Search
2023 Volume 2
Article Contents
LETTER   Open Access    

Co-occurrence networks depict common selection patterns, not interactions

More Information
  • 加载中
  • Supplemental Fig. S1 Cartoon of the experimental design. Soil was collected from a sandy soil grassland field, microbial suspensions extracted from the soil, and 10-fold serially diluted aliquots were re-inoculated into the origin sterilized soil (by gammairradiation). The extraction and re-inoculation procedure was repeated 4 times (given as A, B, C and D). Soils were incubated in the dark at 20 °C for 61 weeks, after which 12 seedlings of Arabidopsis thaliana were transplanted per soil (one plant per pot), 4 pots of which were harvested after 1, 2 and 3 weeks of growth, respectively. Three replicate pots from the 1- and 3-weeks harvest were randomly chosen for 16S rRNA gene tag sequencing and network analysis of rhizosphere soil.
    Supplemental Fig. S2 Input OTUs used in the network analyses (A), number of network nodes using three different models (B), and number of links in co-occurrence networks (C) of rhizosphere microbiota from samples collected from week 1 and week 3 across the given levels of dilution treatment; Spearman correlations between input OTUs and dilutions were listed under panel; Spearman correlations between nodes (or links) and dilutions in each network were listed in Table S1; Asterisk means a statistically significant (*P < 0.05).
    Supplemental Fig. S3 Co-occurrence networks of rhizosphere bacteria microbiota from different dilutions from samples collected after 1 and 3 weeks of plant growth. Connections indicate significant (P < 0.01) correlation calculated by the Pearson method. Nodes represent different OTUs and are colored by the replicates in which the co-occur (A, B, C, and D represent 4 replicates). Grey links represent positive correlations, and pink links represent negative correlations.
    Supplemental Fig. S4 Numbers of intra- and inter-treatment (which was derived from one origin soil, in total of 4 origin soils) links in co-occurrence networks of rhizosphere microbiota from samples collected from week 1 and week 3 across the given levels of dilution.
    Supplemental Table S1 Spearman correlations between node (or link) number with dilutions in each network. Correlations were calculated by cor.test function in R3.4.3, and asterisk means a statistically significant (*P < 0.05, ** P < 0.01).
  • Alexander JM, Diez JM, Levine JM. 2015. Novel competitors shape species’ responses to climate change. Nature 525:515−18

    doi: 10.1038/nature14952

    CrossRef    Google Scholar

    Barberán A, Bates ST, Casamayor EO, Fierer, N. 2012. Using network analysis to explore co-occurrence patterns in soil microbial communities. The ISME Journal 6:343−51

    doi: 10.1038/ismej.2011.119

    CrossRef    Google Scholar

    Berry D, Widder S. 2014. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Frontiers in Microbiology 5:219

    doi: 10.3389/fmicb.2014.00219

    CrossRef    Google Scholar

    Blasche S, Kim Y, Mars RAT, Machado D, Maansson M, et al. 2021. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nature Microbiology 6(2):196−206

    doi: 10.1038/s41564-020-00816-5

    CrossRef    Google Scholar

    Cardona C, Weisenhorn P, Henry C, Gilbert JA. 2016. Network-based metabolic analysis and microbial community modeling. Current Opinion in Microbiology 31:124−31

    doi: 10.1016/j.mib.2016.03.008

    CrossRef    Google Scholar

    Cazelles K, Araújo MB, Mouquet N, Gravel D. 2016. A theory for species co-occurrence in interaction networks. Theoretical Ecology 9:39−48

    doi: 10.1007/s12080-015-0281-9

    CrossRef    Google Scholar

    Ceja-Navarro JA, Wang Y, Ning D, Arellano A, Ramanculova L, et al. 2021. Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop. Microbiome 9:96

    doi: 10.1186/s40168-021-01042-9

    CrossRef    Google Scholar

    Clark JS, Nemergut D, Seyednasrollah B, Turner PJ, Zhang S. 2017. Generalized joint attribute modeling for biodiversity analysis: median-zero, multivariate, multifarious data. Ecological Monographs 87:34−56

    doi: 10.1002/ecm.1241

    CrossRef    Google Scholar

    Dal Co A, van Vliet S, Kiviet DJ, Schlegel S, Ackermann M. 2020. Short-range interactions govern the dynamics and functions of microbial communities. Nature Ecology & Evolution 4(3):366−75

    doi: 10.1038/s41559-019-1080-2

    CrossRef    Google Scholar

    Dini-Andreote F, Stegen JC, van Elsas JD, Salles JF. 2015. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proceedings of the National Academy of Sciences of the United States of America 112:E1326−E1332

    doi: 10.1073/pnas.1414261112

    CrossRef    Google Scholar

    Faust K, Raes J. 2012. Microbial interactions: from networks to models. Nature Reviews Microbiology 10:538−50

    doi: 10.1038/nrmicro2832

    CrossRef    Google Scholar

    Freilich MA, Wieters E, Broitman BR, Marquet PA, Navarrete SA. 2018. Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities? Ecology 99:690−99

    doi: 10.1002/ecy.2142

    CrossRef    Google Scholar

    Gao C, Xu L, Montoya L, Madera M, Hollingsworth J, et al. 2022. Co-occurrence networks reveal more complexity than community composition in resistance and resilience of microbial communities. Nature Communications 13:3867

    doi: 10.1038/s41467-022-31343-y

    CrossRef    Google Scholar

    Hilton S, Picot E, Schreiter S, Bass D, Norman K, et al. 2021. Identification of microbial signatures linked to oilseed rape yield decline at the landscape scale. Microbiome 9:19

    doi: 10.1186/s40168-020-00972-0

    CrossRef    Google Scholar

    Holt RD, Bonsall MB. 2017. Apparent competition. Annual Review of Ecology, Evolution, and Systematics 48:447−71

    doi: 10.1146/annurev-ecolsys-110316-022628

    CrossRef    Google Scholar

    Horner-Devine MC, Bohannan BJM. 2006. Phylogenetic clustering and overdispersion in bacterial communities. Ecology 87:S100−S108

    doi: 10.1890/0012-9658(2006)87[100:PCAOIB]2.0.CO;2

    CrossRef    Google Scholar

    Li B, Roley SS, Duncan DS, Guo J, Quensen JF, et al. 2021. Long-term excess nitrogen fertilizer increases sensitivity of soil microbial community to seasonal change revealed by ecological network and metagenome analyses. Soil Biology and Biochemistry 160:108349

    doi: 10.1016/j.soilbio.2021.108349

    CrossRef    Google Scholar

    Lv X, Zhao K, Xue R, Liu Y, Xu J, et al. 2019. Strengthening insights in microbial ecological networks from theory to applications. mSystems 4:e00124-19

    doi: 10.1128/mSystems.00124-19

    CrossRef    Google Scholar

    Morales-Castilla I, Matias MG, Gravel D, Araújo MB. 2015. Inferring biotic interactions from proxies. Trends in Ecology & Evolution 30:347−56

    doi: 10.1016/j.tree.2015.03.014

    CrossRef    Google Scholar

    Morriën E, Hannula SE, Snoek LB, Helmsing NR, Zweers H, et al. 2017. Soil networks become more connected and take up more carbon as nature restoration progresses. Nature Communications 8:14349

    doi: 10.1038/ncomms14349

    CrossRef    Google Scholar

    Peres-Neto PR, Olden JD, Jackson DA. 2001. Environmentally constrained null models: Site suitability as occupancy criterion. Oikos 93:110−20

    doi: 10.1034/j.1600-0706.2001.930112.x

    CrossRef    Google Scholar

    Röttjers L, Faust K. 2018. From hairballs to hypotheses–biological insights from microbial networks. FEMS Microbiology Reviews 42:761−80

    doi: 10.1093/femsre/fuy030

    CrossRef    Google Scholar

    Sander EL, Wootton JT, Allesina S. 2017. Ecological network inference from long-term presence-absence data. Scientific Reports 7:7154

    doi: 10.1038/s41598-017-07009-x

    CrossRef    Google Scholar

    Shi S, Nuccio EE, Shi ZJ, He Z, Zhou J, et al. 2016. The interconnected rhizosphere: High network complexity dominates rhizosphere assemblages. Ecology Letters 19:926−36

    doi: 10.1111/ele.12630

    CrossRef    Google Scholar

    Stegen JC, Lin X, Konopka AE, Fredrickson JK. 2012. Stochastic and deterministic assembly processes in subsurface microbial communities. The ISME Journal 6:1653−64

    doi: 10.1038/ismej.2012.22

    CrossRef    Google Scholar

    Thakur M P, Geisen S. 2019. Trophic regulations of the soil microbiome. Trends in Microbiology 27(9):771−80

    doi: 10.1016/j.tim.2019.04.008

    CrossRef    Google Scholar

    Ulrich W. 2004. Species co-occurrences and neutral models: reassessing JM Diamond’s assembly rules. Oikos 107:603−9

    doi: 10.1111/j.0030-1299.2004.12981.x

    CrossRef    Google Scholar

    Vos M, Wolf AB, Jennings SJ, Kowalchuk GA. 2013. Micro-scale determinants of bacterial diversity in soil. FEMS Microbiology Reviews 37:936−54

    doi: 10.1111/1574-6976.12023

    CrossRef    Google Scholar

    Weiss S, van Treuren W, Lozupone C, Faust K, Friedman J, et al. 2016. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. The ISME Journal 10:1669−81

    doi: 10.1038/ismej.2015.235

    CrossRef    Google Scholar

    Xiong W, Jousset A, Guo S, Karlsson I, Zhao Q, et al. 2018. Soil protist communities form a dynamic hub in the soil microbiome. The ISME Journal 12:634−38

    doi: 10.1038/ismej.2017.171

    CrossRef    Google Scholar

    Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P, et al. 2015. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proceedings of the National Academy of Sciences of the United States of America 112:6449−54

    doi: 10.1073/pnas.1421834112

    CrossRef    Google Scholar

  • Cite this article

    Li R, Weidner S, Ou Y, Xiong W, Jousset A, et al. 2023. Co-occurrence networks depict common selection patterns, not interactions. Soil Science and Environment 2:1 doi: 10.48130/SSE-2023-0001
    Li R, Weidner S, Ou Y, Xiong W, Jousset A, et al. 2023. Co-occurrence networks depict common selection patterns, not interactions. Soil Science and Environment 2:1 doi: 10.48130/SSE-2023-0001

Figures(2)

Article Metrics

Article views(4329) PDF downloads(1057)

LETTER   Open Access    

Co-occurrence networks depict common selection patterns, not interactions

Soil Science and Environment  2 Article number: 1  (2023)  |  Cite this article
  • High-throughput interrogation of microbial communities has provided a quantum leap in our ability to characterize the phylogenetic composition of our microbial world. However, as ecologists, we aim to go beyond pure stamp collecting of who is present in a community. We seek to understand the drivers of community dynamics and the interactions that dictate community functioning. A range of tools has been developed to visualize co-occurrence patterns, generated for instance by High-throughput (HTP) tag sequencing of ribosomal RNA gene fragments, as networks of taxa that are positively or negatively correlated in their distributions (Barberán et al., 2012). While such studies often reveal interesting changes in network topology as related to specific environmental gradients or experimental manipulations (Hilton et al., 2021; Li et al., 2021), interpretation of how such networks relate to actual interactions and community drivers has remained problematic and can even be misleading (Cardona et al., 2016). Here, we use a set of manipulated soil-borne microbial communities to demonstrate that extra complex and tightly knit microbial co-occurrence networks can be generated by selection processes that have no link to actual ecological interactions. Thus, while co-occurrence network topology and complexity may indeed have ecological meaning, we purport that they are more related to common outcomes of population selection, as opposed to interactive activities.

    As we continue to develop more advanced and thorough means to describe complex microbial communities, we obviously wish to move beyond cataloguing relative population densities toward detecting patterns of ecological significance. To drive such research forward, a number of approaches have been employed to examine evolutionary and ecological signatures within large microbial datasets (Horner-Devine and Bohannan, 2006; Stegen et al., 2012; Dini-Andreote et al., 2015), in order to examine how interspecific interactions, be they positive, negative or neutral, shape microbial community structure and assembly (Faust and Raes, 2012; Zelezniak et al., 2015; Blasche et al., 2021). The use of co-occurrence and correlation networks has increased significantly in recent years, as a means of organizing population distributions across a range of complex microbial communities from oceans to the human gut and for describing not only bacteria, but also fungi, protists and other organisms (Faust and Raes, 2012; Morriën et al., 2017; Xiong et al., 2018; Gao et al., 2022). Indeed, a simple search of the Web of Science (Fig. 1) demonstrates the widespread and growing use of such approaches.

    Figure 1. 

    (a) Number of publications and (b) citations referencing microbial network analysis (Web of Science from 1985 to Dec 20, 2022; search = network and microbiology or microbes or microbiome or microbiota or microflora as two separate subjects).

    Many different approaches (correlation networks (CoNet), local similarity analysis (LSA), maximal information coefficients (MIC), random matrix theory (RMT), sparse correlations for compositional data (SparCC), Pearson correlations, Spearman correlations, Bray–Curtis, and so on) and models (copula, null model, ecological, and lotka–volterra) have been explored for constructing co-occurrence networks, and were reported to exhibit different correlation technique usage (Weiss et al., 2016). Resulting changes in network topology are then typically related to specific environmental gradients or experimental manipulations. While most researchers recognize that such networks do not demonstrate actual ecological interactions between species, the very term 'network' implies that such community portrayals yield interactive information (Morales-Castilla et al., 2015; Cazelles et al., 2016; Sander et al., 2017). Real world interactions are far more complex than a mathematical linkage can convey. They involve specific physiologies, higher-order and indirect interactions, changing environmental conditions and spatially structured environments (Alexander et al., 2015; Thakur and Geisen, 2019; Dal et al., 2020; Vos et al., 2013). Network analysis is clearly booming, but what insight does it lend to illuminating microbial interactions or drivers of community structuring?

    Positive or negative links within co-occurrence networks have been shown to be poor predictors of actual interactions upon examination of one-to-one effects (Freilich et al., 2018). However, such pairwise interactions should also be viewed within the context of other interactions within the community, as interactions may be indirect and other species in the network may impact one or both of the pairwise players. Species can also coexist and exhibit a correlation in their abundances through population selection by a third species (Holt and Bonsall, 2017) or unreported abiotic factors (Röttjers and Faust, 2018; Lv et al., 2019). It has also been suggested that co-occurrence may be a result of dispersal limitation (Ulrich, 2004), or common selection due to specific environmental factors, without any actual direct or indirect interaction (Peres-Neto et al., 2001; Freilich et al., 2018).

    We believe that this latter explanation is in many cases driving the topology of microbial co-occurrence networks. In order to examine this premise, we used a set of engineered soil-borne microbial communities that differed in the degree to which populations were segregated across replicates. We then tracked rhizosphere community assembly and network topology, via a range of methods, over time. Briefly, we used a dilution series of a soil suspension re-inoculated back into its sterilized origin soil to create soil communities that had been subjected to different levels of population segregation (Supplemental Fig. S1). With such a dilution-to-extinction experiment setup, all starting communities derived from low dilutions have very similar species pools, as nearly all species remain present in the starting inoculum. On the other hand, replicates at higher dilutions receive more disparate species pools due to the dilution to extinction of different subsets of the initial community. As such, this imposed community 'drift' acts as a strong experimentally imposed segregation at high dilutions, but not at low dilutions. Soils were incubated for 61 weeks and then used to examine microbial community assembly on plant roots via 16S rRNA gene tag sequencing by primers 341F (5’-CCTACGGGNBGCASCAG-3’) and 806R (5’-GGACTACNVGGGTWTCTAAT-3’) based on the Illumina MiSeq platform and network analyses in a replicated design (Supplemental Fig. S1). There are 12 sequencing samples in each dilution with three replicates per treatment which was derived from one origin soil (in a total of four origin soils) and with 446-814 OTUs in each sample, depending on the dilution and the sampling time, used to compute one network.

    Co-occurrence network analyses of rhizosphere communities showed distinct patterns with respect to the dilution of the starting communities, as well as the age of the plant (Fig. 2), with denser and stronger networks with increased dilution and plant age. In addition, the number of links per network also showed increased trends with dilution, independent of the co-occurrence model used (see Supplemental Fig. S2; e.g. CoNet, Pearson or Spearman). The Spearman correlations between node (or link) number with dilutions in each network were calculated, and significant correlations can be observed in some networks (see Supplemental Table S1). In our manipulated communities, we have no reason to assume that communities at higher dilutions are more 'connected' or contain more microbial interactions, which can be supported by the decreased number of OTUs involved in the network (see Supplemental Fig. S2). Rather, the imposed segregation of populations, which is greater at higher dilutions, resulted in a greater preponderance of positive and negative co-occurrence patterns and an increased number of nodes derived from multiple replicates (see Supplemental Fig. S3). Similarly, network strength is increased with the continued growth of the plant (Fig. 2). We believe that this result is due to the increased selective action of the plant, which increases with plant size. Indeed, it has previously been observed that co-occurrence network complexity increases with plant growth stage (Shi et al., 2016; Ceja-Navarro et al., 2021).

    Figure 2. 

    Co-occurrence networks of rhizosphere bacteria microbiota from samples collected from week 1 and week 3 across the given soil suspension dilution treatments. Connections represent significant (P < 0.01) correlation as calculated by the Pearson method. Nodes represent different OTUs. Nodes and edges are colored by modularity class.

    It has been suggested that co-occurrence networks can be misleading if other factors, such as habitat filtering, result in non-random patterns in the abundance of multiple taxa (Berry and Widder, 2014). In other words, if one seeks to zoom in on one particular parameter driving co-occurrence network structure, it is important to keep all other factors that may influence this structure constant. Here, the numbers of intra- and inter-treatment links both showed increased trends in co-occurrence networks of rhizosphere microbiota with the level of dilution (see Supplemental Fig. S4). We demonstrate that our imposed segregation of populations, which can be seen as a random selection, resulted in more tightly knit network topologies – a result that most likely has nothing to do with increases in actual interactions. The distribution of microbial species in the environment is clearly not independent, and we suggest that estimations of microbial responses to environmental filtering need to be considered, for instance via generalized joint attribute modeling (Clark et al., 2017). Our example provides an empirical warning regarding the ecological interpretation of co-occurrence networks. We show that co-occurrence network structure and complexity can be principally driven by common patterns of imposed selection, thereby providing a strong cautionary message to the interpretation of functional interactions from such approaches.

    • This work was supported by the Key project at central government level: The ability establishment of sustainable use for valuable Chinese medicine resources (2060302); Natural Science Foundation of Jiangsu Province, China (BK20200544); the Priority Academic Program Development of the Jiangsu Higher Education Institutions (PAPD), the National Natural Science Foundation of China (42107141), the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (ALW.870.15.050) and the Koninklijke Nederlandse Akademie van Wetenschappen (530-5CDP18).

    • Li R, Shen Q and Kowalchuk GA are the Editorial Board members of Journal Soil Science and Environment. They were blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of these Editorial Board members and their research groups.

    • Supplemental Fig. S1 Cartoon of the experimental design. Soil was collected from a sandy soil grassland field, microbial suspensions extracted from the soil, and 10-fold serially diluted aliquots were re-inoculated into the origin sterilized soil (by gammairradiation). The extraction and re-inoculation procedure was repeated 4 times (given as A, B, C and D). Soils were incubated in the dark at 20 °C for 61 weeks, after which 12 seedlings of Arabidopsis thaliana were transplanted per soil (one plant per pot), 4 pots of which were harvested after 1, 2 and 3 weeks of growth, respectively. Three replicate pots from the 1- and 3-weeks harvest were randomly chosen for 16S rRNA gene tag sequencing and network analysis of rhizosphere soil.
    • Supplemental Fig. S2 Input OTUs used in the network analyses (A), number of network nodes using three different models (B), and number of links in co-occurrence networks (C) of rhizosphere microbiota from samples collected from week 1 and week 3 across the given levels of dilution treatment; Spearman correlations between input OTUs and dilutions were listed under panel; Spearman correlations between nodes (or links) and dilutions in each network were listed in Table S1; Asterisk means a statistically significant (*P < 0.05).
    • Supplemental Fig. S2
    • Supplemental Fig. S3 Co-occurrence networks of rhizosphere bacteria microbiota from different dilutions from samples collected after 1 and 3 weeks of plant growth. Connections indicate significant (P < 0.01) correlation calculated by the Pearson method. Nodes represent different OTUs and are colored by the replicates in which the co-occur (A, B, C, and D represent 4 replicates). Grey links represent positive correlations, and pink links represent negative correlations.
    • Supplemental Fig. S4 Numbers of intra- and inter-treatment (which was derived from one origin soil, in total of 4 origin soils) links in co-occurrence networks of rhizosphere microbiota from samples collected from week 1 and week 3 across the given levels of dilution.
    • Supplemental Fig. S3
    • Supplemental Table S1 Spearman correlations between node (or link) number with dilutions in each network. Correlations were calculated by cor.test function in R3.4.3, and asterisk means a statistically significant (*P < 0.05, ** P < 0.01).
    • Copyright: © 2023 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
Figure (2)  References (31)
  • About this article
    Cite this article
    Li R, Weidner S, Ou Y, Xiong W, Jousset A, et al. 2023. Co-occurrence networks depict common selection patterns, not interactions. Soil Science and Environment 2:1 doi: 10.48130/SSE-2023-0001
    Li R, Weidner S, Ou Y, Xiong W, Jousset A, et al. 2023. Co-occurrence networks depict common selection patterns, not interactions. Soil Science and Environment 2:1 doi: 10.48130/SSE-2023-0001
  • Catalog

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return