Expanding an existing classification of New Zealand vegetation to include non-forested vegetation

We produced the first national-scale quantitative classification of non-forest vegetation types, including shrubland, based on vegetation plot data from the National Vegetation Survey Databank. Semi-supervised clustering with the fuzzy classification algorithm Noise Clustering was used to incorporate these new data into a pre-existing quantitative classification of New Zealand’s woody vegetation. Fuzzy classification allows plots to be designated as transitional when they are similar to multiple vegetation types; the Noise Clustering algorithm allows plots having unique composition to be designated as outliers. We combined plot data collected using two different methods by transforming abundances to relative ranks and showed our classification results were robust to this. Of the 6362 plots analysed, 505 were assigned to previously defined woody vegetation types. Using the remaining 5857 plots, we defined vegetation types at two hierarchical levels comprising 25 alliances and 56 associations. Ten of the alliances are tussocklands, six are grasslands, four are stonefields or gravelfields, two are herbfields, one is rushland, and two are newly defined woody alliances. The classification defined compositional differences among well-known widely distributed short and tall tussock grasslands of the South Island. Notably it distinguished Chionochloa pallens, C. crassiuscula and C. oreophila tussocklands in wetter western regions from those dominated by C. rigida and C. macra in the east, and the domination of eastern South Island short tussock grasslands by Festuca novae-zelandiae and Poa colensoi. We demonstrate the distinctiveness of the vegetation of four naturally uncommon ecosystems – coastal turfs, northern gumlands, granite sand plains and braided riverbeds. Insufficient plot data precluded the definition of North Island Chionochloa rubra grassland types and many wetland and coastal communities. The 1846 plots designated as outliers mainly occur on warmer, wetter and less invaded sites than classified plots. Semi-supervised clustering allowed us to progress the development of an extendable, plot-based, quantitative classification of all New Zealand’s vegetation despite data gaps.