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Anders Bignert, Dep. of Contaminant Research 06.10.27 Randomization and stratification — a model analysis at different variation scenarios Swedish Museum of Natural History

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Tidsserier Timeseries

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SD(lr) = 0.35 Power as a function of slope (annual change in %) at log-linear regression analysis at a residual standard deviation on a log-scale of 0.35, assuming normally distributed residuals. The graphs, from left to right, represent sampling every, every-second, third and fourth year, respectively based on Monte Carlo simulations at 10,000 runs. Sampling frequency Sampling period = 12 years

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Spatial monitoring Objectives? Estimate mean and variance Regional differences Spatial trends Level in relation to class limit

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Figur II. Variogram som visar hur skillnaden mellan prov (CB-118, pg/g färskvikt i strömmingsmuskel från Bottenhavet) ökar med ökande avstånd. Sample variogram

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N of species – n of samples (nets) Randomization technique

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Sampling design Random design Unaligned square lattice design Sobol sequence

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CV = 10% Distance to class limit: 25, 15, 10, 5%

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CV=20%, normal distributed Distance to class limit: 30, 25, 20, 15, 10% 10 > 34

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CV=20%, Log-normal distribution Distance to class limit: 30, 25, 20, 15, 10% 34 > 50

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75 > 53 random sampling Sobol sequence

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random sampling ”Stratified” sampling 100 > 65

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35% (normally distributed) variation were added (> total variation = 39 %) True mean = 2.5 pg/g. Distance to 4.0, 3.5 and 3.0 pg/g (37.5%, 28%, 17%)

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Tolerance limit Afla-toxines in figs

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PCDD/DF - TEQ Herring, geometric mean Muscle

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PCDD/DF- TEQ Herring, 95% conf. int. Muscle

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PCDD/DF- TEQ Herring, 95% pop int. Muscle

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