Tropical savannas cover over 20% of land surface. They sustain a high diversity of mammalian herbivores and promote frequent fires, both of which are dependent on the underlying grass composition. These habitats are typically dominated by relatively few taxa, and the evolutionary origins of the dominant grass species are largely unknown. Here, we trace the origins of the genus Themeda, which contains a number of widespread grass species dominating tropical savannas. Complete chloroplast genomes were assembled for seven samples and supplemented with chloroplast and nuclear ITS markers for 71 samples representing 18 of the 27 Themeda species. Phylogenetic analysis supports a South Asian origin for both the genus and the widespread dominant T. triandra. This species emerged ~1.5 Ma from a group that had lived in the savannas of Asia for several million years. It migrated to Australia ~1.3 Ma and to mainland Africa ~0.5 Ma, where it rapidly spread in pre-existing savannas and displaced other species. Themeda quadrivalvis, the second most widespread Themeda species, is nested within T. triandra based on whole chloroplast genomes, and may represent a recent evolution of an annual growth form that is otherwise almost indistinguishable from T. triandra. The recent spread and modernday dominance of T. triandra highlight the dynamism of tropical grassy biomes over millennial time-scales that has not been appreciated, with dramatic shifts in species dominance in recent evolutionary times. The ensuing species replacements likely had profound effects on fire and herbivore regimes across tropical savannas.
Recent advances in conceptual frameworks in vegetation classifications, such as the EcoVeg approach that underpins the International Vegetation Classification (IVC) developed by NatureServe staff and colleagues, offer opportunities to enhance national classification initiatives. National level initiatives provide an important stepping-stone between international units and subnational units. Australia has a long history of developing various vegetation typologies at local to regional scales, but ecologists recognise the need for an Australia-wide, plot-based vegetation classification system that incorporates the principles of the EcoVeg approach, and thereby helps build an international classification system. Using two case studies, we provide a comparison of various structures and criteria for relevant Australian classifications in the context of the IVC, and exemplify how Australian classifications of forest, shrublands, grasslands, and deserts could potentially link into the IVC hierarchy to illustrate the capacity of the IVC to summarise the full range of Australian vegetation at a broad formation (biome) scale. We then discuss how the IVC might inform future work towards an Australian vegetation classification system and, vice versa, the implications of an Australian vegetation classification for IVC development.
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.Abbreviations: ALL-dataset consisting of the full species pool; C>1-dataset with species contributing >1% to TFC included; C>5 -dataset with species contributing >5%; C>8 -dataset with species contributing >8%; C>10 -dataset with species contributing >10% to TFC included; IS -Indicator Species; ISA -Indicator Species Analysis; TFC -Total Foliage Cover.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with đź’™ for researchers
Part of the Research Solutions Family.