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The intersection of diversity metrics and spatial mapping: a case study of regional vegetation patterns for a complex community

Tierney, D. A., Wardle, G. M., Erskine, P. D.
Plant ecology 2018 v.219 no.10 pp. 1169-1183
case studies, highlands, mountains, surveys, swamps, vegetation, Australia
We evaluate the contribution of small-scale floristic diversity to regional vegetation patterns, if current vegetation classification methods adequately account for small-scale floristic diversity and the potential role of diversity metrics in contributing to improved classification and mapping of plant community patterns. Upland swamps in the Greater Blue Mountains World Heritage Area, Australia, were used as a case study for this evaluation. Eight hundred and eleven survey plots using two contrasting survey designs were used to generate diversity metrics (α; β; ϒ; ζ; multivariate dispersion) and these were intersected with spatial mapping across 69 swamps. A novel classification informed by small-scale floristic diversity was also implemented. Diversity patterns at the regional scale were significantly affected by survey design (both β and ζ overestimated by a survey design using large plots, but α, ϒ and multivariate dispersion not significantly different among plot designs). Secondly, the novel classification revealed that the majority of assemblages present were previously unreported. Thirdly, floristic assemblages previously mapped only in discrete parts of the region were found to be widespread. A poor correlation exists between current standard classification approaches and a classification informed by small-scale floristic diversity. Thus, recommended and implemented standards for survey and classification in many jurisdictions globally are inadequate for revealing diversity patterns and mapping communities with complex small-scale diversity patterns. Communities of this type are widespread globally. Our study demonstrates that the intersection of advanced diversity metrics and spatial mapping, using small-scale survey data, provides critical insights into regional vegetation patterns that may otherwise remain obscure.