Abstract
Plant communities in extensive landscapes are often mapped remotely using detectable patterns based on vegetation structure and canopy species with a high relative cover. A plot-based classification which includes species with low relative canopy cover and ignores vegetation structure, may result in plant communities not easily reconcilable with the landscape patterns represented in mapping. In our study, we investigate the effects on classification outcomes if we (1) remove rare species based on canopy cover, and (2) incorporate vegetation structure by weighting species’ cover by different measures of vegetation height. Using a dataset of 101 plots of savanna vegetation in north-eastern Australia we investigated first, the effect of removing rare species using four cover thresholds (1, 5, 8 and 10% contribution to total cover) and second, weighting species by four height measures including actual height as well as continuous and categorical transformations. Using agglomerative hierarchical clustering we produced a classification for each dataset and compared them for differences in: patterns of plot similarity, clustering, species richness and evenness, and characteristic species. We estimated the ability of each classification to predict species cover using generalised linear models. We found removing rare species at any cover threshold produced characteristic species appearing to correspond to landscape scale changes and better predicted species cover in grasslands and shrublands. However, in woodlands it made no difference. Using actual height of vegetation layer maintained vegetation structure, emphasised canopy and then sub-canopy species in clustering, and predicted species cover best of the height-measures tested. Thus, removing rare species and weighting species by height are useful techniques for identifying plant communities from plot-based classifications which are conceptually consistent with those in landscape scale mapping. This increases the confidence of end-users in both the classifications and the maps, thus enhancing their use in land management decisions.
Highlights
Plant communities underpin many land management and policy decisions (Margules and Pressey 2000) and much scientific research (De Cáceres et al 2015)
In tropical savanna vegetation of north-eastern Australia, we examined how rarity, species cover, and height influenced classifications and their ability to predict species foliage cover
Removing rare species based on percent contribution to total foliage cover improved the detection of characterising species useful for landscape scale classifications
Summary
Plant communities underpin many land management and policy decisions (Margules and Pressey 2000) and much scientific research (De Cáceres et al 2015). Maps showing the extent and distribution of plant communities across large areas of the landscape are a commonly associated management tool. They are used for exploration of spatial and temporal changes (Accad et al 2017) and ecological patterns of species distribution (Kent 2012, Clarke et al 2014) and provide a predictive role in describing the distribution of plant communities in inaccessible areas. Plot-based inventories of species assemblages are often used as part of the mapping process to describe map units (i.e., plant communities), and may be used to derive or test vegetation classifications applied through mapping. Plot-based, classification using multivariate species data to be relevant to the map-
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