, USA | 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) |
EM Source or Collection
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None | US EPA | US EPA | None | None | None |
EM Source Document ID
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271 | 96 | 386 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
448 | 476 |
Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Day, B., & Smith |
Document Year
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2008 | 2011 | 2018 | None | 2000 | None |
Document Title
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Mapping ecosystem services for planning and management | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | 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) |
Document Status
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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 |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review | Published journal manuscript | Published report |
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
Not applicable | Not applicable | Not applicable | Not applicable | https://ecopath.org/downloads/ | https://www.leep.exeter.ac.uk/orval/ | |
Contact Name
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Benis Egoh | Leah Oliver | Kate K, Mulvaney | Ted Angradi | Carl Walters | Brett Day |
Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | National Health and Environmental Research Effects Laboratory | Not reported | 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 |
Contact Email
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Not reported | leah.oliver@epa.gov | Mulvaney.Kate@EPA.gov | tedangradi@gmail.com | c.walters@oceans.ubc.ca | Brett.Day@exeter.ac.uk |
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
Summary Description
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AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover." | 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: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | 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. |
Specific Policy or Decision Context Cited
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None identified | Not applicable | To assess the number of people who would be impacted by beach closures. | None identified | None | None provided |
Biophysical Context
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Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | nearshore; <1.5 km offshore; <12 m depth | Four separate beaches within the community of Barnstable | Waterfront districts on south Lake Michigan and south lake Erie | None, Ocean ecosystems | Urban greenspaces in the UK |
EM Scenario Drivers
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No scenarios presented | Not applicable | No scenarios presented | N/A | N/A | NA |
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application |
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
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
Document ID for related EM
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Doc-271 | None | Doc-386 | Doc-387 | Doc-422 | None | None |
EM ID for related EM
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EM-85 | EM-86 | EM-88 | None | EM-682 | EM-685 | EM-683 | EM-686 | EM-886 | EM-888 | EM-890 | EM-891 | EM-893 | EM-894 | EM-895 | EM-1055 | None |
EM Modeling Approach
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
EM Temporal Extent
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Not reported | 2006-2007 | 2011 - 2016 | 2022 | Not applicable | 2016-2018 |
EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | past time | Not applicable | both | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable |
discrete ?Comment:Modeller dependent |
Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Day | Not applicable | Day | Not applicable |
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
Bounding Type
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Geopolitical | Physiographic or Ecological | Physiographic or ecological | Geopolitical | Other | Geopolitical |
Spatial Extent Name
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South Africa | St.Croix, U.S. Virgin Islands | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Great Lakes waterfront | Not applicable | West Norwich |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10-100 km^2 | 10-100 ha | 1000-10,000 km^2. | Not applicable | 1-10 km^2 |
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | length, for linear feature (e.g., stream mile) | Not applicable | Not applicable | map scale, for cartographic feature |
Spatial Grain Size
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Distributed across catchments with average size of 65,000 ha | Not applicable | by beach site | Not applicable | Not applicable | Not reported |
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
Model Calibration Reported?
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No | Yes | Yes | No | No | Unclear |
Model Goodness of Fit Reported?
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No | Yes | No | No | No | No |
Goodness of Fit (metric| value | unit)
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None |
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None | None | None | None |
Model Operational Validation Reported?
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No | No | No | No | Not applicable | Unclear |
Model Uncertainty Analysis Reported?
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No | Yes | No | No | Not applicable | Unclear |
Model Sensitivity Analysis Reported?
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No | No | Yes | Yes | Not applicable | Unclear |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
Centroid Latitude
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-30 | 17.75 | 41.64 | 42.26 | Not applicable | 52.62 |
Centroid Longitude
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25 | -64.75 | -70.29 | -87.84 | Not applicable | 1.25 |
Centroid Datum
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WGS84 | NAD83 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated |
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Open Ocean and Seas | Created Greenspace |
Specific Environment Type
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Not applicable | stony coral reef | Saltwater beach | Lake Michigan & Lake Erie waterfront | Pelagic | Urban Greenspaces |
EM Ecological Scale
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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 | 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 of differentiation of organisms modeled
EM ID
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EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
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)
EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-87 | EM-260 | EM-684 | EM-889 | EM-964 | EM-1010 |
None |
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None |
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None |