Marine and Freshwater Research Marine and Freshwater Research Society
Advances in the aquatic sciences
RESEARCH ARTICLE

A multiscale, hierarchical, ecoregional and floristic classification of arid and semi-arid ephemeral wetlands in New South Wales, Australia

John T. Hunter A C and Alex M. Lechner B
+ Author Affiliations
- Author Affiliations

A School of Environmental and Rural Sciences, University of New England, Armidale, NSW 2351, Australia.

B School of Environmental and Geographical Sciences, University of Nottingham, Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor, Kuala Lumpur, Malaysia.

C Corresponding author. Email: jhunter8@bigpond.com

Marine and Freshwater Research - https://doi.org/10.1071/MF17006
Submitted: 11 January 2017  Accepted: 18 September 2017   Published online: 12 December 2017

Abstract

Describing, classifying and quantifying vegetation communities is fundamental for understanding their current distribution, rarity, interrelationships and ecosystem functions. In the present study, we apply a consistent objective classification system for ephemeral wetlands of arid and semi-arid areas of New South Wales (NSW), Australia. Our approach uses a two-step statistically based, hierarchical, multiscale classification of environmental data at broad scales and floristics data at intermediate scales. At broad scales, ecoregionalisation methods were used to describe three wetland macrogroups. Within these groups, we performed unsupervised analyses of 640 floristic survey plots using the Bray–Curtis algorithm, clustering by group averaging and testing of clusters using similarity profile analysis (SIMPROF). From this we delineated 18 vegetation groups with class definition based on a combination of diagnostic and non-diagnostic similarity percentage analysis (SIMPER) outputs and dominant taxa. We show that a consistent classification system can be effectively created for subsets of vegetation that have adequate plot data within a general matrix that is poorly sampled if outputs are restricted to appropriate scales of resolution. We suggest that our approach provides a stable and robust classification system that can be added to as more data become available.

Additional keywords: EcoVeg, functional trait, PAM, partitioning around medoids, PCA, principal component analysis, similarity percentage analysis, SIMPER, similarity profile analysis, SIMPROF, unsupervised.


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