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.
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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 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).
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).
Carscadden, K. A. et al. Niche breadth: causes and consequences for ecology, evolution, and conservation. Q. Rev. Biol. 95, 179–214 (2020).
Muller, E. E. L. Determining microbial niche breadth in the environment for better ecosystem fate predictions. mSystems 4, e00080-19 (2019).
Bonetti, M. F. & Wiens, J. J. Evolution of climatic niche specialization: a phylogenetic analysis in amphibians. Proc. Biol. Sci. 281, 20133229 (2014).
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).
Devictor, V. et al. Defining and measuring ecological specialization. J. Appl. Ecol. 47, 15–25 (2010).
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).
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).
Grime, J. P. Competitive exclusion in herbaceous vegetation. Nature 242, 344–347 (1973).
Warren, M. S. et al. Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature 414, 65–69 (2001).
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).
Feinsinger, P., Spears, E. E. & Poole, R. W. A simple measure of niche breadth. Ecology 62, 27–32 (1981).
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).
Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
Setlow, P., Wang, S. & Li, Y.-Q. Germination of spores of the orders Bacillales and Clostridiales. Annu. Rev. Microbiol. 71, 459–477 (2017).
Felsenstein, J. Parsimony in systematics: biological and statistical issues. Annu. Rev. Ecol. Syst. 14, 313–333 (1983).
Ochman, H. & Moran, N. A. Genes lost and genes found: evolution of bacterial pathogenesis and symbiosis. Science 292, 1096–1099 (2001).
Deeds, E. J., Hennessey, H. & Shakhnovich, E. I. Prokaryotic phylogenies inferred from protein structural domains. Genome Res. 15, 393–402 (2005).
Sriswasdi, S., Yang, C. & Iwasaki, W. Generalist species drive microbial dispersion and evolution. Nat. Commun. 8, 1162 (2017).
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).
Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).
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).
Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).
Xun, W. et al. Specialized metabolic functions of keystone taxa sustain soil microbiome stability. Microbiome 9, 35 (2021).
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).
Ramirez, K. S. et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat. Microbiol. 3, 189–196 (2018).
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).
Tang, S. et al. Microbial coupling mechanisms of nitrogen removal in constructed wetlands: a review. Bioresour. Technol. 314, 123759 (2020).
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).
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).
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).
Xiao, X. et al. Two cultivated legume plants reveal the enrichment process of the microbiome in the rhizocompartments. Mol. Ecol. 26, 1641–1651 (2017).
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).
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).
García, Y., Clara Castellanos, M. & Pausas, J. G. Differential pollinator response underlies plant reproductive resilience after fires. Ann. Bot. 122, 961–971 (2018).
Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. Biol. Sci. 271, 2605–2611 (2004).
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).
Ma, P. et al. Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states. Cell 186, 877–891.e14 (2023).
Yin, J. et al. A droplet-based microfluidic approach to isolating functional bacteria from gut microbiota. Front. Cell. Infect. Microbiol. 12, 920986 (2022).
Young, I. M. & Crawford, J. W. Interactions and self-organization in the soil–microbe complex. Science 304, 1634–1637 (2004).
Schlüter, S., Sammartino, S. & Koestel, J. Exploring the relationship between soil structure and soil functions via pore-scale imaging. Geoderma 370, 114370 (2020).
Bebber, D. P. & Chaloner, T. M. Specialists, generalists and the shape of the ecological niche in fungi. New Phytol. 234, 345–349 (2022).
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).
Davison, J. et al. Temperature and pH define the realised niche space of arbuscular mycorrhizal fungi. New Phytol. 231, 763–776 (2021).
Slatyer, R. A., Hirst, M. & Sexton, J. P. Niche breadth predicts geographical range size: a general ecological pattern. Ecol. Lett. 16, 1104–1114 (2013).
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).
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=
National Ecological Observatory Network (NEON). Soil microbe marker gene sequences (DP1.10108.001) (2022); https://data.neonscience.org/data-products/DP1.10108.001
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
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
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).
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).
National Ecological Observatory Network (NEON). Soil physical and chemical properties, periodic (DP1.10086.001) (2022); https://data.neonscience.org/data-products/DP1.10086.001
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).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
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).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
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).
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).
Sievers, F. & Higgins, D. G. Clustal omega. Curr. Protoc. Bioinformatics 48, 3–13 (2014).
Jukes, T. H. & Cantor, C. R. in Mammalian Protein Metabolism (ed. Munro, H. N.) 21–132 (Academic Press, 1969).
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).
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).
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).
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).
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).
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).
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).
Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: a network perspective. Trends Microbiol. 25, 217–228 (2017).
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).
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).
Röttjers, L. & Faust, K. From hairballs to hypotheses–biological insights from microbial networks. FEMS Microbiol. Rev. 42, 761–780 (2018).
van der Heijden, M. G. A. & Hartmann, M. Networking in the plant microbiome. PLoS Biol. 14, e1002378 (2016).
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).
Jordán, F. Keystone species and food webs. Philos. Trans. R. Soc. B 364, 1733–1741 (2009).
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).
The authors declare no competing interests.
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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.
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.
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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