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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660 
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Rate of Fire Spread | Land capability classification | Chinook salmon value, Yaquina Bay, OR | N removal by wetland restoration, Midwest, USA | RUM: Valuing fishing quality, Michigan, USA | Total duck recruits, CREP wetlands, Iowa, USA | Pollinators on landfill sites, United Kingdom | Wild bees over 26 yrs of restored prairie, IL, USA | Recreational fishery index, USA | Atlantis ecosystem physics submodel | SMOKE emissions model, Asia |
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EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Rate of Fire Spread | Land capability classification | Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Total duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Pollinating insects on landfill sites, East Midlands, United Kingdon | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Recreational fishery index for streams and rivers, USA | Atlantis user's guide part I: general overview, physics & ecology | Development of an anthropogenic emissions processing system for Asia using SMOKE |
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EM Source or Collection
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US EPA | EnviroAtlas | None | None | US EPA | None | None | None | None | None | US EPA | None | None |
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EM Source Document ID
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262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
306 | 340 | 324 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
372 ?Comment:Document 373 is a secondary source for this EM. |
389 | 401 | 414 | 461 | 481 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Rothermel, Richard C. | United States Department of Agriculture - Natural Resources Conservation Service | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | 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 | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | Audzijonyte, A., Gorton, R., Kaplan, I., & Fulton, E. A. | Woo, J.H., Choi, K.C., Kim, H.K., Baek, B.H., Jang, M., Eum, J.H., Song, C.H., Ma, Y.I., Sunwoo, Y., Chang, L.S. and Yoo, S.H. |
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Document Year
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2013 | 1972 | 2013 | 2012 | 2006 | 2014 | 2010 | 2013 | 2017 | 2021 | 2017 | 2012 |
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Document Title
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EnviroAtlas - National | A Mathematical model for predicting fire spread in wildland fuels | National Soil Survey Handbook - Part 622 - Interpretative Groups | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Valuing recreational fishing quality at rivers and streams | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | Atlantis user’s guide part I: general overview, physics & ecology | Development of an anthropogenic emissions processing system for Asia using SMOKE |
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Document Status
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Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published USDA Forest Service report | Published report | Published journal manuscript | Published report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript |
Software and Access
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EM ID
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
| https://www.epa.gov/enviroatlas | http://firelab.org/project/farsite | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://research.csiro.au/atlantis/home/links/ | https://www.cmascenter.org/smoke/ | |
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Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Charles McHugh | United States Department of Agriculture | Stephen Jordan | William G. Crumpton | Richard Melstrom | David Otis | Sam Tarrant | Sean R. Griffin | Gregg Lomnicky | Asta Audzijonyte | Jung-Hun Woo |
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Contact Address
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Not reported | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | Not reported | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | 200 SW 35th St., Corvallis, OR, 97333 | University of Tasmania (Australia); Nature Research Centre (Lithuania) | Department of Advanced Technology Fusion, Room 812, San-Hak Bldg., Konkuk University, Seoul, South Korea |
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Contact Email
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enviroatlas@epa.gov | cmchugh@fs.fed.us | http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ | jordan.steve@epa.gov | crumpton@iastate.edu | melstrom@okstate.edu | dotis@iastate.edu | sam.tarrant@rspb.org.uk | srgriffin108@gmail.com | lomnicky.gregg@epa.gov | Asta.Audzijonyte@utas.edu.au | jwoo@konkuk.ac.kr |
EM Description
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EM ID
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.** | AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2] | 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: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | 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: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each | ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] | Before delving into Atlantis we would like to provide a little bit of background on the modelling framework and this manual. Atlantis is just one of many marine ecosystem models, originally known as BM2 (BoxModel 2) it was christened Atlantis by Villy Christensen in South Africa in 2001. Marine ecosystem models have existed for more than 50 years, though they have only grown in popular use since the advent of (fast) modern computing power. They have grown from a biophysical focus to include more and more of the human dimensions. This is reflected in the structure of this manual, which sequentially works through the physical then biological before getting into the human dimensions. Atlantis was originally developed with an eye to temperate marine ecosystems and fisheries, though it has grown through time. | Air quality modeling is a useful methodology to investigate air quality degradation in various locations and to analyze effectiveness of emission reduction plans. A comprehensive air quality model usually requires a coordinated set of emissions input of all necessary chemical species. We have developed an anthropogenic emissions processing system for Asia in support of air quality modeling and analysis over Asia (named SMOKE-Asia). The SMOKE (Sparse Matrix Operator kernel Emissions) system, which was developed by U.S. EPA and has been maintained by the Carolina Environmental Program (CEP) of the University of North Carolina, was used to develop our emissions processing system. A merged version of INTEX 2006 and TRACE-P 2000 inventories was used as an initial Asian emissions inventory. The IDA (Inventory Data Analyzer) format was used to create SMOKE-ready emissions. Source Classification Codes (SCCs) and country/state/county (FIPS) code, which are the two key data fields of SMOKE IDA data structure, were created for Asia. The 38 SCCs and 2752 FIPS codes were allocated to our SMOKE-ready emissions for more comprehensive processing. US EPA’s MIMS (Multimedia Integrated Modeling System) Spatial Allocator software, along with many global and regional GIS shapes, were used to create spatial allocation profiles for Asia. Temporal allocation and chemical speciation profiles were partly regionalized using Asia-based studies. Initial data production using the developed SMOKE-Asia system was successfully performed. NOx and VOC emissions for the year 2009 were projected to be increased by 50% from those of 1997. The emission hotspots, such as large cities and large point sources, are distinguished in the domain due to spatial allocation. Regional emission peaks were distinguished due to temporally resolved emission information. The PAR (Paraffin carbon bond) and XYL (Xylene and other polyalkyl aromatics) showed the first and second largest emission rate among VOC species. Most of point source emissions are located in layers 3 to 4, which the altitude range reaches 310–550 m AGL. Qualitative inter-comparison between model output and ground/satellite measurement showed good agreements in terms of spatial and temporal patterns. We expect that the result of this study will provide better air quality modeling inputs, which will act as a major step to improve our understanding of Asian air quality. |
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Specific Policy or Decision Context Cited
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None Identified | None identified | None provided | None reported | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None provided |
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Biophysical Context
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No additional description provided | Not applicable | No additional description provided | Yaquina Bay estuary | No additional description provided | stream and river reaches of Michigan | Prairie Pothole Region of Iowa | No additional description provided | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | None | Marine and coastal ecosystems | Asia |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | N/A | More conservative, average and less conservative nitrate loss rate | targeted sport fish biomass | No scenarios presented | No scenarios presented | No scenarios presented | N/A | No scenarios presented | NA |
EM Relationship to Other EMs or Applications
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EM ID
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method Only | Method Only | Method + Application | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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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 | New or revised model | New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
em.detail.idHelp
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
None | None | None | None | None | Doc-372 | Doc-373 | Doc-389 | None | None | Doc-456 | Doc-459 | Doc-478 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | None | None | EM-604 | EM-397 | None | None | EM-632 | EM-700 | EM-701 | EM-702 | EM-703 | EM-704 | EM-697 | None | None | EM-981 | EM-978 | EM-985 | EM-990 | EM-991 | EM-1012 | EM-1021 |
EM Modeling Approach
EM Relationship to Time
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EM ID
em.detail.idHelp
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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EM Temporal Extent
em.detail.tempExtentHelp
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2006-2010 | Not applicable | Not applicable | 2003-2008 | 1973-1999 | 2008-2010 | 1987-2007 | 2007-2008 | 1988-2014 | 2013-2014 | Not applicable | 1997-2009 |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | Not applicable | Not applicable | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | past time |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | continuous | continuous |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
em.detail.idHelp
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Not applicable | Not applicable | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Geopolitical | Not applicable | Geopolitical |
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Spatial Extent Name
em.detail.extentNameHelp
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counterminous United States | Not applicable | Not applicable | Pacific Northwest | Upper Mississippi River and Ohio River basins | HUCS in Michigan | CREP (Conservation Reserve Enhancement Program | East Midlands | Nachusa Grasslands | United States | Not applicable | Asia |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | Not applicable | Not applicable | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | >1,000,000 km^2 | Not applicable | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
Not applicable | Not applicable | 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) | Not applicable | spatially lumped (in all cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | area, for pixel or radial feature | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | Not applicable | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | Not applicable | Not applicable | Not applicable | 1 km2 | reach in HUC | multiple, individual, irregular sites | multiple unrelated locations | Area varies by site | stream reach (site) | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
em.detail.idHelp
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Not applicable | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Model Checking Procedures Used
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EM ID
em.detail.idHelp
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | Not applicable | Not applicable | No | No | No | Unclear | Not applicable | No | No | Not applicable | Unclear |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Not applicable | Not applicable | No | No | Yes | No | Not applicable | No | No | Not applicable | Unclear |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None |
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None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No |
Yes ?Comment:Compared to a second methodological approach |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | No | Not applicable | No | No | Not applicable | Yes |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Not applicable | Not applicable | No | No | No | No | Not applicable | No | No | Not applicable | Unclear |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Not applicable | Not applicable | No | No | No | No | Not applicable | No | No | Not applicable | Unclear |
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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 |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
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| EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
| None | None | None |
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None | None | None | None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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Centroid Latitude
em.detail.ddLatHelp
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39.5 | -9999 | Not applicable | 44.62 | 40.6 | 45.12 | 42.62 | 52.22 | 41.89 | 36.21 | Not applicable | 38.63 |
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Centroid Longitude
em.detail.ddLongHelp
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-98.35 | -9999 | Not applicable | -124.02 | -88.4 | 85.18 | -93.84 | -0.91 | -89.34 | -113.76 | Not applicable | 117.79 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | Not applicable | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Not applicable | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Not applicable | Estimated |
Environments and Scales Modeled
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EM ID
em.detail.idHelp
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Agroecosystems | Rivers and Streams | Inland Wetlands | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Atmosphere |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Terrestrial | Not applicable | None identified | Yaquina Bay | Agroecosystems and associated drainage and wetlands | stream reaches | Wetlands buffered by grassland within agroecosystems | restored landfills and grasslands | Restored prairie, prairie remnants, and cropland | reach | Multiple | Asian atmosphere |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is 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 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 | 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 | Not applicable |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Guild or Assemblage | Individual or population, within a species | Species | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
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EM-862 | EM-983 | EM-1019 |
| None Available | None Available | None Available |
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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.detail.cicesHelp
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| EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
|
EM-705 |
EM-709
|
EM-788
|
EM-862 | EM-983 | EM-1019 |
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None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-63 | EM-337 | EM-434 | EM-603 | EM-627 |
EM-660
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EM-705 |
EM-709
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EM-788
|
EM-862 | EM-983 | EM-1019 |
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None |
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None |
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None | None |
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