EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | EnviroAtlas - Natural biological nitrogen fixation | Soil carbon and plant traits, Central French Alps | Stream nitrogen removal, Mississippi R. basin, USA | AnnAGNPS, Kaskaskia River watershed, IL, USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | SPARROW, Northeastern USA | Land-use change and wildlife products, Europe | Blue crabs and SAV, Chesapeake Bay, USA | Coral and land development, St.Croix, VI, USA | Coastal protection, Europe | Decrease in wave runup, St. Croix, USVI | SolVES, Pike & San Isabel NF, WY | Hunting recreation, Wisconsin, USA | Alwife phosphorus flux in lakes, Connecticut, USA | Alewife nutrients in stream food web, CT, USA | Estuary visitation, Cape Cod, MA | Fish species richness, St. John, USVI, USA | ESII Tool, Michigan, USA | WESP: Marsh & wet meadow, ID, USA | Arthropod flower preference, CA, USA | Neighborhood greenness and health, FL, USA | ETDOT carbon contaminant removal | N-SPECT, Sediment and runoff, Isfahan, Iran |
EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Soil carbon potential estimated from plant functional traits, Central French Alps | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | SPARROW (SPAtially Referenced Regressions On Watershed Attributes), Northeastern USA | Land-use change effects on wildlife products, Europe | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | Coral colony density and land development, St.Croix, Virgin Islands, USA | Coastal protection, Europe | Decrease in wave runup (by reef), St. Croix, USVI | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | Hunting recreation, Wisconsin, USA | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Alewife derived nutrients in stream food web, Connecticut, USA | Value of recreational use of an estuary, Cape Cod, Massachusetts | Fish species richness, St. John, USVI, USA | ESII (Ecosystem Services Identification and Inventory) Tool, Michigan, USA | WESP: Seasonally flooded marsh & wet meadow, Idaho, USA | Arthropod flower type preference, California, USA | Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA | nvironmental Technologies Design Option Tool for contaminant removal | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran |
EM Source or Collection
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Envision | US EPA | EnviroAtlas | EU Biodiversity Action 5 | US EPA | US EPA | US EPA | US EPA | EU Biodiversity Action 5 | None | US EPA | EU Biodiversity Action 5 | US EPA | None | None | None | None | US EPA | None | None | None | None | None | US EPA | None |
EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
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. |
260 | 52 | 137 | 275 | 86 | 228 |
292 ?Comment:Conference paper |
96 | 296 | 335 | 369 | 376 | 383 | 384 | 387 | 355 |
392 ?Comment:Document 391 is an additional source for this EM. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
399 | 417 | 475 | 480 |
Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Hill, B. and Bolgrien, D. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Moore, R. B., Johnston, C.M., Smith, R. A. and Milstead, B. | Haines-Young, R., Potschin, M. and Kienast, F. | Mykoniatis, N. and Ready, R. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Qiu, J. and M. G. Turner | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Walters, A. W., R. T. Barnes, and D. M. Post | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Guertin, F., K. Halsey, T. Polzin, M. Rogers, and B. Witt | Murphy, C. and T. Weekley | Lundin, O., Ward, K.L., and N.M. Williams | Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik | National Center for Clean Industrial and Treatment Technologies at Michigan Technological University (MTU) | Khalili, S., Jamali, A.A., Hasanzadeh, M. and Morovvati, A., |
Document Year
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2008 | 2013 | 2011 | 2011 | 2011 | 2014 | 2011 | 2012 | 2013 | 2011 | 2013 | 2014 | 2014 | 2013 | 2010 | 2009 | 2019 | 2007 | 2019 | 2012 | 2018 | 2016 | 2019 | 2015 |
Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | EnviroAtlas - National | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Source and delivery of nutrients to receiving waters in the northeastern and mid-Atlantic regions of the United States | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Assessment of coastal protection as an ecosystem service in Europe | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Spatial interactions among ecosystem services in an urbanizing agricultural watershed | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | From ash pond to riverside wetlands: Making the business case for engineered natural technologies | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Indentifying native plants for coordinated hanbitat manegement of arthroppod pollinators, herbivores and natural enemies | Neighborhood greenness and chronic health conditions in Medicare beneficiaries | Environmental Technologies Design Option Tool | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran. |
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 | Not formally documented | 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 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 |
Comments on Status
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Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Conference proceedings | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Draft manuscript-work progressing | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript |
EM ID
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.esiitool.com/ | Not applicable | Not applicable | Not applicable | https://github.com/USEPA/Environmental-Technologies-Design-Option-Tool | https://coast.noaa.gov/digitalcoast/tools/qnspect.html | |
Contact Name
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Michael R. Guzy |
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Brian Hill | Yongping Yuan |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Richard Moore | Marion Potschin | Nikolaos Mykoniatis | Leah Oliver | Camino Liquete | Susan H. Yee | Benson Sherrouse | Monica G. Turner | Derek C. West | Annika W. Walters | Mulvaney, Kate | Simon Pittman | Not reported | Chris Murphy | Ola Lundin | Scott C. Brown | David Hokanson | Ali Akbar Jamali |
Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | U.S. Environmental Protection Agency, 27 Tarzwell Drive, Narragansett, Rhode Island 02882 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | National Health and Environmental Research Effects Laboratory | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Not reported | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven CT 06511 | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | 1305 East-West Highway, Silver Spring, MD 20910, USA | Not reported | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Ecology, Swedish Univ. of Agricultural Sciences, Uppsala, Sweden | Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136 | 224 N. Fair Oaks Ave., Floor 2 Pasadena, CA. 91103 | Department of Watershed MGT, Maybod Branch, Islamic Azad University, Maybod, Iran |
Contact Email
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Not reported | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | hill.brian@epa.gov | yuan.yongping@epa.gov | frazier@nceas.ucsb.edu | rmoore@usgs.gov | marion.potschin@nottingham.ac.uk | Not reported | leah.oliver@epa.gov | camino.liquete@gmail.com | yee.susan@epa.gov | bcsherrouse@usgs.gov | turnermg@wisc.edu | derek.west@yale.edu | annika.walters@yale.edu | None reported | simon.pittman@noaa.gov | Not reported | chris.murphy@idfg.idaho.gov | ola.lundin@slu.se | sbrown@med.miami.edu | administrator@trusselltech.com | jamaliaa@maybodiau.ac.ir |
EM ID
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | 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: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The soil carbon ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | AUTHOR'S DESCRIPTION: "SPAtially Referenced Regressions On Watershed attributes (SPARROW) nutrient models were developed for the Northeastern and Mid-Atlantic (NE US) regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the NE US Seaboard indicated that agricultural sources contribute the largest percentage (37%) of the total nitrogen load delivered to the estuaries" | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes…are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Wildlife products); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000." AUTHOR'S DESCRIPTION: "Wildlife products belongs to the service group Biotic Materials in the CICES system; it includes the provisioning of all non-edible raw material products that are gained through non-agricultural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers." | ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | 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, storm damage, or coastal inundation during extreme events...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | AUTHOR'S DESCRIPTION (from Supporting Information): "The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility. Similar assumptions were made for this assessment: larger areas and places with more game species would support more hunting, areas closer to large population centers would be used more than remote areas, and proximity to major roads would increase access and use of an area. We first obtained the boundary of public wild areas from Wisconsin DNR and calculated the amount of areas for each management unit. The number of game species (Spe) for each area was derived from Dane County Parks Division (70). We used the same population density (Pop) and road buffer layer (Road) described in the previous forest recreation section. The variables Spe, Pop, and Road were weighted to ranges of 0–40, 0–40, and 0–20, respectively, based on the relative importance of each in determining this service. We estimated overall hunting recreation service for each 30-m grid cell with the following equation: HRSi = Ai Σ(Spei + Popi +Roadi), where HRS is hunting recreation score, A is the area of public wild areas open for hunting/fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28,000) to a range of 0–100, with 0 representing no hunting recreation service and 100 representing highest service. | ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year. Natural abundance stable isotope analyses indicate that this influx of marine-derived nitrogen is rapidly incorporated into the stream food web. An enriched d15N signal, indicative of a marine origin, is present at all stream trophic levels with the greatest level of enrichment coincident with the timing of the anadromous alewife spawning migration. There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." AUTHOR'S DESCRIPTION: "Here, we examine the effect of alewife-contributed marine- derived nutrients to coastal stream ecosystems in southern New England. We take a comparative approach examining streams with and without anadromous alewife runs. We use natural abundance stable isotope analyses to assess the incorporation of marine-derived nitrogen and carbon into stream food webs." | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | 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." | ABSTRACT: "The 2015 announcement of The Dow Chemical Company's (Dow) Valuing Nature Goal, which aims to identify $1 billion in business value from projects that are better for nature, gives nature a spot at the project design table. To support this goal, Dow and The Nature Conservancy have extended their long-standing collaboration and are now working to develop a defensible methodology to support the implementation of the goal. This paper reviews the nature valuation methodology framework developed by the Collaboration in support of the goal. The nature valuation methodology is a three-step process that engages Dow project managers at multiple stages in the project design and capital allocation processes. The three-step process identifies projects that may have a large impact on nature and then promotes the use of ecosystem service tools, such as the Ecosystem Services Identification and Inventory Tool, to enhance the project design so that it better supports ecosystem health. After reviewing the nature valuation methodology, we describe the results from a case study of redevelopment plans for a 23-acre site adjacent to Dow's Michigan Operations plant along the Tittabawassee River." AUTHOR'S DESCRIPTION: "The ESII Tool measures the environmental impact or proposed land changes through eight specific ecosystem services: (i) water provisioning, (ii) air quality control (nitrogen and particulate removal), (iii) climate regulation (carbon uptake and localized air temperature regulation), (iv) erosion regulation, (v) water quality control (nitrogen and filtration), (vi) water temperature regulation, (vii) water quantity control, and (viii) aesthetics (noise and visual). The ESII Tool allows for direct comparison of the performance of these eight ecosystem services both across project sites and across project design proposals within a site." "The team was also asked to use an iterative design process using the ESII Tool to create alternative restoration scenarios…The project team developed three alternative restoration designs: i) standard brownfield restoration (i.e., cap and plant grass) on the ash pond and 4-D property (referred to as SBR); ii) ecological restoration (i.e., excavate ash and associated soil for secured disposal in approved landfill and restore historic forest, prairie, wetland) of the ash pond only, with SBR on the 4-D property (referred to as ER); and iii) ecological restoration on the ash pond and 4- D property (referred to as ER+)." | 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: " Plant species differed in attractiveness for each arthropod functional group. Floral area of the focal plant species positively affected honeybee, predator, and parasitic wasp attractiveness. Later bloom period was associated with lower numbers of parasitic wasps. Flower type (actinomorphic, composite, or zygomorphic) predicted attractiveness for honeybees, which preferred actinomorphic over composite flowers and for parasitic wasps, which preferred composite flowers over actinomorphic flowers. 4. Across plant species, herbivore, predator, and parasitic wasp abundances were positively correlated, and honeybee abundance correlated negatively to herbivore abundance. 5. Synthesis and applications. We use data from our common garden experiment to inform evidence-based selection of plants that support pollinators and natural enemies without enhancing potential pests. We recommend selecting plant species with a high floral area per ground area unit, as this metric predicts the abundances of several groups of beneficial arthropods. Multiple correlations between functionally important arthropod groups across plant species stress the importance of a multifunctional approach to arthropod habitat management. " Changes in arthropod abundance were estimated for flower type (entered as separate runs); Actinomorphic, Composite, Zygomorphic. 43 plant species evaluated included Amsinckia intermedia, Calandrinia menziesii, Nemophila maculata, Nemophila menziesii, Phacelia ciliata, Achillea millefolium, Collinsia heterophylla, Fagopyrum esculentum, Lasthenia fremontii, Lasthenia glabrata, Limnanthes alba, Lupinus microcarpus densiflorus, Lupinus succelentus, Phacelia californica, Phacelia campanularia, Phacelia tanacetifolia, Salvia columbariae, Sphaeralcea ambigua, Trifolium fucatum, Trifolium gracilentum, Antirrhinum conutum, Clarkia purpurea, Clarkia unguiculata, Clarkia williamsonii, Eriophyllum lanatum, Eschscholzia californica, Monardella villosa, Scrophularia californica, Asclepia eriocarpa, Asclepia fascicularis, Camissoniopsis Cheiranthifolia, Eriogonum fasciculatum, Gilia capitata, Grindelia camporum, Helianthus annuus, Lupinus formosus, Malacothrix saxatilis, Oenothera elata, Helianthus bolanderi, Helianthus californicus, Madia elegans, Trichostema lanceolatum, Heterotheca grandiflora." | ABSTRACT: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation." | Authors description: "The Environmental Technologies Design Option Tool (ETDOT) is a suite of software models that provides engineers with the capability to evaluate and design systems that use granular activated carbon or ion exchange resins for the removal of contaminants, including PFAS, from drinking water and wastewater. Information generated from ETDOT models will provide states and utilities with a better understanding of the fundamentals of carbon adsorption and what that means to the operation, performance, and costs associated with this technology. Even though carbon adsorption can be an effective treatment technology for removing organic compounds, such as PFAS, from water, it can be expensive or may not achieve desired removal objectives if improperly designed. Proper full-scale design of this adsorption process typically results from carefully controlled pilot-scale studies that are used to determine important design variables, such as the type of adsorbent, empty bed contact time, and bed configuration. However, these studies can be time consuming and expensive if they are not properly planned. Information generated from the ETDOT models can be used to help design pilot treatment systems and provide a first-cut prediction of full-scale results.] | Identifying and quantifying the runoff and Sediment yield are the necessary measures in the issues of soil erosion in a watershed. Pelasjan watershed located in West of Isfahan and it is one of the sub basins of Zayanderud which is taken as the study area. In this study the amount of runoff and Sediment yield has been evaluated using the Nonpoint-Source Pollution and Erosion Comparison Tools (N-SPECT) model which is an extension to ArcGIS software. The input layer maps in the GIS environment, including land use, the rain erosion, vegetation, soil erodibility, contour map and watershed boundary map were prepared. By entering the input data and running N-SPECT model, runoff and Sediment yield raster maps of the study area were obtained. To evaluate the model and data comparing, the values obtained from the model and the actual data values of runoff and Sediment yield were converted to the eigenvalues. Special amount of runoff from the model equals 1483 m3/ha/year and the actual runoff is equivalent to 1253 m3/ha/year for 21 water years ,from 1991 to 2012. From the values obtained by the model and the actual data it can be concluded that the model is sufficiently accurate for estimating runoff since the actual runoff value and the value obtained from the model are close to each other and statistically, there is no significant difference between them during this 21 water year. In relation to a Sediment yield, the amount obtained from the model was 7.8 ton/ha/year and the average amount of Sediment yield for 21 water years is 2.1 ton/ha/year, which by comparing with the values obtained for Sediment yield it can be concluded that the model overestimates about three times from the actual amount and there is a significant difference between the real data and data obtained by model so the model has not been very successful in Sediment yield estimating. One of the advantages of this model for estimating runoff and Sediment yield is point to point estimation of runoff and Sediment yield in output maps of the region. This model is particularly recommended for harsh and difficult access regions of the watershed. |
Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None Identified | None identified | Not applicable | Not reported | None identified | water-quality assessment, total maximum daily load(TMDL) determination | None identified | Not applicable | Not applicable | Supports global and EU biodiversity policy | None identified | None | None identified | Restoration and management of diadromous fish runs in coastal New England | Nutrients and water quality related to anadromous alewife restoration efforts | None identified | None provided | Use ESII to answer the following business decision question: how can Dow close the ash pond while enhancing ecosystem services to Dow and the community and creating local habitat, for a lesser overall cost to Dow than the option currently defined? | None identified | None reported | None identified | Not Applicable | None provided |
Biophysical Context
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No additional description provided | No additional description provided | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Agricultural landuse , 1st-10th order streams | Upper Mississipi River basin, elevation 142-194m, | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | Norteneastern region (U.S.); Mid-Atlantic region (U.S.) | No additional description provided | Submerged Aquatic Vegetation (SAV), eelgrass | nearshore; <1.5 km offshore; <12 m depth | No additional description provided | No additional description provided | Rocky mountain conifer forests | No additional description provided | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | No additional description provided | None identified | Hard and soft benthic habitat types approximately to the 33m isobath | No additional description provided | restored, enhanced and created wetlands | Mediteranean climate | No additional description provided | Not applicable | Pelasjan watershed, Zagros mountain range |
EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | Not applicable | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | No scenarios presented | Recent historical land-use change from 1990-2000 | Essential or Facultative habitat | Not applicable | No scenarios presented | No scenarios presented | N/A | No scenarios presented | current and historical run size | No scenarios presented | N/A | No scenarios presented | Alternative restoration designs | Sites, function or habitat focus | Arthropod groups | No scenarios presented | Not applicable | No scenarios presented |
EM ID
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | 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 | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application |
New or Pre-existing EM?
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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 | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
Document ID for related EM
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Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
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-260 | Doc-154 | Doc-155 | Doc-142 | None | None | Doc-228 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-227 | None | None | Doc-335 | Doc-369 | None | None | Doc-384 | Doc-383 | None | Doc-355 | Doc-391 | Doc-390 | None | None | None | Doc-473 |
EM ID for related EM
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EM-333 | EM-369 | None | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | None | None | None | None | EM-122 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | EM-106 | None | None | EM-447 | EM-626 | EM-628 | None | EM-667 | EM-672 | EM-674 | EM-673 | EM-667 | EM-661 | EM-682 | EM-684 | EM-685 | EM-590 | EM-698 | EM-712 | EM-718 | EM-734 | EM-743 | None | None | None | EM-1007 | EM-1003 |
EM Modeling Approach
EM ID
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
EM Temporal Extent
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1990-2050 | 2006-2010 | Not reported | 2000-2008 | 1980-2006 | December 2007 - November 2008 |
2002 ?Comment:Several nationwide database development and modeling efforts were necessary to create models consistent with 2002 conditions. |
1990-2000 | 1993-2011 | 2006-2007 | 1992-2010 | 2006-2007, 2010 | 2004-2008 | 2000-2006 | 1960"s and early 2000's | 2005-2006 (March-July) | Summer 2017 | 2000-2005 | Not reported | 2010-2012 | 2015-2016 | 2010-2011 | Not applicable | 1991-2012 |
EM Time Dependence
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time-dependent | time-stationary | 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-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | Not applicable | time-stationary |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
other or unclear (comment) ?Comment:Sampling conducted at discrete time periods during Alewife migration. Three sampling periods are presented in this entry. |
discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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2 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
Bounding Type
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Geopolitical | Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Geopolitical | Physiographic or ecological | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Point or points | Geopolitical | Not applicable | Watershed/Catchment/HUC |
Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | counterminous United States | Central French Alps | Upper Mississippi, Ohio and Missouri River sub-basins | East Fork Kaskaskia River watershed basin | Yaquina Estuary (intertidal), Oregon, USA | NE U.S. Regions | The EU-25 plus Switzerland and Norway | Chesapeake Bay | St. Croix, U.S. Virgin Islands | Shoreline of the European Union-27 | Coastal zone surrounding St. Croix | National Park | Yahara Watershed, Wisconsin | Bride Lake and Linsley Pond | New London County, Connecticut, USA | Three Bays, Cape Cod | SW Puerto Rico, | Dow Midland Operations facility ash pond and Posey Riverside (4-D property) | Wetlands in idaho | Harry Laidlaw Jr. Honey Bee Research facility | Miami-Dade County | Not applicable | Pelasjan watershed |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1-10 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 10-100 ha | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | 10-100 ha | 100,000-1,000,000 km^2 | <1 ha | 1000-10,000 km^2. | Not applicable | 1000-10,000 km^2. |
EM ID
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
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 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 distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | length, for linear feature (e.g., stream mile) | other (habitat type) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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varies | irregular | 20 m x 20 m | 1 km | 1 km^2 | 0.87-104.29 ha | 30 x 30 m | 1 km x 1 km | Not applicable | Not applicable | Irregular | 10 m x 10 m | 30m2 | 30m x 30m | Not applicable | variable stream lengths | beach length | not reported | map unit | Not applicable | Not applicable | Census block | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Not applicable | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | Not applicable | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Comment:Agent based modeling results in response indices. |
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EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | No | No | No | No | Unclear | Yes | No | Yes | Yes | No | Yes | No | No | Yes | Not applicable | Yes | No | Unclear | No | Not applicable | Not applicable | Not applicable | Unclear |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | No |
Yes ?Comment:R-squared of .97 refers to the modelled loading whereas .83 refers to yield (see table 1, pg 972 for more information) |
No | Yes | Yes | No | No | Yes | No | No | Not applicable | No | Yes | No | No | Not applicable | No | Not applicable | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None |
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None | None |
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None | None |
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None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | Yes | No | Yes | No | Yes | No | No | Yes | No | No | No | Not applicable | No | Yes | Unclear | No | Not applicable | No | Unclear | Unclear |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Yes | Yes | No | Unclear | No | Yes | Yes | No | No | No | No | No | Not applicable | No | No | No | No | No | No | Not applicable | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No ?Comment:Sensitivity analysis performed for agent values only. |
No | No | Unclear | Unclear | No | Yes | No | Yes | No | No | No | No | No | Yes | Not applicable | No | Yes | No | No | No | No | Not applicable | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
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None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
None | None | None | None | 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)
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
Centroid Latitude
em.detail.ddLatHelp
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44.11 | 39.5 | 45.05 | 36.98 | 38.69 | 44.62 | 42 | 50.53 | 36.99 | 17.75 | 48.2 | 17.73 | 38.7 | 43.1 | 41.33 | 41.78 | 41.62 | 17.79 | 43.6 | 44.06 | 38.54 | 25.64 | Not applicable | 32.26 |
Centroid Longitude
em.detail.ddLongHelp
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-123.09 | -98.35 | 6.4 | -89.13 | -89.1 | -124.06 | -73 | 7.6 | -75.95 | -64.75 | 16.35 | -64.77 | 105.89 | -89.4 | -72.24 | -72.17 | -70.42 | -64.62 | -84.24 | -114.69 | -121.79 | -80.5 | Not applicable | 50.22 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Provided | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Not applicable | Provided |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Rivers and Streams | Agroecosystems | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | None | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Forests | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Rivers and Streams | Lakes and Ponds | Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Agroecosystems | Created Greenspace | Not applicable | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Agricultural-urban interface at river junction | Terrestrial | Subalpine terraces, grasslands, and meadows. | Not applicable | Row crop agriculture in Kaskaskia river basin | Estuarine intertidal | none | Not applicable | Yes | stony coral reef | Coastal zones | Coral reefs | Montain forest | Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape | Coastal lakes and ponds and associated streams | Coastal streams | Beaches | shallow coral reefs | Ash pond and surrounding environment | created, restored and enhanced wetlands | Agricultural fields | urban neighborhood greenspace | Not applicable | Desert mountains watershed |
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 is coarser 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 is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Yes | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | 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 coarser than that of the Environmental Sub-class | Not applicable | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Community | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Yes | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
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None Available | None Available | None Available | None Available |
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None Available | None Available | 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 |
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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-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
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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-12 ![]() |
EM-63 | EM-83 | EM-93 | EM-97 | EM-103 | EM-104 | EM-123 | EM-185 | EM-194 | EM-320 | EM-450 | EM-629 | EM-655 |
EM-661 ![]() |
EM-672 ![]() |
EM-683 | EM-699 |
EM-713 ![]() |
EM-760 ![]() |
EM-779 ![]() |
EM-876 | EM-1009 | EM-1017 |
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None |
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None |
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None | None | None |
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None |
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None | None |