Determination of plant communities based on bryophytes: The combined use of Kohonen artificial neural network and indicator species analysis

Abstract

Abstract Mosses and liverworts are good bioindicators, not only of environmental pollution and the substrates on which they develop, but also of the plant communities in which they grow. The present study classifies bryological releves and identifies epigeic species significantly associated with three forest phytocoenoses important for Central Poland: Tilio-Carpinetum with Abies alba, Abietetum polonicum and managed forests; importantly, the study takes an approach using a Kohonen artificial neural network (i.e. self-organising map, SOM), its first use in bryological studies, together with Indicator Species Analysis based on IndVal index. The number of species significantly associated with natural forests was found to be 2–3 times higher than in managed forests, which reflected the differences in the quality of forest habitats. Additionally, the approach not only confirmed that some taxa play a diagnostic role (e.g.: Pleurozium schreberi, Dicranum polysetum, Dicranum scoparium, Thuidium tamariscinum, Atrichum undulatum, Eurhynchium angustirete) but also indicated new species which may have diagnostic potential for the studied communities. Our findings demonstrate that the epigeic bryophyte layer can play an important role in separating the studied phytocoenoses, and thus in the syntaxonomy of forest communities.