Fatty Acids to Quantify Phytoplankton Functional Groups and Their Spatiotemporal Dynamics in a Highly Turbid Estuary
Phytoplankton community composition expresses estuarine functionality and its assessment can be improved by implementing novel quantitative fatty acid–based procedures. Fatty acids have similar potential to pigments for quantifying phytoplankton functional groups but have been far less applied. A recently created dataset containing vast information on fatty acids of phytoplankton taxonomic groups was used as reference to quantify phytoplankton functional groups in the yet undescribed Guadalquivir River Estuary. Twelve phytoplankton groups were quantitatively distinguished by iterative matrix factor analysis of seston fatty acid signatures in this turbid estuary. Those phytoplankton groups including species unfeasible for visual identification (coccoid or microflagellated cells) could be quantified when using fatty acids. Conducting monthly matrix factor analyses over a period of 2 years and the full salinity range of the estuary indicated that diatoms dominated about half of the phytoplankton community spatiotemporally. The abundance of Cyanobacteria and Chlorophytes was inversely related to salinity and little affected by seasonality. Euglenophytes were also more abundant at lower salinity, increasing their presence in autumn–winter. Coccolithophores and Dinophytes contributed more to phytoplankton community at higher salinity and remained little affected by seasonality. Multivariate canonical analysis indicated that the structure of the estuarine phytoplankton community was most influenced by salinity; secondly influenced by water temperature, irradiance, and river flow; and unaffected by nutrients. Fatty acids are especially suited for phytoplankton community research in high turbid estuaries and generate outcomes in synergy with those derived from classical pigment assessments.
KeywordsCHEMTAX Estuary FASTAR Fatty acids Phytoplankton Quantitative structure
This work was supported by projects financed by the European Fisheries Fund (2007–2013) and European Maritime and Fisheries Fund (2014–2020) and by project P11-RNM-7467 executed through the Agriculture and Fisheries and Science Departments of the Government of Andalusia.
- Anderson, M.J., R.N. Gorley, and K.R. Clarke. 2008. PERMANOVA+ for PRIMER: Guide to software and statistical methods. Plymouth: PRIMER-E.Google Scholar
- Bazin, P., F. Jouenne, T. Friedl, A.F. Deton-Cabanillas, B. Le Roy, and B. Véron. 2014b. Phytoplankton diversity and community composition along the estuarine gradient of a temperate macrotidal ecosystem: Combined morphological and molecular approaches. PLoS One 9 (4): e94110. https://doi.org/10.1371/journal.pone.0094110.CrossRefGoogle Scholar
- Bergmann, T.I. 2004. The physiological ecology and natural distribution patterns of Cyrptomonas algae in coastal aquatic ecosystems. PhD thesis, 125 pp. New Brunswick Rutgers, The State University of New Jersey.Google Scholar
- Christie, W.W. 2003. Lipid analysis: Isolation, separation, identification and structural analysis of lipids. Bridgewater: The Oily Press.Google Scholar
- Conn, K.E., R.S. Dinicola, R.W. Black, S.E. Cox, R.W. Sheibley, J.R. Foreman, C.A. Senter and N.T. Peterson. 2016. Continuous-flow centrifugation to collect suspended sediment for chemical analysis: U.S. Geological Survey techniques and methods, book 1, chap. D6, 31 p., plus appendixes, https://doi.org/10.3133/tm1D6.
- Eikrem, W., L.K. Medlin, J. Henderiks, S. Rokitta, B. Rost, I. Probert, J. Throndsen, B. Edvardsen, and B. 2016. Haptophyta. In Handbook of the protists, ed. J. Archibald, A. Simpson, C. Slamovits, L. Margulis, M. Melkonian, D. Chapman, and J. Corliss. Cham: Springer.Google Scholar
- Elser, J.J., M.E. Bracken, E.E. Cleland, D.S. Gruner, W.S. Harpole, H. Hillebrand, J.T. Ngai, E.W. Seabloom, J.B. Shurin, and J.E. Smith. 2007. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecology Letters 10 (12): 1135–1142.CrossRefGoogle Scholar
- Etcheber, H., and J.M. Jouanneau. 1980. Comparison of the different methods for the recovery of suspended matter from estuarine waters: Deposition, filtration and centrifugation; consequences for the determination of some heavy metals. Estuarine, Coastal and Shelf Science 11 (6): 701–707.CrossRefGoogle Scholar
- Folch, J., M. Lees, and G.H. Sloane-Stanley. 1957. A simple method for the isolation and purification of total lipids from animal tissues. Journal of Biological Chemistry 226: 497–509.Google Scholar
- Gescher, C., K. Metfies, S. Frickenhaus, B. Knefelkamp, K.H. Wiltshire, and L.K. Medlin. 2008. Feasibility of assessing the community composition of prasinophytes at the Helgoland Roads sampling site with a DNA microarray. Applied and Environmental Microbiology 74 (17): 5305–5316.CrossRefGoogle Scholar
- Grasshoff, K., M. Ehrhardt, and K. Kremling. 1983. Methods of seawater analysis. Verlag Chemie 419 pp.Google Scholar
- Hastie, T.J., and D. Pregibon. 1993. Generalized linear models. In Statistical models, ed. J.M. Chambers and T.J. Hastie, 194–244. London: Chapman and Hall.Google Scholar
- Higgins, H.W., S.W. Wright, and L. Schlüter. 2011. Quantitative interpretation of chemotaxonomic pigment data. In Phytoplankton pigments: Characterization, chemotaxonomy and applications in oceanography, ed. S. Roy, E. Skarstad, E.G. Johnsen, and C.A. Llewellyn, 257–313. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
- Jaanus, A., A. Andersson, I. Olenina, K. Toming, and K. Kaljurand. 2011. Changes in phytoplankton communities along a north-south gradient in the Baltic Sea between 1990 and 2008. Boreal Environmental Research 16: 191–208.Google Scholar
- Jasprica, N., M. Carić, F. Kršinić, T. Kapetanović, M. Batistić, M. And, and J. Njire. 2012. Planktonic diatoms and their environment in the lower Neretva River estuary (eastern Adriatic Sea, NE Mediterranean). Vol. 141, 405–430. Beiheft: Nova Hedwigia.Google Scholar
- Jeffrey, S.W., and M. Vesk. 1997. Introduction of marine phytoplankton and their pigment signatures. In Phytoplankton pigments in oceanography, ed. S.W. Jeffrey, R.F.C. Mantoura, and S.W. Wright, 37–84. Paris: SCOR-UNESCO.Google Scholar
- Napolitano, G.E., R.J. Pollero, A.M. Gayoso, B.A. Macdonald, and R.J. Thompson. 1997. Fatty acids as trophic markers of phytoplankton blooms in the Bahía Blanca estuary (Buenos Aires, Argentina) and in Trinity Bay (Newfoundland, Canada). Biochemical Systematics and Ecology 25 (8): 739–755.CrossRefGoogle Scholar
- Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), pp. 1–10. http://www.r-pro- ject.org/conferences/DSC-2003/Proceedings/Plummer.pdf.
- Ruiz, J., M. Polo, M. Dìez-Minguito, G. Navarro, E. Morris, E. Huertas, I. Caballero, E. Contreras, and M. Losada. 2015. The Guadalquivir estuary: A hot spot for environmental and human conflicts. In Environmental management and governance (Coastal Research Library), ed. C.W. Finkl and C. Makowski, vol. 8, 199–232. Springer.Google Scholar
- Tilman, D. 1999. The ecological consequences of changes in biodiversity: A search for general principles. Ecology 80: 1455–1475.Google Scholar
- White, D.A., C.E. Widdicombe, P.J. Somerfield, R.L. Airs, G.A. Tarran, J.L. Maud, and A. Atkinson. 2015. The combined effects of seasonal community succession and adaptive algal physiology on lipid profiles of coastal phytoplankton in the Western English Channel. Marine Chemistry 177: 638–652.CrossRefGoogle Scholar
- Wright, S.W., D.P. Thomas, H.J. Marchant, H.W. Higgins, M.D. Mackey, and D.J. Mackey. 1996. Analysis of phytoplankton of the Australian sector of the Southern Ocean: Comparisons of microscopy and size-frequency data with interpretations of pigment HPLC data using the CHEMTAX matrix factorisation program. Marine Ecology Progress Series 144: 285–298.CrossRefGoogle Scholar