Patterning emergent marsh vegetation assemblages in coastal Louisiana, USA, with unsupervised artificial neural networks

Abstract

Questions: Are self-organizing maps (SOMs) useful for patterning coastal wetland vegetation communities? Do SOMs provide robust alternatives to traditional classification methods, particularly when underlying species response functions are unknown or difficult to approximate, or when a need exists to continuously classify new samples obtained under ongoing long-term ecosystem monitoring programs as they become available?. Location: Coastal Louisiana, USA. Methods: A SOM was trained from in-situ observations of 559 vegetation species relative cover data from 2,526 samples collected over 8 years at 343 locations across coastal Louisiana. Hierarchical cluster analysis was applied to the SOM output to delineate vegetation community types, and indicator species analysis was conducted. Salinity and flood duration were compared across the delineated community types. Results: The SOM patterned the 2,526 training samples into 260 output neurons, which were further clustered into eleven community types. Clear gradients in salinity and flood duration existed among the community types, and geographic zonation of the communities was evident across the landscape. At some locations assemblages were temporally stable; at other locations, they varied considerably. Samples not used in training the network were effectively projected onto the SOM and assigned to one of the delineated community types. Conclusions: The SOM was effective in delineating plant communities in the region that were qualitatively similar to those obtained in previous investigations. Being robust to skewed distributions and the presence of outliers, SOMs provide an alternative to traditional distribution-based statistical approaches. Their ability to efficiently classify new data into existing community types makes their use an ideal approach to classifying samples obtained from ongoing, long-term ecological monitoring programs.