Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Multidimensional specialization and generalization are pervasive in soil prokaryotes

Nature Ecology & Evolution volume 7, pages 1408–1418 (2023)Cite this article

Subjects

Abstract

Habitat specialization underpins biological processes from species distributions to speciation. However, organisms are often described as specialists or generalists based on a single niche axis, despite facing complex, multidimensional environments. Here, we analysed 236 environmental soil microbiomes across the United States and demonstrate that 90% of >1,200 prokaryotes followed one of two trajectories: specialization on all niche axes (multidimensional specialization) or generalization on all axes (multidimensional generalization). We then documented that this pervasive multidimensional specialization/generalization had many ecological and evolutionary consequences. First, multidimensional specialization and generalization are highly conserved with very few transitions between these two trajectories. Second, multidimensional generalists dominated communities because they were 73 times more abundant than specialists. Lastly, multidimensional specialists played important roles in community structure with ~220% more connections in microbiome networks. These results indicate that multidimensional generalization and specialization are evolutionarily stable with multidimensional generalists supporting larger populations and multidimensional specialists playing important roles within communities, probably stemming from their overrepresentation among pollutant detoxifiers and nutrient cyclers. Taken together, we demonstrate that the vast majority of soil prokaryotes are restricted to one of two multidimensional niche trajectories, multidimensional specialization or multidimensional generalization, which then has far-reaching consequences for evolutionary transitions, microbial dominance and community roles.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Multidimensional generalization and specialization are widespread in prokaryotes and exceed what can be explained by environmental correlations.
Fig. 2: Multidimensional niche trajectories are phylogenetically conserved.
Fig. 3: Multidimensional generalists are more dominant within communities.
Fig. 4: Multidimensional specialists are more central to microbiome networks.
Fig. 5: Multidimensional specialists are overrepresented in many nutrient-cycling and detoxifying orders of soil prokaryotes.

Data availability

All raw sequencing and environmental data are publicly available through the NEON database (DP1.10081.001). Scripts to download data from NEON and process sequencing data into ESVs and OTUs are available in Supplementary Data 2. OTU abundances from ‘kingdom’ to ‘species’ levels are available in Supplementary Data 1. We used the GreenGenes database (v.13.5) for taxonomic assignments.

Code availability

Code to replicate our analyses and a ‘project’ folder containing all the intermediate files and statistical summaries from RMarkdown scripts are available at Zenodo (https://doi.org/10.5281/zenodo.7747186).

References

  1. Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R. & Slatyer, R. A. Evolution of ecological niche breadth. Annu. Rev. Ecol. Evol. Syst. 48, 183–206 (2017).

    Article  Google Scholar 

  2. Carscadden, K. A. et al. Niche breadth: causes and consequences for ecology, evolution, and conservation. Q. Rev. Biol. 95, 179–214 (2020).

    Article  Google Scholar 

  3. Muller, E. E. L. Determining microbial niche breadth in the environment for better ecosystem fate predictions. mSystems 4, e00080-19 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bonetti, M. F. & Wiens, J. J. Evolution of climatic niche specialization: a phylogenetic analysis in amphibians. Proc. Biol. Sci. 281, 20133229 (2014).

    PubMed  PubMed Central  Google Scholar 

  5. Julliard, R., Clavel, J., Devictor, V., Jiguet, F. & Couvet, D. Spatial segregation of specialists and generalists in bird communities. Ecol. Lett. 9, 1237–1244 (2006).

    Article  PubMed  Google Scholar 

  6. Devictor, V. et al. Defining and measuring ecological specialization. J. Appl. Ecol. 47, 15–25 (2010).

    Article  Google Scholar 

  7. Dehling, D. M., Jordano, P., Schaefer, H. M., Böhning-Gaese, K. & Schleuning, M. Morphology predicts species’ functional roles and their degree of specialization in plant–frugivore interactions. Proc. Biol. Sci. 283, 20152444 (2016).

    PubMed  PubMed Central  Google Scholar 

  8. Hardy, N. B. & Otto, S. P. Specialization and generalization in the diversification of phytophagous insects: tests of the musical chairs and oscillation hypotheses. Proc. Biol. Sci. 281, 20132960 (2014).

    PubMed  PubMed Central  Google Scholar 

  9. Grime, J. P. Competitive exclusion in herbaceous vegetation. Nature 242, 344–347 (1973).

    Article  Google Scholar 

  10. Warren, M. S. et al. Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature 414, 65–69 (2001).

    Article  CAS  PubMed  Google Scholar 

  11. Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Feinsinger, P., Spears, E. E. & Poole, R. W. A simple measure of niche breadth. Ecology 62, 27–32 (1981).

    Article  Google Scholar 

  13. Kim, S. L., Tinker, M. T., Estes, J. A. & Koch, P. L. Ontogenetic and among-individual variation in foraging strategies of northeast Pacific white sharks based on stable isotope analysis. PLoS ONE 7, e45068 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).

    Article  PubMed  Google Scholar 

  15. Setlow, P., Wang, S. & Li, Y.-Q. Germination of spores of the orders Bacillales and Clostridiales. Annu. Rev. Microbiol. 71, 459–477 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Felsenstein, J. Parsimony in systematics: biological and statistical issues. Annu. Rev. Ecol. Syst. 14, 313–333 (1983).

    Article  Google Scholar 

  17. Ochman, H. & Moran, N. A. Genes lost and genes found: evolution of bacterial pathogenesis and symbiosis. Science 292, 1096–1099 (2001).

    Article  CAS  PubMed  Google Scholar 

  18. Deeds, E. J., Hennessey, H. & Shakhnovich, E. I. Prokaryotic phylogenies inferred from protein structural domains. Genome Res. 15, 393–402 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sriswasdi, S., Yang, C. & Iwasaki, W. Generalist species drive microbial dispersion and evolution. Nat. Commun. 8, 1162 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Barberán, A. et al. Why are some microbes more ubiquitous than others? Predicting the habitat breadth of soil bacteria. Ecol. Lett. 17, 794–802 (2014).

    Article  PubMed  Google Scholar 

  21. Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Banerjee, S., Schlaeppi, K. & van der Heijden, M. G. A. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567–576 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).

    Article  Google Scholar 

  24. Xun, W. et al. Specialized metabolic functions of keystone taxa sustain soil microbiome stability. Microbiome 9, 35 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rawstern, A. H., Hernandez, D. J. & Afkhami, M. E. Hub taxa are keystone microbes during early succession. Preprint at bioRxiv https://doi.org/10.1101/2023.03.02.530218 (2023).

  26. Ramirez, K. S. et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat. Microbiol. 3, 189–196 (2018).

    Article  CAS  PubMed  Google Scholar 

  27. Bittleston, L. S., Gralka, M., Leventhal, G. E., Mizrahi, I. & Cordero, O. X. Context-dependent dynamics lead to the assembly of functionally distinct microbial communities. Nat. Commun. 11, 1440 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Tang, S. et al. Microbial coupling mechanisms of nitrogen removal in constructed wetlands: a review. Bioresour. Technol. 314, 123759 (2020).

    Article  CAS  PubMed  Google Scholar 

  29. Deng, J., Xiao, T., Fan, W., Ning, Z. & Xiao, E. Relevance of the microbial community to Sb and As biogeochemical cycling in natural wetlands. Sci. Total Environ. 818, 151826 (2022).

    Article  CAS  PubMed  Google Scholar 

  30. Banerjee, S. et al. Poor nutrient availability in opencast coalmine influences microbial community composition and diversity in exposed and underground soil profiles. Appl. Soil Ecol. 152, 103544 (2020).

    Article  Google Scholar 

  31. Jia, R. et al. Abundance and community succession of nitrogen-fixing bacteria in ferrihydrite enriched cultures of paddy soils is closely related to Fe(III)-reduction. Sci. Total Environ. 720, 137633 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. Xiao, X. et al. Two cultivated legume plants reveal the enrichment process of the microbiome in the rhizocompartments. Mol. Ecol. 26, 1641–1651 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Ozaki, K., Thompson, K. J., Simister, R. L., Crowe, S. A. & Reinhard, C. T. Anoxygenic photosynthesis and the delayed oxygenation of Earth’s atmosphere. Nat. Commun. 10, 3026 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Norden, N., Chazdon, R. L., Chao, A., Jiang, Y.-H. & Vílchez-Alvarado, B. Resilience of tropical rain forests: tree community reassembly in secondary forests. Ecol. Lett. 12, 385–394 (2009).

    Article  PubMed  Google Scholar 

  35. García, Y., Clara Castellanos, M. & Pausas, J. G. Differential pollinator response underlies plant reproductive resilience after fires. Ann. Bot. 122, 961–971 (2018).

    PubMed  PubMed Central  Google Scholar 

  36. Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. Biol. Sci. 271, 2605–2611 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Imdahl, F., Vafadarnejad, E., Homberger, C., Saliba, A.-E. & Vogel, J. Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria. Nat. Microbiol. 5, 1202–1206 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Ma, P. et al. Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states. Cell 186, 877–891.e14 (2023).

    Article  CAS  PubMed  Google Scholar 

  39. Yin, J. et al. A droplet-based microfluidic approach to isolating functional bacteria from gut microbiota. Front. Cell. Infect. Microbiol. 12, 920986 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Young, I. M. & Crawford, J. W. Interactions and self-organization in the soil–microbe complex. Science 304, 1634–1637 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Schlüter, S., Sammartino, S. & Koestel, J. Exploring the relationship between soil structure and soil functions via pore-scale imaging. Geoderma 370, 114370 (2020).

    Article  Google Scholar 

  42. Bebber, D. P. & Chaloner, T. M. Specialists, generalists and the shape of the ecological niche in fungi. New Phytol. 234, 345–349 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Chaloner, T. M., Gurr, S. J. & Bebber, D. P. Geometry and evolution of the ecological niche in plant-associated microbes. Nat. Commun. 11, 2955 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Davison, J. et al. Temperature and pH define the realised niche space of arbuscular mycorrhizal fungi. New Phytol. 231, 763–776 (2021).

    Article  CAS  PubMed  Google Scholar 

  45. Slatyer, R. A., Hirst, M. & Sexton, J. P. Niche breadth predicts geographical range size: a general ecological pattern. Ecol. Lett. 16, 1104–1114 (2013).

    Article  PubMed  Google Scholar 

  46. Keller, M., Schimel, D. S., Hargrove, W. W. & Hoffman, F. M. A continental strategy for the National Ecological Observatory Network. Front. Ecol. Environ. 6, 282–284 (2008).

    Article  Google Scholar 

  47. Stanish, L. & Parker, S. NEON User Guide to Microbe Marker Gene Sequences (DP1.10108.001; DP1.20280.001; DP1.20282.001) (2020); https://data.neonscience.org/documents/10179/2237401/NEON_markerGenes_userGuide_vD/7be3774b-b924-9640-4d89-b154d31993df?version=1.0&previewFileIndex=

  48. National Ecological Observatory Network (NEON). Soil microbe marker gene sequences (DP1.10108.001) (2022); https://data.neonscience.org/data-products/DP1.10108.001

  49. MoBio PowerSoil-htp 96-Well Manual Extraction Method Using a Swing Bucket Centrifuge (Argonne National Laboratory, 2015); https://data.neonscience.org/documents/10179/2655517/ANL_soilDnaExtractionSOP_2015/ce0f07df-ca4d-428e-96f8-8c5a7413cd17

  50. NEON DNA Extraction Standard Operating Procedure v.1 (Battelle Ecology, Inc., 2018); https://data.neonscience.org/documents/10179/2655517/BMI_dnaExtractionSOP_v1/34aa8f9c-819c-48ed-821a-a82168a9dd20

  51. Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).

    Article  Google Scholar 

  53. National Ecological Observatory Network (NEON). Soil physical and chemical properties, periodic (DP1.10086.001) (2022); https://data.neonscience.org/data-products/DP1.10086.001

  54. Dastogeer, K. M. G., Tumpa, F. H., Sultana, A., Akter, M. A. & Chakraborty, A. Plant microbiome–an account of the factors that shape community composition and diversity. Curr. Plant Biol. 23, 100161 (2020).

    Article  Google Scholar 

  55. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

    Article  CAS  PubMed  Google Scholar 

  58. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  59. Labrousse, S. et al. Under the sea ice: exploring the relationship between sea ice and the foraging behaviour of southern elephant seals in East Antarctica. Prog. Oceanogr. 156, 17–40 (2017).

    Article  Google Scholar 

  60. Afkhami, M. E., McIntyre, P. J. & Strauss, S. Y. Mutualist-mediated effects on species’ range limits across large geographic scales. Ecol. Lett. 17, 1265–1273 (2014).

    Article  PubMed  Google Scholar 

  61. Sievers, F. & Higgins, D. G. Clustal omega. Curr. Protoc. Bioinformatics 48, 3–13 (2014).

    Article  Google Scholar 

  62. Jukes, T. H. & Cantor, C. R. in Mammalian Protein Metabolism (ed. Munro, H. N.) 21–132 (Academic Press, 1969).

  63. Goffredi, S. K. et al. Genomic versatility and functional variation between two dominant heterotrophic symbionts of deep-sea Osedax worms. ISME J. 8, 908–924 (2014).

    Article  PubMed  Google Scholar 

  64. Ogata, H. et al. Genome sequence of Rickettsia bellii illuminates the role of amoebae in gene exchanges between intracellular pathogens. PLoS Genet. 2, e76 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Argimón, S. et al. A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch. Nat. Commun. 12, 2879 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Blum, M. G. B. & François, O. Which random processes describe the tree of life? A large-scale study of phylogenetic tree imbalance. Syst. Biol. 55, 685–691 (2006).

    Article  PubMed  Google Scholar 

  67. Keck, F., Rimet, F., Bouchez, A. & Franc, A. phylosignal: an R package to measure, test, and explore the phylogenetic signal. Ecol. Evol. 6, 2774–2780 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Watts, S. C., Ritchie, S. C., Inouye, M. & Holt, K. E. FastSpar: rapid and scalable correlation estimation for compositional data. Bioinformatics 35, 1064–1066 (2019).

    Article  CAS  PubMed  Google Scholar 

  69. Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: a network perspective. Trends Microbiol. 25, 217–228 (2017).

    Article  CAS  PubMed  Google Scholar 

  71. Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6, 343–351 (2012).

    Article  PubMed  Google Scholar 

  72. Hernandez, D. J., David, A. S., Menges, E. S., Searcy, C. A. & Afkhami, M. E. Environmental stress destabilizes microbial networks. ISME J. 15, 1722–1734 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Röttjers, L. & Faust, K. From hairballs to hypotheses–biological insights from microbial networks. FEMS Microbiol. Rev. 42, 761–780 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  74. van der Heijden, M. G. A. & Hartmann, M. Networking in the plant microbiome. PLoS Biol. 14, e1002378 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Gough, E. K. et al. Linear growth faltering in infants is associated with Acidaminococcus sp. and community-level changes in the gut microbiota. Microbiome 3, 24 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Jordán, F. Keystone species and food webs. Philos. Trans. R. Soc. B 364, 1733–1741 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

We thank the National Ecological Observatory Network for making their data publicly available. We also thank A. Zanne (University of Miami), S. Strauss (University of California, Davis), K. Crawford (University of Houston), M. Jayachandran (Florida International University) and A. Rawstern, A. Igwe and G. Ortiz of the Afkhami lab (University of Miami) as well as the labs of A. Wilson, C. Silveira and L. Müller (University of Miami) for their feedback on this manuscript. We also thank the editors A. McKay and L. Grinham for their feedback on this manuscript. We acknowledge funding support from the University of Miami to D.J.H. (Maytag Fellowship, Dean’s Summer Research Fellowship, Dean’s Dissertation Fellowship) and K.N.K. (Lisa D. Anness Fellowship) as well as funding from the United States Department of Agriculture to D.J.H. (National Institute of Food and Agriculture Predoctoral Fellowship 2022-67011-36456) and the National Science Foundation (NSF) to K.N.K. (Graduate Research Fellowship), B.K.A. (Graduate Research Fellowship) and M.E.A. (DEB-1922521 and NSF DEB-2030060).

Author information

Author notes

  1. These authors contributed equally: Damian J. Hernandez, Kasey N. Kiesewetter.

Authors and Affiliations

  1. Department of Biology, University of Miami, Coral Gables, FL, USA

    Damian J. Hernandez, Kasey N. Kiesewetter, Brianna K. Almeida, Daniel Revillini & Michelle E. Afkhami

Authors
  1. Damian J. Hernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kasey N. Kiesewetter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brianna K. Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Revillini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michelle E. Afkhami
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.J.H., K.N.K. and M.E.A. conceptualized the study. D.J.H. and K.N.K. analysed the data. D.J.H., K.N.K., B.K.A. and D.R. collected the data. D.J.H., K.N.K. and M.E.A. wrote the manuscript. D.J.H., K.N.K., B.K.A., D.R. and M.E.A. reviewed and edited the manuscript. D.J.H. and M.E.A. revised the manuscript. M.E.A. supervised the study.

Corresponding author

Correspondence to Damian J. Hernandez.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Sampling design of NEON soil collections.

Map of 30 NEON collection sites across the continental United States. There are 236 plots (up to 10 plots per site) in which complete data on soil pH, soil temperature, litter depth, and soil moisture are collected (‘full dataset’). A subset of the 236 plots (84 plots across 10 sites) had additional biogeochemical data on per cent carbon, per cent nitrogen, and carbon/nitrogen ratios (‘subsetted dataset’). Distribution of the number of plots at each site is displayed in the heatmap for both the full and subsetted datasets. Dark grey squares indicates data was not available (NA).

Extended Data Fig. 2 Rarefaction curves of each sample split by site and robustness of bimodal niche breadth distributions to analysis decisions.

a) All samples reached plateaus in their rarefaction curves indicating that we had enough sequencing depth to fully characterize communities. Each line represents the rarefaction curve of a sample. b-d) To calculate niche breadth along continuous axes, these axes must be broken into bins. Here, we show that bimodal distributions of niche breadth are robust to the important analysis decision of defining ‘habitats’ (that is, bins). In this manuscript, we present the results from the most conservative binning of 10 bins.

Extended Data Fig. 3 Niche breadth and environmental correlations across niche dimensions for Exact Sequence Variants (A-D) and including carbon/nitrogen niche axes (E-H).

ESV analyses are represented by subfigures A-D and taxa level analyses including carbon/nitrogen data are represented by subfigures E-H. a) Heatmap of 14,015 prokaryotic ESV taxa (x-axis) along environmental axes. ESVs are sorted from lowest to highest average niche breadth for visualization. b) Heatmap of Spearman’s ρ from correlations between niche breadths of 14,015 ESVs along different axes. c) Heatmap of Spearman’s ρ from correlations between environmental axes calculated across 236 individual plots. d) Comparison of the absolute values of Spearman’s ρ from correlations between niche breadths and correlations between environmental axes, demonstrating that niche breadth correlations are significantly stronger than correlations in environmental variation among axes. Significance determined by two-tailed Mann-Whitney U test (W = 36, p = 0.002). Box plots show the median (middle line) and interquartile range (the box). e) Heatmap of 1085 prokaryotic taxa (x-axis) along seven environmental axes that include measures of carbon and nitrogen. Taxa are sorted from lowest to highest average niche breadth for visualization. f) Heatmap of Spearman’s ρ from correlations between niche breadths of the 1085 taxa along the seven different axes. g) Heatmap of Spearman’s ρ from correlations between the seven environmental axes across 84 individual plots. h) Comparison of the absolute values of Spearman’s ρ from correlations between niche breadths and correlations between environmental axes, again demonstrating that niche breadth correlations are significantly stronger than correlations in environmental variation among axes. Significance determined by two-tailed Mann-Whitney U test (W = 440, p = 7.43 × 10−12). Box plots show the median (middle line) and interquartile range (the box).

Extended Data Fig. 4 Differences in magnitude of correlations between niche breadths and environmental axes.

Boxplots of the magnitude of Spearman’s coefficients between niche breadths and environmental axes at each of the 21 sites. When we account for the site from which data was collected, niche breadth relationships are still substantially stronger than environmental correlations (p < 2.20 × 10−16, permutational ANOVA accounting for origin site) with the type of the relationship (that is, relationship between niche breadths versus relationship between environmental axes) having an effect size >4 times stronger than site identity (ωcorrelation type/ωsite = 4.17). Boxplots show the median (middle line) and interquartile range (the box).

Extended Data Fig. 5 Randomized state transitions are consistent across all 100 observed representative trees.

Kolomogorov-Smirnov statistics (a measure of how different the shape of two distributions are) of generalist-to-specialist (a) and specialist-to-generalist (b) transitions in all 100 observed representative trees (x-axis). Each point is the comparison of the randomized distribution of the focal representative tree (value of x-axis) against each other representative tree. All values are below a D of 0.1 (dashed horizontal line) indicating that our analyses are robust to changes in which ESV represents each OTU. The higher the D, the more different the distributions are from each other. The lower the D, the more similar the distributions are.

Extended Data Fig. 6 Phylogenetic relationships of niche breadth in closely-related taxa.

Scatter plots of LIPA Moran’s I of average niche breadth for all 1230 taxa (points) in all 100 observed trees (each graph). A LOESS fit (blue line) is plotted to visualize if pattern follows linear or quadratic relationships (compared in Fig. 2). A higher LIPA Moran’s I indicates more phylogenetic conservation of average niche breadth among closely related taxa. A quadratic relationship (a better fit than a linear model in all trees; Fig. 2) indicates that phylogenetic conservation of average niche breadth is strongest when taxa are highly specialized or highly generalized. The quadratic relationship further supports multidimensional specialization and generalization as opposing niche trajectories.

Extended Data Fig. 7 Relationship between average relative abundance and niche breadth of Exact Sequence Variants (A-B) and including carbon/nitrogen niche axes data (C-D).

ESV analyses are represented by subfigures A-B and taxa level analyses including carbon/nitrogen data are represented by subfigures C-D. a) Average abundances of generalist (dark purple) and specialist (light purple) taxa (14,015 ESV taxa total). Significance calculated with a two-tailed permutational test (Z = 5.79, p = 6.87 × 10−9). Boxplots show the median (middle line) and interquartile range (the box). b) Average relative abundances of 14015 prokaryotic ESV taxa regressed against average niche breadth. Direction of the relationship is determined using a Spearman’s correlation test and significance is calculated using a two-tailed permutational test (p < 2.20 × 10−16). c) Average abundances of generalist (dark purple) and specialist (light purple) taxa (1085 taxa total). Significance calculated with a two-tailed permutational test (Z = −6.34, p = 2.27 × 10−10). Boxplots show the median (middle line) and interquartile range (the box). d) Average relative abundances of 1085 taxa regressed against average niche breadth. In B and D, Lines are fitted with LOESS smoothing, shaded regions around the lines are the 95% confidence intervals, and the x-axes are on a log10 scale. Dashed horizontal line indicates the local minima in the bimodal distribution of average niche breadth used to indicate specialists (light purple) and generalists (dark purple). Direction of relationships were determined using a Spearman’s correlation test and significance was calculated using two-tailed permutational tests in which abundances were randomized 10,000 times (p < 2.20 × 10−16).

Extended Data Fig. 8 Average relative abundance within a site is explained by a taxon’s average niche breadth at that site.

Average relative abundances of taxa at each of 21 sites regressed against average niche breadth in the corresponding site. Lines are fitted with LOESS smoothing, and shaded regions around the lines are the 95% confidence interval. The x-axes are displayed on a log10 scale.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Data 1

Excel workbook containing all data necessary to replicate our analyses after processing DNA sequencing data into counts.

Supplementary Data 2

Project folder used to download all raw data from NEON database.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hernandez, D.J., Kiesewetter, K.N., Almeida, B.K. et al. Multidimensional specialization and generalization are pervasive in soil prokaryotes. Nat Ecol Evol 7, 1408–1418 (2023). https://doi.org/10.1038/s41559-023-02149-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-023-02149-y

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing