Imperfect information: Status classification in a world of uncertainty

Transcription

1 WDMSS conference, Malmö 6-7 May 2015 Imperfect information: Status classification in a world of uncertainty Jacob Carstensen Department of Bioscience Aarhus University Mats Lindegarth Department of Biological and Environmental Science Gothenburg University

2 Decision-making is inherently uncertain!

3 Why bother about uncertainty? WFD classification and uncertainty Benefit-of-doubt (polluters option) Face-value (sharing option) Fail-safe (environmental option) CIS guideline #7

4 How can we determine the confidence in status classification?

5 How can we determine the confidence in status classification?

6 Chlorophyll a (µg L -1 ) The simple and convenient approach 10 8 Station 3 m depth Station 7 m depth Spreadsheet solution Mean = 1.61 µg L -1 Mean = 1.52 µg L -1 St.dev. = 1.56 µg L -1 St.dev. = 1.35 µg L -1

7 Chlorophyll a (µg L -1 ) The simple and convenient approach 10 8 Station 3 m depth Station 7 m depth Spreadsheet solution Mean = 1.61 µg L -1 Mean = 1.52 µg L -1 St.dev. = 1.56 µg L -1 St.dev. = 1.35 µg L -1 Mean = 1.56 µg L -1 St.dev. = 1.46 µg L -1

8 Chlorophyll a (µg L -1 ) The simple and convenient approach 10 8 Station 3 m depth Station 7 m depth Spreadsheet solution Mean = 1.61 µg L -1 Mean = 1.52 µg L -1 St.dev. = 1.56 µg L -1 St.dev. = 1.35 µg L -1 Mean = 1.56 µg L -1 St.dev. = 1.46 µg L -1

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10 The uncertainties to be considered Fixed Random

11 Including fixed factors to reduce random variation 20% 70% 10%

12 Including fixed factors to reduce random variation 70% 95% 20% 10% 4.5% 0.5%

13 Chlorophyll a (µg L -1 ) Reducing uncertainty by including seasonal variation 10 1 Station 3 m depth Station 7 m depth Rel. Uncertainty: 108% and 113%

14 Chlorophyll a (µg L -1 ) Chlorophyll a (µg L -1 ) Reducing uncertainty by including seasonal variation 10 1 Station 3 m depth Station 7 m depth Rel. Uncertainty: 108% and 113% Rel. Uncertainty (season): 101% and 99% Station 3 m depth Station 7 m depth Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

15 Chlorophyll a (µg L -1 ) Chlorophyll a (µg L -1 ) Reducing uncertainty by including interannual variation 10 1 Station 3 m depth Station 7 m depth Station 3 m depth Station 7 m depth 0 Jan2007 Feb Mar 2008Apr May 2009Jun Jul 2010 Aug Sep 2011Oct Nov 2012 Dec Rel. Uncertainty: 108% and 113% Rel. Uncertainty (season) 101% and 99% Rel. Uncertainty (season & year): 93% and 95%

16 Chlorophyll a (µg L -1 ) Splitting the residual variation between random components 10 1 Station 3 m depth Station 7 m depth Rel. Uncertainty (season & year): 93% and 95% Component VAR SE Rel. Uncertainty Station 0 0 0% Station month 0 0 0% Station year 0 0 0% year month % Residual %

17 Chlorophyll a uncertainty components Component VAR SE Rel. Unc. Station % Station month % Station year % year % year month % Residual % Total variation % To reduce uncertainty it is important to focus on the largest sources

18 Another example: Eelgrass shoot density

19 Another example: Eelgrass shoot density Area Site # stations # years # divers # observations North Sound Central Sound South Sound Vitsandsbrygga Höganäs Landskrona Bjärred Lomma Limhamn Bunkeflo Klagshamn Bredgrund Ö. Haken Lilla Hammar South coast Segelskär Fredshög

20 Model 1: Analysis of individual sites Area Site V[GRADIENT] V[YEAR] V[PERSON V[G Y] V[PATCHINESS] ] North Vitsandsbrygga Sound Höganäs Central Landskrona Sound Bjärred Lomma Limhamn South Bunkeflo Sound Klagshamn South coast Eelgrass shoot density Bredgrund Ö. Haken Lilla Hammar Segelskär Fredshög

21 Shoot density (m -2 ) Shoot density (m -2 ) odel 2: all sites combined A) B) Component VAR SE Rel. Unc. YEAR % STATION % YEAR STATION % PERSON % PATCHINESS % Total variation %

22 Shoot density (m -2 ) Shoot density (m -2 ) Model 3: Explaining the gradient by depth A) B) Component VAR SE Rel. Unc. YEAR % STATION % YEAR STATION % PERSON % PATCHINESS % Total variation %

23 Estimating indicator variance crossed design Estimation of indicator variance crossed design Number of years Number of stations Number of replicates

24 Uncertainties at the indicator level propagate through the integrated assessment

25 Uncertainties at the indicator level propagate through the integrated assessment

26 Uncertainties at the indicator level propagate through the integrated assessment

27 Conclusions BQE indicators are influenced by many different sources of uncertainty that must be considered Estimation of variance components with a reasonable precision requires a large dataset with a structure that allows for identification of these components The uncertainty associated with the various variance components can be reduced by including fixed, explanatory factors in the model Uncertainties from indicators propagate through the integrated assessment and this allows for estimating the confidence in classification and the risk of misclassification

28 So, how do I calculate uncertainties in my spreadsheet?

29 So, how do I calculate uncertainties in my spreadsheet? YOU DON T

30 Uncertainty

31 Uncertainty Uncertainty

32 Uncertainty Uncertainty