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Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls
Author(s) -
BarrosoBergadà Didac,
Pauvert Charlie,
Vallance Jessica,
Delière Laurent,
Bohan David A.,
Buée Marc,
Vacher Corinne
Publication year - 2021
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/1755-0998.13302
Subject(s) - biomonitoring , ecological network , biology , inference , ecosystem , environmental change , ecosystem services , ecology , community structure , environmental dna , environmental resource management , biodiversity , computer science , climate change , environmental science , artificial intelligence
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.