Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis

Derocles, Stéphane A. P.; Bohan, David; Dumbrell, Alex J.; Kitson, James J. N.; Massol, François; Pauvert, Charlie; Plantegenest, Manuel; Vacher, Corinne; Evans, Darren M.

doi:10.1016/bs.aecr.2017.12.001

Cited by 90 publications

(80 citation statements)

References 279 publications

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“…However, such networks take considerable effort to construct and can be subject to bias because of the limitations of taxonomically selective rearing success as well as the reliance on accurate morphological identification (Evans et al., ). Advances in DNA sequencing technologies provide enormous potential to determine hitherto difficult‐to‐observe species interactions and thus to produce highly resolved ecological networks (Derocles et al., ; Evans et al., ; Wirta et al., ). An accurate and cost‐effective PCR diagnostic approach has recently been developed to allow the rapid construction of quantitative ecological networks of farmland aphid–parasitoid interactions (Derocles, Plantegenest, Simon, Taberlet, & Le Ralec, ; Derocles et al., ) providing new opportunities to examine the impacts of environmental change on network structure and complexity.…”

Section: Introductionmentioning

confidence: 99%

Climate warming alters the structure of farmland tritrophic ecological networks and reduces crop yield

et al. 2018

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It is unclear how sustained increases in temperature and changes in precipitation, as a result of climate change, will affect crops and their interactions with agricultural weeds, insect pests and predators, due to the difficulties in quantifying changes in such complex relationships. We simulated the combined effects of increasing temperature (by an average of 1.4°C over a growing season) and applying additional rainwater (10% of the monthly mean added weekly, 40% total) using a replicated, randomized block experiment within a wheat crop. We examined how this affected the structure of 24 quantitative replicate plant–aphid–parasitoid networks constructed using DNA‐based methods. Simulated climate warming affected species richness, significantly altered consumer–resource asymmetries and reduced network complexity. Increased temperature induced an aphid outbreak, but the parasitism rates of aphids by parasitoid wasps remained unchanged. It also drove changes in the crop, altering in particular the phenology of the wheat as well as its quality (i.e., fewer, lighter seeds). We discuss the importance of considering the wider impacts of climate change on interacting species across trophic levels in agroecosystems.

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Section: Introductionmentioning

confidence: 99%

Climate warming alters the structure of farmland tritrophic ecological networks and reduces crop yield

et al. 2018

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“…In addition, in contrast to the expected view of the nestedness structure of phage–bacteria networks (Flores, Meyer, Valverde, Farr, & Weitz, ), the modular structure of the Mamiellophyceae/ Prasinovirus network observed in the field underpinned the modularity patterns previously observed in phage–bacteria networks from cross‐infection experiments (Flores, Valverde, & Weitz, ). Given that the structure of interaction networks is constrained by the coevolutionary processes between species (Peralta, ), this would mean that we need to take phylogenetic signals into account within co‐occurrence networks (Derocles et al, ). In this context, it would be possible to disentangle the confounding effect of phylogeny from true biotic interactions by developing a partial analysis (ter Braak, Šmillauer, & Dray, ) in the context of CoCA to partial out the phylogenetic effect and focus on patterns of co‐occurrence that are not related to phylogenetic signal.…”

Section: Discussionmentioning

confidence: 99%

“…All computational methods used to infer networks from NGS data sets produce species co‐occurrence networks, where a link between two species represents a significant statistical association (positive or negative) between their abundance (or proportion). This raises a critical issue about the interpretation of inferred associations (Derocles et al, ), because co‐occurrence networks differ from interaction networks constructed on observations of both the species and their interactions (Ings et al, ). For instance, all inferred associations between Mamiellophyceae and Prasinovirus belonging to the expected host–virus system were positive.…”

Section: Discussionmentioning

confidence: 99%

“…Our approach (CoCA combined with network analysis, or another method to infer associations) could be combined with other approaches, such as single cell genomics (Kalisky, Baliney, & Quake, 2011;Martinez-Garcia et al, 2012) or Epic-PCR (Spencer et al, 2016), to validate the predicted associations in interactions. The justification of the association sign between the two functional groups making up the microbial eukaryote community is also not straightforward, although they can be triggered by ecological interactions or by species abiotic requirements (Derocles et al, 2018).…”

Section: For Instance All Inferred Associations Between Mamiellophyceaementioning

confidence: 99%

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Investigating microbial associations from sequencing survey data with co‐correspondence analysis

Alric

Braak

Desdevises

et al. 2020

Molecular Ecology Resources

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Microbial communities, which drive major ecosystem functions, consist of a wide range of interacting species. Understanding how microbial communities are structured and the processes underlying this is crucial to interpreting ecosystem responses to global change but is challenging as microbial interactions cannot usually be directly observed. Multiple efforts are currently focused to combine next‐generation sequencing (NGS) techniques with refined statistical analysis (e.g., network analysis, multivariate analysis) to characterize the structures of microbial communities. However, most of these approaches consider a single table of sequencing data measured for several samples. Technological advances now make it possible to collect NGS data on different taxonomic groups simultaneously for the same samples, allowing us to analyse a pair of tables. Here, an analytical framework based on co‐correspondence analysis (CoCA) is proposed to study the distributions, assemblages and interactions between two microbial communities. We show the ability of this approach to highlight the relationships between two microbial communities, using two data sets exhibiting various types of interactions. CoCA identified strong association patterns between autotrophic and heterotrophic microbial eukaryote assemblages, on the one hand, and between microalgae and viruses, on the other. We demonstrate also how CoCA can be used, complementary to network analysis, to reorder co‐occurrence networks and thus investigate the presence of patterns in ecological networks.

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“…Arising due to: (Derocles et al, 2018;Kitson et al, 2018), and allow for sampling communities simultaneously or at multiple discrete time points (e.g., Bennett et al, 2013). Thus, molecular approaches provide opportunities, especially for phylogenetically structured networks (visualised in Figure 1c; Chen et al, 2017).…”

Section: Considerationsmentioning

confidence: 99%

Potentials and pitfalls in the analysis of bipartite networks to understand plant–microbe interactions in changing environments

2018

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Complex networks of interactions involving multiple plant and microbial species have consequences for ecosystem productivity and energy movement through higher trophic levels, but the sensitivity of these networks to environmental change is uncertain. The fast developing field focused on the construction of ecological networks involving multiple plant and microbial species allows the structure, complexity and robustness of these networks to be examined. However, the choice of methods used to generate data and construct networks can significantly influence network‐level metrics. Here, we provide background and guidance on approaches for building and analysing bipartite networks of plant–microbe interactions. Researchers need to explore these methods (and their advantages and pitfalls) to create high quality and reproducible data that allow comparisons within and between systems. In particular, we highlight the significant advances of DNA‐based approaches for characterising interactions between plants and microbes, while suggesting future work needed to help overcome their current limitations. As more studies embrace these approaches, these guidelines will allow robust characterisation and analysis of networks of plant and microbial interactions, and further enable our understanding of the influence of environmental change on species interactions and, ultimately, communities and ecosystem functioning. http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.13223/suppinfo is available for this article.

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Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis

Cited by 90 publications

References 279 publications

Climate warming alters the structure of farmland tritrophic ecological networks and reduces crop yield

Climate warming alters the structure of farmland tritrophic ecological networks and reduces crop yield

Investigating microbial associations from sequencing survey data with co‐correspondence analysis

Potentials and pitfalls in the analysis of bipartite networks to understand plant–microbe interactions in changing environments

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