CHEMTAX - Not just a ‘Black Box’

1. Denis MackeyHarry Higgins with acknowledgements to Mark MackeySimon Wright CHEMTAX - Not just a ‘Black Box’

2. CHEMTAX workshop - Barcelona

3. Oceanography from Hobart

4. Chl a around Tasmania

5. Chl a - September / October 1997

6. Big Picture for Chl a • Large temporal and spatial variability in measurements of surface colour • Uncertainties in algorithms for converting colour to Chl a • Uncertainties in converting surface Chl a to depth integrated Chl a to biomass • Uncertainties in converting biomass, light and nutrient fields to global productivity • Worth trying to improve information on global carbon cycle BUT we need to compare with field data and we need to beaware of assumptions

7. Dunaliella tertiolecta - HPLC pigments • Large number of pigments • Can get definitive identification and quantification • Different species have quite different pigment compositions • Field samples often contain many more pigments • Potentially a very valuable source of information

8. Big Picture for pigments • Information on ‘algal classes’ is also important for global carbon cycle • Diatoms - major contribution to ‘biological pump’Cyanobacteria - may be able to fix N2 Coccolithophores - change Alkalinity and solubility of CO2Small species - more readily acquire limiting nutrients (Fe) • Measurements of pigments can help to identify algal classes BUT need to use data from complementary techniques

9. Basis of method • Need to factorise a data matrix S of pigment concentrations in a set of samples into matrices F, giving the ratios of different pigments for each phytoplankton class, and C, giving the abundances of each phytoplankton class in each sample • Estimate the initial pigment ratios from literature reports etc • Minimise the difference between S and the calculated value of C  F subject to constraints that concentrations and ratios are always +ve • Use steepest descent algorithm to improve factorisation of S after varying each non-zero pigment ratio within userdefined limits

10. Assumptions • Algae can be characterised on the basis of pigment ratios • Pigment ratios remain constant across the samples that are being analysed and so may need to sub-divide a data set if changes in ratios are likely. This is important for depth profiles. • Initial values for pigment ratios can be estimated from algal cultures under varying conditions or from field data dominated by a particular species • Know something about the algal classes present • Pigments have been positively identified and analytical errors have been minimised

11. Advantages and Disadvantages CHEMTAX • Does not require unique pigment markers • Suitable for cells of all size, shape and type • ‘Pigment Classes’ not necessarily the same as ‘Taxonomic Classes’ • Can provide additional information on pigment ratios • Requires best possible knowledge of community structure and suitable pigment ratios • Has been extensively tested using synthetic matrices with known uncertainties and ‘experimental’ errors

12. Comparison with cell counts • In normal usage, CHEMTAX calculates the contribution of a given algal class, defined in terms of pigments, to total Chl a • There may be overlap between taxonomic classes (some prasinophytes and chlorophytes) • Some taxonomic classes may have species with very different pigment ratios (haptophytes, prasinophytes) • Conversion factor from Chl a to biomass can vary widely. Chavez et al. (1996) report values from < 40 to > 250

13. Comparison with cell counts • For a specific species, conversion from cell number to biomass or Chl a to can vary widely (for Prochlorococcus, Blanchot and Rodier (1996) found a range of 0.2 – 6.4 fg Chl a cell-1) • There may well be different ecotypes having different sizes and compositions • For a taxonomic class such as diatoms, there will be a wide range of species with different shapes and sizes • The conversion factors depend on nutrient status, species composition, light intensity, diel cycle etc • There is generally surprisingly good agreement

14. Answers to common problems • Ensure that input files are correct, eg the .RAT and .DAT files must have the same number of pigments in the same order. • Do not include species unless you know they may be present and do not exclude species that you know are present. • When saving data files from Excel, ensure that the column widths are wide enough to display clear spaces between numbers. Save as space delimited formatted text files. • Check very carefully for outliers and typos.

15. Answers to common problems • Ensure that data files and pigment ratio files are compatible. If your ratio matrix has zeaxanthin whenever Chl b is present, then the same should occur in the data files. • Original program was based on a 486PC. With new PCs, there may be no point in using subiterations to reduce calculation times. • PREPRO is currently limited to 200 samples and 30 pigments • For Matlab V6, use a text editor to replace the function nnls with lsqnoneg in all .m files

16. Answers to common problems • Generally there are minimal changes in the ratios if (a) the particular pigment is a minor contributor to the total pigment complement of a given algal class, (b) the relative abundance of a particular algal class in the given sample is low, (c) the errors in the data set are large and iterative changes in the ratios produce no further substantial reduction in the residual and (d) the .RAT file is inappropriate for the data set. • Take care in selecting pigments and choose about 4 more pigments than algal classes and more samples than pigments. • Use .RLM file to prevent large variations in a given pigment (particularly Chl a).

17. Prochlorococcus (Sept/Oct 1992)Central North Atlantic (from Li et al. 1995)

18. Synechococcus (Sept/Oct 1992)Central North Atlantic (from Li et al. 1995)

19. Eukaryotes (Sept/Oct 1992)Central North Atlantic (from Li et al. 1995)

20. Depth profiles • Concentrations of pigments per cell and pigment ratios are known to be a function of light intensity, and hence depth • For vertical profiles need to group samples into 5 – 7 bands • Can use overlapping bands if sample numbers are limited • For photoprotective pigments, ratio to Chl a should decrease with depth • For light-harvesting pigments, ratio to Chl a may increase, decrease or stay constant with depth • Expect smooth changes in ratios with depth withsimilar behaviour across algal classes

21. FR05/92 - percentage of Chl achlorophytes, chrysophytes, cryptophytes, cyanobacteria

22. FR05/92 - pigment ratioschlorophytes, chrysophytes, cryptophytes, cyanobacteria

23. 240 100 75 50 Mean Chl a (µg m-3) 25 0 Sepik Prochlor Synech Chrys Inshore Pras NGCU Crypt Chlor Hapto Offshore Diat TROPICS 97: Chl a conc(Transect 2)

24. TROPICS 97: % Chl a (Transect 2) 60 40 Chl a (class %) 20 0 Sepik Prochlor Synech Chrys Inshore Pras NGCU Crypt Chlor Hapt Offshore Diat

25. Little Rock Lake – treatment basin

26. Conclusions • CHEMTAX relies on the best possible data sets and information as to algal classes and pigment ratios. • The results complement those from other techniques such as light microscopy, flow cytometry and molecular biology. • Cannot treat CHEMTAX as a ‘Black Box’ • Garbage in – garbage out