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-24 |
EM-224 
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | FORCLIM v2.9, West Cascades, OR, USA | EnviroAtlas - Restorable wetlands | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Hunting recreation, Wisconsin, USA | Gadwall duck recruits, CREP wetlands, Iowa, USA | WESP: Urban Stormwater Treatment, ID, USA |
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EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Hunting recreation, Wisconsin, USA | Gadwall duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | WESP: Urban Stormwater Treament, ID, USA |
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EM Source or Collection
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i-Tree | USDA Forest Service | US EPA | US EPA | EnviroAtlas | None | None | None | None |
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EM Source Document ID
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195 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
262 | 358 | 376 |
372 ?Comment:Document 373 is a secondary source for this EM. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
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Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | US EPA Office of Research and Development - National Exposure Research Laboratory | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Qiu, J. and M. G. Turner | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Murphy, C. and T. Weekley |
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Document Year
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2013 | 2007 | 2013 | 2010 | 2013 | 2010 | 2012 |
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Document Title
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Carbon storage and sequestration by trees in urban and community areas of the United States | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | EnviroAtlas - National | 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 | Spatial interactions among ecosystem services in an urbanizing agricultural watershed | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. |
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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 and published | 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 | Published journal manuscript | Published report | Published report |
Software and Access
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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| Not applicable | Not applicable | https://www.epa.gov/enviroatlas | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | |
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Contact Name
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David J. Nowak | Richard T. Busing | EnviroAtlas Team | M. Abdalla | Monica G. Turner | David Otis | Chris Murphy |
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Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Not reported | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID |
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Contact Email
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dnowak@fs.fed.us | rtbusing@aol.com | enviroatlas@epa.gov | abdallm@tcd.ie | turnermg@wisc.edu | dotis@iastate.edu | chris.murphy@idfg.idaho.gov |
EM Description
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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Summary Description
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ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | 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. | AUTHOR'S DESCRIPTION (from Supporting Information): "The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility. Similar assumptions were made for this assessment: larger areas and places with more game species would support more hunting, areas closer to large population centers would be used more than remote areas, and proximity to major roads would increase access and use of an area. We first obtained the boundary of public wild areas from Wisconsin DNR and calculated the amount of areas for each management unit. The number of game species (Spe) for each area was derived from Dane County Parks Division (70). We used the same population density (Pop) and road buffer layer (Road) described in the previous forest recreation section. The variables Spe, Pop, and Road were weighted to ranges of 0–40, 0–40, and 0–20, respectively, based on the relative importance of each in determining this service. We estimated overall hunting recreation service for each 30-m grid cell with the following equation: HRSi = Ai Σ(Spei + Popi +Roadi), where HRS is hunting recreation score, A is the area of public wild areas open for hunting/fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28,000) to a range of 0–100, with 0 representing no hunting recreation service and 100 representing highest service. | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. |
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Specific Policy or Decision Context Cited
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Not reported | None Identified | None Identified | climate change | None identified | None identified | None identified |
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Biophysical Context
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Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | No additional description provided | Prairie Pothole Region of Iowa | restored, enhanced and created wetlands |
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EM Scenario Drivers
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No scenarios presented | Two scenarios modelled, forests with and without fire | No scenarios presented | fertilization | No scenarios presented | No scenarios presented | Sites, function or habitat focus |
EM Relationship to Other EMs or Applications
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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Method Only, Application of Method or Model Run
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Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | WESP - Urban Stormwater Treatment |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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Document ID for related EM
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None | Doc-22 | Doc-23 | None | None | None | Doc-372 | Doc-373 | Doc-390 |
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EM ID for related EM
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None | EM-146 | EM-208 | EM-186 | None | EM-593 | None | EM-705 | EM-704 | EM-702 | EM-701 | EM-700 | EM-632 | EM-718 | EM-734 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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EM Temporal Extent
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1989-2010 | >650 yrs | 2006-2013 | 1961-1990 | 2000-2006 | 1987-2007 | 2010-2011 |
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EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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future time | past time | Not applicable | both | Not applicable | Not applicable | past time |
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EM Time Continuity
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discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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1 | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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Bounding Type
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Geopolitical | Physiographic or ecological | Geopolitical | Point or points | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) |
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Spatial Extent Name
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United States | West Cascades, Oregon | conterminous United States | Oak Park Research centre | Yahara Watershed, Wisconsin | CREP (Conservation Reserve Enhancement Program | Wetlands in idaho |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 1-10 ha | 1000-10,000 km^2. | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
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Spatial Grain Size
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1 m^2 | 0.08 ha | irregular | Not applicable | 30m x 30m | multiple, individual, irregular sites | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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EM Computational Approach
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Numeric | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric |
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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-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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Model Calibration Reported?
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No | No | No | Yes | No | Unclear | No |
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Model Goodness of Fit Reported?
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No | No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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None | None | None |
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Model Operational Validation Reported?
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No | Yes | No | Yes | No | No | No |
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Model Uncertainty Analysis Reported?
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Yes ?Comment:An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling. |
No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No | No | No | No |
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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-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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| None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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Centroid Latitude
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40.16 | 44.24 | 39.5 | 52.86 | 43.1 | 42.62 | 44.06 |
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Centroid Longitude
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-99.79 | -122.24 | -98.35 | 6.54 | -89.4 | -93.84 | -114.69 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Provided | Provided | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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EM Environmental Sub-Class
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Forests | Created Greenspace | Forests | Agroecosystems | Agroecosystems | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands |
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Specific Environment Type
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Urban forests | Primarily conifer forest | Terrestrial | farm pasture | Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape | Wetlands buffered by grassland within agroecosystems | created, restored and enhanced wetlands |
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EM Ecological Scale
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Zone within an ecosystem | 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 | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of 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-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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EM Organismal Scale
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Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Species | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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| None Available |
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None Available | 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.detail.cicesHelp
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| EM-24 |
EM-224
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EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
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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)
fegs2Help
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| EM-24 |
EM-224
|
EM-492 | EM-598 | EM-655 | EM-703 |
EM-729
|
|
None | None |
|
None |
|
None |