Agroécologie, AgroSup Dijon, INRA, University of Bourgogne Franche-Comté, F-21000 Dijon, France.
BIOGECO, INRA, University of Bordeaux, 33615 Pessac, France.
Trends Ecol Evol. 2017 Jul;32(7):477-487. doi: 10.1016/j.tree.2017.03.001. Epub 2017 Mar 27.
We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would provide data for the relative abundance of operational taxonomic units or ecological functions. Machine-learning methods would then be used to reconstruct the ecological networks of interactions implicit in the raw NGS data. Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction. Large numbers of these samplers, in a global array, would allow sensitive automated biomonitoring of the Earth's major ecosystems at high spatial and temporal resolution, revolutionising our understanding of ecosystem change.
我们预计，未来十年内，一种新的全球性生态生物监测方法将会出现，这种方法能够准确、廉价且普遍地检测生态系统的变化。从地球环境中采集的 DNA 进行下一代测序，将为相对丰度的操作分类单元或生态功能提供数据。然后，机器学习方法将被用于重建原始 NGS 数据中隐含的生态相互作用网络。最终，我们设想开发出自主采样器，从核酸中采样并将 NGS 序列数据上传到云端进行网络重建。大量这样的采样器，在一个全球阵列中，将能够以高时空分辨率对地球的主要生态系统进行敏感的自动生物监测，从而彻底改变我们对生态系统变化的理解。
