期刊
DIVERSITY AND DISTRIBUTIONS
卷 20, 期 4, 页码 369-381出版社
WILEY
DOI: 10.1111/ddi.12136
关键词
Brazilian flora; inventory completeness; online biodiversity primary data; priorities for survey and conservation; speciesLink; taxonomic knowledge gaps
资金
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [573883/2008-4, 563342/2010-2]
- CNPq/EuropeanComission [FP7-ICT-2011/CNPq 590052/2011-0]
- Fulbright Program
AimBiodiversity information is the focus of major initiatives aimed at assembling large-scale primary-data documentation (digital accessible knowledge') of the distribution of life on Earth. Recent efforts within Brazil have assembled a massive amount of such documentation for Brazilian plants, which we analyse in this study. Our aim is to identify areas representing gaps in current knowledge; these gaps can guide future botanical exploration and discovery in Brazil. LocationBrazil. MethodsWe assessed angiosperm inventories across Brazil at diverse spatial scales using statistics that summarize inventory completeness. In particular, we assess the completeness of geographical knowledge of Brazilian floras as measured in terms of geographical distance and climatic difference from well-documented sites. ResultsSpatial knowledge of Brazilian angiosperms is very unevenly distributed: well-known sites are concentrated in eastern and southern regions, whereas the remainder of the country remains poorly documented. Worse still, in many regions, areas lacking detailed botanical documentation coincide with areas of intense habitat destruction, such that many such sites will never be documented scientifically. Main conclusionsThis study illustrates how biodiversity survey and inventory efforts can be guided by existing knowledge. That is, to the extent that existing biodiversity knowledge is made digital and openly available, and to the extent that information is sufficiently comprehensive and informative, spatial summaries of completeness such as that presented here offer clear and strategic directions for maximizing the yield of new knowledge from any de novo field efforts.
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