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Asm2018 4 y.yang

Using uncertainty analysis to provide sampling guidance in monitoring fish and loons for mercury pollution

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Asm2018 4 y.yang

  1. 1. Using uncertainty analysis to provide sampling guidance in monitoring fish and loons for mercury pollution Yang Yang1*, Ruth Yanai1, Charley Driscoll2, Geoffrey Miller2, Nina Schoch3, David Evers4 State University of New York-ESF1, Syracuse University2 Adirondack Center for Loon Conservation3, Biodiversity Research Institute4
  2. 2. https://www.google.com/search?q=no+fishing+mercury+sign&rlz=1C1CHZL_enU S732US732&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiEsaDBxqjaAhUCsl MKHflQDtQQ_AUICygC&biw=1366&bih=662#imgrc=DF16_FEyaNFOQM:
  3. 3. Adirondack Fish Monitoring Program 1992-2016 106 lakes Long-term Resurveyed 43 lakes 2000s vs. 2010s
  4. 4. Adirondack Loon Monitoring Program 111 lakes from 1998 to current https://www.google.com/search?rlz &q=loon&oq=loon&gs_l= https://www.google.com/search
  5. 5. Calculation of minimum detectable changes Minimum detectable change (MDC): A statistical estimate of the smallest amount of change that can be significantly detected EPA Technical Memorandum #3
  6. 6. Calculation of minimum detectable changes A. Detecting a change between two points of a time Two sample t-test Paired t-test B. Detecting changes over a time series 43 resurveyed lakes in 2000s vs. 2010s 111 lakes from 1998 to 2016
  7. 7. MDCs using paired/two sample t-test N = sample size S2 = pooled variance Z = standard normal probabilities Objectives: how many lakes do we need? Resurveyed 43 lakes 2000s vs. 2010s
  8. 8. MDCs varied by species
  9. 9. Changes over time N = number of years CV = temporal variation T = the test statistic of the T-test Objectives: how many lakes per year and how many years? 111 lakes from 1998 to 2016 a. for 5-25 lakes and 5-20 years Resampling from 886 observations CV = temporal variation b. with 5000 iterations
  10. 10. Other methods of uncertainty analysis Statistical programsPower analysis ‘fishmethods’ package (Nelson 2015) in R Green, P., and MacLeod, C. J. 2016. SIMR: an R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution. 7: 493-498 Fitting GLM model Setting a changing rate Adjusting sample size
  11. 11. Acknowledgement Field and laboratory work Financial support The Wild Center The Raquette River Advisory Council And more and more…

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