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-492 | EM-598 |
EM-672 
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EM-840 | EM-938 | EM-944 | EM-990 |
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EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | EnviroAtlas - Restorable wetlands | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Alewife nutrients in stream food web, CT, USA | Eastern bluebird abundance, Piedmont region, USA | OpenNSPECT v. 1.2 | COBRA v 4.1 | Atlantis ecosystem economics submodel |
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EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Alewife derived nutrients in stream food web, Connecticut, USA | Eastern bluebird abundance, Piedmont ecoregion, USA | OpenNSPECT v. 1.2 | COBRA (CO–Benefits Risk Assessment) v 4.1 | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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EM Source or Collection
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i-Tree | USDA Forest Service | US EPA | EnviroAtlas | None | None | None | None | US EPA | None |
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EM Source Document ID
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195 | 262 | 358 | 384 | 405 | 431 |
437 ?Comment:User's manual is provided at the webpage. |
463 |
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Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | US EPA Office of Research and Development - National Exposure Research Laboratory | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Walters, A. W., R. T. Barnes, and D. M. Post | Riffel, S., Scognamillo, D., and L. W. Burger | Eslinger, David L., H. Jamieson Carter, Matt Pendleton, Shan Burkhalter, Margaret Allen | US EPA | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. |
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Document Year
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2013 | 2013 | 2010 | 2009 | 2008 | 2012 | 2021 | 2011 |
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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 | 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 | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | “OpenNSPECT: The Open-source Nonpoint Source Pollution and Erosion Comparison Tool.” NOAA Office for Coastal Management, Charleston, South Carolina. Accessed (11/2022) at https://coast.noaa.gov/digitalcoast/tools/opennspect.html | CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA) | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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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 | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Webpage | Webpage | Published journal manuscript |
Software and Access
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EM ID
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
| Not applicable | https://www.epa.gov/enviroatlas | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | https://www.epa.gov/cobra | https://research.csiro.au/atlantis/home/links/ | |
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Contact Name
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David J. Nowak | EnviroAtlas Team | M. Abdalla | Annika W. Walters | Sam Riffell | Not reported | Emma Zinsmeister | Elizabeth Fulton |
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Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven CT 06511 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | NOAA Coastal Services Center, 2234 South Hobson Avenue Charleston, South Carolina 29405-2413 | EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division | Department of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia |
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Contact Email
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dnowak@fs.fed.us | enviroatlas@epa.gov | abdallm@tcd.ie | annika.walters@yale.edu | sriffell@cfr.msstate.edu | Not reported | zinsmeister.emma@epa.gov | beth.fulton@csiro.au |
EM Description
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EM ID
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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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)." | 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. | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year. Natural abundance stable isotope analyses indicate that this influx of marine-derived nitrogen is rapidly incorporated into the stream food web. An enriched d15N signal, indicative of a marine origin, is present at all stream trophic levels with the greatest level of enrichment coincident with the timing of the anadromous alewife spawning migration. There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." AUTHOR'S DESCRIPTION: "Here, we examine the effect of alewife-contributed marine- derived nutrients to coastal stream ecosystems in southern New England. We take a comparative approach examining streams with and without anadromous alewife runs. We use natural abundance stable isotope analyses to assess the incorporation of marine-derived nitrogen and carbon into stream food webs." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positivelyrelated to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds " | "This open-source version of the Nonpoint Source Pollution and Erosion Comparison Tool is used to investigate potential water quality impacts from climate change and development to other land uses. The downloadable tool is designed to be broadly applicable for coastal and noncoastal areas alike. Tool functions simulate erosion, pollution, and the accumulation from overland flow. OpenNSPECT uses spatial elevation data to calculate flow direction and flow accumulation throughout a watershed. To do this, land cover, precipitation, and soils data are processed to estimate runoff volume at both the local and watershed levels. Coefficients representing the contribution of each land cover class to the expected pollutant load are also applied to land cover data to approximate total pollutant loads. These coefficients are taken from published sources or can be derived from local water quality studies. The output layers display estimates of runoff volume, pollutant loads, pollutant concentration, and total sediment yield. Requires MapWindow GIS v.4.8.8 (open source software)" | Introduction: "COBRA is a screening tool that provides preliminary estimates of the impact of air pollution emission changes on ambient particulate matter (PM) air pollution concentrations, translates this into health effect impacts, and then monetizes these impacts, as illustrated below. The model does not require expertise in air quality modeling, health effects assessment, or economic valuation. Built into COBRA are emissions inventories, a simplified air quality model, health impact equations, and economic valuations ready for use, based on assumptions that EPA currently uses as reasonable best estimates. COBRA also enables advanced users to import their own datasets of emissions inventories, population, incidence, health impact functions, and valuation functions. Analyses can be performed at the state or county level and across the 14 major emissions categories (these categories are called “tiers”) included in the National Emissions Inventory. COBRA presents results in tabular as well as geographic form, and enables policy analysts to obtain a first-order approximation of the benefits of different mitigation scenarios under consideration. However, COBRA is only a screening tool. More sophisticated, albeit time- and resource-intensive, modeling approaches are currently available to obtain a more refined picture of the health and economic impacts of changes in emissions. EPA initially developed COBRA as a desktop application. In 2021, EPA released a web-based version of the tool, known as the COBRA Web Edition. Although the desktop version and web versions of COBRA both use the same methodology to calculate outdoor air quality and health impacts from changes in air pollution emissions, the desktop version offers additional advanced features that are not included in the more streamlined Web Edition. In particular, the desktop version is preloaded with input data on emissions, population, and baseline health incidence for 2016, 2023, and 2028; the Web Edition includes data only for 2023. Similarly, the desktop version allows users to import custom input datasets, while the Web Edition does not. The Web Edition, however, does not require the user to download or install additional software, and it runs more quickly than the desktop version. Users might choose to use the desktop version if they would like to use advanced features, such as custom input data and/or use the preloaded data for 2016 or 2028. Otherwise, users may choose to use the Web Edition for data analysis relevant to 2023. The process for entering emissions input data into COBRA is very similar for the desktop and web versions of the tool. The remainder of this User’s Manual focuses on the steps required to run the desktop version of the tool. The same general process can be used with the Web Edition." | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. |
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Specific Policy or Decision Context Cited
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Not reported | None Identified | climate change | Nutrients and water quality related to anadromous alewife restoration efforts | None reported | 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. | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | No additional description provided | Conservation Reserve Program lands left to go fallow | No additional description provided | No additional description provided | NA |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | fertilization | No scenarios presented | N/A | No scenarios presented | No scenarios presented | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method Only | Method Only |
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New or Pre-existing EM?
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Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model |
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-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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Document ID for related EM
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None | None | None | Doc-384 | Doc-383 | Doc-405 | None | None | Doc-456 | Doc-459 | Doc-461 |
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EM ID for related EM
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None | None | EM-593 | EM-667 | EM-661 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | EM-940 | None | EM-978 | EM-981 | EM-983 | EM-985 | EM-991 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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EM Temporal Extent
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1989-2010 | 2006-2013 | 1961-1990 | 2005-2006 (March-July) | 2008 | Not applicable | Not applicable | Not applicable |
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EM Time Dependence
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time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | Not applicable | time-dependent |
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EM Time Reference (Future/Past)
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future time | Not applicable | both | past time | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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discrete | Not applicable | discrete |
other or unclear (comment) ?Comment:Sampling conducted at discrete time periods during Alewife migration. Three sampling periods are presented in this entry. |
Not applicable | Not applicable | Not applicable | continuous |
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EM Temporal Grain Size Value
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1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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Bounding Type
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Geopolitical | Geopolitical | Point or points | Geopolitical | Physiographic or ecological | Not applicable | Geopolitical | Not applicable |
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Spatial Extent Name
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United States | conterminous United States | Oak Park Research centre | New London County, Connecticut, USA | Piedmont Ecoregion | Not applicable | Not applicable | Not applicable |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 1-10 ha | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | Not applicable | Not applicable | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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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 lumped (in all 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) | Not applicable |
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Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | map scale, for cartographic feature | Not applicable |
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Spatial Grain Size
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1 m^2 | irregular | Not applicable | variable stream lengths | Not applicable | 30 m | user defined | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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EM Computational Approach
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Numeric | Analytic | Numeric | Not applicable | Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | Not applicable | deterministic | deterministic | stochastic | 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-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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Model Calibration Reported?
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No | No | Yes | Not applicable | Yes | Not applicable | Not applicable | Not applicable |
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Model Goodness of Fit Reported?
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No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | No | Not applicable | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None |
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None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | Yes | Not applicable | No | Not applicable | Not applicable | Not applicable |
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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 | Not applicable | No | Not applicable | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | Not applicable | Yes | Not applicable | Not applicable | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Unclear | 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-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
| None | None | None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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Centroid Latitude
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40.16 | 39.5 | 52.86 | 41.78 | 36.23 | Not applicable | Not applicable | Not applicable |
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Centroid Longitude
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-99.79 | -98.35 | 6.54 | -72.17 | -81.9 | Not applicable | Not applicable | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | None provided | WGS84 | WGS84 | Not applicable | Not applicable | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Provided | Estimated | Estimated | Not applicable | Not applicable | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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EM Environmental Sub-Class
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Forests | Created Greenspace | Agroecosystems | Agroecosystems | Rivers and Streams | Grasslands | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
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Specific Environment Type
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Urban forests | Terrestrial | farm pasture | Coastal streams | grasslands | Coastal and non-coastal | Not applicable | Multiple |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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 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 | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-24 | EM-492 | EM-598 |
EM-672
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EM-840 | EM-938 | EM-944 | EM-990 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Not applicable | Not applicable | Individual or population, within a species | Species | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-24 | EM-492 | EM-598 |
EM-672
|
EM-840 | EM-938 | EM-944 | EM-990 |
| None Available | None Available | None Available |
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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.detail.cicesHelp
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| EM-24 | EM-492 | EM-598 |
EM-672
|
EM-840 | EM-938 | EM-944 | EM-990 |
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None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-24 | EM-492 | EM-598 |
EM-672
|
EM-840 | EM-938 | EM-944 | EM-990 |
|
None |
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None |
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None | None | None |