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"E-taxonomy of Sino-Himalayan Umbelliferae (Apiaceae): diversity, phylogeny and species modelling through new web-based tools" (UMBEL)
Start date: Sep 1, 2014, End date: Aug 31, 2016 PROJECT  FINISHED 

"This project addresses fundamental gaps in our knowledge on the diversity and eco-biogeography of Sino-Himalayan Umbelliferae, commonly known as the carrot or parsley family. Many of these plants are medicinal herbs used by Sino-Himalayan communities, and are endangered due to their fragmented and shrinking forest habitat. This study will be achieved through development of a web-based taxonomic workflow supporting phylogenetic and species distribution modelling analyses for four key genera, to shed light on the evolution of mountain Umbelliferae. The three major components of this project are: 1) Aggregation and curation of existing primary datasets, including taxonomic, floristic, morphological, molecular and bibliographic data, into a web-based Virtual Research Environment to create an open online knowledgebase of Sino-Himalayan Umbelliferae; 2) development of the software workflows necessary to integrate the online taxonomic knowledgebase with state-of-the-art Species Distribution Modelling (SDM) tools; and 3) taxonomic, phylogenetic and SDM analyses using the knowledgebase and workflows on four Sino-Himalayan alpine Umbelliferae genera to detect eco-biogeographical patterns of distribution. The results will create a knowledgebase for research and conservation of Sino-Himalayan Umbelliferae; enhance the technical landscape for revisionary taxonomy by adding new species distribution modelling functionality to the widely used Scratchpad Virtual Research Environment (http://scratchpads.eu/); act as an exemplar for sustainable integrative taxonomy in a rapidly changing technological landscape; and contribute to a better understanding of how alpine Umbelliferae evolved in response to their local environment. The analysis will be vital to identify the potential range of these species in their remote mountain habitats, and predict the long-term impact of climate change on their distribution."
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