Overview of plot-based vegetation classification                     approaches within Australia

Gellie, Nicholas J. H.; Hunter, John T.; Benson, John; Kirkpatrick, J. B.; Cheal, David; McCreery, Kelli; Brocklehurst, Peter

doi:10.1127/phyto/2017/0173

Cited by 31 publications

(51 citation statements)

References 21 publications

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“…Differentiating communities is often based on abundance (De Cáceres et al., ) and may be described, as in this study, by unquantified concepts of dominant and sub‐dominant species (ESCAVI, ; Gellie et al., ; Guo et al., ; MacKenzie & Meidinger, ). The communities from un‐supervised analysis had higher proportions of characterising species (Sharpness), which were less likely to be shared across communities (Uniqueness) because un‐supervised analysis can consistently apply abundance thresholds, quantifying dominant and subdominant.…”

Section: Discussionmentioning

confidence: 99%

“…Using un‐supervised or semi‐supervised techniques to recognise communities is recommended to address some of the inconsistencies in many supervised methods such as low repeatability, opaque grouping criteria and their inconsistent application (Mucina, ; Kent, ). However, supervised methods are still widespread, particularly in areas with a long history of vegetation classification (Tichý et al., ) and in geographical areas with small numbers of researchers working in them (Peet & Roberts, ; Gellie et al., ; Guo et al., ).…”

Section: Introductionmentioning

confidence: 99%

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Supervised versus un‐supervised classification: A quantitative comparison of plant communities in savanna vegetation

Addicott

Laurance

2019

Applied Vegetation Science

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Question:What are the differences between plant communities recognised using supervised versus un-supervised methods?Location: Northeastern Australia.Methods: Two classifications of savanna plant communities were formed independently with two different approaches: supervised and un-supervised (using agglomerative hierarchical clustering). Each approach used the same vegetation datasets and, importantly, classification criteria. The communities occur on two different landscapes, with differing environmental gradients, covering an area of 53,500 km 2 . We compared the internal characteristics of plant communities between approaches and landscapes using four evaluation criteria: identifiability, distinctiveness, similarity of internal heterogeneity and predictability of species foliage cover. Additionally, we compared the central floristic concepts and compositional boundaries of communities identified by each approach.Results: Supervised and un-supervised approaches recognised similar floristic community concepts. Compositional boundaries between communities were similar on the landscape with steeper environmental gradients but significantly different on the landscape with gradual environmental gradients. However, communities distinguished using supervised methods were significantly less distinct and identifiable, worse at predicting species foliage cover and significantly more variable in species composition than those identified using un-supervised methods. Conclusions:Using supervised rather than un-supervised approaches to distinguish plant communities can result in less recognisable communities, possibly reducing their usefulness for land management planning. Importantly, we found a large disparity between the two approaches in delineating compositional boundaries between communities on landscapes with gradual environmental gradients. This is particularly relevant to communities in biomes such as the savanna which comprises 20% of the Earth's landmass. Ecologists can be more confident using a supervised approach on landscapes with steep environmental gradients but should target landscapes with gradual environmental gradients for un-supervised classification.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised versus un‐supervised classification: A quantitative comparison of plant communities in savanna vegetation

Addicott

Laurance

2019

Applied Vegetation Science

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“…International Association for Vegetation Science (IAVS) placement, function, importance and rarity Hunter and Lechner 2017). Not all classification systems are hierarchical in nature, and many have no clear analytical proof of conceptual links (De Cáceres et al 2018;Gellie et al 2017). Ideally, hierarchical classification systems facilitate integrated understanding of relationships between vegetation assemblages and also allow conceptualisations at different ranks to match scales at which management and investigations may be applied, from local to global (Faber-Langendoen et al 2018).…”

Section: Research Papermentioning

confidence: 99%

“…This is particularly true for systems that are under greatest threat and impact from human activities and which provide significant ecosystem services. Unfortunately, vegetation within many areas of the globe have poor sur-vey coverage and/or inconsistent survey protocols, leading to insufficient or poor data hampering classification (Gellie et al 2017;De Cáceres et al 2018). Even within areas considered relatively well surveyed, many highly restricted and/or ephemeral systems are likely to be poorly sampled and incompletely treated within current classification systems, leading to misunderstandings of their…”

Section: Introductionmentioning

confidence: 99%

Montane mire vegetation of the New England Tablelands Bioregion of Eastern Australia

Hunter¹,

Hunter²

2020

VCS

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Aims: To use unsupervised techniques to produce a hierarchical classification of montane mires of the study region. Study area: New England Tablelands Bioregion (NETB) of eastern Australia. Methods: A dataset of 280 vascular floristic survey plots placed across the variation in montane mires of the NETB was collated. Vegetation types were identified with the aid of a clustering method based on group averaging and tested using similarity profile analysis (SIMPROF) and through ordinations using Bray-Curtis similarity and non-metric multidimensional scaling (NMDS). A hierarchical schema was developed based on EcoVeg hierarchy and was circumscribed using positive and negative diagnostic taxa via similarity percentage analysis (SIMPER) and importance based on summed cover scores and frequency. Results: We defined one macrogroup to include all montane mire vegetation of the NETB and within these two groups and twelve alliances. Conclusions: Our study re-enforced the separation of bogs from other montane mire systems and confirmed the separation of fens and wet meadows, a distinction that previously had not been independently tested. Based on our results many existing montane mire communities of the NETB have been ill-defined at multiple hierarchical levels, leading to confusion in threat status and mapping. Additionally, nearly half of the alliances we recognise were found to have no correlates within current classification systems, which necessarily has implications for the effectiveness of current conservation planning. Taxonomic reference: PlantNET (http://plantnet.rbgsyd.nsw.gov.au/, accessed June 2016). Abbreviations: BC Act = Biodiversity Conservation Act; EPBC Act = Environmental Protection and Biodiversity Act; NETB = New England Tablelands Bioregion; NMDS = non-metric multidimensional scaling; PCT = plant community type; RE = regional ecosystem; SIMPER = similarity percentage analysis; SIMPROF = similarity profile analysis.

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“…; 宋永昌, 2017)。 2018年, 国际植被调查和分类学期刊《Phytocoenologia》发表了一个植被分类系统和方法的专辑(第48卷2期), 集中刊载了13篇论文, 介绍国际上主要的植被分类系统和方法 (Brown & Bredenkamp, 2018;Chytrý & Tichý, 2018;Faber-Langendoen et al, 2018;Gellie et al, 2018;Gillet & Julve, 2018;Guarino et al, 2018;Guo et al, 2018;MacKenzie & Meidinger, 2018;Peet et al, 2018;Rodwell, 2018;Walker et al, 2018;Wiser & De Cáceres, 2018), 并对这些植被分类系统进行了比较 (侯学煜, 1960;中国植被编辑委员会, 1980;Qian, 2004;Guo et al, 2018)…”

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