Discussion
Though the concept of a core community is prevalent in ecology, specifically microbial ecology, our findings draw attention to the inconsistency of examined methods and the potential analyses based on a core set of taxa to be misleading. Application and comparison of four commonly used core assignment methods to both simulated and empirical datasets yielded conflicting results, with no clear threshold in abundance or commonness defining core membership inclusion. Our results show that in some situations core assignment methods agree, but in many others, the methods do not reach consensus. We also reveal that this variation in core assignments can produce different statistical results and lead to different ecological interpretations. Furthermore, core co-assignment (i.e. assignment by multiple methods) was limited by the most conservative assignment method, which varied between datasets. Rather than consistent assignments of taxa to a core, assignments were not robust and instead differed among methods and their criteria. This was also highlighted in our comparison of assigned core taxa to statistically significant nodes from our cooccurrence network analyses, with many taxa possessing high degree centrality being absent from core assignments, and in the Arabidopsis dataset, many taxa assigned as core possessing zero significant edges. Our finding highlights the statistical nature of core assignments as opposed to an underlying biological phenomenon and demonstrate the importance of the underlying data structure for assigning a core. Researchers could evaluate the statistical support for a distinction between core and non-core taxa and thereby justify focusing on a subset of taxa for convenience. However, use of a subset of the taxa will in all cases lead to some loss of information about the communities. Instead, researchers could use all of the available abundance data including rare taxa in statistical procedures and rely on model-based methods to recognize groups of taxa that differ in abundance (e.g. Harrison, Calder, Shastry, & Buerkle, 2020; Martin, Witten, & Willis, 2020). Beyond the statistical considerations, the contribution of these less common individuals is becoming increasingly recognized (Amor, Ratzke, & Gore, 2020; Jousset et al., 2017).
Community ecology has a long-standing interest in distinguishing between taxa that drive ecological functions and patterns, and taxa that are less obviously important in ecological systems. Dubos et al. (1965) ascribed importance to ‘indigenous flora’, defined as those microorganisms present during the development of an animal that are so ubiquitous they establish in all its members. These autochthonous microbiota are similar to the concept of a climax community within a habitat, where nonindigenous or transient taxa were compared to flowing streams just passing through, not contributing to the functionality of the system (Savage, 1977). Furthermore, it has been hypothesized that core taxa drive and are disproportionately responsible for the functions of ecological systems (Grime, 1998). These viewpoints of classic ecology have been invoked as rationale to consider only the ‘indigenous flora’ or core taxa, in an attempt to reduce the noise arising when considering mixtures of taxa with different levels of association with their host environments (Astudillo-García et al., 2017). Thus, it is common, current practice in studies of microbiomes to consider only taxa that are common enough to meet criteria for membership in the core community (e.g. Turnbaugh & Gordon, 2009; Turnbaugh et al., 2009; Wirth et al., 2018). However, our results indicate this practice may lack statistical support in some natural systems and may even go as far as to exclude taxa that are important for microbial network structure.
Given that analyses of microbial communities are frequently based on taxon counts for thousands of taxa in a single sample (high dimensionality), the appeal of focusing our attention on fewer dimensions and taxa is understandable. Dimension reduction can reduce noise or variation among samples and resolve the strongest patterns in data sets (Nguyen & Holmes, 2019). Because many statistical methods lack power when applied to highly dimensional data (Nguyen & Holmes, 2019), scientists rarely analyze an entire ecological dataset and instead focus on a subset of the most common individuals (Hawinkel, Kerckhof, Bijnens, & Thas, 2019). From a conceptual standpoint, the practice of focusing on a core set of taxa, and discarding variation in the remaining taxa, comes at little cost if the ecological functions of interest are associated with variation in the core set of taxa. There are certainly examples (Winfree, Fox, Williams, Reilly, & Cariveau, 2015) and even theory (mass-ratio hypothesis; (Grime, 1998)) of ecological processes being tied to variation in the abundance of a small number of relatively common taxa. Yet, variation in ecological and especially microbially-driven processes is sometimes associated with rare taxa (e.g. sulfur reduction, nitrification, or methanogenesis) and thus demonstrates the risk of discarding uncommon members from the community of interest (Harrison et al., 2021; Jousset et al., 2017; Mikkelson, Bokman, & Sharp, 2016; Shade et al., 2014). Our results support this latter viewpoint and show that trends observed in the common taxa (i.e. core assignments) do not always accurately represent the entire taxon assemblage.
Similarly, in initial genomic studies of human trait variation that considered millions of variable nucleotides (analogous to the high number of taxa in microbiomes), researchers focused on those nucleotides that had a particular minor allele frequency (commonly at least 0.05). The conceptual rationale for the focus on a subset of genomic sites was the hypothesis that common conditions (e.g., disease) should be associated with common nucleotide variants (Lohmueller, Pearce, Pike, Lander, & Hirschhorn, 2003; Pritchard & Cox, 2002). The statistical rationale included the difficulty of estimating the effect of rarely observed variants, as is true for rare taxa in a microbiome. The hypothesis of common disease-common variant received poor support for some traits of interest (Cirulli & Goldstein, 2010). Instead there was a growing recognition of the potential contribution of rare alleles and the potential exchangeability of neighboring rare variants that, in aggregate, could explain trait variation (Zhou & Stephens, 2012). Likewise, variation in ecological processes could be associated with variation of the common taxa in communities, or through variation among any of their members. As for genomics, this is a hypothesis to be tested in ecology and for some systems discarding rare taxa will preclude understanding ecological processes.
If one accepts the necessity of considering only common and prevalent taxa, our comparison of core assignment methods should raise concern about their subjectivity and inconsistency. In our simulations, we considered core taxa to be those that were 2-25 times more common than non-core taxa from the same samples, representing a range of plausible taxon abundances. Analyses of simulated and two empirical datasets highlight the inconsistency among the four common methods considered for defining a core community and call into question the validity of dichotomizing taxon abundances into core and non-core assignments. The lack of a clear threshold in taxon abundance and coefficient of variation across real datasets suggest that this divide may not be supported in some cases. Furthermore, core taxa were more or less associated with study variables than the entire taxon assemblage and could lead to different ecological interpretation. The categorization of taxa into core and non-core groups is contingent on both the criteria used for identification and the underlying structure of the taxon table. Our results demonstrate the statistical nature of core assignment criteria and the forced dichotomization as opposed to an underlying biological difference between core and non-core taxa. Our comparisons along with other empirical studies (Caporaso et al., 2011; Clooney et al., 2016; Pollock, Glendinning, Wisedchanwet, & Watson, 2018; Shafer et al., 2017) indicate that the size and membership of the core community are not robust to differences in bioinformatic methods and core classification criteria.
The lack of consistency across core assignment methods should concern researchers as inferences drawn from core assignment can change drastically even when using the same dataset. Our simulations covered a range of plausible community structures, providing core assignment methods opportunity to potentially accurately assign core taxa. The individual samples in our simulated datasets contained nearly identical taxon distributions and offer core assignment methods a best case scenario. The lack of agreement and consistency in core assignment even under best case scenarios calls into question whether using these methods in ecological studies is fruitful or misleading. For example, researchers have related energy acquisition to variance in core gut microbiota, and the role this may play in obesity (Ley, 2010; Turnbaugh & Gordon, 2009; Turnbaugh et al., 2009). The observed relationship between the energy acquisition and microbial community composition is likely to depend on which criterion was used for identifying core taxa, thus potentially leading to different interpretations.
While a focus on a core set of taxa has been common, research suggests it may not be entirely warranted (Engel & Moran, 2013; Hammer, Sanders, & Fierer, 2019; Martinson, Moy, & Moran, 2012) and that all taxa could instead be used for analysis. More attention is now being paid to the “rare biosphere” and the contribution these less abundant taxa make to ecosystem processes (reviewed in Jousset et al., 2017), community structure (Mikkelson et al., 2016; Shade et al., 2014), and as a reservoir of metabolic diversity (Mikkelson et al., 2016). In ignoring these rare taxa and focusing solely on common ones, researchers may wrongfully attribute the functions of rare taxa to common ones. This is especially concerning in human microbiome and agricultural studies where environments are scanned for beneficial microbes, e.g. if the functions of rare taxa are attributed to common ones, researchers may be chasing the wrong taxa for biotechnological applications. In addition, this dichotomous assignment of core and non-core ignores situations in which taxa, both common and rare, form networks and function through interactions. Our network analysis highlights many taxa that are characterized by high degree centrality but were excluded from any core assignment method, again demonstrating the danger of focusing solely on taxa assigned to a core.
To proceed with the analysis of a core set of taxa, researchers can either investigate the effects of focal taxa that were chosen based on other information (analogous to candidate gene analysis), or closely examine evidence for categorical differences in the abundance of taxa. Statistical evidence for a distinction between core and non-core taxa could come from consistent categorization by different core assignment methods. Our analyses demonstrate that while a common core can be assigned by multiple methods, co-assignments are limited by the most conservative method, which can change based upon the underlying structure of the taxon table.
Alternatively, researchers can rely on established statistical approaches that incorporate variation in the abundance of all taxa. These include standard methods for multivariate analysis, including dimension reduction, including those implemented in statistical packages such vegan (Oksanen et al., 2018) and phyloseq (McMurdie & Holmes, 2013). Additionally, specialized methods for differential abundance analysis exist, including Dirichlet multinomial models (Grantham, Guan, Reich, Borer, & Gross, 2019; Harrison et al., 2020; La Rosa et al., 2012; Shafiei et al., 2015) and related methods that model the relative abundance of all taxa (Fernandes et al., 2014; Love, Huber, & Anders, 2014; Mandal et al., 2015; Robinson, McCarthy, & Smyth, 2009; Wang et al., 2015).
In summary, our application of core assignment methods to simulated and published data sets demonstrated the inconsistent classifications that resulted from commonly applied criteria for determining membership in the set of core taxa. Changes in the set of taxa assigned to the core could lead to drastically different conclusions regarding statistical associations and ecological consequences. These findings suggest that analyses that rely on the identification of core taxa should be disfavored in many cases and instead researchers can rely on multivariate analyses that make use of all of the abundance data.
Data accessibility: Taxon tables, simulated data and all code used for analysis are available online in the CoreMicro R package found at https://github.com/MayaGans/CoreMicro.git and zenodo repository (10.5281/zenodo.4909346) .
Acknowledgments: This research was supported by the Microbial Ecology Collaborative with funding from NSF award #EPS-1655726.
Author contributions:
M.G. and G.C. conceived the ideas presented. M.G., G.C., and C.A.B. wrote code for testing core hypothesis and simulations. M.G., G.C., L.v.D., and C.A.B. developed and edited manuscript. CoreMicro R package was developed by M.G. and G.C.
Table List:
List of core methods from Web of Science literature review
Summary of published data sets and core inclusion by method
Figure List:
  1. Heat map of true positive rate, false positive rate, and net assignment value for assigning core taxa by method for simulated data
  2. Bivariate plot of core inclusion by method and data set
  3. Venn diagram of core co-assignments for each of the datasets used.
  4. Top panels (A & B): Venn diagrams of all core assignments compared to significant nodes identified from cooccurrence network analysis. Bottom panels (C & D): Venn diagrams of core assignments by individual methods shared with significant nodes from cooccurrence networks.
Supplementary tables – available at 10.5281/zenodo.4909346.
Literature review reference table
Ahrendt, S. R., Quandt, C. A., Ciobanu, D., Clum, A., Salamov, A., Andreopoulos, B., … Grigoriev, I. V. (2018). Leveraging single-cell genomics to expand the fungal tree of life. Nature Microbiology , 3 (12), 1417–1428. https://doi.org/10.1038/s41564-018-0261-0
Amor, D. R., Ratzke, C., & Gore, J. (2020). Transient invaders can induce shifts between alternative stable states of microbial communities. Science Advances , 6 (8), eaay8676. https://doi.org/10.1126/sciadv.aay8676
Astudillo-García, C., Bell, J. J., Webster, N. S., Glasl, B., Jompa, J., Montoya, J. M., & Taylor, M. W. (2017). Evaluating the core microbiota in complex communities: A systematic investigation. Environmental Microbiology , 19 (4), 1450–1462. https://doi.org/10.1111/1462-2920.13647
Banerjee, S., Kirkby, C. A., Schmutter, D., Bissett, A., Kirkegaard, J. A., & Richardson, A. E. (2016). Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biology and Biochemistry , 97 , 188–198. https://doi.org/10.1016/j.soilbio.2016.03.017
Banerjee, S., Schlaeppi, K., & van der Heijden, M. G. A. (2018). Keystone taxa as drivers of microbiome structure and functioning.Nature Reviews Microbiology , 16 (9), 567–576. https://doi.org/10.1038/s41579-018-0024-1
Callahan, B. J., Sankaran, K., Fukuyama, J. A., McMurdie, P. J., & Holmes, S. P. (2016). Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses. F1000Research ,5 (3), 1492. https://doi.org/10.12688/f1000research.8986.2
Caporaso, J. G., Lauber, C. L., Costello, E. K., Berg-Lyons, D., Gonzalez, A., Stombaugh, J., … Knight, R. (2011). Moving pictures of the human microbiome. Genome Biology , 12 (5). https://doi.org/10.1186/gb-2011-12-5-r50
Cirulli, E. T., & Goldstein, D. B. (2010). Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nature Reviews Genetics , 11 (6), 415–425. https://doi.org/10.1038/nrg2779
Clooney, A. G., Fouhy, F., Sleator, R. D., O’Driscoll, A., Stanton, C., Cotter, P. D., & Claesson, M. J. (2016). Comparing apples and oranges?: Next generation sequencing and its impact on microbiome analysis.PLoS ONE , 11 (2), 1–16. https://doi.org/10.1371/journal.pone.0148028
Consortium, T. H. M. P. (2012). Structure, function and diversity of the healthy human microbiome. Nature , 486 (7402), 207–214. https://doi.org/10.1038/nature11234
[Dataset] Gans, M., Custer, G.F., Buerkle, C.A., van Diepen, L.T.A. 2021. Zenodo. 1.4.0. 10.5281/zenodo.4909346.
Delgado-Baquerizo, M., Oliverio, A. M., Brewer, T. E., Benavent-González, A., Eldridge, D. J., Bardgett, R. D., … Fierer, N. (2018). A global atlas of the dominant bacteria found in soil. Science , 359 (6373), 320–325. https://doi.org/10.1126/science.aap9516
Desnues, C., Rodriguez-Brito, B., Rayhawk, S., Kelley, S., Tran, T., Haynes, M., … Rohwer, F. (2008). Biodiversity and biogeography of phages in modern stromatolites and thrombolites. Nature ,452 (7185), 340–343. https://doi.org/10.1038/nature06735
Domagalski, R., Neal, Z., & Sagan, B. (2019). backbone: An R Package for extracting the backbone of bipartite projections, 1–17. Retrieved from http://arxiv.org/abs/1912.12779
Dubos, R., Schaedler, R. W., Costello, R., & Hoet, P. (1965). Indigenous, normal, and autochthonous flora of the gastrointestinal tract. The Journal of Experimental Medicine , 122 (1), 67–76. https://doi.org/10.1084/jem.122.1.67
Engel, P., & Moran, N. A. (2013). The gut microbiota of insects - diversity in structure and function. FEMS Microbiology Reviews ,37 (5), 699–735. https://doi.org/10.1111/1574-6976.12025
Fernandes, A. D., Reid, J. N., Macklaim, J. M., McMurrough, T. A., Edgell, D. R., & Gloor, G. B. (2014). Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome , 2 (1), 15. https://doi.org/10.1186/2049-2618-2-15
Forcino, F. L., Leighton, L. R., Twerdy, P., & Cahill, J. F. (2015). Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be. PloS One , 10 (6), e0128379–e0128379. https://doi.org/10.1371/journal.pone.0128379
Geisen, S., Laros, I., Vizcaíno, A., Bonkowski, M., & de Groot, G. A. (2015). Not all are free-living: high-throughput DNA metabarcoding reveals a diverse community of protists parasitizing soil metazoa.Molecular Ecology , 24 (17), 4556–4569. https://doi.org/10.1111/mec.13238
Grantham, N. S., Guan, Y., Reich, B. J., Borer, E. T., & Gross, K. (2019). MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments. Journal of the American Statistical Association . https://doi.org/10.1080/01621459.2019.1626242
Gray, K. I. . U. and J. S. ., Amjad, S., & Gray, J. S. (1983). Lognormal Distributions and the Concept of Community Equilibrium.Marine Pollution Bulletin. , 39 (2), 178–181. https://doi.org/10.2307/3544482
Grime, J. P. (1998). Benefits of plant diversity to ecosystems: immediate, filter and founder effects. Journal of Ecology ,86 (6), 902–910. https://doi.org/10.1046/j.1365-2745.1998.00306.x
Hamady, M., & Knight, R. (2009). Microbial community profiling for human microbiome projects: Tools, techniques, and challenges.Genome Research . https://doi.org/10.1101/gr.085464.108
Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P., & Fierer, N. (2017). Caterpillars lack a resident gut microbiome. Proceedings of the National Academy of Sciences , 114 (36), 9641–9646. https://doi.org/10.1073/pnas.1707186114
Hammer, T. J., Sanders, J. G., & Fierer, N. (2019). Not all animals need a microbiome. FEMS Microbiology Letters , 366 (10), 1–11. https://doi.org/10.1093/femsle/fnz117
Hanski, I. (1982). Dynamics of Regional Distribution: The Core and Satellite Species Hypothesis. Oikos , 38 (2), 210. https://doi.org/10.2307/3544021
Harrison, J. G., Beltran, L. P., Buerkle, C. A., Cook, D., Gardner, D. R., Parchman, T. L., … Forister, M. L. (2021). A suite of rare microbes interacts with a dominant, heritable, fungal endophyte to influence plant trait expression. The ISME Journal , 608729. https://doi.org/10.1038/s41396-021-00964-4
Harrison, J. G., Calder, W. J., Shastry, V., & Buerkle, C. A. (2020). Dirichlet-multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data. Molecular Ecology Resources , 20 (2), 481–497. https://doi.org/10.1111/1755-0998.13128
Hawinkel, S., Kerckhof, F. M., Bijnens, L., & Thas, O. (2019). A unified approach to unconstrained and constrained ordination of microbiome count data. Under Review , 1–20.
Jousset, A., Bienhold, C., Chatzinotas, A., Gallien, L., Gobet, A., Kurm, V., … Hol, G. W. H. (2017). Where less may be more: How the rare biosphere pulls ecosystems strings. ISME Journal ,11 (4), 853–862. https://doi.org/10.1038/ismej.2016.174
La Rosa, P. S., Brooks, J. P., Deych, E., Boone, E. L., Edwards, D. J., Wang, Q., … Shannon, W. D. (2012). Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data. PLoS ONE ,7 (12), e52078. https://doi.org/10.1371/journal.pone.0052078
Ley, R. E. (2010). Obesity and the human microbiome. Current Opinion in Gastroenterology , 26 (1), 5–11. https://doi.org/10.1097/MOG.0b013e328333d751
Lohmueller, K. E., Pearce, C. L., Pike, M., Lander, E. S., & Hirschhorn, J. N. (2003). Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nature Genetics , 33 (2), 177–182. https://doi.org/10.1038/ng1071
Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology , 15 (12), 550. https://doi.org/10.1186/s13059-014-0550-8
Lundberg, D. S., Lebeis, S. L., Paredes, S. H., Yourstone, S., Gehring, J., Malfatti, S., … Dangl, J. L. (2012). Defining the core <i>Arabidopsis thaliana<i> root microbiome. Nature , 488 (7409), 86–90. https://doi.org/10.1038/nature11237
Lynch, M. D. J., & Neufeld, J. D. (2015). Ecology and exploration of the rare biosphere. Nature Reviews Microbiology , 13 (4), 217–229. https://doi.org/10.1038/nrmicro3400
Mandal, S., Van Treuren, W., White, R. A., Eggesbø, M., Knight, R., & Peddada, S. D. (2015). Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial Ecology in Health & Disease , 26 (0), 1–8. https://doi.org/10.3402/mehd.v26.27663
Martin, B. D., Witten, D., & Willis, A. D. (2020). Modeling microbial abundances and dysbiosis with beta-binomial regression. The Annals of Applied Statistics , 14 (1). https://doi.org/10.1214/19-AOAS1283
Martinson, V. G., Moy, J., & Moran, N. A. (2012). Establishment of Characteristic Gut Bacteria during Development of the Honeybee Worker.Applied and Environmental Microbiology , 78 (8), 2830–2840. https://doi.org/10.1128/aem.07810-11
McMurdie, P. J., & Holmes, S. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE , 8 (4), e61217. https://doi.org/10.1371/journal.pone.0061217
Mikkelson, K. M., Bokman, C. M., & Sharp, J. O. (2016). Rare Taxa Maintain Microbial Diversity and Contribute to Terrestrial Community Dynamics throughout Bark Beetle Infestation. Applied and Environmental Microbiology , 82 (23), 6912–6919. https://doi.org/10.1128/AEM.02245-16
Neal, Z. (2013). Identifying statistically significant edges in one-mode projections. Social Network Analysis and Mining , 3 (4), 915–924. https://doi.org/10.1007/s13278-013-0107-y
Nguyen, L. H., & Holmes, S. (2019). Ten quick tips for effective dimensionality reduction. PLOS Computational Biology ,15 (6), e1006907. https://doi.org/10.1371/journal.pcbi.1006907
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. (2018). vegan: Community Ecology Package.
Pollock, J., Glendinning, L., Wisedchanwet, T., & Watson, M. (2018). The Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies. Applied and Environmental Microbiology , 84 (7), 1–12. https://doi.org/10.1128/AEM.02627-17
Porazinska, D. L., Giblin-Davis, R. M., Esquivel, A., Powers, T. O., Sung, W., & Thomas, W. K. (2010). Ecometagenetics confirm high tropical rainforest nematode diversity. Molecular Ecology , 19 (24), 5521–5530. https://doi.org/10.1111/j.1365-294X.2010.04891.x
Pritchard, J. K., & Cox, N. (2002). The allelic architecture of human disease genes: common disease-common variant… or not? Human Molecular Genetics , 11 (20), 2417–2423. https://doi.org/10.1093/hmg/11.20.2417
R Development Core Team. (2020). A Language and Environment for Statistical Computing. R Foundation for Statistical Computing . Vienna, Austria. Retrieved from http://www.r-project.org
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2009). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics , 26 (1), 139–140. https://doi.org/10.1093/bioinformatics/btp616
Saunders, A. M., Albertsen, M., Vollertsen, J., & Nielsen, P. H. (2016). The activated sludge ecosystem contains a core community of abundant organisms. The ISME Journal , 10 (1), 11–20. https://doi.org/10.1038/ismej.2015.117
Savage, D. C. (1977). Microbial Ecology of the Gastrointestinal Tract.Annual Review of Microbiology , 31 (1), 107–133. https://doi.org/10.1146/annurev.mi.31.100177.000543
Shade, A., & Handelsman, J. (2012). Beyond the Venn diagram: the hunt for a core microbiome. Environmental Microbiology , 14 (1), 4–12. https://doi.org/10.1111/j.1462-2920.2011.02585.x
Shade, A., Jones, S. E., Caporaso, J. G., Handelsman, J., Knight, R., Fierer, N., & Gilbert, J. A. (2014). Conditionally Rare Taxa Disproportionately Contribute to Temporal Changes in Microbial Diversity. MBio , 5 (4), 3–11. https://doi.org/10.1128/mBio.01371-14
Shade, A., & Stopnisek, N. (2019). Abundance-occupancy distributions to prioritize plant core microbiome membership. Current Opinion in Microbiology , 49 , 50–58. https://doi.org/10.1016/j.mib.2019.09.008
Shafer, A. B. A., Peart, C. R., Tusso, S., Maayan, I., Brelsford, A., Wheat, C. W., & Wolf, J. B. W. (2017). Bioinformatic processing of RAD-seq data dramatically impacts downstream population genetic inference. Methods in Ecology and Evolution , 8 (8), 907–917. https://doi.org/10.1111/2041-210X.12700
Shafiei, M., Dunn, K. A., Boon, E., MacDonald, S. M., Walsh, D. A., Gu, H., & Bielawski, J. P. (2015). BioMiCo: A supervised Bayesian model for inference of microbial community structure. Microbiome ,3 (1), 8. https://doi.org/10.1186/s40168-015-0073-x
Shi, Y., Delgado-Baquerizo, M., Li, Y., Yang, Y., Zhu, Y. G., Peñuelas, J., & Chu, H. (2020). Abundance of kinless hubs within soil microbial networks are associated with high functional potential in agricultural ecosystems. Environment International , 142 (April), 105869. https://doi.org/10.1016/j.envint.2020.105869
Soliveres, S., van der Plas, F., Manning, P., Prati, D., Gossner, M. M., Renner, S. C., … Allan, E. (2016). Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality.Nature , 536 (7617), 456–459. https://doi.org/10.1038/nature19092
Stat, M., Huggett, M. J., Bernasconi, R., DiBattista, J. D., Berry, T. E., Newman, S. J., … Bunce, M. (2017). Ecosystem biomonitoring with eDNA: metabarcoding across the tree of life in a tropical marine environment. Scientific Reports , 7 (1), 12240. https://doi.org/10.1038/s41598-017-12501-5
Tedersoo, L., Bahram, M., Põlme, S., Kõljalg, U., Yorou, N. S., Wijesundera, R., … Abarenkov, K. (2014). Global diversity and geography of soil fungi. Science , 346 (6213), 1256688–1256688. https://doi.org/10.1126/science.1256688
Turnbaugh, P. J., & Gordon, J. I. (2009). The core gut microbiome, energy balance and obesity. Journal of Physiology ,587 (17), 4153–4158. https://doi.org/10.1113/jphysiol.2009.174136
Turnbaugh, P. J., Hamady, M., Yatsunenko, T., Cantarel, B. L., Duncan, A., Ley, R. E., … Gordon, J. I. (2009). A core gut microbiome in obese and lean twins. Nature , 457 (7228), 480–484. https://doi.org/10.1038/nature07540
Turnbaugh, P. J., Ley, R. E., Hamady, M., Fraser-Liggett, C. M., Knight, R., & Gordon, J. I. (2007). The Human Microbiome Project.Nature , 449 (7164), 804–810. https://doi.org/10.1038/nature06244
Umaña, M. N., Zhang, C., Cao, M., Lin, L., & Swenson, N. G. (2017). A core-transient framework for trait-based community ecology: an example from a tropical tree seedling community. Ecology Letters ,20 (5), 619–628. https://doi.org/10.1111/ele.12760
Wang, A. Y., Naumann, U., Wright, S., Eddelbuettel, D., Warton, D., & Davidwartonunsweduau, M. D. W. (2015). mvabund: Statistical Methods for Analysing Multivariate Abundance Data. CRAN. Retrieved from https://cran.r-project.org/web/packages/mvabund/index.html
Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R., & Cariveau, D. P. (2015). Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecology Letters ,18 (7), 626–635. https://doi.org/10.1111/ele.12424
Wirth, R., Kádár, G., Kakuk, B., Maróti, G., Bagi, Z., Szilágyi, Á., … Kovács, K. L. (2018). The Planktonic Core Microbiome and Core Functions in the Cattle Rumen by Next Generation Sequencing.Frontiers in Microbiology , 9 (SEP), 2285. https://doi.org/10.3389/fmicb.2018.02285
Zaheer, R., Noyes, N., Ortega Polo, R., Cook, S. R., Marinier, E., Van Domselaar, G., … McAllister, T. A. (2018). Impact of sequencing depth on the characterization of the microbiome and resistome.Scientific Reports , 8 (1), 5890. https://doi.org/10.1038/s41598-018-24280-8
Zhou, X., & Stephens, M. (2012). Genome-wide efficient mixed-model analysis for association studies. Nature Genetics , 44 (7), 821–824. https://doi.org/10.1038/ng.2310
Table 1. Five commonly employed core methods along with descriptions and number of publications found using these methods within Web of Science, accessed April 2018. The Venn Diagram method was not utilized in our analysis due to its similarity to the proportion of replicates method.