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
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EM ID
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Area & hotspots of soil accumulation, South Africa | SPARROW, Northeastern USA | InVEST nutrient retention, Hood Canal, WA, USA | Salmon habitat values, west coast of Canada | Cultural ecosystem services, Bilbao, Spain | Wetland shellfish production, Gulf of Mexico, USA | Sed. denitrification, St. Louis River, MN/WI, USA | Retained rainwater, Guánica Bay, Puerto Rico | Decrease in erosion (shoreline), St. Croix, USVI | Sed. denitrification, St. Louis R., MN/WI, USA | Sedge Wren density, CREP, Iowa, USA | Natural amenities and population migration, USA | Fish species richness, St. Croix, USVI | WESP Method | WESP: Urban Stormwater Treatment, ID, USA | HWB indicator-poor mental health, Great Lakes, USA | HWB indicator-ADI, Great Lakes, USA | MIKE-SHE Munich, Germany | Velma- 6PPD-Q concentrations, Seattle, WA |
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EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Area and hotspots of soil accumulation, South Africa | SPARROW (SPAtially Referenced Regressions On Watershed Attributes), Northeastern USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) nutrient retention, Hood Canal, WA, USA | Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada | Cultural ecosystem services, Bilbao, Spain | Wetland shellfish production, Gulf of Mexico, USA | Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA | Retained rainwater, Guánica Bay, Puerto Rico, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Sediment denitrification, St. Louis River, MN/WI, USA | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Natural amenities and rural population migration, USA | Fish Species Richness, Buck Island, St. Croix , USVI | Method for the Wetland Ecosystem Services Protocol (WESP) | WESP: Urban Stormwater Treament, ID, USA | Human well being indicator-poor mental health,Great Lakes waterfront, USA | Human well being indicator- Area Deprivation Index (ADI) , Great Lakes waterfront, USA | MIKE SHE model, Regulation of urban surface runoff, Munich Germany | VELMA: 6PPD-Quinone stormwater concentrations , Seattle, Washington |
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EM Source or Collection
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US EPA | EnviroAtlas | None | US EPA | InVEST | None |
None ?Comment:EU Mapping Studies |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
US EPA | US EPA | US EPA | US EPA | None | USDA Forest Service | None | None | None | None | US EPA | None | US EPA |
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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. |
271 | 86 | 205 | 286 | 191 | 324 | 333 | 338 | 335 | 333 | 372 | 375 | 355 | 390 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
440 | 465 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Moore, R. B., Johnston, C.M., Smith, R. A. and Milstead, B. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | 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 | Cordell H. K., V. Heboyan, F. Santos, J. C. Bergstrom | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Adamus, P. R. | Murphy, C. and T. Weekley | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Zolch,T., Henze, L., Keilholz, P., and S. Pauleit | Halama JJ, McKane RB, Barnhart BL, Pettus PP, Brookes AF, Adams AK, Gockel CK, Djang KS, Phan V, Chokshi SM, Graham JJ, Tian Z, Peter KT and Kolodziej,EP |
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Document Year
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2013 | 2008 | 2011 | 2013 | 2003 | 2013 | 2012 | 2014 | 2017 | 2014 | 2014 | 2010 | 2011 | 2007 | 2016 | 2012 | None | None | 2017 | 2024 |
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Document Title
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EnviroAtlas - National | Mapping ecosystem services for planning and management | Source and delivery of nutrients to receiving waters in the northeastern and mid-Atlantic regions of the United States | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Valuing freshwater salmon habitat on the west coast of Canada | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Sediment nitrification and denitrification in a Lake Superior estuary | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Sediment nitrification and denitrification in a Lake Superior estuary | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Natural amenities and rural population migration | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Regulating urban surface runoff through nature-based solutions – An assessment at the micro-scale | Watershed analysis of urban stormwater contaminant 6PPD-Quinone hotspots and stream concentrations using a process-based ecohydrological model |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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) | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Published report | Published report | Journal manuscript submitted or in review | Journal manuscript submitted or in review | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
| https://www.epa.gov/enviroatlas | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | Not applicable | Not applicable | Not applicable | Not reported | |
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Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Benis Egoh | Richard Moore | J.E. Toft | Duncan Knowler | Izaskun Casado-Arzuaga | Stephen J. Jordan | Brent J. Bellinger | Susan H. Yee | Susan H. Yee |
Brent J. Bellinger ?Comment:Ph# +1 218 529 5247. Other current address: Superior Water, Light and Power Company, 2915 Hill Ave., Superior, WI 54880, USA. |
David Otis | Ken Cordell | Simon Pittman | Paul R. Adamus | Chris Murphy | Ted Angradi | Ted Angradi | Teresa Zoelch | Jonathan Halama |
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Contact Address
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Not reported | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | U.S. Environmental Protection Agency, 27 Tarzwell Drive, Narragansett, Rhode Island 02882 | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6 | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Department of Agriculture, Forest Service, Southern Research Station, Athens, GA 30602 | 1305 East-West Highway, Silver Spring, MD 20910, USA | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Technical University of Munich, Centre for Urban Ecology and Climate Adaptation (ZSK), Arcisstraße 21, 80333 Munich, Germany | U.S. Environmental Protection Agency, Corvallis, OR |
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Contact Email
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enviroatlas@epa.gov | Not reported | rmoore@usgs.gov | jetoft@stanford.edu | djk@sfu.ca | izaskun.casado@ehu.es | jordan.steve@epa.gov | bellinger.brent@epa.ogv | yee.susan@epa.gov | yee.susan@epa.gov | bellinger.brent@epa.gov | dotis@iastate.edu | Not reported | simon.pittman@noaa.gov | adamus7@comcast.net | chris.murphy@idfg.idaho.gov | tedangradi@gmail.com | tedangradi@gmail.com | teresa.zoelch@tum.de | Halama.Jonathan@epa.gov |
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EM ID
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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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." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover." | AUTHOR'S DESCRIPTION: "SPAtially Referenced Regressions On Watershed attributes (SPARROW) nutrient models were developed for the Northeastern and Mid-Atlantic (NE US) regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the NE US Seaboard indicated that agricultural sources contribute the largest percentage (37%) of the total nitrogen load delivered to the estuaries" | InVEST Nutrient Retention Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub-watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparameterized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)" (2) "We used the InVEST Nutrient Retention model to quantify the total nitrogen load for each subwatershed. Inputs to the Nutrient Retention model include water yield, land use and land cover, and nutrient loading and filtration rates (Table 1; Conte et al., 2011; Tallis et al., 2011). The nutrient model quantifies natural and anthropogenic sources of total nitrogen within each subwatershed, allowing managers to identify subwatersheds potentially at risk of contributing excessive nitrogen loads given the predicted development and climate future." ( P. 4) | ABSTRACT: "In this paper, we present a framework for valuing benefits for fisheries from protecting areas from degradation, using the example of the Strait of Georgia coho salmon fishery in southern British Columbia, Canada. Our study improves upon previous methods used to value fish habitat in two major respects. First, we use a bioeconomic model of the coho fishery to derive estimates of value that are consistent with economic theory. Second, we estimate the value of changing the quality of fish habitat by using empirical analyses to link fish population dynamics with indices of land use in surrounding watersheds." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | ABSTRACT: "We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." |
ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ?Comment:BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification model. |
AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature. We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones…Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates." AUTHOR'S DESCRIPTION: "We used different survey designs in 2011 and 2012. Both designs were based on area-weighted probability sampling methods, similar to those developed for EPA's Environmental Monitoring and Assessment Program (EMAP) (Crane et al., 2005; Stevens and Olsen, 2003, 2004). Sampling sites were assigned to spatial zones: “harbor” (river km 0–13), “bay” (river km 13–24), or “river” (river km 24–35) (Fig. 1). Sites were also grouped by depth zones (“shallow,” <1 m; “intermediate,” 1–2 m; and “deep,” >2 m). In 2011 (“vegetated-habitat survey”), the sample frame consisted of areas of emergent and submergent vegetation in the SLRE… The resulting sample frame included 2370 ha of potentially vegetated area out of a total SLRE area of 4378 ha. Sixty sites were distributed across the total vegetated area in each spatial zone using an uneven spatially balanced probabilistic design. Vegetated areas were more prevalent, and thus had greater sampling effort, in the bay (n = 33) and river (n = 17) than harbor (n=10) zones, and in the shallow (n=44) and intermediate (n =14) than deep (n =2) zones. All sampling was done in July. In 2012 a probabilistic sampling design (“estuary-wide survey”) was implemented to determine N-cycling rates for the entire SLRE (not just vegetated areas as in 2011). Thirty sites unevenly distributed across spatial and depth zones were sampled monthly in May–September (Fig. 1). Area weighting for each sampled site reflects the SLRE area attributable to each sample by month, spatial zone, and depth zone." "…we were able to create significant predictive models for NIT and DeNIT rates using linear combinations of physiochemical parameters…" "…Simulations of changes in DeNIT rates in response to altered water depth and surface NOx-N concentration for spring (Fig. 4A) and summer (Fig. 4B) show that for a given season, altering water depths would have a greater influence on DeNIT than rising NO3- concentration." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) | ABSTRACT: "Research suggests that significant relationships exist between rural population change and natural amenities. Thus, understanding and predicting domestic migration trends as a function of changes in natural amenities is important for effective regional growth and development policies and strategies. In this study, we first estimated an econometric model which showed the effects of natural amenities, such as climate and landscape variables, on rural population migration patterns in the United States between 1990 and 2007. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections. Results suggest that people prefer rural areas with mild winters and cooler summers; thus we can expect a direct impact of climate change on population migration when areas associated with these conditions change. Results also suggest preference for varied landscapes that feature a mix of forest land and open space (e g , pasture and range land). During the projection period from 2010 to 2060 in the United States, changes in natural amenities were predicted to have positive effects on rural population migration trends in most parts of the Intermountain and Pacific Northwest regions, and some parts of the Southeastern, South Central, and Northeastern U S regions (e g , Southern Appalachian Mountains, Ozark Mountains, northern New England). Changes in natural amenities were predicted to have negative effects on rural population migration trends during the projection period in Midwestern regions (e g , Great Plains and North Central regions)." AUTHOR'S DESCRIPTION: "This model was estimated for 2,014 rural counties in the continental United States using various national data bases and sources. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections." | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | [Enter up to 65000 characters] | ABSTRACT: "Coho salmon (Oncorhynchus kisutch) are highly sensitive to 6PPD-Quinone (6PPD-Q). Details of the hydrological and biogeochemical processes controlling spatial and temporal dynamics of 6PPD-Q fate and transport from points of deposition to receiving waters (e.g., streams, estuaries) are poorly understood. To understand the fate and transport of 6PPD and mechanisms leading to salmon mortality Visualizing Ecosystem Land Management Assessments (VELMA), an ecohydrological model developed by US Environmental Protection Agency (EPA), was enhanced to better understand and inform stormwater management planning by municipal, state, and federal partners seeking to reduce stormwater contaminant loads in urban streams draining to the Puget Sound National Estuary. This work focuses on the 5.5 km2 Longfellow Creek upper watershed (Seattle, Washington, United States), which has long exhibited high rates of acute urban runoff mortality syndrome in coho salmon. We present VELMA model results to elucidate these processes for the Longfellow Creek watershed across multiple scales–from 5-m grid cells to the entire watershed. Our results highlight hydrological and biogeochemical controls on 6PPD-Q flow paths, and hotspots within the watershed and its stormwater infrastructure, that ultimately impact contaminant transport to Longfellow Creek and Puget Sound. Simulated daily average 6PPD-Q and available observed 6PPD-Q peak in-stream grab sample concentrations (ng/L) corresponds within plus or minus 10 ng/L. Most importantly, VELMA’s high-resolution spatial and temporal analysis of 6PPD-Q hotspots provides a tool for prioritizing the locations, amounts, and types of green infrastructure that can most effectively reduce 6PPD-Q stream concentrations to levels protective of coho salmon and other aquatic species. " |
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Specific Policy or Decision Context Cited
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None Identified | None identified | water-quality assessment, total maximum daily load(TMDL) determination | Land use change | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | None identified | None identified | Meeting water demands for agriculture and domestic purposes. | None identified | None identified | None identified | None identified | None provided | None identified | None identified | None identified | None identified | None | Not reported |
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Biophysical Context
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No additional description provided | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Norteneastern region (U.S.); Mid-Atlantic region (U.S.) | No additional description provided | No additional description provided | Northern Spain; Bizkaia region | Estuarine environments and marsh-land interfaces | Estuarine system | No additional descriptions provided | No additional description provided | No additional description provided | Prairie pothole region of north-central Iowa | No additional description provided | Hard and soft benthic habitat types approximately to the 33m isobath | None | restored, enhanced and created wetlands | Waterfront districts on south Lake Michigan and south lake Erie | Waterfront districts on south Lake Michigan and south lake Erie | None | 6PPD deposition from vehicle tire wear particles. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Future land use and land cover; climate change | Habitat quality | No scenarios presented | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Climate projections based on the CGCM 3 1 general circulation model of moderate warming (IPCC). The A1B scenario assumes a growing world population that peaks in the mid-century and balanced technological growth. | No scenarios presented | N/A | Sites, function or habitat focus | N/A | N/A | None | N/A |
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EM ID
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | None | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | Application of existing model | New or revised model | WESP - Urban Stormwater Treatment | New or revised model | New or revised model | None | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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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. |
Doc-271 | None | Doc-309 | Doc-338 | None | None | None | None | None | Doc-335 | None | Doc-372 | None | Doc-355 | None | Doc-390 | None | Doc-422 | None | Doc-366 | Doc-423 | Doc-430 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | EM-85 | EM-86 | EM-88 | None | EM-363 | EM-438 | EM-179 | EM-183 | EM-180 | EM-181 | None | EM-604 | EM-603 | None | None | EM-447 | EM-448 | None | EM-652 | EM-651 | EM-649 | EM-648 | None | EM-590 | EM-699 | EM-718 | EM-718 | EM-734 | EM-888 | EM-889 | EM-890 | EM-891 | EM-893 | EM-894 | EM-895 | EM-886 | EM-888 | EM-889 | EM-890 | EM-891 | EM-894 | EM-895 | None | None |
EM Modeling Approach
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EM ID
em.detail.idHelp
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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EM Temporal Extent
em.detail.tempExtentHelp
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2006-2010 | Not reported |
2002 ?Comment:Several nationwide database development and modeling efforts were necessary to create models consistent with 2002 conditions. |
2005-7; 2035-45 | 1989-1999 | 2000 - 2007 | 1950 - 2050 | 2011 - 2012 | 2006 - 2012 | 2006-2007, 2010 |
July 2011 to September 2012 ?Comment:All sampling performed July 2011, and May-September 2012. |
1992-2007 | 1982-2060 | 2000-2005 | Not applicable | 2010-2011 | 2022 | 2022 | None | 9/2020-6/2021 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | None | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | past time | Not applicable | Not applicable | None | past time |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | None | discrete |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Varies by Run | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | None | 1 |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | None | Day |
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EM ID
em.detail.idHelp
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Physiographic or ecological | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Geopolitical | None | Watershed/Catchment/HUC |
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Spatial Extent Name
em.detail.extentNameHelp
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counterminous United States | South Africa | NE U.S. Regions | Hood Canal | South Thompson watershed | Bilbao Metropolitan Greenbelt | Gulf of Mexico (estuarine and coastal) | St. Louis River estuary | Guanica Bay watershed | Coastal zone surrounding St. Croix | St. Louis River Estuary (of western Lake Superior) | CREP (Conservation Reserve Enhancement Program) wetland sites | continental United States | SW Puerto Rico, | Not applicable | Wetlands in idaho | Great Lakes waterfront | Great Lakes waterfront | None | Longfellow creek |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10-100 km^2 | 1-10 km^2 | >1,000,000 km^2 | 100-1000 km^2 | Not applicable | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | None | 1-10 km^2 |
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EM ID
em.detail.idHelp
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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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). |
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) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
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) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | None | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | 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) | map scale, for cartographic feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | Not applicable | None | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | Distributed across catchments with average size of 65,000 ha | 30 x 30 m | 30 m x 30 m | Not applicable | 2 m x 2 m | 55.2 km^2 | Not applicable | 30 m x 30 m | 10 m x 10 m | 35 km river estuary reach, 0 to 5 m depth by 1 m increment | multiple, individual, irregular shaped sites | varies | not reported | not reported | Not applicable | Not applicable | Not applicable | None | Not applicable |
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EM ID
em.detail.idHelp
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Other or unclear (comment) | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | * | Analytic |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | None | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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None |
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EM ID
em.detail.idHelp
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Yes | Yes | Yes | No | Yes | No | No | Yes | Yes | Unclear | Yes | No | Not applicable | No | No | No | None | Yes |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No |
Yes ?Comment:R-squared of .97 refers to the modelled loading whereas .83 refers to yield (see table 1, pg 972 for more information) |
No | No | No | No | No | No | No | Yes | No | No | Yes | Not applicable | No | No | No | None | No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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None | None | None | None | None | None | None |
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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 | Yes | Yes | No | Yes | No | No | No | Yes | No | Unclear | No | Yes | No | No | No | No | None | Yes |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | Unclear | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | None | Unclear |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | Yes | Yes | Yes | No | No | No | No | No | No | No | No | Yes | Not applicable | No | Yes | Yes | None | Unclear |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Unclear | No | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Yes | Not applicable | None | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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None |
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None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
| None | None | None |
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None |
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None | None |
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None | None | None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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Centroid Latitude
em.detail.ddLatHelp
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39.5 | -30 | 42 | 47.8 | 49.29 | 43.25 | 30.44 | 46.75 | 17.96 | 17.73 | 46.74 | 42.62 | 39.8 | 17.79 | Not applicable | 44.06 | 42.26 | 42.26 | None | 47.55 |
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Centroid Longitude
em.detail.ddLongHelp
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-98.35 | 25 | -73 | -122.7 | -123.8 | -2.92 | -87.99 | -92.08 | -67.02 | -64.77 | -96.13 | -93.84 | -98.55 | -64.62 | Not applicable | -114.69 | -87.84 | -87.84 | None | 122.37 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | None | None provided |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | None | Provided |
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EM ID
em.detail.idHelp
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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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) | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Near Coastal Marine and Estuarine | Rivers and Streams | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Inland Wetlands | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | None | Rivers and Streams |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Terrestrial | Not applicable | none | glacier-carved saltwater fjord | Rivers and streams | none | Submerged aquatic vegetation in estuaries and coastal lagoons | Freshwater estuary | 13 LULC were used | Coral reefs | River and riverine estuary (lake) | Grassland buffering inland wetlands set in agricultural land | Terrestrial environments including water bodies and coastlines | shallow coral reefs | Wetlands | created, restored and enhanced wetlands | Lake Michigan waterfront | Lake Michigan & Lake Erie waterfront | None | small stream |
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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 corresponds to the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 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 | None | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Not applicable |
Other (Comment) ?Comment:Coho salmon stock |
Not applicable | Species | Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | None | Species |
Taxonomic level and name of organisms or groups identified
| EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
| None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available | None Available |
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EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
| EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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None | None | None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
| EM-63 | EM-87 | EM-104 |
EM-112 |
EM-177 |
EM-193 |
EM-397 |
EM-416 | EM-428 | EM-449 |
EM-496 |
EM-650 | EM-653 | EM-698 | EM-706 |
EM-729 |
EM-886 | EM-893 | EM-946 | EM-993 |
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None |
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None |
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None | None |
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None |
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None |
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None | None | None | None |
Comment:Model identifies toxicant concentrations relative to the known LC50 for coho juveniles which is 95ng/L (Spromber and Scholz, 2011; |
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