Analysing a large dataset on long-term monitoring of water quality and plankton with the SOM clustering

Abstract

The Self-Organizing Map (SOM) proved to be the method of choice for analysing a large heterogeneous ecological dataset. In addition to distributing the data into clusters, the SOM enabled hunting for correlations between the data components. This revealed logical and plausible relationships between and within the environment and groups of organisms. The main conclusions derived from the results were: (i) the structure of early summer plankton community significantly differed from that of late summer community in Lake Pyhäselkä and (ii) plankton community in late summer was characterized by two functional groups. The first group was formed mainly by phytoplankton, rotifers, and small cladocerans, such as Bosmina spp., and driven by water temperature. The second group was formed by small copepods and the abundant generalist herbivorous cladocerans Daphnia cristata and Limnosida frontosa, which, in turn, associated with chlorophyll a concentration. Biomasses of Bosmina spp. and D. cristata showed decreasing monotonic trends during a 20-year study period supposedly due to oligotrophication. Versatile possibilities to cluster data and hunt for correlations between data components offered by the SOM decisively helped to reveal associations across the original variables and draw conclusions. The results would have been undetectable solely on the basis of unorganised values.

Keywords

• boreal lake
• chlorophyll
• phytoplankton
• Self-Organizing map
• water temperature
• zooplankton