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.

我们预计在未来十年内将出现一种新的全球性生态监测生物方法，该方法可以准确，廉价且一般地检测生态系统的变化。从地球环境中采样的DNA的下一代测序将为操作分类单位或生态功能的相对丰富提供数据。然后将使用机器学习方法来重建原始NGS数据中隐含的相互作用的生态网络。最终，我们构想了自主采样器的开发，该采样器将对核酸进行采样并将NGS序列数据上传到云中以进行网络重建。在全球范围内，大量的此类采样器可以在高时空分辨率下对地球主要生态系统进行灵敏的自动生物监测，