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

Abstract

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.

Vegetation classification in south-western Australia’s Mediterranean jarrah forest: new data, old units, and a conservation conundrum

“…Sentienal-2, Landsat, the soil and landscape grid of Australia) with floristic information in GDM may enable community groups to be better separated across the upland and plateau areas of the NJF. Conversely, given the historic difficulties in defining vegetation communities in this system and findings from work in other Australian systems with gradual environmental gradients, supervised classification may not improve cluster separation (Havel 1975a(Havel , 1975bAddicott and Laurance 2019).…”

“…New data and the centralisation of datasets provide platforms for improvement and are occurring internationally (VegBank, BIEN, sPlot), nationally (TERN Aekos) and at State and Territory levels (e.g. BioNET (New South Wales), COREVeg (Queensland), NatureMap (Western Australia), the Vegetation Site Database (Northern Territory); Benson 2008;Wiser and De Cáceres 2013;Chytrý et al 2016;Faber-Langendoen et al 2018;Gellie et al 2018;Gibson 2018;Addicott and Laurance 2019;Bruelheide et al 2019). Using these products to improve conservation assessment at multiple scales is key to improving decision-making.…”

Aligning quantitative vegetation classification and landscape scale mapping: updating the classification approach of the Regional Ecosystem classification system used in Queensland

“…In saying this, it is important to recognise that expertrecognised communities will reflect some of the biases of the experts and their assumptions regarding the drivers of ecological patterns. When the updated class definition procedures were applied to savanna communities of two landscapes in north-eastern Queensland, a 49% reduction in the number of communities compared to those identified using expert-based techniques resulted (Addicott et al 2018a) and these were more recognisable and useful for conservation planning (Addicott and Laurance 2019). In total, 96% of the suggested changes were accepted during the review process, despite the extensive modifications to the existing expert-based communities.…”