Abstract:
The updating and rethinking of vegetation classifications is important for ecosystem
monitoring in a rapidly changing world, where the distribution of vegetation is changing.
The general assumption that discrete and persistent plant communities exist
that can be monitored efficiently, is rarely tested before undertaking a classification.
Marion Island (MI) is comprised of species-poor
vegetation undergoing rapid environmental
change. It presents a unique opportunity to test the ability to discretely
classify species-poor
vegetation with recently developed objective classification
techniques and relate it to previous classifications. We classified vascular species data
of 476 plots sampled across MI, using Ward hierarchical clustering, divisive analysis
clustering, non-hierarchical
kmeans and partitioning around medoids. Internal cluster
validation was performed using silhouette widths, Dunn index, connectivity of clusters
and gap statistic. Indicator species analyses were also conducted on the best performing
clustering methods. We evaluated the outputs against previously classified
units. Ward clustering performed the best, with the highest average silhouette width
and Dunn index, as well as the lowest connectivity. The number of clusters differed
amongst the clustering methods, but most validation measures, including for Ward
clustering, indicated that two and three clusters are the best fit for the data. However,
all classification methods produced weakly separated, highly connected clusters with
low compactness and low fidelity and specificity to clusters. There was no particularly
robust and effective classification outcome that could group plots into previously suggested
vegetation units based on species composition alone. The relatively recent age
(c. 450,000 years B.P.), glaciation history (last glacial maximum 34,500 years B.P.) and
isolation of the sub-Antarctic
islands may have hindered the development of strong
vascular plant species assemblages with discrete boundaries. Discrete classification at
the community-level
using species composition may not be suitable in such species-poor
environments. Species-level,
rather than community-level,
monitoring may thus be more appropriate in species-poor
environments, aligning with continuum theory
rather than community theory.
