Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island
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 test...
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ftpubmed:oai:pubmedcentral.nih.gov:9811060 2023-05-15T13:47:11+02:00 Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island van der Merwe, Stephni Greve, Michelle Skowno, Andrew Luke Hoffman, Michael Timm Cramer, Michael Denis 2023-01-03 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811060/ https://doi.org/10.1002/ece3.9681 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811060/ http://dx.doi.org/10.1002/ece3.9681 © 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY Ecol Evol Research Articles Text 2023 ftpubmed https://doi.org/10.1002/ece3.9681 2023-01-08T02:13:37Z 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 ... Text Antarc* Antarctic Marion Island PubMed Central (PMC) Antarctic Ecology and Evolution 13 1 |
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Research Articles |
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Research Articles van der Merwe, Stephni Greve, Michelle Skowno, Andrew Luke Hoffman, Michael Timm Cramer, Michael Denis Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island |
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Research Articles |
description |
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 ... |
format |
Text |
author |
van der Merwe, Stephni Greve, Michelle Skowno, Andrew Luke Hoffman, Michael Timm Cramer, Michael Denis |
author_facet |
van der Merwe, Stephni Greve, Michelle Skowno, Andrew Luke Hoffman, Michael Timm Cramer, Michael Denis |
author_sort |
van der Merwe, Stephni |
title |
Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island |
title_short |
Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island |
title_full |
Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island |
title_fullStr |
Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island |
title_full_unstemmed |
Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island |
title_sort |
can vegetation be discretely classified in species‐poor environments? testing plant community concepts for vegetation monitoring on sub‐antarctic marion island |
publisher |
John Wiley and Sons Inc. |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811060/ https://doi.org/10.1002/ece3.9681 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Marion Island |
genre_facet |
Antarc* Antarctic Marion Island |
op_source |
Ecol Evol |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811060/ http://dx.doi.org/10.1002/ece3.9681 |
op_rights |
© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
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CC-BY |
op_doi |
https://doi.org/10.1002/ece3.9681 |
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Ecology and Evolution |
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13 |
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1 |
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1766246737824972800 |