Free Access
Ann. Limnol. - Int. J. Lim.
Volume 52
Page(s) 217 - 233
Published online 14 April 2016
  • Abonyi A., Leitão M., Lançon A.M. and Padisák J., 2012. Phytoplankton functional groups as indicators of human impacts along the River Loire (France). Hydrobiologia, 698, 233–249. [CrossRef] [Google Scholar]
  • Abonyi A., Leitão M., Stanković I., Borics G., Várbíró G. and Padisák J., 2014. A large river (River Loire, France) survey to compare phytoplankton functional approaches: do they display river zones in similar ways? Ecol. Indic., 46, 11–22. [CrossRef] [Google Scholar]
  • Baker K.K. and Baker A.L., 1981. Seasonal succession of the phytoplankton in the upper Mississippi River. Hydrobiologia, 83, 295–301. [CrossRef] [Google Scholar]
  • Billen G., Garnier J. and Rousseau V., 2005. Nutrient fluxes and water quality in the drainage network of the Scheldt basin over the last 50 years. Hydrobiologia, 540, 47–67. [CrossRef] [Google Scholar]
  • Butcher R., 1932. Studies in the ecology of rivers. II. The microflora of rivers with special reference to the algae on the river bed. Ann. Botany, 46, 813–861. [CrossRef] [Google Scholar]
  • Chen N., Liu L., Li Y., Qiao D., Li Y., Zhang Y. and Lv Y., 2015. Morphology-based classification of functional groups for potamoplankton. J. Limnol., 74, 559–571. [Google Scholar]
  • Clarke K.R. and Warwick R.M., 2001. Changes in Marine Communities: an Approach to Statistical Analysis and Interpretation (2nd edn), PRIMER-E, Plymouth. [Google Scholar]
  • De'ath G. and Fabricius K.E., 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81, 3178–3192. [CrossRef] [Google Scholar]
  • De Oliveira M.D. and Calheiros D.F., 2000. Flood pulse influence on phytoplankton communities of the south Pantanal floodplain, Brazil. Hydrobiologia, 427, 101–112. [CrossRef] [Google Scholar]
  • Duarte C.M., Marrasé C., Vaqué D. and Estrada M., 1990. Counting error and the quantitative analysis of phytoplankton communities. J. Plankton Res., 12, 295–304. [CrossRef] [Google Scholar]
  • Gallina N., Salmaso N., Morabito G. and Beniston M., 2013. Phytoplankton configuration in six deep lakes in the peri-Alpine region: are the key drivers related to eutrophication and climate? Aquatic Ecol., 47, 177–193. [CrossRef] [Google Scholar]
  • Gamier J., Billen G. and Coste M., 1995. Seasonal succession of diatoms and Chlorophyceae in the drainage network of the Seine River: observations and modeling. Limnol. Oceanogr, 40, 750–765. [Google Scholar]
  • Garnier J., Billen G., Hannon E., Fonbonne S., Videnina Y. and Soulie M., 2002. Modelling the transfer and retention of nutrients in the drainage network of the Danube River. Estuar. Coast. Shelf Sci., 54, 285–308. [CrossRef] [Google Scholar]
  • Garnier J., Némery J., Billen G. and Théry S., 2005. Nutrient dynamics and control of eutrophication in the Marne River system: modelling the role of exchangeable phosphorus. J. Hydrol., 304, 397–412. [CrossRef] [Google Scholar]
  • Garrad P.N. and Hey R.D., 1987. Boat traffic, sediment resuspension and turbidity in a Broadland river. J. Hydrol., 95, 289–297. [CrossRef] [Google Scholar]
  • Ger K.A., Hansson L.A. and Lürling M., 2014. Understanding cyanobacteria-zooplankton interactions in a more eutrophic world. Freshwater Biol., 59, 1783–1798. [CrossRef] [Google Scholar]
  • Gliwicz Z.M. and Pijanowska J., 1989. The role of predation in zooplankton succession. In: Sommer U. (ed.), Plankton Ecology, Brock/Springer Series in Contemporary Bioscience. Springer, Berlin, Heidelberg, 253–296. [CrossRef] [Google 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. Phytoplankton and Trophic Gradients, 129, 217–227. [CrossRef] [Google Scholar]
  • Ha K., Jang M.-H. and Joo G.-J., 2002. Spatial and temporal dynamics of phytoplankton communities along a regulated river system, the Nakdong River, Korea. Hydrobiologia, 470, 235–245. [CrossRef] [Google Scholar]
  • Hasle G., 1978. The inverted microscope method. In: Sournia A. (ed.), Phytoplankton Manual. Unesco, Paris, France, 88–96. [Google Scholar]
  • Hillebrand H., Dürselen C.D., Kirschtel D., Pollingher U. and Zohary T., 1999. Biovolume calculation for pelagic and benthic microalgae. J. Phycol., 35, 403–424. [Google Scholar]
  • Hilton J., O'hare M., Bowes M.J. and Jones J.I., 2006. How green is my river? A new paradigm of eutrophication in rivers. Sci. Total Environ., 365, 66–83. [CrossRef] [PubMed] [Google Scholar]
  • Hu R., Han B. and Naselli-Flores L., 2013. Comparing biological classifications of freshwater phytoplankton: a case study from South China. Hydrobiologia, 701, 219–233. [CrossRef] [Google Scholar]
  • Javornicky P. and Komárková J., 1973. The changes in several parameters of plankton primary productivity in Slapy Reservoir 1960–1967, their mutual correlations and correlations with the main ecological factors. [Google Scholar]
  • Kirk J.T.O., 1994. Light and Photosynthesis in Aquatic Ecosystems, Cambridge University Press, Cambridge. [CrossRef] [Google Scholar]
  • Kiss K., 1994. Trophic level and eutrophication of the River Danube in Hungary. Int. Ver. Theor. Angew. Limnol. Verh., 25, 1688–1691. [Google Scholar]
  • Kruk C. and Segura A., 2012. The habitat template of phytoplankton morphology-based functional groups. Hydrobiologia, 698, 191–202. [CrossRef] [Google Scholar]
  • Kruk C., Huszar V.L., Peeters E.T., Bonilla S., Costa L., Lrling M., Reynolds C.S. and Scheffer M., 2010. A morphological classification capturing functional variation in phytoplankton. Freshwater Biol., 55, 614–627. [Google Scholar]
  • Lehman P., Sevier J., Giulianotti J. and Johnson M., 2004. Sources of oxygen demand in the lower San Joaquin River, California. Estuaries, 27, 405–418. [CrossRef] [Google Scholar]
  • Lepš J. and Šmilauer P., 2003. Multivariate Analysis of Ecological Data Using CANOCO, Cambridge University Press, Cambridge. [Google Scholar]
  • Lund J.W.G., Kipling C. and Cren E.D., 1958. The inverted microscope method of estimating algal numbers and the statistical basis of estimations by counting. Hydrobiologia, 11, 143–170. [CrossRef] [Google Scholar]
  • Mihaljević M., Špoljarić D., Stević F. and Pfeiffer T.Ž., 2013. Assessment of flood-induced changes of phytoplankton along a river–floodplain system using the morpho-functional approach. Environ. Monit. Assess., 185, 8601–8619. [CrossRef] [PubMed] [Google Scholar]
  • Mihaljević M., Stević F., Špoljarić D. and Pfeiffer T.Ž., 2014. Application of Morpho-Functional Classifications in the evaluation of phytoplankton changes in the Danube River. Acta Zool. Bulg., 66, 153–158. [Google Scholar]
  • Moss B., 1977. Conservation problems in the Norfolk Broads and rivers of East Anglia, England – Phytoplankton, boats and the causes of turbidity. Biol. Conserv., 12, 95–114. [CrossRef] [Google Scholar]
  • Naselli-Flores L., Padisák J., Dokulil M.T. and Chorus I., 2003. Equilibrium/steady-state concept in phytoplankton ecology. 502, 395–403. [Google Scholar]
  • Padisák J., Soróczki-Pintér E. and Rezner Z., 2003. Sinking properties of some phytoplankton shapes and the relation of form resistance to morphological diversity of plankton – an experimental study. Aquat. Biodiv., 500, 243–257. [CrossRef] [Google Scholar]
  • Palau A., 2006. Integrated environmental management of current reservoirs and regulated rivers. Limnetica, 25, 287–302. [Google Scholar]
  • Reynolds C. and Descy J.-P., 1996. The production, biomass and structure of phytoplankton in large rivers. Large Rivers, 10, 161–187. [Google Scholar]
  • Reynolds C.S., 1994. The long, the short and the stalled: on the attributes of phytoplankton selected by physical mixing in lakes and rivers. In: Descy P., Reynolds C. and Padisák J. (eds.), Phytoplankton in Turbid Environments: Rivers and Shallow Lakes, Developments in Hydrobiology, Springer, Netherlands, 9–21. [CrossRef] [Google Scholar]
  • Reynolds C.S., 1998. What factors influence the species composition of phytoplankton in lakes of different trophic status? Hydrobiologia, 369, 11–26. [CrossRef] [Google Scholar]
  • Reynolds C.S., 2006. The Ecology of Phytoplankton, Cambridge University Press, Cambridge. [Google Scholar]
  • Salmaso N. and Padisák J., 2007. Morpho-Functional Groups and phytoplankton development in two deep lakes (Lake Garda, Italy and Lake Stechlin, Germany). Hydrobiologia, 578, 97–112. [CrossRef] [Google Scholar]
  • Salmaso N., Naselli-Flores L. and Padisák J., 2012. Impairing the largest and most productive forest on our planet: how do human activities impact phytoplankton? Hydrobiologia, 698, 375–384. [CrossRef] [Google Scholar]
  • Sommer U., Gliwicz Z.M., Lampert W. and Duncan A., 1986. The PE-model of seasonal succession of planktonic events in fresh waters. Arch. Hydrobiol., 106, 433–471. [Google Scholar]
  • Sparks R.E., 1995. Need for ecosystem management of large rivers and their floodplains. BioScience, 45, 168–182. [CrossRef] [Google Scholar]
  • Tolotti M., Boscaini A. and Salmaso N., 2010. Comparative analysis of phytoplankton patterns in two modified lakes with contrasting hydrological features. Aquat. Sci., 72, 213–226. [CrossRef] [Google Scholar]
  • Tolotti M., Thies H., Nickus U. and Psenner R., 2012. Temperature modulated effects of nutrients on phytoplankton changes in a mountain lake. Hydrobiologia, 698, 61–75. [CrossRef] [Google Scholar]
  • Townsend C., 1996. Concepts in river ecology: pattern and process in the catchment hierarchy. Large Rivers, 10, 3–21. [Google Scholar]
  • Turner R.E., Rabalais N.N., Justic D. and Dortch Q., 2003. Global patterns of dissolved N, P and Si in large rivers. Biogeochemistry, 64, 297–317. [CrossRef] [Google Scholar]
  • Wehr J.D. and Descy J.P., 1998. Use of phytoplankton in large river management. J. Phycol., 34, 741–749. [CrossRef] [Google Scholar]
  • Weithoff G., 2003. The concepts of ‘plant functional types’ and ‘functional diversity’ in lake phytoplankton – a new understanding of phytoplankton ecology? Freshwater Biol., 48, 1669–1675. [CrossRef] [Google Scholar]

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