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大数据方法在底栖大型动物基线评估、监测和可持续开发中的应用。

A big data approach to macrofaunal baseline assessment, monitoring and sustainable exploitation of the seabed.

机构信息

Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk, NR33 0HT, United Kingdom.

出版信息

Sci Rep. 2017 Sep 29;7(1):12431. doi: 10.1038/s41598-017-11377-9.

Abstract

In this study we produce a standardised dataset for benthic macrofauna and sediments through integration of data (33,198 samples) from 777 grab surveys. The resulting dataset is used to identify spatial and temporal patterns in faunal distribution around the UK, and the role of sediment composition and other explanatory variables in determining such patterns. We show how insight into natural variability afforded by the dataset can be used to improve the sustainability of activities which affect sediment composition, by identifying conditions which should remain favourable for faunal recolonisation. Other big data applications and uses of the dataset are discussed.

摘要

在这项研究中,我们通过整合来自 777 次抓斗调查的数据(33198 个样本),生成了一个标准化的底栖大型动物和沉积物数据集。该数据集用于识别英国周边地区动物群分布的时空模式,以及沉积物组成和其他解释变量在确定这些模式中的作用。我们展示了如何通过数据集提供的对自然变异性的深入了解,通过确定有利于动物重新定居的条件,来提高影响沉积物组成的活动的可持续性。还讨论了该数据集的其他大数据应用和用途。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84b0/5622093/9c134e9eb830/41598_2017_11377_Fig1_HTML.jpg

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