Covariation of diet and gut microbiome in African megafauna

Tyler R Kartzinel, Julianna C Hsing, Paul M Musili, Bianca R P Brown, Robert M Pringle, Tyler R Kartzinel, Julianna C Hsing, Paul M Musili, Bianca R P Brown, Robert M Pringle

Abstract

A major challenge in biology is to understand how phylogeny, diet, and environment shape the mammalian gut microbiome. Yet most studies of nonhuman microbiomes have relied on relatively coarse dietary categorizations and have focused either on individual wild populations or on captive animals that are sheltered from environmental pressures, which may obscure the effects of dietary and environmental variation on microbiome composition in diverse natural communities. We analyzed plant and bacterial DNA in fecal samples from an assemblage of 33 sympatric large-herbivore species (27 native, 6 domesticated) in a semiarid East African savanna, which enabled high-resolution assessment of seasonal variation in both diet and microbiome composition. Phylogenetic relatedness strongly predicted microbiome composition (r = 0.91) and was weakly but significantly correlated with diet composition (r = 0.20). Dietary diversity did not significantly predict microbiome diversity across species or within any species except kudu; however, diet composition was significantly correlated with microbiome composition both across and within most species. We found a spectrum of seasonal sensitivity at the diet-microbiome nexus: Seasonal changes in diet composition explained 25% of seasonal variation in microbiome composition across species. Species' positions on (and deviations from) this spectrum were not obviously driven by phylogeny, body size, digestive strategy, or diet composition; however, domesticated species tended to exhibit greater diet-microbiome turnover than wildlife. Our results reveal marked differences in the influence of environment on the degree of diet-microbiome covariation in free-ranging African megafauna, and this variation is not well explained by canonical predictors of nutritional ecology.

Keywords: 16S rRNA; DNA metabarcoding; megaherbivores; phylosymbiosis.

Conflict of interest statement

The authors declare no competing interest.

Copyright © 2019 the Author(s). Published by PNAS.

Figures

Fig. 1.
Fig. 1.
Phylogenetic variation in diet and gut microbiome composition. (A) The phylogeny of 33 sympatric mammalian herbivores in central Kenya, grouped by family and order (identified here by the first 3 letters of families and orders; see also Dataset S1). Species names are in gray for ruminants (toward the top), orange for pseudoruminants (hippo and camel), and black for nonruminants. Sample sizes for each species are listed parenthetically (diet, microbiome). (B) The mean RRA of the 9 most eaten plant families and the 45 other plant families, expressed as percentages. There was modest phylogenetic signal in grass (Poaceae) RRA (Pagel’s λ = 0.55, P = 0.03), but no phylogenetic signal in the RRA of other abundant plant families (Fabaceae: λ = 0.20, P > 0.05; Malvaceae: λ = 0.62, P > 0.05). (C) Mean dietary richness (±1 SE) did not exhibit significant phylogenetic signal (λ < 0.01, P ≈ 1.0; diversity yielded similar results: λ < 0.01, P ≈ 1.0; Dataset S1). (D) Mean RRA of the 9 most prevalent clades of gut bacteria (identified to family when possible and listed by phylum), along with the remaining ≥238 other clades (gray). There was significant phylogenetic signal in mean RRA for 2 of the 3 predominant bacterial families that were identified (Ruminococcaceae: λ ≈ 1.0, P < 0.001; Bacteroidaceae: λ = 0.93, P < 0.001; not Lachnospiraceae: λ = 0.98, P > 0.05). (E) Mean microbial richness (±SE) exhibited significant phylogenetic signal (λ ≈ 1.0, P < 0.001; diversity yielded similar results: λ ≈ 1.0, P < 0.001; Dataset S1).
Fig. 2.
Fig. 2.
Dietary richness did not predict microbiome richness across species, but diet composition did predict microbiome composition. (A) We found no relationship between mean dietary and microbiome richness (±1 SE) across species (ordinary least squares, OLS: F1,15 < 0.01, R2 < 0.001, P = 0.96; phylogenetic generalized least squares, PGLS: F1,15 < 0.01, R2 < 0.001, P = 0.95). Point colors correspond to the ordering of species from top (oryx) to bottom (elephant) of the phylogeny in Fig. 1 (squares, nonruminants; circles, ruminants/camels). Intraspecific correlations between dietary and microbiome richness within these 17 species are shown in SI Appendix, Fig. S3. (B) Microbiome dissimilarity within and between pairs of species increased with dietary dissimilarity. Intraspecific comparisons were the most similar (gray crosses), followed by interspecific comparisons between species with similar digestive systems (black crosses). Comparisons between species with dissimilar digestive systems (i.e., ruminants/camels vs. nonruminants, red crosses) had almost entirely distinct microbiomes (all dissimilarities > 0.99), irrespective of dietary overlap (dissimilarities ranging 0.67 to 0.97). Shading represents 95% confidence ellipses. Interspecific comparisons (black and red) revealed a significant increase in microbiome dissimilarity with diet dissimilarity, after accounting for the phylogenetic relatedness of species (partial Mantel: r = 0.28, P = 0.005). The correlation between intraspecific diet−microbiome dissimilarities across species (gray) was not statistically significant, but the trend was positive (OLS: F1,15 = 0.93, R2 = 0.06, P = 0.35; PGLS: F1,15 = 0.24, R2 = 0.02, P = 0.62). Analagous diet−microbiome comparisons among samples within each species were generally strong and positive (SI Appendix, Fig. S4).
Fig. 3.
Fig. 3.
A spectrum of seasonal sensitivity in diet−microbiome covariation. (A) Seasonal turnover in diet composition was positively correlated with seasonal turnover in microbiome composition (squares, nonruminants; circles, ruminants/camels). Trend lines were fit using OLS (F1,15 = 5.0, R2 = 0.25, P = 0.041) and PGLS (F1,15 = 5.5, R2 = 0.27, P = 0.034). Excluding camels, an extreme outlier in diet turnover, would yield similar results, although OLS regression would only be marginally significant (OLS: F1,14 = 3.3, R2 = 0.19, P = 0.090; PGLS: F1,14 = 7.5, R2 = 0.35, P = 0.016). (B and C) There was greater turnover in domesticated vs. wild species in (B) microbiome (phylogenetic ANOVA: F1,15 = 19.2, P < 0.001) and (C) diet (F1,15 = 4.95, P = 0.042). Boxplots show ranges (whiskers), interquartile ranges (boxes), and medians (central lines). (D–G) Using examples of 4 species at increasing distances from the origin in A, we performed Procrustes analyses to visualize how seasonal diet and microbiome compositions mapped onto each other. Procrustes rotates the results of separate principal coordinates analyses of diet composition (open symbols) and microbiome composition (closed symbols) so that results can be compared. For illustration, we show results from the driest and wettest sampling periods, with color-coded minimum convex hulls drawn around the set of points representing diet (open hulls) and microbiome (shaded hulls) compositions in each season. Vectors (arrows) show correspondence between diet and microbiome for each fecal sample; shorter vectors indicate closer compositional correspondence. Below each panel is the Procrustes sum of squares and P value testing whether there is significant dissimilarity in configuration between the diet and microbiome ordinations. Procrustes analyses for all 17 species and all 3 seasons are provided in SI Appendix, Fig. S5.

References

    1. Arnolds K. L., Lozupone C. A., Striking a balance with help from our little friends—How the gut microbiota contributes to immune homeostasis. Yale J. Biol. Med. 89, 389–395 (2016).
    1. Duvallet C., Gibbons S. M., Gurry T., Irizarry R. A., Alm E. J., Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Nat. Commun. 8, 1784 (2017).
    1. Dearing M. D., Kohl K. D., Beyond fermentation: Other important services provided to endothermic herbivores by their gut microbiota. Integr. Comp. Biol. 57, 723–731 (2017).
    1. McKenney E. A., Koelle K., Dunn R. R., Yoder A. D., The ecosystem services of animal microbiomes. Mol. Ecol. 27, 2164–2172 (2018).
    1. Ley R. E., et al. , Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).
    1. Brooks A. W., Kohl K. D., Brucker R. M., van Opstal E. J., Bordenstein S. R., Phylosymbiosis: Relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol. 14, e2000225 (2016).
    1. Kohl K. D., Dearing M. D., The woodrat gut microbiota as an experimental system for understanding microbial metabolism of dietary toxins. Front. Microbiol. 7, 1165 (2016).
    1. David L. A., et al. , Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
    1. McKenzie V. J., et al. , The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol., 57, 690–704 (2017).
    1. Muegge B. D., et al. , Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).
    1. Reese A. T., Dunn R. R., Drivers of microbiome diversity: A review of general rules, feces, and ignorance. MBio 9, e01294-18 (2018).
    1. Groussin M., et al. , Unraveling the processes shaping mammalian gut microbiomes over evolutionary time. Nat. Commun. 8, 14319 (2017).
    1. Amato K. R., et al. , Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2019).
    1. Gomez A., et al. , Temporal variation selects for diet-microbe co-metabolic traits in the gut of Gorilla spp. ISME J. 10, 514–526 (2016).
    1. Tung J., et al. , Social networks predict gut microbiome composition in wild baboons. eLife 4, e05224 (2015).
    1. Pringle R. M., et al. , Predator-induced collapse of niche structure and species coexistence. Nature 570, 58–64 (2019).
    1. Hairston N. G., Smith F. E., Slobodkin L. B., Community structure, population control, and competition. Am. Nat. 94, 421–425 (1960).
    1. Paine R. T., Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).
    1. Metcalf J. L., et al. , Evaluating the impact of domestication and captivity on the horse gut microbiome. Sci. Rep. 7, 15497 (2017).
    1. Bergmann G. T., Craine J. M., Robeson M. S. I. 2nd, Fierer N., Seasonal shifts in diet and gut microbiota of the American Bison (Bison bison). PLoS One 10, e0142409 (2015).
    1. Codron D., Brink J. S., Rossouw L., Clauss M., The evolution of ecological specialization in southern African ungulates: Competition- or physical environmental turnover. Oikos 117, 344–353 (2008).
    1. Brashares J. S., Garland T., Arcese P., Phylogenetic analysis of coadaptation in behavior, diet, and body size in the African antelope. Behav. Ecol. 11, 452–463 (2000).
    1. Bolnick D. I., et al. , The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).
    1. Taberlet P., et al. , Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. 35, e14 (2007).
    1. Caporaso J. G., et al. , QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
    1. DeSantis T. Z., et al. , Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).
    1. Gill B. A., et al. , Plant DNA-barcode library and community phylogeny for a semi-arid East African savanna. Mol. Ecol. Resour. 19, 838–846 (2019).
    1. Jarman P. J., The social organization of antelope in relation to their ecology. Behaviour 48, 215–266 (1974).
    1. Moyers B. T., Morrell P. L., McKay J. K., Genetic costs of domestication and improvement. J. Hered. 109, 103–116 (2018).
    1. Moeller A. H., Suzuki T. A., Phifer-Rixey M., Nachman M. W., Transmission modes of the mammalian gut microbiota. Science 362, 453–457 (2018).
    1. Goheen J. R., et al. , Conservation lessons from large-mammal manipulations in East African savannas: The KLEE, UHURU, and GLADE experiments. Ann. N. Y. Acad. Sci. 1429, 31–49 (2018).
    1. Belovsky G. E., Optimal foraging and community structure: The allometry of herbivore food selection and competition. Evol. Ecol. 11, 641–672 (1997).
    1. Price E. O., Behavioral aspects of animal domestication. Q. Rev. Biol. 59, 1–32 (1984).
    1. Gustafsson M., Jensen P., de Jonge F. H., Schuurman T., Domestication effects on foraging strategies in pigs (Sus scrofa). Appl. Anim. Behav. Sci. 62, 305–317 (1999).
    1. Kie J. G., Optimal foraging and risk of predation: Effects on behavior and social structure in ungulates. J. Mammal. 80, 1114–1129 (1999).
    1. Letten A. D., Ke P.-J., Fukami T., Linking modern coexistence theory and contemporary niche theory. Ecol. Monogr. 87, 161–177 (2016).
    1. McCann K. S., The diversity-stability debate. Nature 405, 228–233 (2000).
    1. Kartzinel T. R., et al. , DNA metabarcoding illuminates dietary niche partitioning by African large herbivores. Proc. Natl. Acad. Sci. U.S.A. 112, 8019–8024 (2015).
    1. Deagle B. E., et al. , Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Mol. Ecol. 28, 391–406 (2019).
    1. Willerslev E., et al. , Fifty thousand years of Arctic vegetation and megafaunal diet. Nature 506, 47–51 (2014).
    1. Craine J. M., Towne E. G., Miller M., Fierer N., Climatic warming and the future of bison as grazers. Sci. Rep. 5, 16738 (2015).
    1. Fritz S. A., Bininda-Emonds O. R. P., Purvis A., Geographical variation in predictors of mammalian extinction risk: Big is bad, but only in the tropics. Ecol. Lett. 12, 538–549 (2009).

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