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Marine Ecology Progress Series

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MEPS 473:53-66 (2013)  -  DOI:

Harmonizing large data sets reveals novel patterns in the Baltic Sea phytoplankton community structure

Kalle Olli1,*, Olga Trikk1, Riina Klais1, Robert Ptacnik2, Tom Andersen3, Sirpa Lehtinen4, Timo Tamminen4

1Institute of Ecology and Earth Sciences, University of Tartu, 51005 Tartu, Estonia
2ICBM, University of Oldenburg, 26382 Wilhelmshaven, Germany
3Department of Biology, University of Oslo, 0316 Oslo, Norway
4Marine Research Centre, Finnish Environment Institute, 00251 Helsinki, Finland

ABSTRACT: Phytoplankton forms the basis of aquatic food webs, and shifts in community composition reflect changes in environmental conditions. Despite the accepted importance, the processes behind maintaining spatial and temporal community structure and biodiversity at the base of the aquatic food web remain poorly described and understood. A recognized challenge hampering validation of ecological models and meta-analysis is the scarcity of large phytoplankton data sets. Compared to other aquatic data, harmonization of quantitative phytoplankton data sets from different sources and academic institutions has remained a major challenge. Here we demonstrate and examine processes used to compile and harmonize a large multi-sourced phytoplankton data set covering 40 yr of monitoring and over 15000 quantitative samples from the Baltic Sea. We show differences in the quality of data among countries and analyze autocorrelation scales in field data. Phytoplankton community composition showed positive autocorrelation at a temporal scale of less than 30 d and had a recurrent pattern at a yearly interval. Both total biomass and community composition showed a positive spatial autocorrelation, but the extent of the data determines the autocorrelation scale and strength. We introduce a new strategy to select the best performing model to assess regional taxon richness in phytoplankton field data. The Weibull 4-parameter model showed both the best fit with data and robust parameter estimates at varying sample size.

KEY WORDS:  Meta-analysis · Phytoplankton · Monitoring · Time-series · Autocorrelation · Species richness

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Cite this article as: Olli K, Trikk O, Klais R, Ptacnik R, Andersen T, Lehtinen S, Tamminen T (2013) Harmonizing large data sets reveals novel patterns in the Baltic Sea phytoplankton community structure. Mar Ecol Prog Ser 473:53-66.

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