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.
EM Identity and Description
EM ID
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | Landscape importance for habitat diversity, Europe | 3-PG, South Australia | KINEROS2, River Ravna watershed, Bulgaria | EnviroAtlas - Water recharge | InVEST crop pollination, NJ and PA, USA | MIMES: For Massachusetts Ocean (v1.0) | Esocid spawning, St. Louis River, MN/WI, USA | Carbon sequestration, Guánica Bay, Puerto Rico | Reef density of S. gigas, St. Croix, USVI | Chinook salmon value, Yaquina Bay, OR | WaterWorld v2, Santa Basin, Peru | Sedge Wren density, CREP, Iowa, USA | Estuary recreational use, Cape Cod, MA | Blue-winged Teal recruits, CREP wetlands, IA, USA | Northern Pintail recruits, CREP wetlands, IA, USA | ESII Tool method | WESP: Urban Stormwater Treatment, ID, USA | Indigo bunting abund, Piedmont region, USA | InVEST Coastal Vulnerability, New York, USA |
EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | Landscape importance for habitat diversity, Europe | 3-PG (Physiological Principles Predicting Growth), South Australia | KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria | US EPA EnviroAtlas - Annual water recharge by tree cover; Example is shown for Durham NC and vicinity, USA | InVEST crop pollination, New Jersey and Pennsylvania, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Esocid spawning, St. Louis River estuary, MN & WI, USA | Carbon sequestration, Guánica Bay, Puerto Rico, USA | Relative density of Strombus gigas (on reef), St. Croix, USVI | Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR | WaterWorld v2, Santa Basin, Peru | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Estuary recreational use, Cape Cod, MA | Blue-winged Teal duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Northern Pintail duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | ESII (Ecosystem Services Identification & Inventory) Tool method | WESP: Urban Stormwater Treament, ID, USA | Indigo bunting abundance, Piedmont ecoregion, USA | InVEST Coastal Vulnerability, Jamaica Bay, New York, USA |
EM Source or Collection
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i-Tree | USDA Forest Service | EU Biodiversity Action 5 | None | EU Biodiversity Action 5 |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
InVEST | US EPA | US EPA | US EPA | US EPA | US EPA | None | None | US EPA | None | None | None | None | None | InVEST |
EM Source Document ID
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195 | 228 | 243 |
248 ?Comment:Document 277 is also a source document for this EM |
223 ?Comment:Parameter default values used in the i-Tree Hydro model were obtained from the i-Tree website (Document ID 198, EM 137). |
279 | 316 | 332 | 338 | 335 | 324 | 368 | 372 | 387 |
372 ?Comment:Document 373 is a secondary source for this EM. |
372 ?Comment:Document 373 is a secondary source for this EM. |
391 ?Comment:Website for online user support |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
405 |
410 ?Comment:Sharp R, Tallis H, Ricketts T, Guerry A, Wood S, Chaplin-Kramer R, et al. InVEST User?s Guide. User Guide. Stanford (CA): The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, World Wildlife Fund; 2015. |
Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | Haines-Young, R., Potschin, M. and Kienast, F. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Nedkov, S., Burkhard, B. | US EPA Office of Research and Development - National Exposure Research Laboratory | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Altman, I., R.Boumans, J. Roman, L. Kaufman | Ted R. Angradi, David W. Bolgrien, Jonathon J. Launspach, Brent J. Bellinger, Matthew A. Starry, Joel C. Hoffman, Mike E. Sierszen, Anett S. Trebitz, and Tom P. Hollenhorst | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Van Soesbergen, A. and M. Mulligan | 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 | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | 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 | 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 | EcoMetrix Solutions Group (ESG) | Murphy, C. and T. Weekley | Riffel, S., Scognamillo, D., and L. W. Burger | Hopper T. and M. S. Meixler |
Document Year
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2013 | 2012 | 2011 | 2012 | 2013 | 2009 | 2012 | 2016 | 2017 | 2014 | 2012 | 2018 | 2010 | 2019 | 2010 | 2010 | 2016 | 2012 | 2008 | 2016 |
Document Title
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Carbon storage and sequestration by trees in urban and community areas of the United States | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Carbon payments and low-cost conservation | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | EnviroAtlas - Featured Community | Modelling pollination services across agricultural landscapes | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Mapping ecosystem service indicators of a Great Lakes estuarine Area of Concern | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | 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 | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | ESII Tool | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Modeling coastal vulnerability through space and time |
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 | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Draft manuscript-work progressing | Published report | Published report | Website | Published report | Published journal manuscript | Published journal manuscript |
EM ID
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
Not applicable | Not applicable | http://www.csiro.au/products/3PGProductivity#a1 | http://www.tucson.ars.ag.gov/agwa/ | https://www.epa.gov/enviroatlas | http://www.naturalcapitalproject.org/models/crop_pollination.html | http://www.afordablefutures.com/orientation-to-what-we-do | Not applicable | Not applicable | Not applicable | Not applicable | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | Not applicable | https://www.esiitool.com/ | Not applicable | Not applicable | https://naturalcapitalproject.stanford.edu/software/invest-models/coastal-vulnerability | |
Contact Name
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David J. Nowak | Marion Potschin | Anders Siggins | David C. Goodrich | EnviroAtlas Team | Eric Lonsdorf | Irit Altman | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Stephen Jordan | Arnout van Soesbergen | David Otis | Mulvaney, Kate | David Otis | David Otis | Not reported | Chris Murphy | Sam Riffell | Thomas Hopper |
Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Not reported | USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719 | Not reported | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Boston University, Portland, Maine | United States Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboraty, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804 USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, 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 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Not reported |
Contact Email
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dnowak@fs.fed.us | marion.potschin@nottingham.ac.uk | Anders.Siggins@csiro.au | agwa@tucson.ars.ag.gov | enviroatlas@epa.gov | ericlonsdorf@lpzoo.org | iritaltman@bu.edu | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | jordan.steve@epa.gov | arnout.van_soesbergen@kcl.ac.uk | dotis@iastate.edu | Mulvaney.Kate@epa.gov | dotis@iastate.edu | dotis@iastate.edu | Not reported | chris.murphy@idfg.idaho.gov | sriffell@cfr.msstate.edu | Tjhop1123@gmail.com |
EM ID
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
Summary Description
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ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)." | ABSTRACT: "The 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." | AUTHOR'S DESCRIPTION: "Carbon trading and its resultant market for carbon offsets are expected to drive investment in the sequestration of carbon through tree plantings (i.e., carbon plantings). Most carbon-planting investment has been in monocultures of trees that offer a rapid return on investment but have relatively little compositional and structural diversity (Bekessy & Wintle 2008; Munro et al. 2009). There are additional benefits available should carbon plantings comprise native species of diverse composition and age that are planted strategically to meet conservation and restoration objectives (hereafter ecological carbon plantings) (Bekessy &Wintle 2008; Dwyer et al. 2009; Bekessy et al. 2010). Ecological carbon plantings may increase availability of resources and refugia for native animals, but they often yield less carbon and are more expensive to establish than monocultures and therefore are less profitable…We used the tree-stand growth model 3-PG (physiological principles predicting growth) (Landsberg & Waring 1997) to simulate annual carbon sequestration under permanent carbon plantings in the part of the study area cleared for agriculture. The 3-PG model calculates total above- and below-ground biomass of a stand after accounting for soil water deficit, atmospheric vapor pressure deficits, and stand age…The 3-PG model was originally parameterized for a generic species, but species-specific parameters have since been calibrated for many commercially valuable trees (Paul et al. 2007) and most recently for mixed species used in permanent ecological restoration plantings (Polglase et al. 2008). We simulated four carbon-planting systems described in Polglase et al. (2008) for which the plants in the systems would grow in our study area. All species were native to areas of Australia with climate similar to that in the study area. We simulated the annual growth of three trees typically grown in monoculture (Eucalyptus globulus, native to Tasmania, constrained to precipitation ≥ 550 mm/year; Eucalyptus camaldulensis, native to the study area, constrained to 350–549 mm/year; Eucalyptus kochii, native to Western Australia, constrained to <350 mm/year). For the simulations of ecological carbon plantings we used a set of trees and shrubs representative of those planted for ecological restoration in temperate southern Australia (species list in England et al. 2006).We assumed the ecological carbon plantings were planted and managed in such a way as to comply with the definition of ecological restoration (Society for Ecological Restoration International Science and PolicyWorking Group 2004)." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. The use of the catchment based hydrologic model KINEROS and the GIS AGWA tool provided data about peak rivers’ flows and the capability of different land cover types to “capture” and regulate some parts of the water." AUTHOR'S DESCRIPTION: "KINEROS is a distributed, physically based, event model describing the processes of interception, dynamic infiltration, surface runoff and erosion from watersheds characterized by predominantly overland flow. The watershed is conceptualized as a cascade and the channels, over which the flow is routed in a top–down approach, are using a finite difference solution of the one-dimensional kinematic wave equations (Semmens et al., 2005). Rainfall excess, which leads to runoff, is defined as the difference between precipitation amount and interception and infiltration depth. The rate at which infiltration occurs is not constant but depends on the rainfall rate and the accumulated infiltration amount, or the available moisture condition of the soil. The AGWA tool is a multipurpose hydrologic analysis system addressed to: (1) provide a simple, direct and repeatable method for hydrologic modeling; (2) use basic, attainable GIS data; (3) be compatible with other geospatial basin-based environmental analysis software; and (4) be useful for scenario development and alternative future simulation work at multiple scales (Miller et al., 2002). AGWA provides the functionality to conduct the processes of modeling and assessment for…KINEROS." | The Water Recharge model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects 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 DESCRIPTION: The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results. Calibration coefficients (0-1 with 1.0 = perfect fit) were calculated for peak flow, base flow, and balance flow (peak and base). To estimate the effect of trees at the block group level for Durham, the Hydro model was run for: Gauging Station Name: SANDY CREEK AT CORNWALLIS RD NEAR DURHAM, NC, Gauging Station Location: 35°58'59.6",-78°57'24.5", Gauging Station Number: 0209722970. After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed. To estimate block group effects, the block group was assumed to act similarly to the watershed in terms of hydrologic effects. To estimate the block group effect, the outputs of the watershed were determined for each possible combination of tree cover (0-100%) and impervious cover (0-100%). Thus, there were a total of 10,201 possible responses (101 x 101). For each block group, the percent tree cover and percent impervious cover combination (e.g., 30% tree / 20% impervious) was matched to the appropriate watershed hydrologic response output for that combination. The hydrologic response outputs were calculated as either percent change or absolute change in units of cubic meters of water per square meter of land area for water flow or kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level. | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." | AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors. | ABSTRACT: "Estuaries provide multiple ecosystem services from which humans benefit…We described an approach, with examples, for assessing how local-scale actions affect the extent and distribution of coastal ecosystem services, using the St. Louis River estuary (SLRE) of western Lake Superior as a case study. We based our approach on simple models applied to spatially explicity biophysical data that allows us to map the providing area of ecosystem services at high resolution (10-m^2 pixel) across aquatic and riparian habitats…Aspects of our approach can be adapted by communities for use in support of local decision-making." AUTHOR'S DESCRIPTION: "We derived the decision criteria used to map the IEGS habitat proxy of esocid spawning from habitat suitability information for two species that have similar but not identical spawning habitat and behavior." | 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." | 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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(2) density of the queen conch Strombus gigas" | 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 | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. 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... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This model focused on the various use by access point type (beaches, landings and way to water, boat use). This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | 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). | 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). | AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance." | 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. | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | ABSTRACT: "Coastal ecosystems experience a wide range of stressors including wave forces, storm surge, sea-level rise, and anthropogenic modification and are thus vulnerable to erosion. Urban coastal ecosystems are especially important due to the large populations these limited ecosystems serve. However, few studies have addressed the issue of urban coastal vulnerability at the landscape scale with spatial data that are finely resolved. The purpose of this study was to model and map coastal vulnerability and the role of natural habitats in reducing vulnerability in Jamaica Bay, New York, in terms of nine coastal vulnerability metrics (relief, wave exposure, geomorphology, natural habitats, exposure, exposure with no habitat, habitat role, erodible shoreline, and surge) under past (1609), current (2015), and future (2080) scenarios using InVEST 3.2.0. We analyzed vulnerability results both spatially and across all time periods, by stakeholder (ownership) and by distance to damage from Hurricane Sandy. We found significant differences in vulnerability metrics between past, current and future scenarios for all nine metrics except relief and wave exposure…" |
Specific Policy or Decision Context Cited
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Not reported | None identified | None identified | None identified | None identified | None identified | None identified | Federal delisting of an area of concern (AOC) | None identified | None identified | None reported | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None reported | None identified |
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 | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | Range of tree and impervious covers in urban setting | No additional description provided | No additional description provided | No additional description provided | 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. | Prairie pothole region of north-central Iowa | None identified | Prairie Pothole Region of Iowa | Prairie Pothole Region of Iowa | Not applicable | restored, enhanced and created wetlands | Conservation Reserve Program lands left to go fallow | Jamaica Bay, New York, situated on the southern shore of Long Island, and characterized by extensive coastal ecosystems in the central bay juxtaposed with a largely urbanized shoreline containing fragmented and fringing coastal habitat. |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | The effect of habitat restoration on esocid spawning area was simulated by varying biophysical changes. | No scenarios presented | No scenarios presented | N/A | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | N/A | No scenarios presented | No scenarios presented | No scenarios presented | Sites, function or habitat focus | N/A | Past (1609), current (2015), and future (2080) scenarios |
EM ID
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the average annual biomass flux and carbon sequestration from two types of carbon plantings: 1) Tree-based monocultures of three different species (i.e., monoculture carbon planting) and 2) Diverse plantings of nine different native tree and shrub species (i.e., ecological carbon planting) |
Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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Application of existing model | New or revised model | Application of existing model | Application of existing model |
Application of existing model ?Comment:EnviroAtlas uses an application of the i-Tree Hydro 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 |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | New or revised model | New or revised model | New or revised model | WESP - Urban Stormwater Treatment | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
EM Temporal Extent
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1989-2010 | 2000 | 2009-2050 | Not reported | 2008-2010 | 2000-2002 | Not applicable | 2013 | 1978 - 2013 | 2006-2007, 2010 | 2003-2008 | 1950-2071 | 1992-2007 | Summer 2017 | 1987-2007 | 1987-2007 | Not applicable | 2010-2011 | 2008 | 1609-2080 |
EM Time Dependence
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time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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future time | Not applicable | future time | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | both | Not applicable | past time | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable |
EM Time Continuity
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discrete | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable | 1 | Not reported | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Month | Not reported | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Month | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
Bounding Type
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Geopolitical | Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Other | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Physiographic or ecological |
Spatial Extent Name
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United States | The EU-25 plus Switzerland and Norway | Agricultural districts of the state of South Australia | River Ravna watershed | Durham, NC and vicinity | Central New Jersey and east-central Pennsylvania | Massachusetts Ocean | St. Louis River estuary | Guanica Bay watershed | Coastal zone surrounding St. Croix | Pacific Northwest | Santa Basin | CREP (Conservation Reserve Enhancement Program) wetland sites | Three Bays, Cape Cod | CREP (Conservation Reserve Enhancement Program | CREP (Conservation Reserve Enhancement Program | Not applicable | Wetlands in idaho | Piedmont Ecoregion | Jamaica Bay, Long Island, New York |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 10-100 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 1-10 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 10,000-100,000 km^2 | Not applicable | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 |
EM ID
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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:map units delineated by user based on project. |
spatially lumped (in all cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:by coastal segment |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | length, for linear feature (e.g., stream mile) |
Spatial Grain Size
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1 m^2 | 1 km x 1 km | 1 ha x 1 ha | 25 m x 25 m | irregular | 30 m x 30 m | 1 km x1 km | 10 m x 10 m | 30 m x 30 m | 10 m x 10 m | Not applicable | 1 km2 | multiple, individual, irregular shaped sites | beach length | multiple, individual, irregular sites | multiple, individual, irregular sites | map units | Not applicable | Not applicable | 80 m |
EM ID
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
EM Computational Approach
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Numeric | Logic- or rule-based | Numeric | Numeric | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | * | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
Model Calibration Reported?
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No | No | Yes | Yes | Yes | Unclear | No | No | No | Yes | No | No | Unclear | Yes | Unclear | Unclear | Not applicable | No | Yes | No |
Model Goodness of Fit Reported?
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No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
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No | Yes | No | No | No |
Yes ?Comment:Aggregate native bee abundance on watermelon flowers was measured at 23 sites in 2005. Species richness was measured using the specimens collected from watermelon flowers at the end of the sampling period. |
No | No | No | Yes |
Yes ?Comment:Compared to a second methodological approach |
Yes | Unclear | No | No | No | Not applicable | No | No | No |
Model Uncertainty Analysis Reported?
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Yes ?Comment:An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling. |
No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | Unclear | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | Yes | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None |
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None |
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None |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
None | None | None | None | None | None |
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None |
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None | None |
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None | None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
Centroid Latitude
em.detail.ddLatHelp
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40.16 | 50.53 | -34.9 | 42.8 | 35.99 | 40.2 | 41.72 | 46.74 | 17.96 | 17.73 | 44.62 | -9.05 | 42.62 | 41.62 | 42.62 | 42.62 | Not applicable | 44.06 | 36.23 | 40.61 |
Centroid Longitude
em.detail.ddLongHelp
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-99.79 | 7.6 | 138.7 | 24 | -78.96 | -74.8 | -69.87 | -92.14 | -67.02 | -64.77 | -124.02 | -77.81 | -93.84 | -70.42 | -93.84 | -93.84 | Not applicable | -114.69 | -81.9 | -73.84 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Provided |
EM ID
em.detail.idHelp
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Forests | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Rivers and Streams | Ground Water | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | None | Inland Wetlands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Grasslands | Near Coastal Marine and Estuarine |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban forests | Not applicable | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Primarily forested watershed | Urban areas including streams | Cropland and surrounding landscape | None identified | freshwater estuary | 13 LULC were used | Coral reefs | Yaquina Bay | tropical, coastal to montane | Grassland buffering inland wetlands set in agricultural land | Beaches | Wetlands buffered by grassland within agroecosystems | Wetlands buffered by grassland within agroecosystems | Not applicable | created, restored and enhanced wetlands | grasslands | Coastal |
EM Ecological Scale
em.detail.ecoScaleHelp
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Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 | 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 corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
EM Organismal Scale
em.detail.orgScaleHelp
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Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Not applicable | Species | Not applicable | Community | Species | Species | Not applicable | Not applicable | Species | Individual or population, within a species | Not applicable | Species | Not applicable | Individual or population, within a species | Individual or population, within a species | Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
None Available | None Available |
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None Available | None Available |
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None Available |
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None Available |
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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-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
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None |
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None |
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None | 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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-24 | EM-120 |
EM-129 ![]() |
EM-130 | EM-142 | EM-339 | EM-376 | EM-415 | EM-430 | EM-459 | EM-603 | EM-630 | EM-650 |
EM-686 ![]() |
EM-701 | EM-704 | EM-712 |
EM-729 ![]() |
EM-846 |
EM-851 ![]() |
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None |
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None |
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None |
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None | None |
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None |
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None |