Environmental DNA contains information on the species interaction networks that support ecosystem functions and services. Next‐generation biomonitoring proposes the use of this data to reconstruct ecological networks in real time and then compute network‐level properties to assess ecosystem change. We investigated the relevance of this proposal by assessing: (i) the replicability of DNA‐based networks in the absence of ecosystem change, and (ii) the benefits and shortcomings of community‐ and network‐level properties for monitoring change. We selected crop‐associated microbial networks as a case study because they support disease regulation services in agroecosystems and analysed their response to change in agricultural practice between organic and conventional systems. Using two statistical methods of network inference, we showed that network‐level properties, especially β‐properties, could detect change. Moreover, consensus networks revealed robust signals of interactions between the most abundant species, which differed between agricultural systems. These findings complemented those obtained with community‐level data that showed, in particular, a greater microbial diversity in the organic system. The limitations of network‐level data included (i) the very high variability of network replicates within each system; (ii) the low number of network replicates per system, due to the large number of samples needed to build each network; and (iii) the difficulty in interpreting links of inferred networks. Tools and frameworks developed over the last decade to infer and compare microbial networks are therefore relevant to biomonitoring, provided that the DNA metabarcoding data sets are large enough to build many network replicates and progress is made to increase network replicability and interpretation.

中文翻译：

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从用于生物监测生态系统变化的环境 DNA 数据推断的微生物网络：优势和缺陷

环境 DNA 包含有关支持生态系统功能和服务的物种相互作用网络的信息。下一代生物监测建议使用这些数据实时重建生态网络，然后计算网络级属性以评估生态系统变化。我们通过评估以下内容来研究该提案的相关性：（i）在没有生态系统变化的情况下基于 DNA 的网络的可复制性，以及（ii）用于监测变化的社区和网络级属性的优点和缺点。我们选择与作物相关的微生物网络作为案例研究，因为它们支持农业生态系统中的疾病调节服务，并分析了它们对有机系统和传统系统之间农业实践变化的反应。使用两种网络推理的统计方法，我们展示了网络级别的属性，尤其是 β 属性，可以检测到变化。此外，共识网络揭示了最丰富的物种之间相互作用的强大信号，这在农业系统之间有所不同。这些发现补充了通过群落级数据获得的发现，特别是有机系统中微生物多样性更高。网络级数据的局限性包括（i）每个系统内网络复制的高度可变性；(ii) 由于构建每个网络需要大量样本，因此每个系统的网络重复次数较少；(iii) 难以解释推断网络的链接。因此，过去十年开发的用于推断和比较微生物网络的工具和框架与生物监测相关，