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-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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EM Short Name
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Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | ARIES carbon, Puget Sound Region, USA | HexSim v2.4, San Joaquin kit fox, CA, USA | Decrease in wave runup, St. Croix, USVI | Value of finfish, St. Croix, USVI | Yasso 15 - soil carbon model | EnviroAtlas - Restorable wetlands | EnviroAtlas-Carbon sequestered by trees | InVEST fisheries, lobster, South Africa | VELMA v2.0, Ohio, USA | SolVES, Shoshone NF, WY | Floral resources on landfill sites, United Kingdom | WESP: Marsh and open water, ID, USA | Wildflower mix supporting bees, Florida, USA | Bird abundance on restored landfills, UK | COVID-19 space disease model, Oregon, USA |
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EM Full Name
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Reduction in pesticide runoff risk, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | ARIES (Artificial Intelligence for Ecosystem Services) Carbon Storage and Sequestration, Puget Sound Region, Washington, USA | HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA | Decrease in wave runup (by reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | Yasso 15 - soil carbon | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | SolVES, Social Values for Ecosystem Services, Shoshone National Forest, WY | Floral resources on landfill sites, East Midlands, United Kingdom | WESP: Deepwater marsh and open Water waterfowl habitat, Idaho, USA | Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA | Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK | HexSim- Adding space to Covid19 disease model Oregon, USA |
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EM Source or Collection
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None | US EPA | ARIES | US EPA | US EPA | US EPA | None | US EPA | EnviroAtlas | US EPA | EnviroAtlas | i-Tree | InVEST | US EPA | None | None | None | None | None | US EPA |
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EM Source Document ID
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255 | 137 | 302 |
337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
335 | 335 |
342 ?Comment:Webpage pdf users manual for model. |
262 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
369 | 389 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
400 | 406 | 500 |
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Document Author
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Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | US EPA Office of Research and Development - National Exposure Research Laboratory | US EPA Office of Research and Development - National Exposure Research Laboratory | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Murphy, C. and T. Weekley | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Schumaker, N. and S.M. Watkins |
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Document Year
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2012 | 2011 | 2014 | 2015 | 2014 | 2014 | 2016 | 2013 | 2013 | 2018 | 2018 | 2014 | 2013 | 2012 | 2015 | 2011 | 2021 |
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Document Title
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Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Yasso 15 graphical user-interface manual | EnviroAtlas - National | EnviroAtlas - Featured Community | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities | Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA |
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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 | Other or unclear (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 | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Not applicable | Published on US EPA EnviroAtlas website | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
| Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | http://aries.integratedmodelling.org/ | http://www.hexsim.net/ | Not applicable | Not applicable |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?Comment:User's manual states that the software will be downloadable at this site. |
https://www.epa.gov/enviroatlas | https://www.epa.gov/enviroatlas | https://www.naturalcapitalproject.org/invest/ | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | www,hexsim.net | |
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Contact Name
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Sven Lautenbach | Yongping Yuan | Ken Bagstad | Theresa M. Nogeire | Susan H. Yee | Susan H. Yee | Jari Liski | EnviroAtlas Team | EnviroAtlas Team | Michelle Ward | Heather Golden | Benson Sherrouse | Sam Tarrant | Chris Murphy | Neal Williams | Lutfor Rahman | Nathan Schumaker |
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Contact Address
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Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Geosciences and Environmental Change Science Center, US Geological Survey | School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | Not reported | Not reported | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | Department of Fisheries and Wildlife, Oregon State University Corvallis, Corvallis, OR 97331, USA |
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Contact Email
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sven.lautenbach@ufz.de | yuan.yongping@epa.gov | kjbagstad@usgs.gov | tnogeire@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | jari.liski@ymparisto.fi | enviroatlas@epa.gov | enviroatlas@epa.gov | m.ward@uq.edu.au | Golden.Heather@epa.gov | bcsherrouse@usgs.gov | sam.tarrant@rspb.org.uk | chris.murphy@idfg.idaho.gov | nmwilliams@ucdavis.edu | lutfor.rahman@northampton.ac.uk | schumakn@onid.oregonstate.edu |
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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Summary Description
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AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | 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" | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We quantified carbon sequestration and storage in vegetation and soils using Bayesian models (Bagstad et al. 2011) calibrated with Moderate-resolution Imaging Spectroradiometer Net Primary Productivity (MODIS GPP/NPP Project, http://secure.ntsg.umt. edu/projects/index.php/ID/ca2901a0/fuseaction/prohttp://www.whrc.org/ational Bwww.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627)vey Geographic Dahttp://www.geomac.gov/index.shtml)wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627) soils data, respectively. By overlaying fire boundary polygons from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov/index.shtml) we estimated carbon storage losses caused by wildfire, using fuel consumption coefficients from Spracklen et al. (2009) and carbon pool data from Smith et al. (2006). By incorporating the impacts of land-cover change from urbanization (Bolte and Vache 2010) within carbon models, we quantified resultant changes in carbon storage." | ABSTRACT: "...Here, we use an individual-based population model to assess potential population-wide effects of rodenticide exposures on the endangered San Joaquin kit fox (Vulpes macrotis mutica). We estimate likelihood of rodenticide exposure across the species range for each land cover type based on a database of reported pesticide use and literature…" AUTHOR'S DESCRIPTION: "We simulated individual kit foxes across their range using HexSim [33], a computer modeling platform for constructing spatially explicit population models. Our model integrated life history traits, repeated exposures to rodenticides, and spatial data layers describing habitat and locations of likely exposures. We modeled female kit foxes using yearly time steps in which each individual had the potential to disperse, establish a home range, acquire resources from their habitat, reproduce, accumulate rodenticide exposures, and die." "Simulated kit foxes assembled home ranges based on local habitat suitability, with range size inversely related to habitat suitability [34,35]. Kit foxes aimed to acquire a home range with a target score corresponding to the observed 544 ha home range size in the most suitable habitat [26]. Modeled home ranges varied in size from 170 ha to 1000 ha. Kit foxes were assigned to a resource class depending on the quality of the habitat in their acquired home range. The resource class then influenced rates of kit fox survival," "Juveniles and adults without ranges searched for a home range across 30 km2 outside of their natal range, using HexSim’s ‘adaptive’ exploration algorithm [33]." | 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: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | ABSTRACT: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | 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, social value information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.” | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML) | 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: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." | ABSTRACT: "We selected the COVID-19 outbreak in the state of Oregon, USA as a system for developing a general geographically nuanced epidemiological forecasting model that balances simplicity, realism, and accessibility. Using the life history simulator HexSim, we inserted a mathematical SIRD disease model into a spatially explicit framework, creating a distributed array of linked compartment models. Our spatial model introduced few additional parameters, but casting the SIRD equations into a geographic setting significantly altered the system’s emergent dynamics. Relative to the non-spatial model, our simple spatial model better replicated the record of observed infection rates in Oregon. We also observed that estimates of vaccination efficacy drawn from the non-spatial model tended to be higher than those obtained from models that incorporate geographic variation. Our spatially explicit SIRD simulations of COVID-19 in Oregon suggest that modest additions of spatial complexity can bring considerable realism to a traditional disease model. |
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Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | None identified | None identified | None identified | None identified | None identified | None Identified | None identified | Future rock lobster fisheries management | None identified | None | None identified | None identified | None identrified | None identified | None |
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Biophysical Context
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Not applicable | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Not applicable | No additional description provided | No additional description provided | No additional description provided | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | Rocky mountain conifer forests | No additional description provided | restored, enhanced and created wetlands | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). | spatial density affecting transmission rates |
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EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | N/A | No scenarios presented | Sites, function or habitat focus | Varied wildflower planting mixes of annuals and perennials | No scenarios presented | None reported |
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
Method + Application | Method + Application | Method Only | 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 | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing 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 | WESP Deepwater Marsh | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
Doc-142 | Doc-303 | Doc-305 | Doc-328 | Doc-327 | Doc-2 | Doc-335 | None | Doc-343 | Doc-344 | None | Doc-345 | None | Doc-13 | Doc-366 | Doc-369 | None | Doc-390 | None | None | None |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | None | None | EM-403 | EM-98 | EM-447 | None | EM-467 | EM-469 | EM-480 | EM-485 | None | None | None | EM-375 | EM-377 | EM-378 | EM-884 | EM-883 | EM-887 | EM-629 | EM-628 | EM-709 | EM-718 | EM-729 | EM-743 | EM-756 | EM-757 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-751 | EM-768 | EM-796 | EM-797 | EM-804 | EM-805 | EM-806 | EM-812 | EM-814 | EM-837 | None |
EM Modeling Approach
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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EM Temporal Extent
em.detail.tempExtentHelp
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2000 | 1980-2006 | 1950-2007 | 60 yr | 2006-2007, 2010 | 2006-2007, 2010 | Not applicable | 2006-2013 | 2010-2013 | 1986-2115 | Jan 1, 2009 to Dec 31, 2011 | 2004-2008 | 2007-2008 | 2010-2013 | 2011-2012 | Not applicable | 2021 |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | past time | Not applicable | Not applicable | past time | past time | Not applicable | past time |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Not applicable | Year | Not applicable | Day |
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Not applicable | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Multiple unrelated locations (e.g., meta-analysis) | Geopolitical |
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Spatial Extent Name
em.detail.extentNameHelp
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EU-27 | East Fork Kaskaskia River watershed basin | Puget Sound Region | San Joaquin Valley, CA | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Not applicable | conterminous United States | Durham NC and vicinity | Table Mountain National Park Marine Protected Area | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | National Forest | East Midlands | Wetlands in Idaho | Agricultural plots | East Midland | Oregon |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | Not applicable | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10-100 ha | 1000-10,000 km^2. | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 |
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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:Census block groups |
spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | 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) | area, for pixel or radial feature |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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10 km x 10 km | 1 km^2 | 200m x 200m | 14 ha | 10 m x 10 m | 10 m x 10 m | Not applicable | irregular | irregular | Not applicable | 10m x 10m | 30m2 | multiple unrelated locations | Not applicable | Not applicable | multiple unrelated sites | 21.65 ha |
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Numeric | Numeric | Analytic | Numeric | Numeric | Analytic | Numeric |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Yes | Unclear | Yes | Yes | Not applicable | No | No | No | Yes | No | Not applicable | No | No | Not applicable | No |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | No | Not applicable | No | No | No |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
Yes | Not applicable | No | No | Not applicable | No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None | None | None |
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None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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Yes | Yes | No | No | Yes | Yes | Not applicable | No | No |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Yes | No | Not applicable | No | No | Not applicable | Yes |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Yes | No | No | No | No | Not applicable | No | No | No | No | No | Not applicable | No | No | Not applicable | Unclear |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Unclear | No | Yes | No | No | Not applicable | No | No | No | No | No | Not applicable | No | No | Not applicable | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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None | None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
| None | None | None | None |
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None | None | None |
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None | 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-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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Centroid Latitude
em.detail.ddLatHelp
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50.53 | 38.69 | 48 | 36.13 | 17.73 | 17.73 | Not applicable | 39.5 | 35.99 | -34.18 | 39.19 | 43.98 | 52.22 | 44.06 | 29.4 | 52.22 | 43.8 |
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Centroid Longitude
em.detail.ddLongHelp
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7.6 | -89.1 | -123 | -120 | -64.77 | -64.77 | Not applicable | -98.35 | -78.96 | 18.35 | -84.29 | 109.52 | -0.91 | -114.69 | -82.18 | -0.91 | 120.55 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Estimated |
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Forests | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Grasslands | Scrubland/Shrubland | Tundra | Agroecosystems | Created Greenspace | Atmosphere | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Forests | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Created Greenspace | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Streams and near upstream environments | Row crop agriculture in Kaskaskia river basin | Terrestrial environment surrounding a large estuary | Agricultural region (converted desert) and terrestrial perimeter | Coral reefs | Coral reefs | Not applicable | Terrestrial | Urban and vicinity | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Mixed land cover suburban watershed | Montain forest | restored landfills and grasslands | created, restored and enhanced wetlands | Agricultural landscape | restored landfills and conserved grasslands | urban settings |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser 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 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 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 corresponds to the Environmental Sub-class | 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-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Guild or Assemblage | Species | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
| None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available |
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None Available | None Available |
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None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
| EM-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
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None |
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None |
<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-94 | EM-97 | EM-317 |
EM-422 |
EM-450 | EM-462 | EM-466 | EM-492 | EM-493 |
EM-541 |
EM-605 |
EM-626 |
EM-697 |
EM-734 |
EM-784 |
EM-836 | EM-1050 |
| None |
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None | None |
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None | None | None |
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None | None |
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None | None |
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