TIME-SERIES OF MULTIVARIATE DATA IN AQUATIC ECOLOGY

Coupling of multivariate methods and time series analysis can be ueful for studying dynamics of aquatic communities. This is demonstratred w ith a data set from the pelagic area of an oligo-mesotrophic lake in C entral Spain during 61 consecutive days of Autumn overturn. Abiotic va riables, phytoplankton species and their total biomass were traced. Sp ecies abundance and specific biomass were considered as indices of com munity 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 abilit ies and cell size was associated to the main factors of the community structure matrix whereas phosphorus limitation and species responses t o buoyancy represented the main factors of the biomass matrix. Coordin ates 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 i n most time series. Lagged responses of atmospheric forcing and nitrog en on phytoplankton community structure and resource partitioning at s cales of 1-7 days were also shown. Overall phytoplankton biomass did n ot show significant delayed responses, thereby suggesting that it migh t be resulting from the interplay of other non-studied factors.