A combined judgment and random sample planning tool to support biological sampling using VSP

1. A combined judgment and random sample planning tool to support biologicalsampling using VSP David Graham Microbial Ecology & Physiology Group Biosciences Division Oak Ridge National Laboratory ASP 2013 Workshop September 25, 2013

2. Common adaptive sampling workflow

3. Microbial Distribution

4. Common Sampling Strategies Samples required: Low (L), Medium (M) or High (H) Advanced training or collector knowledge required to select samples (Y/N) Statistical confidence of finding positive site can be estimated (Y/N) Uses expert and collector knowledge (Y/N) Sampling error likelihood: Low (L), Medium (M) or High (H)

5. Underlying Premises • VSP has many features; Allow flexibility from novice to sophisticated/experienced VSP users. Need a simple interface and a workflow tailored for microbial sampling. • To determine number of samples required, must define the sampling objective and statistical parameter(s) of interest • VSPlay introduces more specific guidance for judgment sampling in a combined judgment-random sampling plans prescribed by VSP. • Easy to evaluate the effects of changing confidence intervals and judgment-random sampling proportions on sample numbers and coverage • Quick calculator to estimate statistical confidence based on number of samples

6. VSPlay A functional prototype tool for the design of microbial sampling plans. Written in VBScript. It combines judgment and random sampling methods. It integrates statistical and graphical features of VSP with decision support tools from Playbook. The program produces a sampling plan report and recommendations for collecting judgment and random samples. It is simple.

7. Combined Judgment-RandomSampling Plan Workflow • Develop site representation in VSP and define sampling area. • Select sampling scenario. • Select target microbe and describe sampling areas. • Explore & select relevant objects for each area from prioritized visual menu. • Program calculates recommended number of random samples based on confidence requirements and judgment samples. • Accept or adjust proposed number of samples • Report produced documenting sampling plan and statistical confidence • User takes samples • Post-sampling analysis

8. Playbook • A decision support tool for prioritizing and selecting judgment samples. • Decision analysis matrices - Prediction • Agents, Sampling Areas, Objects • Guidance for object sampling – Training & Reference • Based on prior research, SME opinion • Framework for mission-specific applications

9. Sample Decision Matrix For each factor, the agent or area is assigned a score: 1 (not likely) 2 (likely) 3 (very likely)

10. Object SamplingGuidance

11. Object SamplingGuidance

12. Scenario Sampling Objectives Background What is the proportion of possible samples that contain the organism? Statistical Framework: Determine the upper x% confidence limit (UCL) on the estimated proportion. 2. Contaminated Is the organism present, with a high probability of detection? Statistical Framework: Want x% confidence of getting 1 or more contaminated samples if y% of the area is contaminated. 3. Contaminated with Background Is the proportion of contaminated samples greater than expected from background? Statistical Framework: Want high probability of concluding the estimated proportion of contaminated samples is greater than the background UCL

13. Background Sampling Simulation2046 ft2 area, 95% confidence, 3% UCL window

14. Background Sampling Simulation2046 ft2 area, 95% confidence, 1% expected positive

15. Contaminated Site Sampling Simulation2046 ft2 area, 97% area negative, 10 judgment samples

16. Contaminated Site Sampling Simulation2046 ft2 area, 95% confidence, 10 judgment samples

17. Combined Judgment and Random Sampling Uses both weighted judgment samples and unweighted random samples to design a sampling plan with a reduced number of samples and a statistical estimate of confidence. How should we weight judgment samples?

18. Applications across the DOE complex:Mercury methylation Figure from Parks, J. M., A. Johs, M. Podar, R. Bridou, R. A. Hurt, S. D. Smith, S. J. Tomanicek, Y. Qian, S. D. Brown, C. C. Brandt, A. V. Palumbo, J. C. Smith, J. D. Wall, D. A. Elias, and L. Liang. 2013. The Genetic basis for bacterial mercury methylation. Science 339:1332-1335.

19. Applications across the DOE complex:Anaerobic Hg(0) oxidation & Hg(II) methylation Figure from Hu, H., H. Lin, W. Zheng, S. J. Tomanicek, A. Johs, X. Feng, D. A. Elias, L. Liang, and B. Gu. 2013. Oxidation and methylation of dissolved elemental mercury by anaerobic bacteria. Nature Geosci. 6:751-754.

20. Applications across the DOE complex:Identifying Hg methylation potential • Adaptive management • Statistical framework includes learning, iterative improvement • Stressor identification • Sampling for Hg oxidation and methylation activity as well as Hg abundance and speciation • Combining random and judgment sampling Figure from Hu, H., H. Lin, W. Zheng, S. J. Tomanicek, A. Johs, X. Feng, D. A. Elias, L. Liang, and B. Gu. 2013. Oxidation and methylation of dissolved elemental mercury by anaerobic bacteria. Nature Geosci. 6:751-754.

21. Acknowledgments Stanton Martin Andrea Sjoreen Craig Brandt April McMillan Anthony Palumbo Tommy Joe Phelps Brent Pulsipher John Wilson Karen Wahl Mary Lancaster David Wunschel ORNL Hg Science Focus Area Task Leads Liyuan Liang Scott Brooks Dwayne Elias Baohua Gu Jeremy Smith