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Application of pigment analysis and CHEMTAX to field studies of phytoplankton communities Simon Wright Australian Antarctic Division

Dec 18, 2015

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  • Slide 1
  • Application of pigment analysis and CHEMTAX to field studies of phytoplankton communities Simon Wright Australian Antarctic Division
  • Slide 2
  • This powerpoint presentation has been cut back considerably to reduce its size from 29MB to somewhat closer to the 2MB requested. In doing so, I have had to exclude all of my antarctic and shipboard photos (not a great scientific loss), but also a photo sequence on exactly how we filter and extract our samples. I am placing these separately on a Pigment HPLC web site via the Australian Antarctic Division. I will forward the address to the PICODIV site. I have also annotated some of the slides to make them more stand-alone.
  • Slide 3
  • Find a simple chemical technique for determining the abundance of phytoplankton This talk will consider how far we have come toward that goal. The application of pigment analysis to biological oceanography was largely pioneered by Shirley Jeffrey. In one of her first post- docs with George Humphrey, she was given the challenge to:
  • Slide 4
  • Outline Historical perspective development of CHEMTAX BROKE 1996 CHEMTAX at work Optimising pigment analysis and data CHEMTAX problems unusual algae choice of inputs variability of algal pigment content Modelling pigments in the underwater light field Current directions Conclusions
  • Slide 5
  • How do we measure the abundance of phytoplankton in the presence of protozoa, bacteria, detritus and viruses?
  • Slide 6
  • Many species can be identified by electron microscopy but cannot be identified by light microscopy [photos omitted] Even if they could be identified by light microscopy, the statistics of enumeration means that 10000 cells of each type must be counted to ensure 1% precision. And. Die numerische Erfassung von Phytoplankton-Arten gleicht einer Danaiden- Arbeit die mit einer Zerstoerung von Koerper und Seele einhergeht Haeckel, 1890 (roughly =). Plankton counting is a task that cannot be achieved without ruin of body and soul Chlorophylls and carotenoids are useful chemical markers that, in the open ocean, are only found in living phytoplankton. By chromatographically separating them, we can determine the composition and abundance of phytoplankton populations.
  • Slide 7
  • Chl aAstaxanthin Diadinoxanthin Fucoxanthin Neofucoxanthin Phaeophytin a Carotenes Chl b Peridinin Neoperidinin Neoxanthin Chlorophyllide a Chl c Phaeophorbide a Origin TLC Jeffrey 1974
  • Slide 8
  • Jeffrey 1974 PigmentsAlgal types or biological processes indicated Chl a Chl c Diatoms and / or chrysomonads Fucoxanthin Diadinoxanthin Chl bGreen algae Neoxanthin PeridininDinoflagellates Chlorophyllide aSenescent diatoms (due to chlorophyllase) Phaeophorbide aFaecal pellets of copepods Phaeophytin aUs. Trace amounts on all cgrams AstaxanthinCopepods present High chl c:a ratiosSenescent phytoplankton or detritus In earlier times, we thought in terms of individual marker pigments indicating particular algal types or processes.
  • Slide 9
  • HPLC systems development Steady improvement in HPLC techniques led to recognition of many more pigment markers
  • Slide 10
  • Major marker pigments Ubiquitous Chl a Unambiguous Alloxanthin Peridinin Prasinoxanthin Jeffrey and Vesk (1997)
  • Slide 11
  • Major marker pigments Ubiquitous Chl a Unambiguous Alloxanthin Peridinin Prasinoxanthin Shared e.g. Fucoxanthin Chl b Zeaxanthin Violaxanthin
  • Slide 12
  • Major marker pigments We can no longer talk in terms of individual marker pigments. Instead we talk of SUITES of pigments that may cross conventional taxonomic boundaries. By the late 80s it became very apparent that normal interpretation of pigment data amounted to little more than guesswork. There was an urgent need for objective computational methods for determining the phytoplankton community composition from pigment data.
  • Slide 13
  • Computational methods in pigment analysis
  • Slide 14
  • 1. Simple or multiple linear regression e.g. Gieskes and Kraay 1983 Statistically sound Does not distinguish algal groups with shared marker pigments Computation methods
  • Slide 15
  • 1. Simple or multiple linear regression 2. Multiple simultaneous equations Everitt et al. 1990 Letelier et al.1993 Peekin 1997 van Leeuwe et al. 1998 Computation methods
  • Slide 16
  • 1. Simple or multiple linear regression 2. Multiple simultaneous equations Letelier et al. 1993 [Chla] Prochl = 0.91([Chlb] - 2.5[Prasino]) [Chla] Cyano = 2.1{[zeax] -0.07([Chlb] - 2.5[Prasino])} [Chla] Chrys = 0.9[19-but] Chrys [Chla] Prym = 1.3[19-hex] Prym [Chla] Bacill = 0.8{[fuco] - (0.02[19-hex] Prym + 0.14[19-but] Chrys )} [Chla] Dino = 1.5[perid] [Chla] Pras = 2.1[prasino] Computation methods
  • Slide 17
  • 1. Simple or multiple linear regression 2. Multiple simultaneous equations Allowed shared marker pigments Difficult to set up Computation methods
  • Slide 18
  • 1. Simple or multiple linear regression 2. Multiple simultaneous equations 3. Matrix factorization Computation methods
  • Slide 19
  • 1. Simple or multiple linear regression 2. Multiple simultaneous equations 3. Matrix factorization CHEMTAX (Mackey et al. 1996, Wright et al. 1996) Computation methods
  • Slide 20
  • uses a table of concentration ratios of all pigments for each algal group Algal Class Pigment Chl c3 Peridinin 19-but Fucox 19-hex Prasinox Diatom - - - 0.75-- Hapto3 0.045 - - - 1.7- Hapto4 0.048 - 0.25 0.58 0.54- Cryptophyte - - - --- Prasinophyte - - - --0.32 Chlorophyte - - - --- Dinoflagellate - 1.06 - --- Cyanobacteria - - - --- Each ratio is iteratively modified to minimize the difference between observed and calculated total pigment concentration Matrix factorization (Half of table only)
  • Slide 21
  • Currently based on a MATLAB platform Can distinguish algal groups with qualitatively identical pigment compositions using differences in pigment ratios (Wright et al, 1996) Requires the user to enter the expected mix of algal components which the software then optimises Microscopic examination of the samples is thus essential CHEMTAX software
  • Slide 22
  • Changes in pigment ratios with depth It is essential to split samples into a series of depth strata that are computed independently (Mackey et al., 1998, Higgins and Mackey, 2000, Wright and van den Enden, 2000) 1004 Samples were split into 8 depth layers. Samples from each layer were computed independently. Graph at left shows the computed ratios for type 4 haptophytes (e.g. Phaeocystis spp.) vs. depth. The smooth change with depth suggests that CHEMTAX is measuring something real.
  • Slide 23
  • Phytoplankton community structure and stocks in the East Antarctic marginal ice zone (BROKE survey, January - March 1996) determined by CHEMTAX analysis of HPLC pigment signatures S. W. Wright and R. L. van den Enden (2000) Deep-Sea Research II, 47, 2363 - 2400 Does CHEMTAX work? An example where it worked well to map phytoplankton communities in the Southern Ocean
  • Slide 24
  • Study Area Chlorophyll by satellite
  • Slide 25
  • Ice shelf 1000m
  • Slide 26
  • Antarctic Slope Front Tmin Pycnocline
  • Slide 27
  • ASF Tmin Pycnocline
  • Slide 28
  • ASF Tmin Pycnocline
  • Slide 29
  • ASF Tmin Pycnocline
  • Slide 30
  • ASF Tmin Pycnocline
  • Slide 31
  • ASF Tmin Pycnocline
  • Slide 32
  • ASF Tmin Pycnocline
  • Slide 33
  • ASF Tmin Pycnocline
  • Slide 34
  • ASF Tmin Pycnocline
  • Slide 35
  • ASF Tmin Pycnocline
  • Slide 36
  • BROKE conclusions 1. Effect of Stratification MIXED STRATIFIED Chl a (g.L -1 ) 0.4 2.0 Diatoms Pycnocline Pycnocline Prasinophytes Tmin Pycnocline Hapto4s Tmin Pycnocline 2. Hole in algal distribution at the ice edge, except for Cryptophytes 3. Generally uniform to pycnocline under ice 4. Importance of frontal features - downwelling tongue from Tmin layer These observations could not have been obtained using microscopy or any other method currently available.
  • Slide 37
  • Optimizing pigment data
  • Slide 38
  • Aim:Sensitivity maximum peak height Accuracy Integrity lack of pigment degradation Reproducibility of retention times Data reliability These aims require care at each step of the process Optimising pigment data
  • Slide 39
  • Field sampling It is important to realise that the pigment composition of the sample starts changing from the moment it is enclosed in a dark Niskin bottle. For maximum reproducibility of pigment ratios, all samples should be subjected to the same time delay from collection to end of filtration, and all should be handled in the same light regime (preferably very dim). For example, in our cruises, it is normally 40 minutes before we can sample the Niskin bottles after the physical and chemical oceanographers have collected their samples. Thus we never see diatoxanthin. It has all been converted to diadinoxanthin in the dark.
  • Slide 40
  • Sample filtration Dim light, cool lab