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在生物多样性的空间和系统发育模式研究中调动和整合大数据。

Mobilizing and integrating big data in studies of spatial and phylogenetic patterns of biodiversity.

作者信息

Soltis Douglas E, Soltis Pamela S

机构信息

Florida Museum of Natural History, University of Florida, Gainesville, FL, USA.

Genetics Institute, University of Florida, Gainesville, FL, USA.

出版信息

Plant Divers. 2016 Dec 24;38(6):264-270. doi: 10.1016/j.pld.2016.12.001. eCollection 2016 Dec.

Abstract

The current global challenges that threaten biodiversity are immense and rapidly growing. These biodiversity challenges demand approaches that meld bioinformatics, large-scale phylogeny reconstruction, use of digitized specimen data, and complex post-tree analyses (e.g. niche modeling, niche diversification, and other ecological analyses). Recent developments in phylogenetics coupled with emerging cyberinfrastructure and new data sources provide unparalleled opportunities for mobilizing and integrating massive amounts of biological data, driving the discovery of complex patterns and new hypotheses for further study. These developments are not trivial in that biodiversity data on the global scale now being collected and analyzed are inherently complex. The ongoing integration and maturation of biodiversity tools discussed here is transforming biodiversity science, enabling what we broadly term "next-generation" investigations in systematics, ecology, and evolution (i.e., "biodiversity science"). New training that integrates domain knowledge in biodiversity and data science skills is also needed to accelerate research in these areas. Integrative biodiversity science is crucial to the future of global biodiversity. We cannot simply react to continued threats to biodiversity, but via the use of an integrative, multifaceted, big data approach, researchers can now make biodiversity projections to provide crucial data not only for scientists, but also for the public, land managers, policy makers, urban planners, and agriculture.

摘要

当前威胁生物多样性的全球挑战巨大且迅速加剧。这些生物多样性挑战需要融合生物信息学、大规模系统发育重建、数字化标本数据的使用以及复杂的树后分析(如生态位建模、生态位多样化和其他生态分析)的方法。系统发育学的最新进展,加上新兴的网络基础设施和新的数据来源,为调动和整合大量生物数据提供了前所未有的机会,推动了复杂模式的发现和有待进一步研究的新假设。这些进展并非微不足道,因为目前正在收集和分析的全球范围内的生物多样性数据本质上是复杂的。这里讨论的生物多样性工具的持续整合和成熟正在改变生物多样性科学,使我们能够在系统学、生态学和进化领域(即“生物多样性科学”)进行我们广泛称之为“下一代”的研究。还需要将生物多样性领域知识与数据科学技能相结合的新培训,以加速这些领域的研究。综合生物多样性科学对全球生物多样性的未来至关重要。我们不能仅仅应对生物多样性持续面临的威胁,而是通过使用综合、多方面的大数据方法,研究人员现在可以做出生物多样性预测,不仅为科学家,也为公众、土地管理者、政策制定者、城市规划者和农业提供关键数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b75a/6112245/885323f5fabd/gr1.jpg

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