1 / 28

Data comparison - Bioaccumulation – (Lichen transplants)

Data comparison - Bioaccumulation – (Lichen transplants). Background. Lichen transplants:. Background. Previously available “interpretative tool“ for bioaccumulation data from the lichen transplant technique is based on the so-called…. EC ratio : E xposed-to- C ontrol ratio *. “ C ontrol“

mmohr
Download Presentation

Data comparison - Bioaccumulation – (Lichen transplants)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data comparison- Bioaccumulation –(Lichen transplants)

  2. Background Lichen transplants:

  3. Background Previously available “interpretative tool“ for bioaccumulation data from the lichen transplant technique is based on the so-called… EC ratio: Exposed-to-Control ratio * “Control“ YC(e.g., µg g-1) Both should be fully reported in biomonitoring reports Exposed YE(e.g., µg g-1) YE Y: concentration of a generic element in the lichen matrix EC ratio = YC * Frati, L., Brunialti, G., & Loppi, S. (2005). Journal of Atmospheric Chemistry, 52(3), 221-230.

  4. Background Previouslyavailable “interpretative tool“: EC ratio: Exposed-to-Control ratio “A 5-class interpretative scale was elaborated (Table I) based on the deviation of the EC ratio from “normal” conditions, assumed to be ±25% from the ratio of 1. As a rule of thumb, this deviation should account for natural fluctuations in trace element concentrations in the biomonitor (Loppi et al., 2002). Classes of accumulation/loss were based on progressive ±25% deviations.” Frati, L., Brunialti, G., & Loppi, S. (2005). Journal of Atmospheric Chemistry, 52(3), 221-230.

  5. Background Main criticalities EC ratio: Exposed-to-Control ratio • Terminology: “Control” is not a real control; • Species-specificity: It is not even mentioned; • Again, it is a completely expert-assessed scale! • Note. An EC ratio of 1.76 and one of 5.0 are both indication of “severe bioaccumulation”. Does it make sense? • EC = 1.76 → Percent change: + 76% • EC = 5 → Percent change: +400%

  6. Background Main criticalities EC ratio: Exposed-to-Control ratio • Expert-assessed scale: “A 5-class interpretative scale was elaborated (Table I) based on the deviation of the EC ratio from “normal” conditions, assumed to be ±25% from the ratio of 1. As a rule of thumb, this deviation should account for natural fluctuations in trace element concentrations in the biomonitor (Loppi et al., 2002).” Something’s missing… !

  7. Objective Develop BRAND NEW, interpretative scales for bioaccumulation data from lichen transplants, similarly to what was done for native lichens. Preliminary actions A brand new name for the ratio: Exposed-to-Unexposed ratio… EU ratio Literature survey: • How many studies in Italy? • How many studies reported full data (i.e., pre- and post-exposure element concentrations or EC ratios) with TOTAL acid digestion? • How many species? • What about the exposure period (duration)? • Is it possible (are there enough data) to build up period-specific scales?

  8. Literature survey - results • How many Italian studies reporting full data (i.e., pre- and post-exposure element concentrations or EC ratios) with TOTAL acid DIGESTION? 1994 – 2017 (vs. 1980 – 2017 of native lichens) • How many species? 1 2 • Evernia prunastri(1) • c. 20% of data • Pseudevernia furfuracea (2) • c. 80% of data

  9. Towards the dataset - Species comparison EU ratio Pseudevernia furfuracea Evernia prunastri Still comparable distribution shapes…

  10. Towards the dataset – exposure periods • What about the exposure period (duration)? • Are there enough data to build up period-specific scales? Exposure period ranged between 4 and 12 weeks After a preliminary assessment of data distributions, we decided to merge data for… - 6, 8 and 9 weeks; - 11 and 12 weeks. 293 174 164 4 8 12 77 68 6 Henceforth referred to: → 4, 8, 12 weeks

  11. The dataset(s) 789 data, 15 elements, 11 admin. regions 3 period-specific sub-datasets ... … … … …

  12. Data cleaning For the overall dataset • Removal of elements with a number of record below n = 25 that don’t fall in all the selected exposure periods (few cases of elements of scarce environmental concern).. Separately for the three datasets (4, 8 and 12 weeks) • Tukey’s extreme values removal (q3 + 3 IQR)

  13. Percentile thresholds and classes Calculating %iles as class thresholds: %ile thresholds (25th, 75th, 90th, 95th %ile; similarly to Native scale) were separately calculated for each dataset (4, 8 , 12 weeks). Accounting for uncertainty in small size datasets Correction of %ile thresholds for the overall uncertainty of the data series, calculated as… …the ratio between 95% confidence interval (95% C.I. *) and twice the mean value of the series (µ). 95% C.I. u% = % 2µ The u value was used to correct the class thresholds as follows: Confidence interval (CI) *: a type of interval estimate, computed from the statistics of the observed data, that contains the true value of a parameter (e.g. the mean) of the statistical population based on a certain statistical significance.

  14. Percentile thresholds and classes Numerical example - 12 weeks dataset - … …

  15. Percentile thresholds and classes Numerical example - 12 weeks dataset - … … … Result: slight decrease of class thresholds values (cautious, “pro-environment” approach)

  16. Interpretative bioaccumulation scale - Lichen transplants - Increasing EU values * The upper threshold of class 1 (and the lower of class 2) was aprioristically established at the unitary value since this represents the discernibility threshold between the occurrence of bioaccumulation (EU > 1) and its absence (EU ≤ 1). In fact, the offset between the actual value corresponding to the 25th %ile of the EU ratio distribution and the unit is negligible, since it ranges between 0.9 and 1.1.

  17. Class ascription Actually the answer is not so obvious… How to assign bioaccumulation values to a class?

  18. Class ascription A step back… How many lichen samples in the Sampling Units of our Study area? Traditionally… It depends on the sampling strategy However, the best choice would be to transplant a suitable number of lichen samples in order to assess the infra-SU element content variability… This can be easily achieved by transplanting n ≥ 3 samples per SU, so that it would be feasible to properly assess such variability for our target variable. We decided to include this constraint in the Protocol draft, supporting a proper statistical approach (at the expense of budgets!)

  19. Class ascription Study area Now… At least 3 samples per SU, in order to catch the infra-SU variability of elemental content.

  20. Class ascription Study area Depending on the number of samples exposed at each sampling unit (SU) in the study area, we could use different approaches to ascribe the SU to a bioaccumulation class… It depends on the sampling strategy • Single value / SU ↔ 1 attribution / SU… • 3 values / SU ↔ 3 attributions / SU… But difficult to represent (and explain)! • 1 mean value ↔ 1 attribution / SU… Maybe…

  21. Class ascription We came up with a new approach…

  22. Class ascription How to calculate Δ(EU)? - Numerical example Suppose we want to assess the bioaccumulation level of a generic element y in 5 SUs having m = 6 pre-exposure samples and n = 3 samples exposed for 12 weeks in each SU.

  23. Class ascription How to calculate Δ(EU)? - Numerical example Step 1: calculating EU ratios for the 5 SUs

  24. Class ascription How to calculate Δ(EU)? - Numerical example Step 2: calculating the uncertainties associated to Ye(i) and Yu Continuous triangular data distribution is useful in ecology to calculate the uncertainty of measurement in case of limited sample size.

  25. Class ascription How to calculate Δ(EU)? - Numerical example Step 2: calculating the uncertainties associated to Ye(i) and Yu

  26. Class ascription How to calculate Δ(EU)? - Numerical example Step 3: propagating the uncertainties of Ye(i) and Yuto their ratio…

  27. Class ascription How to calculate Δ(EU)? - Numerical example Step 4: subtract Δ(EU) to EU and assign the class!

More Related