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Patterning emergent marsh vegetation assemblages in coastal Louisiana, USA , with unsupervised artificial neural networks
Author(s) -
Snedden Gregg A.
Publication year - 2019
Publication title -
applied vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.096
H-Index - 64
eISSN - 1654-109X
pISSN - 1402-2001
DOI - 10.1111/avsc.12425
Subject(s) - vegetation (pathology) , wetland , marsh , geography , self organizing map , plant community , vegetation classification , ecology , cartography , environmental science , physical geography , artificial neural network , species richness , computer science , machine learning , biology , medicine , pathology
Abstract Questions Are self‐organizing maps ( SOM s) useful for patterning coastal wetland vegetation communities? Do SOM s 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, SOM s 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.