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-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | Pollination ES, Central French Alps | RHyME2, Upper Mississippi River basin, USA | PATCH, western USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | Coral and land development, St.Croix, VI, USA | Urban Temperature, Baltimore, MD, USA | EnviroAtlas - Restorable wetlands | Coastal protection in Belize | LUCI, New Zealand | Alwife phosphorus flux in lakes, Connecticut, USA | Gadwall duck recruits, CREP wetlands, Iowa, USA | Seed mix and mowing in prairie reconstruction, USA | Pollutant dispersion by vegetation barriers | Co$ting Nature model method |
EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | Pollination ecosystem service estimated from plant functional traits, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | PATCH (Program to Assist in Tracking Critical Habitat), western USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | Coral colony density and land development, St.Croix, Virgin Islands, USA | Urban Air Temperature Change, Baltimore, MD, USA | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | LUCI (Land Utilisation and Capability Indicator), New Zealand | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Gadwall duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Seed mix design and first year management in prairie reconstruction, IA, USA | Pollutant dispersion by vegetation barriers | Co$ting Nature model method |
EM Source or Collection
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i-Tree | USDA Forest Service | EU Biodiversity Action 5 | US EPA | US EPA | US EPA | US EPA | i-Tree | USDA Forest Service | US EPA | EnviroAtlas | InVEST | None | None | None | None | US EPA | None |
EM Source Document ID
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195 | 260 | 123 | 2 | 275 | 96 | 217 | 262 | 350 |
380 ?Comment:Document 381 is an additional source for this EM. |
383 |
372 ?Comment:Document 373 is a secondary source for this EM. |
395 | 435 | 466 |
Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | US EPA Office of Research and Development - National Exposure Research Laboratory | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Trodahl, M. I., B. M. Jackson, J. R. Deslippe, and A. K. Metherell | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | 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 | Meissen, J. C., A. J. Glidden, M. E. Sherrard, K. J. Elgersma, and L. L. Jackson | Hashad, K. B. Yang, J. T. Steffens, R. W. Baldauf, P. Deshmukh, K. M. Zhang | Mulligan, M. |
Document Year
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2013 | 2011 | 2013 | 2006 | 2014 | 2011 | 2016 | 2013 | 2016 | 2017 | 2010 | 2010 | 2019 | 2021 | None |
Document Title
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Carbon storage and sequestration by trees in urban and community areas of the United States | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Defining recovery goals and strategies for endangered species: The wolf as a case study | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | EnviroAtlas - National | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Investigating trade-offs between water quality and agricultural productivity using the Land Utilisation and Capability Indicator (LUCI)-A New Zealand application | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Seed mix design and first year management influence multifunctionality and cost-effectiveness in prairie reconstruction | Parameterizing pollutant dispersion downwind of roadside vegetation barriers | Conservation prioritisation and Ecostystem services mapping with Co$ting Nature |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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) | Other or unclear (explain in Comment) |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Journal manuscript submitted or in review | Web page so cannot tell if documentation is reviewed |
EM ID
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | Not identified in paper |
info@lucitools.org ?Comment:To obtain LUCI, email us your enquiry at info@lucitools.org with information about: The problem you are seeking to solve or your research question. The country and region you wish to apply LUCI in. What data you have with as much detail as possible about the data sources. Your timeframe or deadlines. |
Not applicable | Not applicable | Not applicable | Not applicable | http://www1.policysupport.org/cgi-bin/ecoengine/start.cgi?project=costingnature&version=3 | |
Contact Name
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David J. Nowak | Sandra Lavorel | Liem Tran | Carlos Carroll |
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 |
Leah Oliver | Gordon M. Heisler | EnviroAtlas Team | Greg Guannel | Martha I. Trodahl | Derek C. West | David Otis | Justin Meissen | K. Max Zhang | Mark Mulligan |
Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | Klamath Center for Conservation Research, Orleans, CA 95556 | 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 | National Health and Environmental Research Effects Laboratory | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | Not reported | The Nature Conservancy, Coral Gables, FL. USA | School of Geography, Environment & Earth Sciences, Victoria University of Wellington, New Zealand | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Tallgrass Prairie Center, 2412 West 27th Street, Cedar Falls, IA 50614-0294, USA | Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA | King's College London, Dept. of Geography |
Contact Email
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dnowak@fs.fed.us | sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | carlos@cklamathconservation.org | frazier@nceas.ucsb.edu | leah.oliver@epa.gov | gheisler@fs.fed.us | enviroatlas@epa.gov | greg.guannel@gmail.com | Not reported | derek.west@yale.edu | dotis@iastate.edu | justin.meissen@uni.edu | kz33@cornell.edu | mark.mulligan@kcl.uk |
EM ID
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
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: "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 pollination ecosystem service map was a simple sums 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 pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | 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: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | ABSTRACT: "...The Land Utilisation & Capability Indicator (LUCI) is a GIS framework that considers impacts of land use on multiple ecosystem services in a holistic and spatially explicit manner. Due to its fine spatial scale and focus on the rural environment, LUCI is well-placed to help both farm and catchment managers to explore and quantify spatially explicit solutions to improve water quality while also maintaining or enhancing other ecosystem service outcomes. LUCI water quality and agricultural productivity models were applied to a catchment in the Bay of Plenty, New Zealand. Nitrogen (N) and phosphorus (P) sources, sinks and pathways in the landscape were identified and trade-offs and synergies between water quality and agricultural productivity were investigated. Results indicate that interventions to improve water quality are likely to come at the expense of agriculturally productive land. Nonetheless, loss of agriculturally productive land can be minimised by using LUCI to identify, at a fine spatial scale, the most appropriate locations for nutrient intervention. Spatially targeted and strategic nutrient source management and pathway interception can improve water quality, while minimising negative financial impacts on farms. Our results provide spatially explicit solutions to optimize agricultural productivity and water quality, which will inform better farm, land and catchment management as well as national and international policy." AUTHOR'S DESCRIPTION (of OVERSEER submodel): "Water quality models within LUCI use an enhanced, spatially representative export co-efficient (EC) approach to model total nitrogen (TN) and total phosphorus (TP) exports to water… Here, ECs for pastoral land cover are calculated by LUCI using algorithms derived from a large ( > 14 000 samples), pastorally based national dataset. The dataset consists of detailed farm nutrient input and management variables that have been entered and run using OVERSEER® to generate nutrient loss predictions, which are also included in the dataset." NOTE: The LUCI model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-658. https://esml.epa.gov/detail/em/658 | 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: "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: "Agricultural intensification continues to diminish many ecosystem services in the North American Corn Belt. Conservation programs may be able to combat these losses more efficiently by developing initiatives that attempt to balance multiple ecological benefits. In this study, we examine how seed mix design and first year management influence three ecosystem services commonly provided by tallgrass prairie reconstructions (erosion control, weed resistance, and pollinator resources). We established research plots with three seed mixes, with and without first year mowing. The grass-dominated “Economy” mix had 21 species and a 3:1 grass-to-forb seeding ratio. The forb-dominated “Pollinator”mix had 38 species and a 1:3 grass-to-forb seeding ratio. The grass:forb balanced “Diversity” mix, which was designed to resemble regional prairie remnants, had 71 species and a 1:1 grass-to-forb ratio. To assess ecosystem services, we measured native stem density, cover, inflorescence production, and floral richness from 2015 to 2018. The Economy mix had high native cover and stem density, but produced few inflorescences and had low floral richness. The Pollinator mix had high inflorescence production and floral richness, but also had high bare ground and weed cover. The Diversity mix had high inflorescence production and floral richness (comparable to the Pollinator mix) and high native cover and stem density (comparable to the Economy mix). First year mowing accelerated native plant establishment and inflorescence production, enhancing the provisioning of ecosystem services during the early stages of a reconstruction. Our results indicate that prairie reconstructions with thoughtfully designed seed mixes can effectively address multiple conservation challenges." | ABSTRACT: "Communities living and working in near-road environments are exposed to elevated levels of traffic-related air pollution (TRAP), causing adverse health effects. Roadside vegetation may help reduce TRAP through enhanced deposition and mixing….there are no studies that developed a dispersion model to characterize pollutant concentrations downwind of vegetation barriers. To account for the physical mechanisms, by which the vegetation barrier deposits and disperses pollutants, we propose a multi-region approach that describes the parameters of the standard Gaussian equations in each region. The four regions include the vegetation, a downwind wake, a transition, and a recovery zone. For each region, we fit the relevant Gaussian plume equation parameters as a function of the vegetation properties and the local wind speed. Furthermore, the model captures particle deposition which is a major factor in pollutant reduction by vegetation barriers. We generated data from 75 (CFD)-based simulations, using the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model, to parameterize the Gaussian-based equations. The simulations used reflected a wide range of vegetation barriers, with heights from 2-10 m, and various densities, represented by leaf area index values from 4-11, and evaluated under different urban conditions, represented by wind speeds from 1-5 m/s. The CTAG model has been evaluated against two field measurements to ensure that it can properly represent the vegetation barrier’s pollutant deposition and dispersion. The proposed multi-region Gaussian-based model was evaluated across 9 particle sizes and a tracer gas to assess its capability of capturing deposition. The multi-region model’s normalized mean error (NME) ranged between 0.18-0.3, the fractional bias (FB) ranged between -0.12-0.09, and R2 value ranged from 0.47-0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable range. Even though the multi-region model is parameterized for coniferous trees, our sensitivity study indicates that the parameterized Gaussian-based model can provide useful predictions for hedge/bushes vegetative barriers as well." ADDITIONAL DESCRIPTION: Detailed variable relationships are described in the source document. The VRD associated with the ESML entry provides variables in a simplified form. | ABSTRACT: " Co$tingNature is a sophisticated web-based spatial policy support system for natural capital accounting and analysing the ecosystem services provided by natural environments (i.e. nature's benefits), identifying the beneficiaries of these services and assessing the impacts of human interventions. This PSS is a testbed for the development and implementation of conservation strategies focused on sustaining and improving ecosystem services. It also focused on enabling the intended and unintended consequences of development actions on ecosystem service provision to be tested in silico before they are tested in vivo . The PSS incorporates detailed spatial datasets at 1-square km and 1 hectare resolution for the entire World, spatial models for biophysical and socioeconomic processes along with scenarios for climate and land use. The PSS calculates a baseline for current ecosystem service provision and allows a series of interventions (policy options) or scenarios of change to be used to understand their impact on ecosystem service delivery. We do not focus on valuing nature (how much someone is willing to pay for it) but rather costing it (understanding the resource e.g. land area and opportunity cost of nature being protected to produce the ecosystem services that we need and value). " |
Specific Policy or Decision Context Cited
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Not reported | None identified | Not reported | AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | None identified | Not applicable | None identified | None Identified | Future rock lobster fisheries management | Land management trade off between agricultural productivity and water quality | Restoration and management of diadromous fish runs in coastal New England | None identified | None identified | None identified | Conservation priorities |
Biophysical Context
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Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | nearshore; <1.5 km offshore; <12 m depth | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | No additional description provided | barrier reef and fringing reef in nearshore coastal marine system | Groundwater dominated, volcanic caldera catchment, largely comprised of porous allophanic and pumice soils. | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | Prairie Pothole Region of Iowa | The site, located at the Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa, is relatively level with slopes not exceeding a 5% grade. Soil composition is primarily poorly drained Clyde clay loams with a minor component of somewhat poorly drained Floyd loams. Sub-surface tile drains exist on site and are spaced approximately 18–24m apart. The land was used for corn and soybean production prior to site establishment in 2015. | Communities living and working in near-road environments | Woldwide coverage |
EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | No scenarios presented | No scenarios presented | Population growth, road development (density) on public vs private land | No scenarios presented | Not applicable | No scenarios presented | No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | No scenarios presented | current and historical run size | No scenarios presented | Seed mix design | None scenarios presented | Policy decisions affecting future land use |
EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
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Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised 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 | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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None | Doc-260 | Doc-123 | Doc-328 | Doc-337 | None | None | Doc-220 | Doc-219 | Doc-218 | None | None | Doc-379 | Doc-381 | None | Doc-372 | Doc-373 | Doc-394 | None | None |
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-80 | EM-81 | EM-83 | None | EM-403 | EM-422 | None | None | None | None | None | EM-658 | EM-667 | EM-672 | EM-674 | EM-673 | EM-705 | EM-704 | EM-702 | EM-701 | EM-700 | EM-632 | EM-719 | None | None |
EM Modeling Approach
EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
EM Temporal Extent
em.detail.tempExtentHelp
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1989-2010 | Not reported | 1987-1997 | 2000-2025 | December 2007 - November 2008 | 2006-2007 | May 5-Sept 30 2006 | 2006-2013 | 2005-2013 | 1930-2013 | 1960"s and early 2000's | 1987-2007 | 2015-2018 | Not applicable | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | Not applicable | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Year | Not applicable | Not applicable | Year | Not applicable | Not applicable | Hour | Not applicable | Second | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Other | Not applicable | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
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United States | Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | Western United States | Yaquina Estuary (intertidal), Oregon, USA | St. Croix, U.S. Virgin Islands | Baltimore, MD | conterminous United States | Coast of Belize | Lake Rotorua catchment | Bride Lake and Linsley Pond | CREP (Conservation Reserve Enhancement Program | Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 1-10 km^2 | 10-100 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10-100 ha | 10,000-100,000 km^2 | <1 ha | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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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 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 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 lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | NHDplus v1 | area, for pixel or radial feature | other (habitat type) | 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) | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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1 m^2 | 20 m x 20 m | NHDplus v1 | 504 km^2 | 0.87-104.29 ha | Not applicable | 10m x 10m | irregular | 1 meter | 5m x 5m | Not applicable | multiple, individual, irregular sites | 6.1 m x 8.53 m | user defined | Not applicable |
EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Yes | Unclear | Unclear | Yes | Yes | No | No | No | Yes | Unclear | Not applicable | Yes | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | Yes | No | No | Yes | Yes | No | No | No | No | No | No | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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None | None |
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None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | No | No | No | No |
No ?Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. |
No | No | No | No | Not applicable | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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Yes ?Comment:An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling. |
No | No | No | No | Yes | No | No | No | No | No | No | No | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
No | No | No | No | No | No | Yes | No | No | Not applicable | Not applicable |
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 | Unclear | 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-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
None | None | None | None |
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None | None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
Centroid Latitude
em.detail.ddLatHelp
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40.16 | 45.05 | 42.5 | 39.88 | 44.62 | 17.75 | 39.28 | 39.5 | 18.63 | -38.14 | 41.33 | 42.62 | 42.93 | Not applicable | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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-99.79 | 6.4 | -90.63 | -113.81 | -124.06 | -64.75 | -76.62 | -98.35 | -88.22 | 176.25 | -72.24 | -93.84 | -92.57 | Not applicable | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | None provided | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Forests | Created Greenspace | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Agroecosystems | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Ground Water | Forests | Agroecosystems | Scrubland/Shrubland | Rivers and Streams | Lakes and Ponds | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban forests | Subalpine terraces, grasslands, and meadows. | None | Not reported | Estuarine intertidal | stony coral reef | Urban landscape and surrounding area | Terrestrial | coral reefs | Largely agricultural, commercial forestry, non-commercial forest and shrubland and urban | Coastal lakes and ponds and associated streams | Wetlands buffered by grassland within agroecosystems | prairie/grassland reconstruction at demonstration farm site | Communities living and working in near-road environments | Non urban |
EM Ecological Scale
em.detail.ecoScaleHelp
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Zone within an ecosystem | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
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. |
Community | Not applicable | Species | Guild or Assemblage | Guild or Assemblage | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Individual or population, within a species | Individual or population, within a species | Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-24 | EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-194 | EM-306 | EM-492 |
EM-542 ![]() |
EM-659 |
EM-661 ![]() |
EM-703 |
EM-728 ![]() |
EM-942 | EM-996 |
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None | None |
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None |
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None |
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None | None |
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