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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-94 | EM-97 |
EM-102 
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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EM Short Name
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Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | Fish species habitat value, Tampa Bay, FL, USA | ARIES carbon, Puget Sound Region, USA | HexSim v2.4, San Joaquin kit fox, CA, USA | Decrease in wave runup, St. Croix, USVI | Yasso07 v1.0.1, Switzerland | EnviroAtlas - Restorable wetlands | EnviroAtlas-Carbon sequestered by trees | InVEST fisheries, lobster, South Africa | VELMA v2.0, Ohio, USA | WESP: Marsh and open water, ID, USA | Bird abundance on restored landfills, UK | HAWQS model method |
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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 | Fish species habitat value, Tampa Bay, FL, 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 | Yasso07 v1.0.1 forest litter decomposition, Switzerland | 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 | WESP: Deepwater marsh and open Water waterfowl habitat, Idaho, USA | Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK | Hydrologic and water quality system (HAWQS) model v.1.1 user's guide methodology |
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EM Source or Collection
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None | US EPA | US EPA | ARIES | US EPA | US EPA | None | US EPA | EnviroAtlas | US EPA | EnviroAtlas | i-Tree | InVEST | US EPA | None | None | US EPA |
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EM Source Document ID
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255 | 137 | 187 | 302 |
337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
335 | 343 | 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. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
406 | 445 |
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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. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | 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 | Didion, M., B. Frey, N. Rogiers, and E. Thurig | 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 | Murphy, C. and T. Weekley | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | United States Environmental Protection Agency |
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Document Year
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2012 | 2011 | 2016 | 2014 | 2015 | 2014 | 2014 | 2013 | 2013 | 2018 | 2018 | 2012 | 2011 | 2019 |
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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 | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | 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 | Validating tree litter decomposition in the Yasso07 carbon model | 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 | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | 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 | HAWQS 1.0 (Hydrologic and Water Quality System) modeling framework |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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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 | Published journal manuscript | Published on US EPA EnviroAtlas website | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published EPA report |
Software and Access
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EM ID
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
| 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 | http://aries.integratedmodelling.org/ | http://www.hexsim.net/ | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | 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 | https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/GDOPBA | |
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Contact Name
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Sven Lautenbach | Yongping Yuan | Richard Fulford | Ken Bagstad | Theresa M. Nogeire | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
EnviroAtlas Team | EnviroAtlas Team | Michelle Ward | Heather Golden | Chris Murphy | Lutfor Rahman | Raghavan Srinivasan |
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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 | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | 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 | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | 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 | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | Spatial Sciences Laboratory, Dept. of ecology and conservatin Biology, Texas A&M university |
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Contact Email
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sven.lautenbach@ufz.de | yuan.yongping@epa.gov | Fulford.Richard@epa.gov | kjbagstad@usgs.gov | tnogeire@gmail.com | yee.susan@epa.gov | markus.didion@wsl.ch | enviroatlas@epa.gov | enviroatlas@epa.gov | m.ward@uq.edu.au | Golden.Heather@epa.gov | chris.murphy@idfg.idaho.gov | lutfor.rahman@northampton.ac.uk | r-srinivasan@tamu.edu |
EM Description
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EM ID
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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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: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | 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 examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | 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]." | 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: "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)." | Author overview: " The Hydrologic and Water Quality System (HAWQS) is a web-based interactive water quantity and water quality modeling system that employs the internationally-recognized public domain model Soil and Water Assessment Tool (SWAT) as its core modeling engine. HAWQS provides users with: 1) interactive web interfaces and maps and pre-loaded input data; 2) Output data includes tables, charts, graphs, and raw data; 3) A user guide; and 4) Online development, execution, and storage for users modeling projects. HAWQS enables use of SWAT to simulate the effects of management practices based on an extensive array of crops, soils, natural vegetation types, land uses, and climate change scenarios for hydrology and the following water quality parameters: Sediment pathogens, nutrients, biological oxygen demand, dissolved oxygen, pesticides, and water temperature. HAWQS users can select from three watershed scales, or hydrologic unit codes (HUCs)—small (HUC 12), medium (HUC 10), and large (HUC 8)—to run simulations. HAWQS allows for further aggregation and scalability of annual, monthly, and daily estimates of water quality across large geographic areas up to and including the continental United States. The United States Environmental Protection Agency (USEPA) Office of Water (OW) supports and provides project management and funding for HAWQS. The Texas A&M University Spatial Sciences Laboratory and EPA subject matter experts provide ongoing technical support including system design, modeling, and software development. The United States Department of Agriculture (USDA) and Texas A&M University jointly developed SWAT and have actively supported the model for more than 25 years. The system was developed to meet the needs of the USEPA Office of Water. It can also be employed by other Federal Agencies, State and local governments, academics, and contractors. " |
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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 identifed | None identified | None identified | None identified | None identified | None Identified | None identified | Future rock lobster fisheries management | None identified | None identified | None identified | None identified |
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Biophysical Context
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Not applicable | Upper Mississipi River basin, elevation 142-194m, | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | No additional description provided | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | 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. | restored, enhanced and created wetlands | 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). | N/A |
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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 | No scenarios presented | Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
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. | Sites, function or habitat focus | No scenarios presented | N/A |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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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 (multiple runs exist) View EM Runs | 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 (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
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 Only | Method Only |
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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 | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | Application of existing model | New or revised model | WESP Deepwater Marsh | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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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 | None | Doc-303 | Doc-305 | Doc-328 | Doc-327 | Doc-2 | Doc-335 | Doc-342 | Doc-344 | None | Doc-345 | None | Doc-13 | Doc-366 | Doc-390 | None | None |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | None | None | None | EM-403 | EM-98 | EM-447 | EM-466 | EM-469 | EM-480 | EM-485 | None | None | None | EM-375 | EM-377 | EM-378 | EM-884 | EM-883 | EM-887 | 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-837 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
|
EM Temporal Extent
em.detail.tempExtentHelp
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2000 | 1980-2006 | 2006-2011 | 1950-2007 | 60 yr | 2006-2007, 2010 | 1993-2013 | 2006-2013 | 2010-2013 | 1986-2115 | Jan 1, 2009 to Dec 31, 2011 | 2010-2013 | Not applicable | Not applicable |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | 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 | Not applicable | future time | Not applicable | future time | Not applicable | Not applicable | future time | past time | past time | Not applicable | future time |
|
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable |
discrete ?Comment:Time can be in day, month or year increments |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | 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 | Not applicable | Year | Not applicable | Year | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Year |
EM Spatial Extent
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EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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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 | Geopolitical | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Not applicable |
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Spatial Extent Name
em.detail.extentNameHelp
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EU-27 | East Fork Kaskaskia River watershed basin | Tampa Bay | Puget Sound Region | San Joaquin Valley, CA | Coastal zone surrounding St. Croix | Switzerland | 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 | Wetlands in Idaho | East Midland | Not applicable |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10-100 ha | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | Not applicable |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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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 distributed (in at least some 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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all 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 | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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10 km x 10 km | 1 km^2 | 1 km^2 | 200m x 200m | 14 ha | 10 m x 10 m | 5 sites | irregular | irregular | Not applicable | 10m x 10m | Not applicable | multiple unrelated sites | Not applicable |
EM Structure and Computation Approach
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EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Numeric | Numeric | Numeric | Numeric | Analytic | Numeric |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Model Checking Procedures Used
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EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | Yes | Unclear | Yes | No | No | No | No | Yes | 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 | No | No | No | No |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
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 | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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Yes | Yes | No | No | No | Yes | Yes | 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 |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Yes | No | No | No | No | No | No | No | No | No | No | Not applicable | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Unclear | No | No | Yes | No | No | No | No | No | 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 | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
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| EM-94 | EM-97 |
EM-102
|
EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
|
EM-605
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EM-734
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EM-836 | EM-960 |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
|
EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
| None | None |
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None | None |
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None | None | None |
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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-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
|
EM-734
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EM-836 | EM-960 |
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Centroid Latitude
em.detail.ddLatHelp
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50.53 | 38.69 | 27.74 | 48 | 36.13 | 17.73 | 46.82 | 39.5 | 35.99 | -34.18 | 39.19 | 44.06 | 52.22 | Not applicable |
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Centroid Longitude
em.detail.ddLongHelp
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7.6 | -89.1 | -82.57 | -123 | -120 | -64.77 | 8.23 | -98.35 | -78.96 | 18.35 | -84.29 | -114.69 | -0.91 | Not applicable |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Near Coastal Marine and Estuarine | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Forests | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Created Greenspace | Atmosphere | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Inland Wetlands | Created Greenspace | Grasslands | Agroecosystems |
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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 | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Terrestrial environment surrounding a large estuary | Agricultural region (converted desert) and terrestrial perimeter | Coral reefs | forests | Terrestrial | Urban and vicinity | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Mixed land cover suburban watershed | created, restored and enhanced wetlands | restored landfills and conserved grasslands | HUCs |
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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 | Zone within an ecosystem | 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 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
|
EM-734
|
EM-836 | EM-960 |
|
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Species | Not applicable | Individual or population, within a species | Not applicable | Community | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
|
EM-734
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EM-836 | EM-960 |
| None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available |
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None Available | 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.detail.cicesHelp
?
| EM-94 | EM-97 |
EM-102
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EM-317 |
EM-422
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EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
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EM-605
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EM-734
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EM-836 | EM-960 |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-94 | EM-97 |
EM-102
|
EM-317 |
EM-422
|
EM-450 |
EM-467
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EM-492 | EM-493 |
EM-541
|
EM-605
|
EM-734
|
EM-836 | EM-960 |
| None |
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None | None |
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None | None | None |
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None | None | None |