Free Access
Issue
Ann. Limnol. - Int. J. Lim.
Volume 51, Number 3, 2015
Page(s) 249 - 259
DOI https://doi.org/10.1051/limn/2015019
Published online 05 October 2015
  • Abonyi A., Leitão M., Stanković I. et al., 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]
  • Alkawri A. and Gamoyo M., 2014. Remote sensing of phytoplankton distribution in the Red Sea and Gulf of Aden. Acta Oceanol. Sin., 33(9), 93–99. [CrossRef] [Google Scholar]
  • Améziane T., Dauta A. and Le Cohu R., 2003. Origin and transport of phytoplankton in a large river: the Garonne, France. Arch. Hydrobiol., 156(3), 385–404. [CrossRef] [Google Scholar]
  • Barron R.K., Siegel D.A. and Guillocheau N., 2014. Evaluating the importance of phytoplankton community structure to the optical properties of the Santa Barbara Channel, California. Limnol. Oceanogr., 59(3), 927–946. [CrossRef] [Google Scholar]
  • Battauz Y.S., de Paggi S.B.J. and Paggi J.C., 2014. Passive zooplankton community in dry littoral sediment: reservoir of diversity and potential source of dispersal in a subtropical floodplain lake of the Middle Parana River (Santa Fe, Argentina). Int. Rev. Hydrobiol., 99(3), 277–286. [CrossRef] [Google Scholar]
  • Bayer A.E. and Folger J., 1966. Some correlates of a citation measure of productivity in science. Sociol. Educ., 39(4), 381–390. [CrossRef] [Google Scholar]
  • Ben Mustapha Z., Alvain S., Jamet C. et al., 2014. Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote Sens. Environ., 146, 97–112. [CrossRef] [Google Scholar]
  • Biddanda B.A., Coleman D.F., Johengen T.H. et al., 2006. Exploration of a submerged sinkhole ecosystem in Lake Huron. Ecosystems, 9(5), 828–842. [CrossRef] [Google Scholar]
  • Bolpagni R., Bresciani M., Laini A. et al., 2014. Remote sensing of phytoplankton–macrophyte coexistence in shallow hypereutrophic fluvial lakes. Hydrobiologia, 737(1), 67–76. [CrossRef] [Google Scholar]
  • Boyce D.G., Lewis M.R. and Worm B., 2010. Global phytoplankton decline over the past century. Nature, 466, 591–596. [CrossRef] [PubMed] [Google Scholar]
  • Callon M., Courtial J.P. and Laville F., 1991. Co-word analysis as a tool for describing the network of interactions between basic and technological research – the case of polymer chemistry. Scientometrics, 22(1), 155–205. [CrossRef] [Google Scholar]
  • Carneiro F.M., Nabout J.C. and Bini L.M., 2008. Trends in the scientific literature on phytoplankton. Limnology, 9, 153–158. [CrossRef] [Google Scholar]
  • Cheng B., Wang M.H., Morch A.I. et al., 2014. Research on e-learning in the workplace 2000–2012: a bibliometric analysis of the literature. Educ. Res. Rev., 11, 56–72. [CrossRef] [Google Scholar]
  • Coale K.H., Johnson K.S., Fitzwater S.E. et al., 1996. A massive phytoplankton bloom induced by an ecosystem-scale iron fertilization experiment in the equatorial Pacific Ocean. Nature, 383, 495–501. [CrossRef] [PubMed] [Google Scholar]
  • Costa L.S., Huszar V.L.M. and Ovalle A.R., 2009. Phytoplankton functional groups in a tropical estuary: hydrological control and nutrient limitation. Estuaries Coasts, 32(3), 508–521. [CrossRef] [Google Scholar]
  • Davis J.R. and Koop K., 2006. Eutrophication in Australian rivers, reservoirs and estuaries ― a southern hemisphere perspective on the science and its implications. Hydrobiologia, 559(1), 23–76. [CrossRef] [Google Scholar]
  • Elliott J.A., Irish A.E., Reynolds C.S. et al., 2000. Modelling freshwater phytoplankton communities: an exercise in validation. Ecol. Modell., 128(1), 19–26. [CrossRef] [Google Scholar]
  • Elliott J.A., Persson I., Thackeray S.J. et al., 2007. Phytoplankton modeling of Lake Erken, Sweden by linking the models PROBE and PROTECH. Ecol. Modell., 202(3–4), 421–426. [CrossRef] [Google Scholar]
  • Gaston K.J., 2000. Global patterns in biodiversity. Nature, 405, 220–227. [CrossRef] [PubMed] [Google Scholar]
  • Grelowski A., Pastuszak M., Sitek S. et al., 2000. Budget calculations of nitrogen, phosphorus and BOD5 passing through the Oder estuary. J. Mar. Syst., 25(3–4), 221–237. [CrossRef] [Google Scholar]
  • Ho Y.S., 2014. Classic articles on social work field in Social Science Citation Index: a bibliometric analysis. Scientometrics, 98, 137–155. [CrossRef] [Google Scholar]
  • Irigoien X., Huisman J. and Harris R.P., 2004. Global biodiversity patterns of marine phytoplankton and zooplankton. Nature, 429, 863–867. [CrossRef] [PubMed] [Google Scholar]
  • Keiser J. and Utzinger J., 2005. Trends in the core literature on tropical medicine: a bibliometric analysis from 1952–2002. Scientometrics, 62(3), 351–365. [CrossRef] [Google Scholar]
  • Li L.L., Ding G.H., Feng N. et al., 2009. Global stem cell research trend: bibliometric analysis as a tool for mapping of trends from 1991 to 2006. Scientometrics, 80(1), 39–58. [CrossRef] [Google Scholar]
  • Li Q.H., Chen L.L., Chen F.F. et al., 2013. Maixi River estuary to the Baihua Reservoir in the Maotiao River catchment: phytoplankton community and environmental factors. Chin. J. Oceanol. Limnol., 31(2), 290–299. [CrossRef] [Google Scholar]
  • Liao J.Q. and Huang Y., 2014. Global trend in aquatic ecosystem research from 1992 to 2011. Scientometrics, 98, 1203–1219. [CrossRef] [Google Scholar]
  • Lunetta R.S., Shao Y., Ediriwickrema J. et al., 2010. Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data. Int. J. Appl. Earth OBS, 12(2), 81–88. [CrossRef] [Google Scholar]
  • Luo Y.Q., Melillo J., Niu S.L. et al., 2010. Coordinated approaches to quantify long-term ecosystem dynamics in response to global change. Glob. Change Biol., 17(2), 843–854. [CrossRef] [Google Scholar]
  • Ma F.C., Lyu P.H., Yao Q. et al., 2013. Publication trends and knowledge maps of global translational medicine research. Scientometrics, 98(1), 221–246. [Google Scholar]
  • Macias D., Navarro G., Echevarria F. et al., 2007. Phytoplankton pigment distribution in the northwestern Alboran Sea and meteorological forcing: a remote sensing study. J. Mar. Res., 65(4), 523–543. [CrossRef] [Google Scholar]
  • Mieleitner J. and Reichert P., 2008. Modelling functional groups of phytoplankton in three lakes of different trophic state. Ecol. Modell., 211(3–4), 279–291. [CrossRef] [Google Scholar]
  • Mihaljevic M., Stevic F., Spoljaric D. et al., 2014. Application of morpho-functional classifications in the evaluation of phytoplankton changes in the Danube River. Acta Zool. Bulg. (Suppl. 7), 153–158. [Google Scholar]
  • Niu B.B., Hong S., Yuan J.F. et al., 2014. Global trends in sediment-related research in earth science during 1992–2011: a bibliometric analysis. Scientometrics, 98(1), 511–529. [CrossRef] [Google Scholar]
  • Odermatt D., Pomati F., Pitarch J. et al., 2012. MERIS observations of phytoplankton blooms in a stratified eutrophic lake. Remote Sens. Environ., 126, 232–239. [CrossRef] [Google Scholar]
  • Ohniwa R.L., Hibino A. and Takeyasu K., 2010. Trends in research foci in life science fields over the last 30 years monitored by emerging topics. Scientometrics, 85(1), 111–127. [CrossRef] [Google Scholar]
  • Ozhan K. and Bargu S., 2014. Distinct responses of Gulf of Mexico phytoplankton communities to crude oil and the dispersant corexit(A (R)) Ec9500A under different nutrient regimes. Ecotoxicology, 23(3), 370–384. [CrossRef] [PubMed] [Google Scholar]
  • Perkins M., Effler S.W. and Strait C.M., 2014. Phytoplankton absorption and the chlorophyll a-specific absorption coefficient in dynamic Onondaga Lake. Inland Waters, 4(2), 133–146. [CrossRef] [Google Scholar]
  • Reynolds C.S., 2006. The Ecology of Phytoplankton. Cambridge University Press, Cambridge. [CrossRef] [Google Scholar]
  • Serizawa H., Amemiya T. and Itoh K., 2009. Patchiness and bistability in the comprehensive cyanobacterial model (CCM). Ecol. Modell., 220(6), 764–773. [CrossRef] [Google Scholar]
  • Sin Y., Hyun B., Jeong B. et al., 2013. Impacts of eutrophic freshwater inputs on water quality and phytoplankton size structure in a temperate estuary altered by a sea dike. Mar. Environ. Res., 85, 54–63. [CrossRef] [PubMed] [Google Scholar]
  • Smetacek V., Klaas C., Strass V.H. et al., 2012. Deep carbon export from a Southern Ocean iron-fertilized diatom bloom. Nature, 487, 313–319. [CrossRef] [PubMed] [Google Scholar]
  • Smith V.H., 2003. Eutrophication of freshwater and coastal marine ecosystems a global problem. Environ. Sci. Pollut. Res., 10(2), 126–139. [CrossRef] [Google Scholar]
  • Sugimoto R., Sato T., Yoshida T. et al., 2014. Using stable nitrogen isotopes to evaluate the relative importance of external and internal nitrogen loadings on phytoplankton production in a shallow eutrophic lake (Lake Mikata, Japan). Limnol. Oceanogr., 59(1), 37–47. [CrossRef] [Google Scholar]
  • Talling J.F. and Prowse G.A., 2010. Selective recruitment and resurgence of tropical river phytoplankton: evidence from the Nile system of lakes, rivers, reservoirs and ponds. Hydrobiologia, 637(1), 187–195. [CrossRef] [Google Scholar]
  • Tekwani N., Majdi N., Mialet B. et al., 2013. Contribution of epilithic diatoms to benthic–pelagic coupling in a temperate river. Aquat. Microb. Ecol., 69, 47–57. [CrossRef] [Google Scholar]
  • Treusch A.H., Demir-Hilton E., Vergin K.L. et al., 2012. Phytoplankton distribution patterns in the northwestern Sargasso Sea revealed by small subunit rRNA genes from plastids. ISME J., 6, 481–492. [CrossRef] [PubMed] [Google Scholar]
  • Van de Waal D.B., Smith V.H., Declerck S.A.J. et al., 2014. Stoichiometric regulation of phytoplankton toxins. Ecol. Lett., 17(6), 736–742. [CrossRef] [PubMed] [Google Scholar]
  • Vannote R.L., Minshall G.W., Cummins K.W. et al., 1980. The river continuum concept. Can. J. Fish. Aquat. Sci., 37(1), 130–137. [CrossRef] [Google Scholar]
  • Van Raan A.F.J., 2005. For your citations only? Hot topics in bibliometric analysis measurement. Interdisc. Res. Perspect., 3, 50–62. [CrossRef] [Google Scholar]
  • Wen H. and Huang Y., 2012. Trends and performance of oxidative stress research from 1991 to 2010. Scientometrics, 91(1), 51–63. [CrossRef] [Google Scholar]
  • Willig M.R., Kaufman D.M. and Stevens R.D., 2003. Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Annu. Rev. Ecol. Evol. Syst., 34, 273–309. [CrossRef] [Google Scholar]
  • Yu S.P., Yang J.S. and Liu G.M., 2014. Impact assessment of Three Gorges Dam's impoundment on river dynamics in the north branch of Yangtze River estuary, China. Environ. Earth Sci., 72(2), 499–509. [CrossRef] [Google Scholar]
  • Zhang Y.H., Liu X.J., Nguyen T. et al., 2013. Global remote sensing research trends during 1991–2010: a bibliometric analysis. Scientometrics, 96, 203–219. [CrossRef] [Google Scholar]
  • Zhao J., Peter H.U., Zhang H.S. et al., 2014. Short- and long-term response of phytoplankton to ENSO in Prydz Bay, Antarctica: evidences from field measurements, remote sensing data and stratigraphic biomarker records. J. Ocean Univ. China, 13(3), 437–444. [CrossRef] [Google Scholar]
  • Zhu K.X., Bi Y.H. and Hu Z.Y., 2013. Responses of phytoplankton functional groups to the hydrologic regime in the Daning River, a tributary of Three Gorges Reservoir, China. Sci. Total Environ., 450–451, 169–177. [CrossRef] [PubMed] [Google Scholar]

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