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Time series of multivariate data in aquatic ecology

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Coupling of multivariate methods and time series analysis can be ueful for studying dynamics of aquatic communities. This is demonstratred with a data set from the pelagic area of an oligo-mesotrophic lake in Central Spain during 61 consecutive days of Autumn overturn. Abiotic variables, phytoplankton species and their total biomass were traced. Species abundance and specific biomass were considered as indices of community structure and resource partitioning, respectively. Abiotic and algal data sets were subjected to factor analyses of cases separately. Atmospheric forcing and nitrogen could be considered as the main (2) driving variables of the abiotic matrix. The coupling of motile abilities and cell size was associated to the main factors of the community structure matrix whereas phosphorus limitation and species responses to buoyancy represented the main factors of the biomass matrix. Coordinates of the two first factors could be used to mimic the trajectories in the data space. Significant short term lags (1–4 days) were found in most time series. Lagged responses of atmospheric forcing and nitrogen on phytoplankton community structure and resource partitioning at scales of 1–7 days were also shown. Overall phytoplankton biomass did not show significant delayed responses, thereby suggesting that it might be resulting from the interplay of other non-studied factors.

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Cobelas, M.A., Verdugo, M. & Rojo, C. Time series of multivariate data in aquatic ecology. Aquatic Science 57, 185–198 (1995). https://doi.org/10.1007/BF00877426

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Key words

  • Time series
  • multivariate data
  • phytoplankton
  • abiotic factors