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-654 |
EM-735 ![]() |
EM-893 |
EM Short Name
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Forest recreation, Wisconsin, USA | C sequestration in grassland restoration, England | HWB indicator-ADI, Great Lakes, USA |
EM Full Name
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Forest recreation, Wisconsin, USA | Carbon sequestration in grassland diversity restoration, England | Human well being indicator- Area Deprivation Index (ADI) , Great Lakes waterfront, USA |
EM Source or Collection
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None | None | US EPA |
EM Source Document ID
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376 | 396 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
Document Author
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Qiu, J. and M. G. Turner | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
Document Year
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2013 | 2011 | None |
Document Title
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Spatial interactions among ecosystem services in an urbanizing agricultural watershed | Additional carbon sequestration benefits of grassland diversity restoration | Human well-being and natural capital indictors for Great Lakes waterfront revitalization |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) |
Comments on Status
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Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review |
EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
Not applicable | Not applicable | Not applicable | |
Contact Name
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Monica G. Turner | Gerlinde B. De Deyn | Ted Angradi |
Contact Address
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Not reported | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 |
Contact Email
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turnermg@wisc.edu | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | tedangradi@gmail.com |
EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
Summary Description
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AUTHOR'S DESCRIPTION (from Supporting Information): "Forest recreation service as a function of the amount of forest habitat, recreational opportunities provided, proximity to population center, and accessibility of the area. Several assumptions were made for this assessment approach: larger areas and places with more recreational opportunities would provide more recreational service; areas near large population centers would be visited and used more than remote areas; and proximity to major roads would increase access and thus recreational use of an area… we quantified forest recreation service for each 30-m grid cells using the equation below: FRSi = Ai Σ(Oppti + Popi +Roadi), where FRS is forest recreation score, A is the area of forest habitat, Oppt represents the recreation opportunities, Pop is the proximity to population centers, and Road stands for the distance to major roads. To simplify interpretation, we rescaled the original forest recreation score (ranging from 0 to 5,200) to a range of 0–100, with 0 representing no forest recreation service and 100 representing highest service. | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" | 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." |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
Biophysical Context
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No additional description provided | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. | Waterfront districts on south Lake Michigan and south lake Erie |
EM Scenario Drivers
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No scenarios presented | Additional benefits due to biodiversity restoration practices | N/A |
EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
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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-654 |
EM-735 ![]() |
EM-893 |
Document ID for related EM
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None | None | Doc-422 |
EM ID for related EM
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None | None | EM-886 | EM-888 | EM-889 | EM-890 | EM-891 | EM-894 | EM-895 |
EM Modeling Approach
EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
EM Temporal Extent
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2000-2006 | 1990-2007 | 2022 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable |
EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
Bounding Type
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Watershed/Catchment/HUC | Other | Geopolitical |
Spatial Extent Name
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Yahara Watershed, Wisconsin | Colt Park meadows, Ingleborough National Nature Reserve, northern England | Great Lakes waterfront |
Spatial Extent Area (Magnitude)
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1000-10,000 km^2. | <1 ha | 1000-10,000 km^2. |
EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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30m x 30m | 3 m x 3 m | Not applicable |
EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
EM Computational Approach
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Analytic | Analytic | Numeric |
EM Determinism
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deterministic | stochastic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
Model Calibration Reported?
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No | Not applicable | No |
Model Goodness of Fit Reported?
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No | Not applicable | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | No | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-654 |
EM-735 ![]() |
EM-893 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-654 |
EM-735 ![]() |
EM-893 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
Centroid Latitude
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43.1 | 54.2 | 42.26 |
Centroid Longitude
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-89.4 | -2.35 | -87.84 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Provided | Estimated |
EM ID
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EM-654 |
EM-735 ![]() |
EM-893 |
EM Environmental Sub-Class
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Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape | fertilized grassland (historically hayed) | Lake Michigan & Lake Erie waterfront |
EM Ecological Scale
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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-654 |
EM-735 ![]() |
EM-893 |
EM Organismal Scale
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Not applicable | Community | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-654 |
EM-735 ![]() |
EM-893 |
None Available | None Available | None Available |
EnviroAtlas URL
EM-654 |
EM-735 ![]() |
EM-893 |
Dasymetric Allocation of Population | None Available | GAP Ecological Systems, Enabling Conditions |
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-654 |
EM-735 ![]() |
EM-893 |
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None |
<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-654 |
EM-735 ![]() |
EM-893 |
None | None | None |