Microbial community structure across grazing treatments and environmental gradients in the Serengeti

Highlights

• Volcanic inputs and grazing impact the distribution of microbes in Serengeti soil.

• Soil texture and phosphorus are important environmental filters structuring soil microbes.

• Herbivores impact microbial communities via environmental filtering not stochastic dispersal.

Abstract

As one of the last remaining naturally grazed ecosystems on Earth, the Serengeti National Park is an ideal location to study the influence of migratory mammals on the structure of microbial communities and the factors that generate biogeography of soil microbes. Furthermore, volcanic inputs generate environmental gradients that may also structure microbial communities. We studied 16S rRNA amplicons in a 13-year herbivore removal experiment to examine the influence of grazing and environmental gradients on the natural distribution of soil microbes. Removal of mammalian herbivores shifted microbial community structure, with 31 taxa that were significant indicator taxa of the ungrazed treatment and three taxa that were indicators of the grazed treatment. The abundance of many taxa were correlated with soil texture, phosphorus, iron, calcium and rainfall, and the evenness of taxa within samples was also correlated with these variables. Bayesian general linear mixed effects models with single predictors of multiple, highly correlated variables of beta diversity were consistent with a significant, but weak (2%), effect of grazing, and stronger effects of phosphorus (14%). Beta diversity of microbial communities was greater in grazed than in ungrazed plots; suggesting that the impacts of grazing on community assembly of microbes results from deterministic environmental filtering caused by the influence of herbivores on plant communities and soil properties rather than stochastic dispersal via herds of large mammals. These herbivore effects are superimposed on deterministic environmental filtering by natural soil and precipitation gradients across the Serengeti.

Graphical abstract

Keywords

Soil bacteria / Community ecology / Grazing / Serengeti National Park / Soil texture / Phosphorus

References

[1] Anderson, G.D., Talbot, L.M., 1965. Soil factors affecting the distribution of the grassland types and their utilization by wild animals on the Serengeti plains, Tanganyika. Journal of Ecology 53, 33–56

[2] Anderson, M.J., Crist, T.O., Chase, J.M., Vellend, M., Inouye, B.D., Freestone, A.L., Sanders, N.J., Cornell, H.V., Comita, L.S., Davies, K.F., Harrison, S.P., Kraft, N.J.B., Stegen, J.C., Swenson, N.G., 2011. Navigating the multiple meanings of β diversity: A roadmap for the practicing ecologist. Ecology Letters 14, 19–28

[3] Anderson, T.M., Ritchie, M.E., McNaughton, S.J., 2007. Rainfall and soils modify plant community response to grazing in Serengeti National Park. Ecology 88, 1191–1201

[4] Andrews, L. (2018). akutils-v1.2.4: Facilitating analyses of microbial communities through QIIME. Zenodo. 10.5281/zenodo.1205079

[5] Andrews, S., (2010). FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc

[6] Antoninka, A.J., Ritchie, M.E., Johnson, N.C., 2015. The hidden Serengeti—Mycorrhizal fungi respond to environmental gradients. Pedobiologia 58, 165–176

[7] Artursson, V., Finlay, R.D., Jansson, J.K., 2006. Interactions between arbuscular mycorrhizal fungi and bacteria and their potential for stimulating plant growth. Environmental Microbiology 8, 1–10

[8] Ashley, G.M., Beverly, E.J., Sikes, N.E., Driese, S.G., 2014. Paleosol diversity in the Olduvai Basin, Tanzania: Effects of geomorphology, parent material, depositional environment, and groundwater on soil development. Quaternary International 322–323, 66–77

[9] Bardgett, R.D., Wardle, D.A., 2003. Herbivore-mediated linkages between aboveground and belowground communities. Ecology 84, 2258–2268

[10] Bardgett, R.D., Wardle, D.A., Yeates, G.W., 1998. Linking above-ground and below-ground interactions: How plant responses to foliar herbivory influence soil organisms. Soil Biology & Biochemistry 30, 1867–1878

[11] Bates, S.T., Berg-Lyons, D., Caporaso, J.G., Walters, W.A., Knight, R., Fierer, N., 2011. Examining the global distribution of dominant archaeal populations in soil. ISME Journal 5, 908–917

[12] Beauregard, M.S., Hamel, C., Atul-Nayyar, St-Arnaud, M., 2009. Long-term phosphorus fertilization impacts soil fungal and bacterial diversity but not AM fungal community in Alfalfa. Microbial Ecology 59, 379–389

[13] Berry, D., Mahfoudh, K.B., Wagner, M., Loy, A., 2011. Barcoded primers used in multiplex amplicon pyrosequencing bias amplification. Applied and Environmental Microbiology 77, 7846–7849

[14] Beuselinck, L., Govers, G., Poesen, J., Degraer, G., Froyen, L., 1998. Grain-size analysis by laser diffractometry: Comparison with the sieve-pipette method. Catena 32, 193–208

[15] Bokulich, N.A., Subramanian, S., Faith, J.J., Gevers, D., Gordon, J.I., Knight, R., Mills, D.A., Caporaso, J.G., 2013. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nature Methods 10, 57–59

[16] Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodríguez, A. M., Chase, J., Caporaso, J. G. (2018). QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science (e27295v2). Peer J Inc. https://doi.org/10.7287/peerj.preprints.27295v2

[17] Brady, N.C., Weil, R.R., 2017. The nature and properties of soil (15th ed.). Pearson.

[18] Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Peña, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336

[19] Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Huntley, J., Fierer, N., Owens, S.M., Betley, J., Fraser, L., Bauer, M., Gormley, N., Gilbert, J.A., Smith, G., Knight, R., 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME Journal 6, 1621–1624

[20] Carson, J.K., Gonzalez-Quiñones, V., Murphy, D.V., Hinz, C., Shaw, J.A., Gleeson, D.B., 2010. Low pore connectivity increases bacterial diversity in soil. Applied and Environmental Microbiology 76, 3936–3942

[21] Chau, J.F., Bagtzoglou, A.C., Willig, M.R., 2011. The effect of soil texture on richness and diversity of bacterial communities. Environmental Forensics 12, 333–341

[22] de Cáceres, M., Legendre, P., 2009. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574

[23] Dechesne, A., Or, D., Smets, B.F., 2008. Limited diffusive fluxes of substrate facilitate coexistence of two competing bacterial strains. FEMS Microbiology Ecology 64, 1–8

[24] Eby, S.L., Anderson, T.M., Mayemba, E.P., Ritchie, M.E., 2014. The effect of fire on habitat selection of mammalian herbivores: The role of body size and vegetation characteristics. Journal of Animal Ecology 83, 1196–1205

[25] Faith, D.P., 1992. Conservation evaluation and phylogenetic diversity. Biological Conservation 61, 1–10

[26] Fierer, N., 2017. Embracing the unknown: Disentangling the complexities of the soil microbiome. Nature Reviews. Microbiology 15, 579–590

[27] Fierer, N., Jackson, R.B., 2006. The diversity and biogeography of soil bacterial communities. Proceedings of the National Academy of Sciences of the United States of America 103, 626–631

[28] Griffiths, R.I., Thomson, B.C., James, P., Bell, T., Bailey, M., Whiteley, A.S., 2011. The bacterial biogeography of British soils. Environmental Microbiology 13, 1642–1654

[29] Grimshaw, H.M., 1987. The determination of total phosphorus in soils by acid digestion. In Rowland A.P. Ed., Chemical analysis in environmental research. NERC/ITE. pp. 92–65

[30] Hamilton, N., (2014, January 15). USDA Textural Soil Classification. R-Bloggers. https://www.r-bloggers.com/usda-textural-soil-classification/

[31] Hanson, C.A., Fuhrman, J.A., Horner-Devine, M.C., Martiny, J.B.H., 2012. Beyond biogeographic patterns: Processes shaping the microbial landscape. Nature Reviews. Microbiology 10, 497–506

[32] Hardin, G., 1960. The competitive exclusion principle. Science 131, 1292–1297

[33] Hassink, J., Bouwman, L.A., Zwart, K.B., Bloem, J., Brussaard, L., (1993). Relationships between soil texture, physical protection of organic matter, soil biota, and C and N mineralization in grassland soils. In: Brussaard, L., Kooistra, M.J., eds. Soil Structure/Soil Biota Interrelationships. Elsevier. pp. 105–128. https://doi.org/10.1016/B978-0-444-81490-6.50059-5

[34] Heiri, O., Lotter, A.F., Lemcke, G., 2001. Loss on ignition as a method for estimating organic and carbonate content in sediments: Reproducibility and comparability of results. Journal of Paleolimnology 25, 101–110

[35] Hou, A.Y., Kakar, R.K., Neeck, S., Azarbarzin, A.A., Kummerow, C.D., Kojima, M., Oki, R., Nakamura, K., Iguchi, T., 2013. The global precipitation measurement mission. Bulletin of the American Meteorological Society 95, 701–722

[36] Kaiser, K., Wemheuer, B., Korolkow, V., Wemheuer, F., Nacke, H., Schöning, I., Schrumpf, M., Daniel, R., 2016. Driving forces of soil bacterial community structure, diversity, and function in temperate grasslands and forests. Scientific Reports 6, 33696

[37] Klute, A., 1986. Water retention: Laboratory methods. Methods of Soil Analysis: Part 1—Physical and Mineralogical Methods, SSSA book series(methodsofsoilan1), 635–662. https://doi.org/10.2136/sssabookser5.1.2ed.c26

[38] Kuramae, E.E., Gamper, H., van Veen, J., Kowalchuk, G., 2011. Soil and plant factors driving the community of soil-borne microorganisms across chronosequences of secondary succession of chalk grasslands with a neutral pH. FEMS Microbiology Ecology 77, 285–294

[39] Kuramae, E.E., Yergeau, E., Wong, L.C., Pijl, A.S., van Veen, J.A., Kowalchuk, G.A., 2012. Soil characteristics more strongly influence soil bacterial communities than land-use type. FEMS Microbiology Ecology 79, 12–24

[40] Lauber, C.L., Hamady, M., Knight, R., Fierer, N., 2009. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Applied and Environmental Microbiology 75, 5111–5120

[41] Legendre, P., Anderson, M. J., 1999. Distance-Based Redundancy Analysis: Testing Multispecies Responses in Multifactorial Ecological Experiments. Ecological Monographs,69, 1–24.

[42] Long, T., Or, D., 2009. Dynamics of microbial growth and coexistence on variably saturated rough surfaces. Microbial Ecology 58, 262–275

[43] Lozupone, C., Knight, R., 2005. UniFrac: A new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology 71, 8228–8235

[44] Martiny, J.B.H., Bohannan, B.J.M., Brown, J.H., Colwell, R.K., Fuhrman, J.A., Green, J.L., Horner-Devine, M.C., Kane, M., Krumins, J.A., Kuske, C.R., Morin, P.J., Naeem, S., Øvreås, L., Reysenbach, A.L., Smith, V.H., Staley, J.T., 2006. Microbial biogeography: Putting microorganisms on the map. Nature Reviews Microbiology 4, 102–112

[45] McNaughton, S.J., 1979. Grazing as an optimization process: Grass-ungulate relationships in the Serengeti. American Naturalist 113, 691–703

[46] McNaughton, S.J., 1985. Ecology of a grazing ecosystem: The Serengeti. Ecological Monographs 55, 259–294

[47] McNaughton, S.J., Zuniga, G., McNaughton, M.M., Banyikwa, F.F., 1997. Ecosystem catalysis: Soil urease activity and grazing in the serengeti ecosystem. Oikos 80, 467–469

[48] Neumann, D., Heuer, A., Hemkemeyer, M., Martens, R., Tebbe, C.C., 2013. Response of microbial communities to long-term fertilization depends on their microhabitat. FEMS Microbiology Ecology 86, 71–84

[49] Nielsen, U.N., Ayres, E., Wall, D.H., Li, G., Bardgett, R.D., Wu, T., Garey, J.R., 2014. Global-scale patterns of assemblage structure of soil nematodes in relation to climate and ecosystem properties. Global Ecology and Biogeography 23, 968–978

[50] Oksanen, J., Blanchet, F., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P., O’Hara, R., Simpson, G., Solymos, P., Stevens, M., 2018. Package ‘vegan’. Community ecology package, R package version 2.5–4. Https://CRAN.R-project.org/package=vegan.

[51] Pigna, M., Violante, A., 2003. Adsorption of sulfate and phosphate on andisols. Communications in Soil Science and Plant Analysis 34, 2099–2113

[52] Prober, S.M., Leff, J.W., Bates, S.T., Borer, E.T., Firn, J., Harpole, W.S., Lind, E.M., Seabloom, E.W., Adler, P.B., Bakker, J.D., Cleland, E.E., DeCrappeo, N.M., DeLorenze, E., Hagenah, N., Hautier, Y., Hofmockel, K.S., Kirkman, K.P., Knops, J.M.H., La Pierre, K.J., MacDougall, A.S., McCulley, R.L., Mitchell, C.E., Risch, A.C., Schuetz, M., Stevens, C.J., Williams, R.J., Fierer, N., 2015. Plant diversity predicts beta but not alpha diversity of soil microbes across grasslands worldwide. Ecology Letters 18, 85–95

[53] Ramula, S., Paige, K.N., Lennartsson, T., Tuomi, J., 2019. Overcompensation: A 30-year perspective. Ecology 100, e02667

[54] Rohland, N., Reich, D., 2012. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Research 22, 939–946

[55] Ruess, R.W., Seagle, S.W., 1994. Landscape patterns in soil microbial processes in the serengeti national park, tanzania. Ecology 75, 892–904

[56] Saxton, K.E., Rawls, W.J., 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Science Society of America Journal 70, 1569–1578

[57] Schachtman, D.P., Reid, R.J., Ayling, S.M., 1998. Phosphorus uptake by plants: From soil to cell. Plant Physiology 116, 447–453

[58] Serna‐Chavez, H.M., Fierer, N.van Bodegom, P.M., , 2013. Global drivers and patterns of microbial abundance in soil. Global Ecology and Biogeography 22, 1162–1172

[59] Sessitsch, A., Weilharter, A., Gerzabek, M.H., Kirchmann, H., Kandeler, E., 2001. Microbial population structures in soil particle size fractions of a long-term fertilizer field experiment. Applied and Environmental Microbiology 67, 4215–4224

[60] Sinclair, A.R.E., Arcese, P., 1995. Serengeti II: Dynamics, Management, and Conservation of an Ecosystem. University of Chicago Press.

[61] Sinclair, A.R.E., Metzger, K., Brashares, J.S., Nkwabi, A., Sharam, G., Fryxell, J.M., 2010. Trophic Cascades in African Savanna: Serengeti as a Case Study. In Terborgh, J., Estes, J. A., eds. Trophic Cascades: Predators, Prey and the Changing Dynamics of Nature. Island Press, Washington pp. 255–274.

[62] Spaink, H.P., Kondorosi, A., Hooykaas, P.J.J., 2012. The Rhizobiaceae: Molecular Biology of Model Plant-associated Bacteria. Springer, Dordrecht.

[63] Stan Development Team, 2017. RStanArm: Bayesian applied regression modelling via Stan (Version R package version 2.15.3) [Computer software]. http://mc-stan.org

[64] Stevens, B.M., Propster, J., Wilson, G.W.T., Abraham, A., Ridenour, C., Doughty, C., Johnson, N.C., 2018. Mycorrhizal symbioses influence the trophic structure of the Serengeti. Journal of Ecology 106, 536–546

[65] Stevens, B.M., Propster, J.R., Öpik, M., Wilson, G.W.T., Alloway, S.L., Mayemba, E., Johnson, N.C., 2020. Arbuscular mycorrhizal fungi in roots and soil respond differently to biotic and abiotic factors in the Serengeti. Mycorrhiza 30, 79–95

[66] Tan, H., Barret, M., Mooij, M.J., Rice, O., Morrissey, J.P., Dobson, A., Griffiths, B., O’Gara, F., 2012. Long-term phosphorus fertilisation increased the diversity of the total bacterial community and the phoD phosphorus mineraliser group in pasture soils. Biology and Fertility of Soils 49, 661–672

[67] Treves, D.S., Xia, B., Zhou, J., Tiedje, J.M., 2003. A two-species test of the hypothesis that spatial isolation influences microbial diversity in soil. Microbial Ecology 45, 20–28

[68] Vance, C.P., Uhde‐Stone, C., Allan, D.L., 2003. Phosphorus acquisition and use: Critical adaptations by plants for securing a nonrenewable resource. New Phytologist 157, 423–447

[69] Vaughan, R.G., Kervyn, M., Realmuto, V., Abrams, M., Hook, S.J., 2008. Satellite measurements of recent volcanic activity at Oldoinyo Lengai, Tanzania. Journal of Volcanology and Geothermal Research 173, 196–206

[70] Vos, M., Wolf, A.B., Jennings, S.J., Kowalchuk, G.A., 2013. Micro-scale determinants of bacterial diversity in soil. FEMS Microbiology Reviews 37, 936–954

Acknowledgments

The authors thank Emilian Mayemba for his help collecting samples and Michael Anderson, Mark Ritchie, Jeffrey Propster, Anita Antoninka and Daniel Revillini for their advice and input. We also thank Gustavo Zimmer for help with DNA extractions, and Lela Andrews and the NAU Environmental Genetics and Genomics Laboratory (EnGGen), Northern Arizona University, Flagstaff, AZ (nau.edu/enggen) for help with sequencing. Thanks to Emery Cowan for her contribution to the efficacy of science communication, and thanks to two anonymous reviewers for their insightful suggestions. The National Science Foundation provided funding for this work to Nancy Collins Johnson (DEB-0842327). This research was partially supported by the Microbial Ecology Collaborative with funding from NSF award #EPS-1655726.

Data availability statement

Our environmental data, correlation matrix, and ASV table are made publicly available on Dryad Digital Repository (https://doi.org/10.5061/dryad.hhmgqnkf6), and the sequences are be archived in the NCBI Sequence Read Archive (BioProject ID: PRJNA662920).

Author contribution

The research interests of BMS and NCJ focus on the microbial communities within soil, and particularly the distribution, abundance, function, and biogeography of bacteria and mycorrhizal fungi. DLS is a statistician with an interest in ecology. NCJ planned, designed the research, and collected samples. BMS performed bioinformatics, statistical analyses, and created figures. DLS and BMS created and analyzed linear models. BMS analyzed and, with the help of NCJ, interpreted data. BMS created figures and wrote the first draft of the manuscript. NCJ edited the final version.

RIGHTS & PERMISSIONS

2020 Higher Education Press