EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-24 | EM-63 | EM-103 |
EM-208 
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EM-418 |
EM-774
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EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | EnviroAtlas - Natural biological nitrogen fixation | Birds in estuary habitats, Yaquina Estuary, WA, USA | FORCLIM v2.9, Santiam watershed, OR, USA | SIRHI, St. Croix, USVI | Plant-pollinator networks at reclaimed mine, USA |
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EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA | SIRHI (SImplified Reef Health Index), St. Croix, USVI | Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA |
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EM Source or Collection
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i-Tree | USDA Forest Service | US EPA | EnviroAtlas | US EPA | US EPA | US EPA | None |
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EM Source Document ID
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195 |
262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
275 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
335 | 397 |
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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 | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Cusser, S. and K. Goodell |
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Document Year
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2013 | 2013 | 2014 | 2007 | 2014 | 2013 |
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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 | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine |
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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 |
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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 | Published journal manuscript |
Software and Access
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EM ID
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EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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| Not applicable | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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David J. Nowak |
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Richard T. Busing | Susan H. Yee |
Sarah Cusser ?Comment:Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A. |
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Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Not reported | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A. |
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Contact Email
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dnowak@fs.fed.us | enviroatlas@epa.gov | frazier@nceas.ucsb.edu | rtbusing@aol.com | yee.susan@epa.gov | sarah.cusser@gmail.com |
EM Description
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EM ID
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EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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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 displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | 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. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045. | 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: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context." |
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Specific Policy or Decision Context Cited
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Not reported | None Identified | None identified | 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 | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | No additional description provided | No additional description provided | The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Land Management (3); Climate Change (3) | 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-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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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 ?Comment:Runs differentiated by scenario combination. |
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 |
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-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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Document ID for related EM
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None |
Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
None |
Doc-22 | Doc-23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
None | None |
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EM ID for related EM
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None | None | None | EM-146 | EM-186 | EM-224 | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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EM Temporal Extent
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1989-2010 | 2006-2010 | December 2007 - November 2008 | 1990-2050 | 2006-2007, 2010 | 2009-2010 |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | 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 |
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EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | 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 |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Year | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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Bounding Type
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Geopolitical | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological |
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Spatial Extent Name
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United States | counterminous United States | Yaquina Estuary (intertidal), Oregon, USA | South Santiam watershed | Coastal zone surrounding St. Croix | The Wilds |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 1-10 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1-10 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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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) ?Comment:Watersheds (12-digit HUCs). |
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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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (habitat type) | 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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1 m^2 | irregular | 0.87-104.29 ha | 0.08 ha | 10 m x 10 m | 10 m radius |
EM Structure and Computation Approach
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EM ID
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EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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EM Computational Approach
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Numeric | Analytic | Analytic | Numeric | Analytic | Analytic |
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EM Determinism
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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-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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Model Calibration Reported?
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No | No | Unclear | No | Yes | Not applicable |
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Model Goodness of Fit Reported?
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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 |
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Model Operational Validation Reported?
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No | No | No | No | Yes | Yes |
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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 | Yes |
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Model Sensitivity Analysis Reported?
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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 | N/A | 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-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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| None | None |
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None |
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None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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Centroid Latitude
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40.16 | 39.5 | 44.62 | 44.24 | 17.73 | 39.82 |
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Centroid Longitude
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-99.79 | -98.35 | -124.06 | -122.24 | -64.77 | -81.75 |
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Centroid Datum
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WGS84 | WGS84 | None provided | None provided | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Provided | Provided | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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EM Environmental Sub-Class
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Forests | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Forests | Near Coastal Marine and Estuarine | Grasslands |
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Specific Environment Type
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Urban forests | Terrestrial | Estuarine intertidal | primarily Conifer Forest | Coral reefs | Grassland |
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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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to 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-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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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. |
Not applicable | Guild or Assemblage | Species | Guild or Assemblage | Species |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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| None Available | None Available |
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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)
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| EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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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)
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| EM-24 | EM-63 | EM-103 |
EM-208
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EM-418 |
EM-774
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None | None | None |