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Knowledge about the biogeographic affinities of the world’s tropical forests helps to better understand regional differences in forest structure, diversity, composition, and dynamics. Such understanding will enable anticipation of region-specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present a classification of the world’s tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (i) Indo-Pacific, (ii) Subtropical, (iii) African, (iv) American, and (v) Dry forests. Our results do not support the traditional neo- versus paleotropical forest division but instead separate the combined American and African forests from their Indo-Pacific counterparts. We also find indications for the existence of a global dry forest region, with representatives in America, Africa, Madagascar, and India. Additionally, a northern-hemisphere Subtropical forest region was identified with representatives in Asia and America, providing support for a link between Asian and American northern-hemisphere forests.
Using Australian Virtual Herbarium data to find all the woody rain forest plants in Australia
(2012)
Data bases that provide continental and global scale information about species distributions provide a valuable resource for environmental, ecological and evolutionary research. However to bring a large dataset to a standard that is suitable for quantitative analysis, data quality needed to be checked. Here we provide a worked example using a large dataset (c. 320,000 records) from Australia’s Virtual Herbarium (AVH) database, based on an initial data request for full distribution data for c. 2600 woody rain forest species known to occur in Australia. To reconcile inconsistencies around taxonomic identity prior to merging with our trait data-base, and resolve issues around spatial resolution and accuracy, we implemented extensive data filtering using a ‘cloud-based’ solution (Google Refine). This systematic process resulted in 1) the removal of close to 45% of the records originally downloaded, and 2) a clean and powerful data set based on herbarium backed distribution records for Australia’s woody rain forest species. Such resources can contribute significantly to improving research outcomes related to understanding Australia’s vegetation.