Implementation of Learning Algorithms in Artificial Neural Networks in Patterning Long-term Ecological Data: Community Dynamics of Benthic Macroinvertebrates and Chironomids in Streams


Chon, Tae-Soo, Inn Sil Kwak and Young Seuk Park

Dept. of Biology, Pusan National University, Pusan 609-735, KOREA





To community data, sampled in a regular interval on the long-term basis, artificial nerual networks were implemented to extract information characterizing community patterns.   The Kohonen network and Adaptive Resonance Theory were utilized in combination in learning benthic macroinvertebrate communities in streams of the Suyong River collected monthly for three years.  In static manner, by regarding each monthly collection as a separate sample unit, communities were grouped into similar patterns after the training with the network.  Subsequently changes in communities in a sequenc of samplings (e.g., two-months, four-months, etc.) were also given as input to the networks to train community dynamics.  After training it was possible, both in static and dynamic mode, to recognize new data set on the on-time basis as sampling proceeded.  Through the comparative study on benthic macroinvertebrates with these learning processes, it was shown that patterns of commnity changes in chironomids were diverging, being more sensitive to the impacts of internal or external factors, while those of benthic macro-invertebrates in total appeared to be more persistent.



Key Words: Patterning community dynamics, Artificial neural network, Adaptive Resonance Theory, Kohonen network, Benthic macroinvertebrates, Chironomids