Advertisement

Phytoplankton pigments and community structure in the northeastern tropical pacific using HPLC-CHEMTAX analysis

  • Cristina Miranda-Alvarez
  • Adriana González-SilveraEmail author
  • Eduardo Santamaría-del-Angel
  • Jorge López-Calderón
  • Victor M. Godínez
  • Laura Sánchez-Velasco
  • Rafael Hernández-Walls
Original Article

Abstract

This study investigated the relationship between phytoplankton biomass and taxonomic composition and hydrographic variables in the Northeastern Tropical Pacific for June 2015, March 2016, and September 2016. Hydrographic data were measured between surface and 100 m; samples were collected at the surface and the deep chlorophyll maximum (DCM) along one or two transects. Pigments were quantified by high-performance liquid chromatography; the CHEMTAX software was used to determine the relative contribution to chlorophyll a of the main taxonomic groups. Our results show that the studied region is characterized by a stable vertical distribution of phytoplankton biomass regardless of the season (winter, spring or summer). A subsurface maximum (DCM) is always observed close to the bottom of the mixed layer where there is a higher abundance of larger groups (diatoms and Prymnesiophytes) while at the surface community, composition was generally dominated by picoplankton (Cyanobacteria and Prochlorococcus), and occasionally Prymnesiophytes. However, during the spring cruise (June 2015), affected by the 2015–2016 El Niño, phytoplankton biomass at the DCM markedly decreased along with an increase in the abundance of Chlorophytes. On the other hand, in September 2016, there was an unexpected increase in phytoplankton biomass at the DCM, although stratification was strong, and Prymnesiophytes comprised 60% of the community at the surface. The evaluation of nutrient and light will be necessary in future studies to determine their role in this temporal variability. Finally, chemotaxonomy (HPLC/CHEMTAX) proved to be a valuable tool for describing phytoplankton distribution at group level in this region and its relationship with physical processes.

Keywords

Phytoplankton taxonomic composition HPLC CHEMTAX Northeastern Tropical Pacific 2015–2016 El Niño 

Notes

Acknowledgements

This study was supported by the projects "Influence of mesoscale eddies on fish larval habitats (with emphasis on commercially important species) in the oxygen minimum zone of the Pacific Ocean off Mexico: open ocean and island effect" (SEP-CONACyT 236864) and "Testing paradigms on the expansion of the oxygen minimum zone: reduction of the vertical habitat of zooplankton and its effect on the pelagic ecosystem by means of autonomous sampling methods" (CONACYT 8662). The first author received support from CONACyT (Mexican Council of Science) through a Ph.D. scholarship (No. 446005). The authors gratefully acknowledge the comments of two anonymous reviewers. María Elena Sánchez-Salazar edited the English manuscript.

Supplementary material

10872_2019_528_MOESM1_ESM.docx (49 kb)
Supplementary file1 (DOCX 49 kb)

References

  1. Araujo MLV, Borges-Mendes CR, Tavano VM et al (2016) Contrasting patterns of phytoplankton pigments and chemotaxonomic groups along 30°S in the subtropical South Atlantic Ocean. Deep Res Part I.  https://doi.org/10.1016/j.dsr.2016.12.004 CrossRefGoogle Scholar
  2. Avila-Alonso D, Baetens JM, Cardenas R, De Baets B (2019) The impact of hurricanes on the oceanographic conditions in the Exclusive Economic Zone of Cuba. Rem Sen Environ 233:111339.  https://doi.org/10.1016/j.rse.2019.111339 CrossRefGoogle Scholar
  3. Babin SM, Carton JA, Dickey TD, Wiggert JD (2004) Satellite evidence of hurricane-induced phytoplankton blooms in an oceanic desert. J Geoph Res 109:C03043.  https://doi.org/10.1029/2003JC001938 CrossRefGoogle Scholar
  4. Barnett ML, Kemp AE, Hickman AE, Purdie DA (2019) Shelf sea subsurface chlorophyll maximum thin layers have a distinct phytoplankton community structure. Con Shelf Res 174:140–157.  https://doi.org/10.1016/j.csr.2018.12.007 CrossRefGoogle Scholar
  5. Berg R (2017) Hurricane Newton (EP152016). National Hurricane Center. Tropical cyclone report. https://www.nhc.noaa.gov/data/tcr/EP152016_Newton.pdf. Accessed 20 June 2019Google Scholar
  6. Billard C, Inouye I (2004) What is new in coccolithophore biology? In: Thierstein HR, Young JR (eds) Coccolithophores. Springer, BerlinGoogle Scholar
  7. Bouman HA, Ulloa O, Barlow R et al (2011) Water-column stratification governs the community structure of subtropical marine picophytoplankton. Environ Microbiol Rep 3:473–482.  https://doi.org/10.1111/j.1758-2229.2011.00241.x CrossRefGoogle Scholar
  8. Braeken J, Van Assen MA (2017) An empirical Kaiser criterion. Psychol Methods 22(3):450CrossRefGoogle Scholar
  9. Bustos-Serrano H, Castro-Valdéz R (2006) Flux of nutrients in the Gulf of California: Geostrophic approach. Mar Chem 99:210–2019.  https://doi.org/10.1016/j.marchem.2005.09.012 CrossRefGoogle Scholar
  10. Carswell T, Costa M, Young E et al (2017) Evaluation of MODIS-Aqua atmospheric correction and chlorophyll products of Western North American coastal waters based on 13 years of data. Remote Sens 9(10):1063CrossRefGoogle Scholar
  11. Castro R, Mascarenhas AS, Durazo R, Collins CA (2000) Seasonal variation of the temperature and salinity at the entrance to the Gulf of California, México. Cienc Mar 26:561–583CrossRefGoogle Scholar
  12. Cavole LM, Demko AM, Diner RE (2016) Biological impacts of the 2013–2015 warm-water anomaly in the Northeast Pacific: Winners, losers, and the future. Oceanography 29(2):273–285CrossRefGoogle Scholar
  13. Cepeda-Morales J, Beier E, Lavín MF, Godínez VM (2009) Effect of the oxygen minimum zone on the second chlorophyll maximum in the Eastern Tropical Pacific off Mexico. Ciencias Mar 35:389–403CrossRefGoogle Scholar
  14. Chavez FP, Pennington JT, Castro CG et al (2002) Biological and chemical consequences of the 1997–1998 El Niño in central California waters. Prog Oceanogr 54(1–4):205–232CrossRefGoogle Scholar
  15. Collins CA, Castro R, Mascarenhas A (2015) Properties of an upper ocean front associated with water mass boundaries at the entrance to the Gulf of California, November 2004. Deep Res Part II 119:48–60.  https://doi.org/10.1016/j.dsr2.2014.06.002 CrossRefGoogle Scholar
  16. Cullen JJ (2015) Subsurface Chlorophyll Maximum Layers: Enduring engima or mystery solved? Annu Rev Mar Sci 7:207–239.  https://doi.org/10.1146/annurev-marine-010213-135111 CrossRefGoogle Scholar
  17. Di Lorenzo E, Mantua N (2016) Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat Clim Chang 6(11):1042CrossRefGoogle Scholar
  18. Fiedler PC (2002) Environmental change in the eastern tropical Pacific Ocean: review of ENSO and decadal variability. Mar Ecol Prog Ser 244:265–283CrossRefGoogle Scholar
  19. Fiedler PC, Talley LD (2006) Hydrography of the eastern tropical Pacific: a review. Progr Oceanogr 69:143–180.  https://doi.org/10.1016/j.pocean.2006.03.008 CrossRefGoogle Scholar
  20. Fiedler PC, Redfern JV, Noord JV, Hall C, Pitman RL, Ballance LT (2013) Effects of a tropical cyclone on a pelagic ecosystem from the physical environment to top predators. Mar Ecol Progr Ser 484:1–16.  https://doi.org/10.3354/meps10378 CrossRefGoogle Scholar
  21. Finkel ZV, Beardall J, Flynn KJ et al (2010) Phytoplankton in a changing world: cell size and elemental stoichiometry. J Plankton Res 32:119–137CrossRefGoogle Scholar
  22. Gierach MM, Subrahmanyam B (2007) Satellite data analysis of the upper ocean response to Hurricanes Katrina and Rita (2005) in the Gulf of Mexico. IEEE Geol Remote Sens Lett 4(1):132–136CrossRefGoogle Scholar
  23. Godínez VM, Beier E, Lavín MF, Kurczyn JA (2010) Circulation at the entrance of the Gulf of California from satellite altimeter and hydrographic observations. J Geophys Res.  https://doi.org/10.1029/2009jc005705 CrossRefGoogle Scholar
  24. Gregg WW, Casey NW (2007) Sampling biases in MODIS and SeaWiFS ocean chlorophyll data. Remote Sens Environ 111:25–35.  https://doi.org/10.1016/j.rse.2007.03.008 CrossRefGoogle Scholar
  25. Guidi L, Stemmann L, Jackson GA (2009) Effects of phyto community on production, size and export of large aggregates. Limnol Oceanogr 54:1951–1963.  https://doi.org/10.4319/lo.2009.54.6.1951 CrossRefGoogle Scholar
  26. Hall NS, Paerl HW (2011) Vertical migration patterns of phytoflagellates in relation to light and nutrient availability in a shallow microtidal estuary. Mar Ecol Prog Ser 425:1–19.  https://doi.org/10.3354/meps09031 CrossRefGoogle Scholar
  27. Hernández-Becerril D, Pastén-Miranda N (2015) Abundancia y distribución de la cianobacteria picoplanctónica Synechococcus en Bahía de La Paz y Cuenca Carmen, Golfo de California (junio, 2001). Hidrobiológica 25:357–364Google Scholar
  28. Higgins HW, Wright SW, Schlüter L (2011) Quantitative interpretation of chemotaxonomic pigment data. In: Roy S, Llewellyn CA, Egeland SA, Johnsen G (eds) Phytoplankon pigments: characterization chemotaxonomy and applications in oceanography. Cambridge University Press, Cambridge, pp 257–313CrossRefGoogle Scholar
  29. Hooker SB, Rees NW, Aiken J (2000) An objective methodology for identifying oceanic provinces. Prog Oceanogr 45:313–338.  https://doi.org/10.1016/S0079-6611(00)00006-9 CrossRefGoogle Scholar
  30. Hooker SB, Van Heukelem L, Thomas CS et al (2005) Second SeaWiFS HPLC analysis round-robin experiment (SeaHARRE-2). National Aeronautics and Space Administration Goddard Space Flight Center, GreenbeltGoogle Scholar
  31. Huisman J, Thi NNP, Karl DM, Sommeijer B (2006) Reduced mixing generates oscillations and chaos in the oceanic deep chlorophyll maximum. Nature 439:322–325.  https://doi.org/10.1038/nature04245 CrossRefGoogle Scholar
  32. Jacox MG, Hazen EL, Zaba KD et al (2016) Impacts of the 2015–2016 El Niño on the California Current System: Early assessment and comparison to past events. Geophys Res Lett 43(13):7072–7080CrossRefGoogle Scholar
  33. Jeffrey SW, Writght SW, Zapata M (2011) Microalgal classes and their signature pigments, In: Roy S, Llewellyn CA, Egeland E, Johnsen G (eds) Phytoplankon pigments: characterization, chemotaxonomy and applications in oceanography. Cambridge University Press, Cambridge, pp. 3–77. https://doi.org/10.1017/CBO9780511732263.004
  34. Kahru M, Di Lorenzo E, Manzano-Sarabia M, Mitchell BG (2012) Spatial and temporal statistics of sea surface temperature and chlorophyll fronts in the California Current. J Plankton Res 34:749–760.  https://doi.org/10.1093/plankt/fbs010 CrossRefGoogle Scholar
  35. Kahru M, Kudela RM, Anderson CR, Mitchell BG (2015) Optimized merger of ocean chlorophyll algorithms of MODIS-aqua and VIIRS. IEEE Geosci Remote Sens Lett 12:2282–2285.  https://doi.org/10.1109/LGRS.2015.2470250 CrossRefGoogle Scholar
  36. Kheireddine M, Ouhssain M, Claustre H et al (2017) Assessing pigment-based phytoplankton community distributions in the Red Sea. Front Mar Sci 4:1–18.  https://doi.org/10.3389/fmars.2017.00132 CrossRefGoogle Scholar
  37. Kurczyn JA, Beier E, Lavín MF, Chaigneau A (2012) Mesoscale eddies in the northeastern Pacific tropical-subtropical transition zone: statistical characterization from satellite altimetry. J Geophys Res Ocean.  https://doi.org/10.1029/2012jc007970 CrossRefGoogle Scholar
  38. Latasa M, Cabello AM, Morán XA et al (2017) Distribution of phytoplankton groups with the deep chlorophyll maximum. Limnol Oceanogr 62:665–685.  https://doi.org/10.1002/lno.10452 CrossRefGoogle Scholar
  39. Latasa M, Gutiérrez-Rodríguez A, Cabello AMM, Scharek R (2016) Influence of light and nutrients on the vertical distribution of marine phytoplankton groups in the deep chlorophyll maximum. Sci Mar 80:S157–S162CrossRefGoogle Scholar
  40. Lavín MF, Beier E, Godínez VM, Amador A (2009) SST, thermohaline structure, and circulation in the southern Gulf of California in June 2004 during the North American Monsoon Experiment. J Geophys Res 114:1–22.  https://doi.org/10.1029/2008JC004896 CrossRefGoogle Scholar
  41. Lavín MF, Fiedler PC, Amador JA et al (2006) A review of eastern tropical Pacific oceanography : Summary Progress in Oceanography. A review of eastern tropical Pacific oceanography : summary. Prog Oceanogr.  https://doi.org/10.1016/j.pocean.2006.03.005 CrossRefGoogle Scholar
  42. Mackey MD, Mackey DJ, Higgins HW, Wright SW (1996) CHEMTAX- a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar Ecol Prog Ser 144:265–283CrossRefGoogle Scholar
  43. Martínez-Ortega RM, Tuya Pendás LC, Martínez-Ortega M, et al. (2009) El coeficiente de correlacion de Spearman caracterización. Rev Habanera de Cienc Méd 8(2)Google Scholar
  44. Moeller HV, Laufkötter C, Sweeney EM, Johnson MD (2019) Light-dependent grazing can drive formation and deepening of deep chlorophyll maxima. Nat Commun 10(1):1978Google Scholar
  45. Molinari J, Vollaro D, Skubis S, Dickinson M (2000) Origins and mechanisms of eastern pacific tropical cyclogenesis: a case study. Mon Weather Rev 128:125–139CrossRefGoogle Scholar
  46. Morales SE, Meyer M, Currie K, Baltar F (2018) Are oceanic fronts ecotones? Seasonal changes along the subtropical front show fronts as bacterioplankton transition zones but not diversity hotspots. Environ Microbiol Rep 10:184–189.  https://doi.org/10.1111/1758-2229.12618 CrossRefGoogle Scholar
  47. Nair A, Sathyendranath S, Platt T et al (2008) Remote sensing of phytoplankton functional types. Remote Sens Environ 112:3366–3375.  https://doi.org/10.1016/j.rse.2008.01.021 CrossRefGoogle Scholar
  48. O'Reilly JE, Maritorena S, Mitchell BG et al (1998) Ocean color chlorophyll algorithms for SeaWiFS. J Geophys Res Oceans 103(C11):24937–24953CrossRefGoogle Scholar
  49. O’Reilly JE, Maritorena S, Siegel DA et al (2000) Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4. SeaWiFS Postlaunch Calib Valid Anal Part 3:9–23Google Scholar
  50. Ohman MD (2018) Introduction to collection of papers on the response of the southern California Current Ecosystem to the Warm Anomaly and El Niño, 2014–16. Deep Sea Res Part 1 Oceanogr Res Pap 140:1–3CrossRefGoogle Scholar
  51. Painter SC, Finlav M, Hemslev VS, Martin AP (2016) Seasonality, phytoplankton succession and the biogeochemical impacts of an autumn storm in the northeast Atlantic Ocean. Progr Oceanogr 142:72–104.  https://doi.org/10.1016/j.pocean.2016.02.001 CrossRefGoogle Scholar
  52. Portela E, Beier E, Barton ED et al (2016) Water masses and circulation in the tropical pacific off central mexico and surrounding areas. J Phys Oceanogr 46:3069–3081.  https://doi.org/10.1175/JPO-D-16-0068.1 CrossRefGoogle Scholar
  53. Prasad TG, Hogan PJ (2007) Upper-ocean response to Hurricane Ivan in a 1/25 nested Gulf of Mexico HYCOM. J Geophys Res.  https://doi.org/10.1029/2006JC003695 CrossRefGoogle Scholar
  54. Rodríguez-Salazar ME, Álvarez-Hernández S, Núñez Bravo (2001) Coeficientes de Asociación. Universidad Autónoma Metropolitana-Iztapalapa, Plaza y Váldes S.A. de C.V., México, p 155Google Scholar
  55. Romero-Vadillo E, Zaytsev O, Morales-Pérez R (2007) Tropical cyclone statistics in the Northeastern Pacific. Atmosfera 20:197–213Google Scholar
  56. Sánchez-Velasco L, Beier E, Godínez VM et al (2017) Hydrographic and fish larvae distribution during the ‘“Godzilla El Niño 2015–2016”’ in the northern end of the shallow oxygen minimum zone of the Eastern Tropical Pacific Ocean. J Geophys Res Ocean 122:2156–2170.  https://doi.org/10.1002/2016JC012622 CrossRefGoogle Scholar
  57. Santamaría-del-Ángel E, González-Silvera A, Millán-Núñez R et al (2011) Determining dynamic biogeographic regions using remote sensing data. Handbook of satellite remote sensing image interpretation: applications for marine living resources conservation and management. EU PRESPO and IOCCG, Germany, pp 273–291Google Scholar
  58. Santana-Vega Z, Morales-Blake AR, Varona-Cordero F (2018) Prokaryotic picoplankton distribution within the oxygen minimum zone of the central Mexican Pacific across environmental gradients. Braz J Oceanogr 66:157–171.  https://doi.org/10.1590/S1679-87592018004806602 CrossRefGoogle Scholar
  59. Sharples J, Ellis JR, Nolan G et al (2013) Fishing and the oceanography of a stratified shelf sea. Program Oceanogr 117:130–139CrossRefGoogle Scholar
  60. Sieburth J, Smetacek V, Lenz J (1978) Pelagic ecosystem structure: heterotrophic. Limnol Oceanogr 23:1256–1263CrossRefGoogle Scholar
  61. Stramma L, Schmidtko S, Levin LA, Johnson GC (2010) Ocean oxygen minima expansions and their biological impacts. Deep Res Part I:1–9.  https://doi.org/10.1016/j.dsr.2010.01.005 CrossRefGoogle Scholar
  62. Taylor AG, Landry MR (2018) Phytoplankton biomass and size structure across trophic gradients in the southern California Current and adjacent ocean ecosystems. Mar Ecol Prog Ser 592:1–17.  https://doi.org/10.3354/meps12526 CrossRefGoogle Scholar
  63. Thomas CS (2012) The HPLC method. In: The fifth SeaWiFS HPLC analysis round-robin experiment (SeaHARRE-5), pp 63–72Google Scholar
  64. Throndsen J (1997) The planktonic marine flagellates. In: Tomas CR (ed) Identifying marine phytoplankton. Academic, San Diego, pp 591–729CrossRefGoogle Scholar
  65. Van Heukelem L, Thomas CS (2001) Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments. J Chromatogr A.  https://doi.org/10.1016/S0378-4347(00)00603-4 CrossRefGoogle Scholar
  66. Wang D, Zhao H (2008) Estimation of phytoplankton responses to hurricane gonu over the arabian sea based on ocean color data. Sensors 8:4878–4893.  https://doi.org/10.3390/s8084878 CrossRefGoogle Scholar
  67. Wilcoxon F (1945) Individual comparison by ranking methods. Biometrics 1:80–83CrossRefGoogle Scholar
  68. Wilcoxon F, Katti SK, Wilcox RA (1970) Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Select Tables Math Stat 1:171–259Google Scholar
  69. Wilcoxon F, Wilcoxon RA (1964) Some rapid approximate statistical procedures. N.Y Laboratories, Division of the American Cyanamid Company, Pear RiverGoogle Scholar
  70. Wright SW, Ishikawa A, Marchant HJ et al (2009) Composition and significance of picophytoplankton in Antarctic waters. Polar Biol.  https://doi.org/10.1007/s00300-009-0582-9 CrossRefGoogle Scholar
  71. Zhao H, Shao J, Han G, Yang D, Lv J (2015) Influence of typhoon matsa on phytoplankton chlorophyll-a off East China. PLoS ONE 10(9):e0137863.  https://doi.org/10.1371/journal.pone.0137863 CrossRefGoogle Scholar

Copyright information

© The Oceanographic Society of Japan and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Cristina Miranda-Alvarez
    • 1
  • Adriana González-Silvera
    • 2
    Email author
  • Eduardo Santamaría-del-Angel
    • 2
  • Jorge López-Calderón
    • 2
  • Victor M. Godínez
    • 3
  • Laura Sánchez-Velasco
    • 4
  • Rafael Hernández-Walls
    • 2
  1. 1.Post-Graduate Program in Coastal OceanographyFacultad de Ciencias MarinasEnsenadaMexico
  2. 2.Universidad Autónoma de Baja California, Facultad de Ciencias MarinasEnsenadaMexico
  3. 3.Departamento de Oceanografía FísicaCICESEEnsenadaMexico
  4. 4.Instituto Politécnico NacionalCICIMAR-IPN, BCSLa PazMexico

Personalised recommendations