Can vegetation be discretely classified in species-poor environments? Testing plant community concepts for vegetation monitoring on sub-Antarctic Marion Island.

Autor: van der Merwe S; Department of Biological Sciences University of Cape Town Cape Town South Africa.; Kirstenbosch Research Centre South African National Biodiversity Institute Cape Town South Africa., Greve M; Department of Plant and Soil Sciences University of Pretoria Pretoria South Africa., Skowno AL; Department of Biological Sciences University of Cape Town Cape Town South Africa.; Kirstenbosch Research Centre South African National Biodiversity Institute Cape Town South Africa., Hoffman MT; Department of Biological Sciences University of Cape Town Cape Town South Africa., Cramer MD; Department of Biological Sciences University of Cape Town Cape Town South Africa.
Jazyk: angličtina
Zdroj: Ecology and evolution [Ecol Evol] 2023 Jan 03; Vol. 13 (1), pp. e9681. Date of Electronic Publication: 2023 Jan 03 (Print Publication: 2023).
DOI: 10.1002/ece3.9681
Abstrakt: 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.
(© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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