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Changes in precipitation patterns can destabilize plant species coexistence via changes in plant–soil feedback

  • Pereira, H. M. et al. Scenarios for global biodiversity in the 21st century. Science 330, 1496–1501 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).

    CAS 
    PubMed 

    Google Scholar 

  • Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • Feeley, K. J., Bravo-Avila, C., Fadrique, B., Perez, T. M. & Zuleta, D. Climate-driven changes in the composition of New World plant communities. Nat. Clim. Change 10, 965–970 (2020).

    CAS 

    Google Scholar 

  • Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).

    PubMed 

    Google Scholar 

  • Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).

    CAS 
    PubMed 

    Google Scholar 

  • Suttle, K. B., Thomsen, M. A. & Power, M. E. Species interactions reverse grassland responses to changing climate. Science 315, 640–642 (2007).

    CAS 
    PubMed 

    Google Scholar 

  • van der Putten, W. H., Macel, M. & Visser, M. E. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels. Proc. R. Soc. B 365, 2025–2034 (2010).

    Google Scholar 

  • Gaüzère, P., Iversen, L. L., Barnagaud, J.-Y., Svenning, J.-C. & Blonder, B. Empirical predictability of community responses to climate change. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00186 (2018).

  • Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • Bennett, J. A. et al. Plant–soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science 355, 181–184 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • Teste, F. P. et al. Plant–soil feedback and the maintenance of diversity in Mediterranean-climate shrublands. Science 355, 173–176 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • Kardol, P., Bezemer, T. M. & van der Putten, W. H. Temporal variation in plant–soil feedback controls succession. Ecol. Lett. 9, 1080–1088 (2006).

    PubMed 

    Google Scholar 

  • van der Putten, W. H., van Dijk, C. & Peters, B. A. M. Plant-specific soil-borne diseases contribute to succession in foredune vegetation. Nature 362, 53–56 (1993).

    Google Scholar 

  • Bever, J. D. Feedback between plants and their soil communities in an old field community. Ecology 75, 1965–1977 (1994).

    Google Scholar 

  • Bever, J. D., Westover, K. M. & Antonovics, J. Incorporating the soil community into plant population dynamics: the utility of the feedback approach. J. Ecol. 85, 561–573 (1997).

    Google Scholar 

  • Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).

    Google Scholar 

  • Bever, J. D. Soil community feedback and the coexistence of competitors: conceptual frameworks and empirical tests. New Phytol. 157, 465–473 (2003).

    PubMed 

    Google Scholar 

  • Revilla, T. A., Veen, G. F., Eppinga, M. B. & Weissig, F. J. Plant–soil feedbacks and the coexistence of competing plants. Theor. Ecol. 6, 99–113 (2013).

    Google Scholar 

  • Molofsky, J. & Bever, J. D. A novel theory to explain species diversity in landscapes: positive frequency dependence and habitat suitability. Proc. R. Soc. B 269, 2389–2393 (2002).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Ke, P. J. & Wan, J. Effects of soil microbes on plant competition: a perspective from modern coexistence theory. Ecol. Monogr. 90, e01391 (2020).

    Google Scholar 

  • Mack, K. M. L. & Bever, J. D. Coexistence and relative abundance in plant communities are determined by feedbacks when the scale of feedback and dispersal is local. J. Ecol. 102, 1195–1201 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Bauer, J. T., Mack, K. M. L. & Bever, J. D. Plant–soil feedbacks as drivers of succession: evidence from remnant and restored tallgrass prairies. Ecosphere 6, art158 (2015).

    Google Scholar 

  • Kulmatiski, A., Beard, K. H., Grenzer, J., Forero, L. & Heavilin, J. Using plant–soil feedbacks to predict plant biomass in diverse communities. Ecology 97, 2064–2073 (2016).

    PubMed 

    Google Scholar 

  • Reinhart, K. O. et al. Globally, plant–soil feedbacks are weak predictors of plant abundance. Ecol. Evol. 11, 1756–1768 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Casper, B. B. & Castelli, J. P. Evaluating plant–soil feedback together with competition in a serpentine grassland. Ecol. Lett. 10, 394–400 (2007).

    PubMed 

    Google Scholar 

  • Shannon, S., Flory, S. L. & Reynolds, H. Competitive context alters plant–soil feedback in an experimental woodland community. Oecologia 169, 235–243 (2012).

    PubMed 

    Google Scholar 

  • Lekberg, Y. et al. Relative importance of competition and plant–soil feedback, their synergy, context dependency and implications for coexistence. Ecol. Lett. 21, 1268–1281 (2018).

    PubMed 

    Google Scholar 

  • Kostenko, O., van de Voorde, T. F. J., Mulder, P. P. J., van der Putten, W. H. & Bezemer, M. T. Legacy effects of aboveground–belowground interactions. Ecol. Lett. 15, 813–821 (2012).

    PubMed 

    Google Scholar 

  • Bezemer, M. T. et al. Above- and below-ground herbivory effects on below-ground plant–fungus interactions and plant–soil feedback responses. J. Ecol. 101, 325–333 (2013).

    Google Scholar 

  • Classen, A. T. et al. Direct and indirect effects of climate change on soil microbial and soil microbial–plant interactions: what lies ahead? Ecosphere 6, art130 (2015).

    Google Scholar 

  • McCarthy-Neumann, S. & Kobe, R. K. Site soil-fertility and light availability influence plant–soil feedback. Front. Ecol. Evol. 7, 383 (2019).

    Google Scholar 

  • Smith-Ramesh, L. M. & Reynolds, H. L. The next frontier of plant–soil feedback research: unraveling context dependence across biotic and abiotic gradients. J. Veg. Sci. 28, 484–494 (2017).

    Google Scholar 

  • Crawford, K. M. et al. When and where plant–soil feedback may promote plant coexistence: a meta-analysis. Ecol. Lett. 22, 1274–1284 (2019).

    PubMed 

    Google Scholar 

  • de Long, J. R., Fry, E. L., Veen, G. F. & Kardol, P. Why are plant–soil feedbacks so unpredictable, and what to do about it? Funct. Ecol. 33, 118–128 (2019).

    Google Scholar 

  • Beals, K. K. et al. Predicting plant–soil feedback in the field: meta-analysis reveals that competition and environmental stress differentially influence PSF. Front. Ecol. Evol. 8, 191 (2020).

    Google Scholar 

  • van der Putten, W. H., Bradford, M. A., Brinkman, P. E., van de Voorde, T. F. J. & Veen, G. F. Where, when and how plant–soil feedback matters in a changing world. Funct. Ecol. 30, 1109–1121 (2016).

    Google Scholar 

  • Pugnaire, F. I. et al. Climate change effects on plant–soil feedbacks and consequences for biodiversity and functioning of terrestrial ecosystems. Sci. Adv. 5, eaaz1834 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).

    Google Scholar 

  • Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Fierer, N., Schimel, J. P. & Holden, P. A. Influence of drying–rewetting frequency on soil bacterial community structure. Microb. Ecol. 45, 63–71 (2003).

    CAS 
    PubMed 

    Google Scholar 

  • Drenovsky, R. E., Vo, D., Graham, K. J. & Scow, K. M. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).

    CAS 
    PubMed 

    Google Scholar 

  • Brockett, B. F., Prescott, C. E. & Grayston, S. J. Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil Biol. Biochem. 44, 9–20 (2012).

    CAS 

    Google Scholar 

  • Manzoni, S., Schimel, J. P. & Porporato, A. Responses of soil microbial communities to water stress: results from a meta-analysis. Ecology 93, 930–938 (2012).

    PubMed 

    Google Scholar 

  • de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • de Oliveira, T. B. et al. Fungal communities differentially respond to warming and drought in tropical grassland soil. Mol. Ecol. 29, 1550–1559 (2020).

    PubMed 

    Google Scholar 

  • Eastburn, D. M., McElrone, A. J. & Bilgin, D. D. Influence of atmospheric and climatic change on plant–pathogen interactions. Plant Pathol. 60, 54–69 (2011).

    Google Scholar 

  • Suzuki, N., Rivero, R. M., Shulaev, V., Blumwald, E. & Mittler, R. Abiotic and biotic stress combinations. New Phytol. 203, 32–43 (2014).

    PubMed 

    Google Scholar 

  • Cavagnaro, T. R. Soil moisture legacy effects: impacts on soil nutrients, plants and mycorrhizal responsiveness. Soil Biol. Biochem. 95, 173–179 (2016).

    CAS 

    Google Scholar 

  • Crawford, K. M. & Hawkes, C. V. Soil precipitation legacies influence intraspecific plant–soil feedback. Ecology 101, e03142 (2020).

    PubMed 

    Google Scholar 

  • Fry, E. L. et al. Drought neutralises plant–soil feedback of two mesic grassland forbs. Oecologia 186, 1113–1125 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Snyder, A. E. & Harmon-Threatt, A. N. Reduced water-availability lowers the strength of negative plant–soil feedbacks of two Asclepias species. Oecologia 190, 425–432 (2019).

    PubMed 

    Google Scholar 

  • Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant–soil feedbacks: a meta-analytical review. Ecol. Lett. 11, 980–992 (2008).

    PubMed 

    Google Scholar 

  • Brinkman, P. E., van der Putten, W. H., Bakker, E.-J. & Verhoeven, K. J. Plant–soil feedback: experimental approaches, statistical analyses and ecological interpretations. J. Ecol. 98, 1063–1073 (2010).

    Google Scholar 

  • Bever, J. D. Negative feedback within a mutualism: host-specific growth of mycorrhizal fungi reduces plant benefit. Proc. R. Soc. B 269, 2595–2601 (2002).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Castelli, J. P. & Casper, B. B. Intraspecific AM fungal variation contributes to plant–fungal feedback in a serpentine grassland. Ecology 84, 323–336 (2003).

    Google Scholar 

  • Mangan, S. A., Herre, E. A. & Bever, J. D. Specificity between neotropical tree seedlings and their fungal mutualists leads to plant–soil feedback. Ecology 91, 2594–2603 (2010).

    PubMed 

    Google Scholar 

  • Bever, J. D., Mangan, S. A. & Alexander, H. M. Maintenance of plant species diversity by pathogens. Annu. Rev. Ecol. Evol. Syst. 46, 305–325 (2015).

    Google Scholar 

  • Gilbert, G. S. & Parker, I. M. The evolutionary ecology of plant disease: a phylogenetic perspective. Annu. Rev. Phytopathol. 54, 549–578 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • Milici, V. R., Dalui, D., Mickley, J. G. & Bagchi, R. Responses of plant–pathogen interactions to precipitation: implications for tropical tree richness in a changing world. J. Ecol. 108, 1800–1809 (2020).

    Google Scholar 

  • Kaisermann, A., de Vries, F. T., Griffiths, R. I. & Bardgett, R. D. Legacy effects of drought on plant–soil feedbacks and plant–plant interactions. New Phytol. 215, 1413–1424 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • Revillini, D., Gehring, C. A. & Johnson, N. C. The role of locally adapted mycorrhizas and rhizobacteria in plant–soil feedback systems. Funct. Ecol. 30, 1086–1098 (2016).

    Google Scholar 

  • Ji, B. & Bever, J. D. Plant preferential allocation and fungal reward decline with soil phosphorus: implications for mycorrhizal mutualism. Ecosphere 7, e01256 (2016).

    Google Scholar 

  • Rubin, R. L., van Groenigen, K. J. & Hungate, B. A. Plant growth promoting rhizobacteria are more effective under drought: a meta-analysis. Plant Soil 416, 309–323 (2017).

    CAS 

    Google Scholar 

  • Brinkman, E. P., Duyts, H., Karssen, G., van der Stoel, C. D. & van der Putten, W. H. Plant-feeding nematodes in coastal sand dunes: occurrence, host specificity and effects on plant growth. Plant Soil 397, 17–30 (2015).

    CAS 

    Google Scholar 

  • Hoeksema, J. D. et al. A meta-analysis of context-dependency in plant response to inoculation with mycorrhizal fungi. Ecol. Lett. 13, 394–407 (2010).

    PubMed 

    Google Scholar 

  • Chase, J. M. Community assembly: when should history matter? Oecologia 136, 489–498 (2003).

    PubMed 

    Google Scholar 

  • Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).

    Google Scholar 

  • Reinhart, K. O. & Rinella, M. J. A common soil handling technique can generate incorrect estimates of soil biota effects on plants. New Phytol. 210, 786–789 (2016).

    PubMed 

    Google Scholar 

  • Mehlich, A. Mehlich-3 soil test extractant: a modification of Mehlich-2 extractant. Commun. Soil Sci. Plant Anal. 15, 1409–1416 (1984).

    CAS 

    Google Scholar 

  • Rhoades, J. D. in Methods of Soil Analysis: Part 2 (eds Page, A. L. et al.) Ch. 10 (American Society of Agronomy and Soil Science Society of America, 1982).

  • Schofield, R. K. & Taylor, A. W. The measurement of soil pH. Soil Sci. Soc. Am. Proc. 19, 164–167 (1955).

    CAS 

    Google Scholar 

  • Keeney, D. R. in Methods of Soil Analysis: Part 2 (eds Page, A. L. et al.) Ch. 35 (American Society of Agronomy and Soil Science Society of America, 1982).

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

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pauvert, C. et al. Bioinformatics matters: the accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline. Fungal Ecol. 41, 23–33 (2019).

    Google Scholar 

  • Abarenkov, K. et al UNITE QIIME Release for Fungi. Version 04.02.2020 (UNITE Community, 2020).

  • Francioli, D., van Ruijven, J., Bakker, L. & Mommer, L. Drivers of total and pathogenic soil-borne fungal communities in grassland plant species. Fungal Ecol. 48, 100987 (2020).

    Google Scholar 

  • Nhu, H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).

    Google Scholar 

  • Brooks, M. B. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).

    Google Scholar 

  • Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Google Scholar 

  • Lou, J. Entropy and diversity. Oikos 113, 363–375 (2006).

    Google Scholar 

  • Oksanen, J. et al. vegan: Community Ecology Package. R version 2.5–7 https://CRAN.R-project.org/package=vegan (2020).

  • Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2020).

    Google Scholar 

  • Wilensky, U. NetLogo http://ccl.northwestern.edu/netlogo (1999).

  • Salecker, J., Sciaini, M., Meyer, K. M. & Wiegand, K. The NLRX R package: a next-generation framework for reproducible NetLogo model analyses. Methods Ecol. Evol. 10, 1854–1863 (2019).

    Google Scholar 

  • Wickham et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).

    Google Scholar 

  • R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).


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