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Combining citizen science and weather radar data to study large‐scale bird movements
IBIS ( IF 1.8 ) Pub Date : 2020-11-20 , DOI: 10.1111/ibi.12906
Nadja Weisshaupt 1 , Teemu Lehtiniemi 2 , Jarmo Koistinen 1
Affiliation  

The study of large‐scale animal mass movements requires suitable large‐scale sampling methods. Weather radar (WR) has been known to register biological targets since the 1960s. Arranged in large networks, radar is suitable to study regional to continent‐wide dynamics of aerofauna and to respond to increasing human–wildlife conflicts in the air. Tools for the spatiotemporal validation of faunistic interpretations of WR measurements in situ are only sparsely available. Citizen science (CS) bird observation repositories established in the past 20 years have accumulated millions of entries of species‐specific information well beyond their time of existence across vast areas. Together with other CS data sources, these databases can relieve the taxonomic shortcomings of WR and thus extend and refine the use of WR data. CS and WR data combined can efficiently provide large amounts of species‐specific data in three dimensions in a short time at low or no cost. Species‐specific data are particularly relevant to tackle loss of biodiversity, one of the greatest challenges in today's world. In this forum paper, we present features and qualities of ornithological CS and WR data, and their potential to provide unprecedented insights into regional to continent‐wide aerial movements of birds. We aim to discuss specific fields of applications where maximum information yield is to be expected, which is otherwise inaccessible, and in which way combined approaches would support biological research and derived data products and services for stakeholders, e.g. in aviation and the general public as beneficiaries.

中文翻译:

结合公民科学和天气雷达数据来研究大型鸟类运动

对大规模动物运动的研究需要合适的大规模采样方法。自1960年代以来,众所周知天气雷达(WR)可以记录生物目标。雷达布置在大型网络中,非常适合研究区域性到整个大陆的航空动植物动态,并应对空中日益增加的人类与野生生物之间的冲突。工具WR测量动物学解释的时空验证原位仅稀疏可用。在过去20年中建立的公民科学(CS)鸟类观测资料库已经积累了数百万种特定物种信息,远远超出了它们在广阔地区的生存时间。这些数据库与其他CS数据源一起可以缓解WR的分类缺陷,从而扩展和完善WR数据的使用。CS和WR数据的组合可以在短时间内以低成本或无成本高效地提供大量三维物种特定数据。特定物种的数据与解决生物多样性丧失特别相关,这是当今世界上最大的挑战之一。在本论坛论文中,我们介绍了鸟类学CS和WR数据的特征和质量,以及它们为深入了解鸟类从区域到整个大陆的空中运动提供的潜力。
更新日期:2020-11-20
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