national descriptive model of mercury in fish tissue
DESCRIPTION
National Descriptive Model of Mercury in Fish Tissue. Stephen P. Wente Mounds View, MN. U.S. Department of the Interior U.S. Geological Survey. Sampling for Mercury in Fish. Spatial variation Temporal variation Fish consumption advisories. Problem 1: Cost of Information. - PowerPoint PPT PresentationTRANSCRIPT
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National Descriptive Model of Mercury in Fish TissueStephen P. WenteMounds View, MNU.S. Department of the Interior U.S. Geological Survey
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Sampling for Mercury in Fish Spatial variationTemporal variationFish consumption advisories
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Problem 1: Cost of Information
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Possible Sample DesignsCollect fish samples with consistent characteristicsSpecies confined to definite rangesCannot compare to existing dataUse existing dataMust use model to make samples comparableVariations in quality of existing studies
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Problem 2: Difficult Comparisons
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Fish Mercury (Hg) Model DetailsRegression method (Covariance model)Accounts for:Less than detection limit valuesSpecies (Hg increases with trophic position)Tissues sampled (skin-off fillet > skin-on > whole fish)Fish length (larger fish are higher in Hg)Calibrated to national dataset (N=45,605)
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Simple yet Realistic DescriptionLog-log SpaceArithmetic Space
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Consumption Advisory
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Standardize Sample Type
Sampling EventsSpecies1234567AXXXBXXXXXCXXXXDXXXX
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Modeled Spatial Variation(14 inch Largemouth Bass Skin-off Fillets)
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Northwestern US Fish Hg Trends:NCBP Data19651990(NCBP = National Contaminant Biomonitoring Program)
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Analytical Cost Reduction*Sample types is number of species size classes tissue types
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Model Reduces Compliance Costs by Reducing Uncertainty
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Project Website (emmma.usgs.gov)
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Benefits of National ModelConsistent nationwide interpretation of many independent monitoring effortsMore comprehensive fish consumption advisoriesBetter description of spatial and temporal trendsReduced analytical and compliance costsContact: Steve Wente ([email protected])
2004 Mercury Workshop in Reston2004 Mercury Workshop in RestonSlopes become exponents describing curvatureIntercepts become multiplication factorsError has a log-normal distribution