Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-10T16:32:07.692Z Has data issue: false hasContentIssue false
  • English
  • Français

A comparison of phytoplankton assemblages generated by two sampling protocols in a German lowland catchment

Published online by Cambridge University Press:  21 December 2011

Naicheng Wu ,
Britta Schmalz  and
Nicola Fohrer
Naicheng Wu*
Affiliation:
Department of Hydrology and Water Resources Management, Institute for the Conservation of Natural Resources, Kiel University, 24118 Kiel, Germany
Britta Schmalz
Affiliation:
Department of Hydrology and Water Resources Management, Institute for the Conservation of Natural Resources, Kiel University, 24118 Kiel, Germany
Nicola Fohrer
Affiliation:
Department of Hydrology and Water Resources Management, Institute for the Conservation of Natural Resources, Kiel University, 24118 Kiel, Germany
*
*Corresponding author: nwu@hydrology.uni-kiel.de; wunaicheng2003@yahoo.com.cn
Get access

Abstract

Research in the phytoplankton community has become an important part of the overall water-quality monitoring. However, to date studies in small rivers and streams were still scarce and sampling methods were also diverse and not as well developed as in lakes or large rivers. We investigated whether two sampling protocols collected different phytoplankton assemblages within a lowland catchment and, consequently, influenced the outcome of bio-assessment. Data collected from 77 sites by plankton net (PLNET) collection and sedimentation (SEDIM) protocols were analyzed. Median Bray–Curtis (BC) similarity between phytoplankton assemblages generated by the two protocols was 48.5% (range: 7.5–82.0%), and sites with the lowest BC similarities tended to have lower chlorophyll a (Chla), water temperature (WT), total suspended solid (TSS) and volatile suspended solid (VSS), but higher channel width and water depth, than other sites with higher BC similarities. Reduced total algal density and biomass, but higher species richness, were observed by the PLNET protocol. However, overall phytoplankton assemblages generated by the two protocols were similar, as indicated by dominant species (paired t-test, P>0.05) and non-metric multidimensional scaling (NMDS) ordination. Nevertheless, from the phytoplankton-based bio-assessment point of view, PLNET protocol was a better method compared with SEDIM protocol because algal data collected by PLNET protocol had higher relationship with environmental variables as indicated by ‘Correlation Index’ (CoI), Cumulative_R2 and canonical correspondence analysis (CCA).

Keywords

Algal metricscanonical correspondence analysis (CCA)correlation index (CoI)plankton net (PLNET)sedimentation (SEDIM)
Type
Research Article
Information
Annales de Limnologie - International Journal of Limnology , Volume 47 , Issue 4 , 2011 , pp. 313 - 323
Copyright
© EDP Sciences, 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abrantes, F., 1988. Diatom assemblages as upwelling indicators in surface sediments in Portugal. Mar. Geol. , 85, 15–39.CrossRefGoogle Scholar
Abrantes, F., Gil, I., Lopes, C. and Castro, M., 2005. Quantitative diatom analyses – a faster cleaning procedure. Deep-Sea Res. I , 52, 189–198.CrossRefGoogle Scholar
APHA, 1992. Standard methods for the examination of water and wastewater, American Public Health Association, New York.Google Scholar
Blanco, S., Bécares, E., Cauchie, H., Hoffmann, L. and Ector, L., 2007. Comparison of biotic indices for water quality diagnosis in the Duero Basin (Spain). Arch. Hydrobiol. Suppl. , 161, 267–286.Google Scholar
Borics, G., Várbíró, G., Grigorszky, I., Krasznai, E., Szabó, S. and Kiss, K.T., 2007. A new evaluation technique of potamo-plankton for the assessment of the ecological status of rivers. Arch. Hydrobiol. Suppl. , 161 (3–4), 465–486.Google Scholar
Cabecinha, E., Cortes, R., Cabral, J.A., Ferreira, T., Lourenço, M. and Pardal, M.Â., 2009. Multi-scale approach using phytoplankton as a first step towards the definition of the ecological status of reservoirs. Ecol. Indicators , 9, 240–255.CrossRefGoogle Scholar
Cao, Y., Williams, D.D. and Larsen, D.P., 2002. Comparison of ecological communities: the problem of sample representativeness. Ecol. Monogr. , 72, 41–56.CrossRefGoogle Scholar
Cao, Y., Williams, W.P. and Bark, A.W., 1997. Effects of sample size (replicate number) on similarity measured in river benthic Aufwuchs community analysis. Water Environ. Res. , 69, 107–114.CrossRefGoogle Scholar
CDC, 2010. Centers for Disease Control and Prevention. http://www.cdc.gov/epiinfo/europe.htm (downloaded 10 October 2010).Google Scholar
Centis, B., Tolotti, M. and Salmaso, N., 2010. Structure of the diatom community of the River Adige (North-Eastern Italy) along a hydrological gradient. Hydrobiologia , 639, 37–42.CrossRefGoogle Scholar
Domingues, R.B. and Galvão, H., 2007. Phytoplancton and environmental variability in a dam regulated temperate estuary. Hydrobiologia , 586, 117–134.CrossRefGoogle Scholar
DWD, 2010. Mean values of the precipitation and temperature for the period 1961–1990. http://www.dwd.de (last accessed 18 June 2010).Google Scholar
Fohrer, N., Schmalz, B., Tavares, F. and Golon, J., 2007. Ansätze zur Integration von landwirtschaftlichen Drainagen in die Modellierung des Landschaftswasserhaushalts von mesoskaligen Tieflandeinzugsgebieten. Hydrol. Wasserbewirtschaftung , 51, 164–169.Google Scholar
Friedrich, G. and Pohlmann, M., 2009. Long-term plankton studies at the lower Rhine/Germany. Limnologica , 39, 14–39.CrossRefGoogle Scholar
Ha, K., Kim, H.W. and Joo, G.J., 1998. The phytoplankton succession in the lower part of hypertrophic Nakdong River (Mulgum), South Korea. Hydrobiologia , 369/370, 217–227.CrossRefGoogle Scholar
Hillebrand, H., Dürselen, C.D., Kirschtel, D., Pollingher, U. and Zohary, Y., 1999. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. , 35, 403–424.CrossRefGoogle Scholar
Hötzel, G. and Croome, R., 1999. A Phytoplankton Methods Manual for Australian Freshwaters. Land and Water Resources Research and Development Corporation, Canberra.Google Scholar
Huang, X.F., 2000. Survey, observation and analysis of lake ecology, Standard Press of China, Beijing.Google Scholar
Jeong, K.S., Kim, D.K., Jung, J.M., Kim, M.C. and Joo, G.J., 2008. Non-linear autoregressive modelling by temporal recurrent neural networks for the prediction of freshwater phytoplankton dynamics. Ecol. Indicators , 211, 292–300.Google Scholar
Köhler, J., Bahnwart, M. and Ockenfeld, K., 2002. Growth and loss processes of riverine phytoplankton in relation to water depth. Int. Rev. Hydrobiol. , 87, 241–254.3.0.CO;2-A>CrossRefGoogle Scholar
Kraatz, W.C., 1940. A comparison of plankton counts from the trap-net and water bottle centrifuge techniques. Ohio J. Sci. , 40, 151–161.Google Scholar
Lange-Bertalot, H., 2000a. Iconographia diatomologica. Annotated diatom micrographs, 7. Diversity-taxonomy-identification. Diatom flora of marine coastes I, Koeltz Scientific Books, Koenigstein, Germany.Google Scholar
Lange-Bertalot, H., 2000b. Iconographia diatomologica. Annotated diatom micrographs, 9. Phytogeography-diversity-taxonomy. Diatoms of Andes, Koeltz Scientific Books, Koenigstein, Germany.Google Scholar
Lange-Bertalot, H., 2005. Iconographia diatomologica. Annotated diatom micrographs, 15. Taxonomy-biogeography-diversity. Diatoms of Uruguay, Koeltz Scientific Books, Koenigstein, Germany.Google Scholar
Lange-Bertalot, H., 2007. Iconographia diatomologica. Annotated diatom micrographs, 18. Diversity-taxonomy-biogeography. Tropical diatoms of South America II, Koeltz Scientific Books, Koenigstein, Germany.Google Scholar
Lepš, J. and Šmilauer, P., 2003. Multivariate analysis of ecological data using CANOCO, Cambridge University Press, New York.CrossRefGoogle Scholar
Majaneva, M., Autio, R., Huttunen, M., Kuosa, H. and Kuparinen, J., 2009. Phytoplankton monitoring: the effect of sampling methods used during different stratification and bloom conditions in the Baltic Sea. Boreal Environ. Res. , 14, 313–322.Google Scholar
Mischke, U. and Behrendt, H., 2007. Handbuch zum Bewertungsverfahren von Fließgewässern mittels Phytoplankton zur Umsetzung der EU-Wasserrahmenrichtlinie in Deutschland, WeißenseeVerlag, Berlin. ISBN 978-3-89998-105-6, 88 pp. (in German).Google Scholar
Paasche, E. and Ostergren, I., 1980. The annual cycle of plankton diatom growth and silica production in the inner Oslofjord. Limn. Oceanogr. , 25, 481–494.Google Scholar
Padisák, J. and Adrian, R., 1999. Biovolumen. In: Tümpling, V.W. and Friedrich, G. (eds.), Methoden der biologischen wasseruntersuchung 2. Biologische gewässeruntersuchung, Gustav Fischer Verlag, Jena, Chapter 5.1, 334–367.Google Scholar
Padisák, J., Krienitz, L. and Scheffler, W., 1999. Phytoplankton. In: Tümpling, V.W. and Friedrich, G. (eds.), Biologischen wasseruntersuchung 2. Biologische gewässeruntersuchung, Gustav Fischer Verlag, Jena, Chapter 3.6 , 35–53.Google Scholar
Piirsoo, K., Pall, P., Tuvikene, A. and Viik, M., 2008. Temporal and spatial patterns of phytoplankton in a temperate lowland river (Emajõgi, Estonia). J. Plank. Res. , 30, 1285–1295.CrossRefGoogle Scholar
Plenković-Moraj, A., Gligora, M., Kralj, K. and Mustafić, P., 2007. Diatoms in monitoring of Drava River, Croatia. Arch. Hydrobiol. Suppl. , 161 (3–4), 511–525.Google Scholar
Porter, S.D., 2008. Algal attributes: an autecological classification of algal taxa collected by the National Water-Quality Assessment Program. U.S. Geological Survey Data Series 329, http://pubs.usgs.gov/ds/ds329/ (accessed October 2010).CrossRefGoogle Scholar
Reavie, E., Jicha, T., Angradi, T., Bolgrien, D. and Hill, B., 2010. Algal assemblages for large river monitoring: comparison among biovolume, absolute and relative abundance metrics. Ecol. Indicators , 10, 167–177.CrossRefGoogle Scholar
Round, F.E., Crawford, R.M. and Mann, D.G., 1990. The diatoms: biology and morphology of the genera, Cambridge University Press, Cambridge.Google Scholar
Sabater, S., Artigas, J., Duran, C., Pardos, M., Romani, A.M., Tornes, E. and Ylla, I., 2008. Longitudinal development of chlorophyll and phytoplankton assemblages in a regulated large river (the Ebro River). Sci. Total Environ. , 404, 196–206.CrossRefGoogle Scholar
Schmalz, B. and Fohrer, N., 2010. Ecohydrological research in the German lowland catchment Kielstu. IAHS Publ. , 336, 115–120.Google Scholar
Simonsen, R., 1987. Atlas and catalogue of the diatom types of Friedrich Hustedt, Volumes 1–3, Catalog. J. Cramer, Berlin.Google Scholar
Stevenson, R.J. and Hashim, S., 1989. Variation in diatom community structure among habitats in sandy streams. J. Phycol. , 25, 678–686.CrossRefGoogle Scholar
Sumorok, B., Zelazna-Wieczorek, J. and Kostrzewa, K., 2009. Qualitative and quantitative phytoseston changes in two different stream-order river segments over a period of twelve years (Grabia and Brodnia, central Poland). Inst. Oceanogr. , 38, 55–63.Google Scholar
Tangen, K., 1978. Nets. In: Sournia, A. (ed.), Phytoplankton manual, UNESCO, Norwich, p. 50.Google Scholar
Trifonova, I.S. and Pavlova, O.A., 2004. Assessment of the trophic state of Lake Ladoga tributaries and the Neva River by phytoplankton. Water Resour. , 31, 679–688.CrossRefGoogle Scholar
Trifonova, I.S., Pavlova, O.A. and Rusanov, A.G., 2007. Phytoplankton as an indicator of water quality in the rivers of the Lake Ladoga basin and its relation to environmental factors. Arch. Hydrobiol. Suppl. , 161 (3–4), 527–549.Google Scholar
U.S. Environmental Protection Agency (U.S. EPA), 1997. Lake Michigan Mass Balance, Methods Compendium, Vol. 3: LMMB 065 (ESS Method 340.2), U.S. Environmental Protection Agency, Great Lakes National Program Office, Chicago.Google Scholar
van Dam, H., Mertens, A. and Sinkeldam, J., 1994. A coded checklist and ecological indicator values of freshwater diatoms from the Netherlands. Netherlands J. Aquat. Ecol. , 28, 117–133.Google Scholar
Wang, Y.K., Stevenson, R.J. and Metzmeier, L., 2005. Development and evaluation of a diatom-based index of biotic integrity for the Interior Plateau Ecoregion, USA. J. North Am. Bentholog. Soc. , 24, 990–1008.CrossRefGoogle Scholar
Weilhoefer, C.L. and Pan, Y.D., 2007. A comparison of diatom assemblages generated by two sampling protocols. J. North Am. Bentholog. Soc. , 26, 308–318.CrossRefGoogle Scholar
Wu, N.C., Schmalz, B. and Fohrer, N., 2011. Distribution of phytoplankton in a German lowland river in relation to environmental factors. J. Plankt. Res. , 33, 807–820.CrossRefGoogle Scholar
Wu, N.C., Tang, T., Zhou, S.C., Fu, X.C., Jiang, W.X., Li, F.Q. and Cai, Q.H., 2007. Influence of cascaded exploitation of small hydropower on phytoplankton in Xiangxi River. Chin. J. Appl. Ecol. , 18, 1091–1096.Google ScholarPubMed
Xu, Y.Y., Cai, Q.H., Ye, L., Zhou, S.C. and Han, X.Q., 2009. Spring diatom blooming phases in a representative eutrophic bay of the Three-Gorges Reservoir, China. J. Freshw. Ecol. , 24, 191–198.CrossRefGoogle Scholar
Zhou, G.J., Kuang, Q.J., Hu, Z.Y. and Cai, Q.H., 2006. Study on the succession of algae and the trend of water-bloom occurred in Xiangxi Bay. Acta Hydrobiol. Sin. , 30, 42–46.Google Scholar