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-462 | EM-598 |
EM-863 ![]() |
EM-945 |
EM Short Name
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Value of finfish, St. Croix, USVI | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | SLAMM, Tampa Bay, FL, USA | Air pollution removal by green roofs, Chicago, USA |
EM Full Name
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Relative value of finfish (on reef), St. Croix, USVI | DeNitrification-DeComposition simulation of N2O flux Ireland | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA | Air pollution removal by green roofs, Chigago, USA |
EM Source or Collection
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US EPA | None | None | None |
EM Source Document ID
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335 | 358 |
415 ?Comment:Secondary sources: Documents 412 and 413. |
438 ?Comment:Document 439 is an additional source for this EM. |
Document Author
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Yee, S. H., Dittmar, J. A., and L. M. Oliver | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Sherwood, E. T. and H. S. Greening | Yang, J., Q. Yu and P. Gong |
Document Year
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2014 | 2010 | 2014 | 2008 |
Document Title
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Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats | Quantifying air pollution removal by green roofs in Chicago |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
Not applicable | http://www.dndc.sr.unh.edu | http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | Not applicable | |
Contact Name
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Susan H. Yee | M. Abdalla | Edward T. Sherwood | Jun Yang |
Contact Address
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US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA | Department of Landscape Architecture and Horticulture, Temple University, 580 Meetinghouse Road, Ambler, PA 19002, USA. |
Contact Email
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yee.susan@epa.gov | abdallm@tcd.ie | esherwood@tbep.org | juny@temple.edu |
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
Summary Description
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ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. | ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" | ABSTRACT: "The level of air pollution removal by green roofs in Chicago was quantified using a dry deposition model. The result showed that a total of 1675 kg of air pollutants was removed by 19.8 ha of green roofs in one year with O3 accounting for 52% of the total, NO2 (27%), PM10 (14%), and SO2 (7%). The highest level of air pollution removal occurred in May and the lowest in February. The annual removal per hectare of green roof was 85 kg/ha/yr. The amount of pollutants removed would increase to 2046.89 metric tons if all rooftops in Chicago were covered with intensive green roofs. Although costly, the installation of green roofs could be justified in the long run if the environmental benefits were considered. The green roof can be used to supplement the use of urban trees in air pollution control, especially in situations where land and public funds are not readily available." |
Specific Policy or Decision Context Cited
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None identified | climate change | None identified | None identified |
Biophysical Context
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No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | No additional description provided | No additional description provided |
EM Scenario Drivers
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No scenarios presented | fertilization | Varying sea level rise (baseline - 2m), and two habitat adaption strategies | No scenarios presented |
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
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Application of existing model | Application of existing model | Application of existing 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-462 | EM-598 |
EM-863 ![]() |
EM-945 |
Document ID for related EM
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None | None | Doc-412 | Doc-413 | Doc-439 |
EM ID for related EM
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None | EM-593 | EM-857 | None |
EM Modeling Approach
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
EM Temporal Extent
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2006-2007, 2010 | 1961-1990 | 2002-2100 | July 2006 to July 2007 |
EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | both | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable | Month |
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
Bounding Type
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Physiographic or ecological | Point or points | Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
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Coastal zone surrounding St. Croix | Oak Park Research centre | Tampa Bay estuary watershed | Chicago |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 1-10 ha | 1000-10,000 km^2. | 100-1000 km^2 |
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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10 m x 10 m | Not applicable | 10 x 10 m | plot (green roof) size |
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
EM Computational Approach
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Analytic | Numeric | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
Model Calibration Reported?
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Yes | Yes | No | Unclear |
Model Goodness of Fit Reported?
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No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
No | No |
Goodness of Fit (metric| value | unit)
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None |
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None | None |
Model Operational Validation Reported?
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Yes | Yes | No | No |
Model Uncertainty Analysis Reported?
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No | No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No | No |
Model Sensitivity Analysis Include Interactions?
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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-462 | EM-598 |
EM-863 ![]() |
EM-945 |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
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None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
Centroid Latitude
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17.73 | 52.86 | 27.76 | 41.88 |
Centroid Longitude
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-64.77 | 6.54 | -82.54 | 87.65 |
Centroid Datum
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WGS84 | None provided | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Provided | Estimated | Provided |
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace |
Specific Environment Type
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Coral reefs | farm pasture | Esturary and associated urban and terrestrial environment | urban green roofs |
EM Ecological Scale
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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 | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
EM Organismal Scale
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Guild or Assemblage | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-462 | EM-598 |
EM-863 ![]() |
EM-945 |
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None Available | None Available | 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-462 | EM-598 |
EM-863 ![]() |
EM-945 |
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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-462 | EM-598 |
EM-863 ![]() |
EM-945 |
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None | None |