Ecological relationships between phytoplankton communities and different spatial scales in European reservoirs: implications at catchment level monitoring programmes
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Phytoplankton communities are structured by factors acting over temporal and spatial scales. Identifying which factors are driving spatial patterns in aquatic communities is the central aim of ecology. In this study, data sets of phytoplankton communities and environmental data of two Portuguese reservoirs types (lowland “riverine reservoirs” and higher altitude “artificial lake reservoirs”) were used to determine the importance of environmental variables at different spatial (geographical, regional and local) and time scales (seasons, years) on the community structure. In all the data sets, the multivariate ordination technique Canonical Correspondence Analysis (CCA) showed that regional and local scales explained the majority (9–18% and 13–19%, respectively) of the taxa variance. However, for “riverine reservoirs”, time variables were more important, explaining 27% of the variability in phytoplankton assemblages. Variance partitioning was used to assess the individual importance of the three spatial scales and time for the community structure of the two reservoir types. The majority of among-site variability (5.9–21.4%) was accounted for by time variables, with local, regional, and geographical scale variables accounting for 3.3–5.6%, 3.7–4.5% and 2.6–2.9%, respectively. The effects of different spatial scales on phytoplankton communities were clearly interrelated; thus, implying that phytoplankton assemblages are capable of detecting stress from catchment to site scales.
KeywordsPhytoplankton Ecological status Reservoirs Spatial scales Multivariate analysis Partial constrained ordination
This study was carried out within the framework of collaboration agreements between INAG (National Water Institute) and other universities, namely the UTAD (University of Trás-os-Montes e Alto Douro) for the study of Portuguese reservoirs. We would like to thank the LABELEC staff for the environmental and phytoplankton data, namely to Engº. Lourenço Gil. The authors also thank the anonymous reviewers, and the Editor who helped to improve the manuscript.
- APHA, 1995. Standard Methods for the Examination of Water and Wastewater, 19th ed. American Public Health Association, Washington, DC.Google Scholar
- Brazner, J. C., N. P. Danz, G. J. Niemi, R. R. Regal, A. S. Trebitz, R. W. Howe, J. M. Hanowski, L. B. Johnson, J. J. H. Ciborowski, C. A. Johnston, E. D. Reavie, V. J. Brady & G. V. Sgro, 2007. Evaluation of geographic, geomorphic and human influences on Great Lakes wetland indicators: a multi-assemblage approach. Ecological Indicators 7: 610–635.CrossRefGoogle Scholar
- Çelik, K. & T. Ongun, 2007. The relationships between certain physical and chemichal variables and the seasonal dynamics of phytoplankton assemblages of two inlets of a shallow hypertrophic lake with different nutrient inputs. Environmental Monitoring and Assessment 124: 321–330.PubMedCrossRefGoogle Scholar
- Clarke, G., M. Kerman, A. Marchetto, S. Sorvari & J. Catalan, 2005. Using diatoms to assess geographical patterns of change in high-altitude European lakes from pre-industrial times to the present day. Aquatic Sciences 67: 224–236.Google Scholar
- Danz, N. P., G. J. Niemi, R. R. Regal, T. P. Hollenhorst, L. B. Johnson, J. M. Hanowski, R. P. Axler, J. J. H. Ciborowski, T. Hrabik, V. J. Brady, J. R. Kelly, J. C. Brazner, R. W. Howe, C. A. Johnston & G. E. Host, 2007. Integrated gradients of anthropogenic stress in the U.S. Great Lakes basin. Environmental Management 39: 631–647.PubMedCrossRefGoogle Scholar
- Downes, B. J., L. A. Barmuta, P. G. Fairweather, D. P. Faith, M. J. Keought, P. S. Lake, B. D. Mapstone & G. P. Quinn, 2002. Monitoring Ecological Impacts Concepts and Practice in Flowing Waters. Cambridge University Press, Cambridge: 434.Google Scholar
- European Commission, 2000. Directive 2000/60/EC of The European Parliament and of the Council—Establishing a Framework for Community Action in the Field of Water Policy. Brussels, Belgium, 23 October 2000.Google Scholar
- Fallu, M. A., N. Allaire & R. Pienitz, 2002. Distribution of freshwater diatoms in 64 Labrador (Canada) lakes: species–environment relationships along latitudinal gradients and reconstruction models for water colour and alkalinity. Canadian Journal of Fisheries and Aquatic Sciences 59: 329–349.CrossRefGoogle Scholar
- GIG, 2007. Lake Mediterranean GIG. Joint Research Centre, European Commission. URL: http://circa.europa.eu/Public/irc/jrc/jrc_eewai/library?l=/milestone_reports/milestone_reports_2007/lakes&vm=detailed&sb=Title.
- IGEOE, Instituto Geográfico do Exército (Geografic Military Institute), 2006. Corine Land Cover 1990 and 2000. http://www.igeoe.pt/.
- INAG, Instituto Nacional da Água (National Water Institute), 2006. Relatório intercalar do projecto “Qualidade ecológica e gestão integrada de albufeiras”. (in Portuguese).Google Scholar
- INE, Instituto Nacional de Estatística (National Statistics Institute), 2006. http://www.ine.pt.
- Legendre, P. & L. Legendre, 1998. Numerical Ecology, 2nd ed. Elsevier, New York.Google Scholar
- Moss, B., S. Stephen, C. Alvarez, E. Becares, W. van de Bund, E. van Donk, E. de Eyto, T. Feldmann, C. Fernández-Aláez, M. Fernández-Aláez, R. J. M. Franken, F. García-Criado, E. Gross, M. Gyllstrom, L.-A. Hansson, K. Irvine, A. Järvalt, J.-P. Jenssen, E. Jeppesen, T. Kairesalo, R. Kornijow, T. Krause, H. Künnap, A. Laas, L. Lill, H. Luup, M. A. Miracle, P. Nõges, T. Nõges, M. Nykannen, O. Ott, E. T. H. M. Peeters, G. Phillips, S. Romo, J. Salujõe, M. Scheffer, K. Siewertsen, T. Tesch, H. Timm, L. Tuvikene, I. Tonno, K. Vakilainnen & T. Virro, 2003. The determination of ecological quality in shallow lakes—a tested expert system (ECOFRAME) for implementation of the European Water Framework Directive. Aquatic Conservation: Marine and Freshwater Systems 13: 507–550.CrossRefGoogle Scholar
- Negro, A. I. & C. De Hoyos, 2005. Relationships between diatoms and the environment in Spanish reservoirs. Limnetica 24: 133–144.Google Scholar
- Podani, J., 2000. Introduction to the Exploration of Multivariate Biological Data. Backhuys Publishers, Leiden.Google Scholar
- Rosenberg, D. M. & V. H. Resh, 1993. Introduction to freshwater biomonitoring and benthic macroinvertebrates. In Rosenberg, D. M. & V. H. Resh (eds), Freshwater Biomonitoring and Benthic Macroinvertebrates. Chapman and Hall, New York: 1–9.Google Scholar
- Sabater, S. & J. Nolla, 1991. Distributional patterns of phytoplankton in Spanish reservoirs. First results and comparison after fifteen years. Verhandlungen der internationale Vereinigung für Limnologie 24: 1371–1375.Google Scholar
- Soininen, J., 2007. Environmental and spatial control of freshwater diatoms–a review. Diatom Research 22: 473–490.Google Scholar
- ter Braak, C. J. F., 1987. Ordination. In Jongman, R. H. G., C. J. F. ter Braak & O. F. R. van Tongeren (eds), Data Analysis in Community and Landscape Ecology. Pudoc, Wageningen: 91–173.Google Scholar
- ter Braak, C. J. F. & P. Šmilauer, 2002. CANOCO Reference Manual and User’s Guide to Canoco for Windows Software for Canonical Community Ordination (Version 4.5). Microcomputer Power, Ithaca, NY: 352.Google Scholar
- Vasconcelos, V. M., 2001. Toxic freshwater cyanobacteria and their toxins in Portugal. In Chorus, I. (ed.), Cyanotoxins—Occurrence, Effects, Controlling Factors. Springer Publishers, Heidelberg: 64–69.Google Scholar
- Venrick, E. L., 1978. How many cells to count? In Sournia, A. (ed.), Phytoplankton Manual. UNESCO: 167–180.Google Scholar