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-59 
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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EM Short Name
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EnviroAtlas-Air pollutant removal | Area & hotspots of soil accumulation, South Africa | Stream nitrogen removal, Mississippi R. basin, USA | 3-PG, South Australia | SIRHI, St. Croix, USVI | ARIES viewsheds, Puget Sound Region, USA | C sequestration in grassland restoration, England |
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EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | Area and hotspots of soil accumulation, South Africa | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | 3-PG (Physiological Principles Predicting Growth), South Australia | SIRHI (SImplified Reef Health Index), St. Croix, USVI | ARIES (Artificial Intelligence for Ecosystem Services) Scenic viewsheds for homeowners, Puget Sound Region, Washington, USA | Carbon sequestration in grassland diversity restoration, England |
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EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
None | US EPA | None | US EPA | ARIES | None |
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EM Source Document ID
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223 | 271 | 52 | 243 | 335 | 302 | 396 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Hill, B. and Bolgrien, D. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett |
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Document Year
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2013 | 2008 | 2011 | 2011 | 2014 | 2014 | 2011 |
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Document Title
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EnviroAtlas - Featured Community | Mapping ecosystem services for planning and management | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Carbon payments and low-cost conservation | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Additional carbon sequestration benefits of grassland diversity restoration |
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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 on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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| https://www.epa.gov/enviroatlas | Not applicable | Not applicable | http://www.csiro.au/products/3PGProductivity#a1 | Not applicable | http://aries.integratedmodelling.org/ | Not applicable | |
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Contact Name
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EnviroAtlas Team | Benis Egoh | Brian Hill | Anders Siggins | Susan H. Yee | Ken Bagstad | Gerlinde B. De Deyn |
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Contact Address
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Not reported | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | Not reported | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Geosciences and Environmental Change Science Center, US Geological Survey | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands |
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Contact Email
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enviroatlas@epa.gov | Not reported | hill.brian@epa.gov | Anders.Siggins@csiro.au | yee.susan@epa.gov | kjbagstad@usgs.gov | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com |
EM Description
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EM ID
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover." | ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | AUTHOR'S DESCRIPTION: "Carbon trading and its resultant market for carbon offsets are expected to drive investment in the sequestration of carbon through tree plantings (i.e., carbon plantings). Most carbon-planting investment has been in monocultures of trees that offer a rapid return on investment but have relatively little compositional and structural diversity (Bekessy & Wintle 2008; Munro et al. 2009). There are additional benefits available should carbon plantings comprise native species of diverse composition and age that are planted strategically to meet conservation and restoration objectives (hereafter ecological carbon plantings) (Bekessy &Wintle 2008; Dwyer et al. 2009; Bekessy et al. 2010). Ecological carbon plantings may increase availability of resources and refugia for native animals, but they often yield less carbon and are more expensive to establish than monocultures and therefore are less profitable…We used the tree-stand growth model 3-PG (physiological principles predicting growth) (Landsberg & Waring 1997) to simulate annual carbon sequestration under permanent carbon plantings in the part of the study area cleared for agriculture. The 3-PG model calculates total above- and below-ground biomass of a stand after accounting for soil water deficit, atmospheric vapor pressure deficits, and stand age…The 3-PG model was originally parameterized for a generic species, but species-specific parameters have since been calibrated for many commercially valuable trees (Paul et al. 2007) and most recently for mixed species used in permanent ecological restoration plantings (Polglase et al. 2008). We simulated four carbon-planting systems described in Polglase et al. (2008) for which the plants in the systems would grow in our study area. All species were native to areas of Australia with climate similar to that in the study area. We simulated the annual growth of three trees typically grown in monoculture (Eucalyptus globulus, native to Tasmania, constrained to precipitation ≥ 550 mm/year; Eucalyptus camaldulensis, native to the study area, constrained to 350–549 mm/year; Eucalyptus kochii, native to Western Australia, constrained to <350 mm/year). For the simulations of ecological carbon plantings we used a set of trees and shrubs representative of those planted for ecological restoration in temperate southern Australia (species list in England et al. 2006).We assumed the ecological carbon plantings were planted and managed in such a way as to comply with the definition of ecological restoration (Society for Ecological Restoration International Science and PolicyWorking Group 2004)." | 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...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "Within a given viewshed, our models quantified the contribution of viewshed source features such as mountains and water bodies and sinks that detract from view quality, including obstructions or visual blight such as industrial or commercial development. Source, sink, and use locations were linked by a flow model that computed visibility along lines of sight from use locations to scenic viewshed features. The model includes a distance decay function that accounts for changes with distance in the value of views. We then computed the ratio of actual to theoretical provision of scenic views to compare the values accruing to homeowners relative to those for the entire landscape." | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" |
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Specific Policy or Decision Context Cited
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None identified | None identified | Not applicable | None identified | None identified | None identified | None identified |
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Biophysical Context
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No additional description provided | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Agricultural landuse , 1st-10th order streams | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | No additional description provided | No additional description provided | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Not applicable | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | No scenarios presented | No scenarios presented | Additional benefits due to biodiversity restoration practices |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the average annual biomass flux and carbon sequestration from two types of carbon plantings: 1) Tree-based monocultures of three different species (i.e., monoculture carbon planting) and 2) Diverse plantings of nine different native tree and shrub species (i.e., ecological carbon planting) |
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 | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised 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-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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Document ID for related EM
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Doc-345 | Doc-271 | Doc-154 | Doc-155 | Doc-243 | Doc-246 | Doc-245 | None | Doc-303 | Doc-305 | None |
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EM ID for related EM
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None | EM-85 | EM-86 | EM-88 | None | None | None | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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EM Temporal Extent
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2008-2010 | Not reported | 2000-2008 | 2009-2050 | 2006-2007, 2010 | 1992-2006 | 1990-2007 |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Hour | Not applicable | Not applicable | Month | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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Bounding Type
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Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Other |
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Spatial Extent Name
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Durham NC and vicinity | South Africa | Upper Mississippi, Ohio and Missouri River sub-basins | Agricultural districts of the state of South Australia | Coastal zone surrounding St. Croix | Puget Sound Region | Colt Park meadows, Ingleborough National Nature Reserve, northern England |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | <1 ha |
Spatial Distribution of Computations
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EM ID
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
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Spatial Grain Size
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irregular | Distributed across catchments with average size of 65,000 ha | 1 km | 1 ha x 1 ha | 10 m x 10 m | 200m x 200m | 3 m x 3 m |
EM Structure and Computation Approach
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EM ID
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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EM Computational Approach
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Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic |
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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-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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Model Calibration Reported?
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Unclear | No | No | Yes | Yes | No | Not applicable |
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Model Goodness of Fit Reported?
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No | No | No | No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | No | Yes | No | No |
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Model Uncertainty Analysis Reported?
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No | No | Yes | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | Unclear | 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-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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| None | None | None | None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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Centroid Latitude
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35.99 | -30 | 36.98 | -34.9 | 17.73 | 48 | 54.2 |
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Centroid Longitude
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-78.96 | 25 | -89.13 | 138.7 | -64.77 | -123 | -2.35 |
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Centroid Datum
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None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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EM Environmental Sub-Class
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Created Greenspace | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Near Coastal Marine and Estuarine | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | Not applicable | Not applicable | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Coral reefs | Terrestrial environment surrounding a large estuary | fertilized grassland (historically hayed) |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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 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-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Species | Guild or Assemblage | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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EM-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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| None Available | None Available | None Available |
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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-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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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-59
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EM-87 | EM-93 |
EM-129
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EM-418 | EM-419 |
EM-735
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None |
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None |
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None |