analysing a large dataset on long-term monitoring of water quality and plankton with the som clustering
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2012
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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.
| Reference Key |
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|---|---|
| Authors | ;Voutilainen A.;Rahkola-Sorsa M.;Parviainen J.;Huttunen M. J.;Viljanen M. |
| Journal | american journal of physiology renal physiology |
| Year | 2012 |
| DOI |
10.1051/kmae/2012021
|
| URL | |
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