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-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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EM Short Name
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EnviroAtlas-Air pollutant removal | Agronomic ES and plant traits, Central French Alps | Birds in estuary habitats, Yaquina Estuary, WA, USA | SPARROW, Northeastern USA | Landscape importance for habitat diversity, Europe | InVEST Coastal Blue Carbon | Wetland shellfish production, Gulf of Mexico, USA | Carbon sequestration, Guánica Bay, Puerto Rico | Nitrogen fixation rates, Guánica Bay, Puerto Rico | Chinook salmon value (household), Yaquina Bay, OR | WaterWorld v2, Santa Basin, Peru | P8 UCM | Alwife phosphorus flux in lakes, Connecticut, USA | Floral resources on landfill sites, United Kingdom | Gadwall duck recruits, CREP wetlands, Iowa, USA | WESP: Riparian & stream habitat, ID, USA |
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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 | Agronomic ecosystem service estimated from plant functional traits, Central French Alps | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | SPARROW (SPAtially Referenced Regressions On Watershed Attributes), Northeastern USA | Landscape importance for habitat diversity, Europe | InVEST v3.0 Coastal Blue Carbon | Wetland shellfish production, Gulf of Mexico, USA | Carbon sequestration, Guánica Bay, Puerto Rico, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA | Economic value of Chinook salmon per household method, Yaquina Bay, OR | WaterWorld v2, Santa Basin, Peru | P8 Urban Catchment model method | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Floral resources on landfill sites, East Midlands, United Kingdom | Gadwall duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | WESP: Riparian and stream habitat focus projects, ID, USA |
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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. |
EU Biodiversity Action 5 | US EPA | US EPA | EU Biodiversity Action 5 | InVEST |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
US EPA | US EPA | US EPA | None | None | None | None | None | None |
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EM Source Document ID
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223 | 260 | 275 | 86 | 228 | 310 | 324 | 338 |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
324 | 368 |
377 ?Comment:Published to the web. Previously versions prepared for EPA. |
383 | 389 |
372 ?Comment:Document 373 is a secondary source for this EM. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Moore, R. B., Johnston, C.M., Smith, R. A. and Milstead, B. | Haines-Young, R., Potschin, M. and Kienast, F. | Natural Capital Project | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Van Soesbergen, A. and M. Mulligan | Walker, W. Jr., and J.D. Walker | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Murphy, C. and T. Weekley |
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Document Year
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2013 | 2011 | 2014 | 2011 | 2012 | 2014 | 2012 | 2017 | 2017 | 2012 | 2018 | 2015 | 2010 | 2013 | 2010 | 2012 |
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Document Title
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EnviroAtlas - Featured Community | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Source and delivery of nutrients to receiving waters in the northeastern and mid-Atlantic regions of the United States | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Blue Carbon model - InVEST (v3.0) | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | P8 Urban Catchment Model Version 3.5 | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | 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 | other | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published report | Published report |
Software and Access
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EM ID
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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| https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | Not applicable | Not applicable | Not applicable | Not applicable | www.policysupport.org/waterworld | http://www.wwwalker.net/p8/v35/webhelp/splash.htm | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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EnviroAtlas Team | Sandra Lavorel |
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 Moore | Marion Potschin | Gregg Verutes | Stephen J. Jordan | Susan H. Yee | Susan H. Yee | Stephen Jordan | Arnout van Soesbergen | William Walker Jr., PhD | Derek C. West | Sam Tarrant | David Otis | Chris Murphy |
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Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | 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. Environmental Protection Agency, 27 Tarzwell Drive, Narragansett, Rhode Island 02882 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Stanford University | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Concord, Massachusetts | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID |
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Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | frazier@nceas.ucsb.edu | rmoore@usgs.gov | marion.potschin@nottingham.ac.uk | gverutes@stanford.edu | jordan.steve@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | jordan.steve@epa.gov | arnout.van_soesbergen@kcl.ac.uk | bill@wwwalker.net | derek.west@yale.edu | sam.tarrant@rspb.org.uk | dotis@iastate.edu | chris.murphy@idfg.idaho.gov |
EM Description
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EM ID
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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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." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions." | 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." | AUTHOR'S DESCRIPTION: "SPAtially Referenced Regressions On Watershed attributes (SPARROW) nutrient models were developed for the Northeastern and Mid-Atlantic (NE US) regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the NE US Seaboard indicated that agricultural sources contribute the largest percentage (37%) of the total nitrogen load delivered to the estuaries" | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Habitat diversity” … The potential to deliver services is assumed to be influenced by land-use … and bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "The analysis for the regulating service "Habitat Diversity" seeks to identify all the areas with potential to support biodiversity." | Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. | ABSTRACT: "We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | AUTHOR'S DESCRIPTION: "In addition to affecting water quality, the ecosystem services of nitrogen retention, phosphorous retention, and sediment retention were also considered to contribute to stakeholder goals of maintaining the productivity of agricultural land and reducing soil loss. Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature." | AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class" | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | Author description: " P8 simulates the generation and transport of stormwater runoff pollutants in urban watersheds. Continuous water-balance and mass-balance calculations are performed on a user-defined drainage system consisting of the following elements: - Watersheds (<= 250 nonpoint source areas) - Devices (<=75 runoff storage/treatment areas or BMP's) - Particles (<= 5 fractions with different settling velocities) - Water Quality Components (<= 10 associated with particles) Simulations are driven by hourly precipitation and daily air temperature time series. Runoff contributions from snowmelt are also simulated. 'P8' abbreviates "Program for Predicting Polluting Particle Passage Thru Pits, Puddles, and Ponds", which more or less captures the basic features and functions of the model. It has been developed for use by engineers and planners in designing and evaluating runoff treatment schemes for existing or proposed urban developments. Design objectives are typically expressed in terms of percentage reduction in suspended solids or other water quality component. Despite its limitations, P8 has been used by state and local regulatory agencies as a consistent framework for evaluating proposed developments. Depending on applications, other models could be either too simple (easily used, but ignoring important factors) or too complex (requiring considerable site-specific data and/or user expertise). P8 attempts to strike a balance to between those extremes. Predicted water quality components include total suspended solids (sum of the individual particle fractions), total phosphorus, total Kjeldahl nitrogen, copper, lead, zinc, and total hydrocarbons. Simulated BMP types include detention ponds (wet, dry, extended), infiltration basins, swales, buffer strips, or other devices with user-specified stage/discharge curves and infiltration rates. A simple water budget algorithm can be used to estimate groundwater storage and stream base flow in watershed-scale applications. Initial calibrations were based upon runoff quality and particle settling velocity data collected under the EPA's Nationwide Urban Runoff Program (Athayede et al., 1983). Calibrations to impervious area runoff parameters for Wisconsin watersheds have been subsequently developed. Inputs are structured in terms which should be familiar to planners and engineers involved in hydrologic evaluation. Several tabular and graphic output formats are provided. " | ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML) | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. |
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Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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None identified | None identified | None identified | water-quality assessment, total maximum daily load(TMDL) determination | None identified | None identified | None identified | None identified | None provided | None identified | None identified | None identified | Restoration and management of diadromous fish runs in coastal New England | None identified | None identified | None identified |
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Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | Norteneastern region (U.S.); Mid-Atlantic region (U.S.) | No additional description provided | Land use land class; habitat type | Estuarine environments and marsh-land interfaces | No additional description provided | No additional description provided | Yaquina Bay estuary | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Urban setting | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | No additional description provided | Prairie Pothole Region of Iowa | restored, enhanced and created wetlands |
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EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Land use land cover changes; habitat disturbance | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | No scenarios presented | No scenarios presented | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | N/A | current and historical run size | No scenarios presented | No scenarios presented | Sites, function or habitat focus |
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-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method Only |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | 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
em.detail.relatedEmHelp
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EM ID
em.detail.idHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-345 | Doc-260 | Doc-270 | None | None | Doc-231 | Doc-228 | None | None | None | None | Doc-324 | None | None | None | None | Doc-372 | Doc-373 | Doc-390 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-81 | EM-82 | EM-83 | None | None | EM-99 | EM-119 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | None | EM-604 | EM-603 | None | None | EM-603 | EM-397 | None | None | EM-667 | EM-672 | EM-674 | EM-673 | EM-709 | EM-705 | EM-704 | EM-702 | EM-701 | EM-700 | EM-632 | EM-706 | EM-729 | EM-730 | EM-734 | EM-743 | EM-749 | EM-750 | EM-756 | EM-757 | EM-758 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-732 | EM-737 | EM-738 | EM-739 | EM-741 | EM-742 | EM-751 | EM-768 |
EM Modeling Approach
EM Relationship to Time
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EM ID
em.detail.idHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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EM Temporal Extent
em.detail.tempExtentHelp
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2008-2010 | Not reported | December 2007 - November 2008 |
2002 ?Comment:Several nationwide database development and modeling efforts were necessary to create models consistent with 2002 conditions. |
2000 | Not applicable | 1950 - 2050 | 1978 - 2013 | 1978 - 2009 | 2003-2008 | 1950-2071 | Not applicable | 1960"s and early 2000's | 2007-2008 | 1987-2007 | 2010-2011 |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | both | Not applicable | Not applicable | Not applicable | Not applicable | past time |
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EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Varies by Run | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Hour | Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Not applicable | Not applicable | Month | Hour | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
em.detail.idHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or Ecological | Physiographic or ecological | Geopolitical | Geopolitical | Not applicable | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Not applicable | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) |
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Spatial Extent Name
em.detail.extentNameHelp
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Durham NC and vicinity | Central French Alps | Yaquina Estuary (intertidal), Oregon, USA | NE U.S. Regions | The EU-25 plus Switzerland and Norway | Not applicable | Gulf of Mexico (estuarine and coastal) | Guanica Bay watershed | Guanica Bay watershed | Pacific Northwest | Santa Basin | Not applicable | Bride Lake and Linsley Pond | East Midlands | CREP (Conservation Reserve Enhancement Program | Wetlands in idaho |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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100-1000 km^2 | 10-100 km^2 | 1-10 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | Not applicable | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | Not applicable | 10-100 ha | 1000-10,000 km^2. | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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EM Spatial Distribution
em.detail.distributeLumpHelp
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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) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (habitat type) | area, for pixel or radial feature | area, for pixel or radial feature | volume, for 3-D feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | 20 m x 20 m | 0.87-104.29 ha | 30 x 30 m | 1 km x 1 km | user-specified | 55.2 km^2 | 30 m x 30 m | HUC | Not applicable | 1 km2 | Not applicable | Not applicable | multiple unrelated locations | multiple, individual, irregular sites | Not applicable |
EM Structure and Computation Approach
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EM ID
em.detail.idHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Analytic | Analytic | Analytic | Logic- or rule-based | Analytic | Numeric | Analytic | Analytic | Analytic | * | Numeric | Analytic | Analytic | Analytic | Numeric |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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None |
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Model Checking Procedures Used
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EM ID
em.detail.idHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | No | Unclear | Yes | No | Not applicable | Yes | No | No | No | No | Yes | Yes | Not applicable | Unclear | No |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No |
Yes ?Comment:R-squared of .97 refers to the modelled loading whereas .83 refers to yield (see table 1, pg 972 for more information) |
No | Not applicable | No | No | No | No | No | Not applicable | No | Not applicable | No | No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None |
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None | None | None | None | None | None | None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | Yes | Yes | Not applicable | No | No | No | Yes | Yes | Not applicable | No | Not applicable | No | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Unclear | No | Not applicable | No | No | No | No | No | Not applicable | No | Not applicable | No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | Yes | No | Not applicable | No | No | No | No | No | Not applicable | Yes | Not applicable | No | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | 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])
em.detail.relationToSpaceTerrestrialHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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| None | None |
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None | None | None |
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None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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Centroid Latitude
em.detail.ddLatHelp
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35.99 | 45.05 | 44.62 | 42 | 50.53 | -9999 | 30.44 | 17.96 | 17.96 | 44.62 | -9.05 | Not applicable | 41.33 | 52.22 | 42.62 | 44.06 |
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Centroid Longitude
em.detail.ddLongHelp
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-78.96 | 6.4 | -124.06 | -73 | 7.6 | -9999 | -87.99 | -67.02 | -67.02 | -124.02 | -77.81 | Not applicable | -72.24 | -0.91 | -93.84 | -114.69 |
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Centroid Datum
em.detail.datumHelp
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None provided | WGS84 | None provided | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Provided | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
em.detail.idHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Created Greenspace | Atmosphere | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | None | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Lakes and Ponds | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | Subalpine terraces, grasslands, and meadows. | Estuarine intertidal | none | Not applicable | user specified | Submerged aquatic vegetation in estuaries and coastal lagoons | 13 LULC were used | Tropical terrestrial | Yaquina Bay estuary and ocean | tropical, coastal to montane | Urban catchments | Coastal lakes and ponds and associated streams | restored landfills and grasslands | Wetlands buffered by grassland within agroecosystems | created, restored and enhanced wetlands |
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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 is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser 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 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 | Other or unclear (comment) | 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 corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Other (multiple scales) | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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| None Available | None Available |
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None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
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EM-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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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-59
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EM-80 | EM-103 | EM-104 | EM-120 | EM-367 |
EM-397
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EM-430 | EM-432 | EM-604 | EM-630 | EM-656 |
EM-661
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EM-697
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EM-703 |
EM-718
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None | None |
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None |
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None | None | None | None |
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None |