Climate change can alter species coexistence through changes in biotic interactions. By describing reciprocal interactions between plants and soil microbes, plant–soil feedback (PSF) has emerged as a powerful framework for predicting plant species coexistence and community dynamics, but little is known about how PSF will respond to changing climate conditions. Hence, the context dependency of PSF has recently gained attention. Water availability is a major driver of all biotic interactions, and it is expected that precipitation patterns will change with ongoing climate change. We tested how soil water content affects PSF by conducting a full factorial pairwise PSF experiment using eight plant species common to southeastern United States coastal prairies under three watering treatments. We found coexistence-stabilizing negative PSF at drier-than-average conditions shifted to coexistence-destabilizing positive PSF under wetter-than-average conditions. A simulation model parameterized with the experimental results supports the prediction that more positive PSF accelerates the erosion of diversity within communities while decreasing the predictability in plant community composition. Our results underline the importance of considering environmental context dependency of PSF in light of a rapidly changing climate.
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The code for the simulation model is available in the Open Science Framework repository: https://doi.org/10.17605/osf.io/x2wds.
Pereira, H. M. et al. Scenarios for global biodiversity in the 21st century. Science 330, 1496–1501 (2010).
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).
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).
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).
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).
Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).
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).
Suttle, K. B., Thomsen, M. A. & Power, M. E. Species interactions reverse grassland responses to changing climate. Science 315, 640–642 (2007).
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).
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).
Bennett, J. A. et al. Plant–soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science 355, 181–184 (2017).
Teste, F. P. et al. Plant–soil feedback and the maintenance of diversity in Mediterranean-climate shrublands. Science 355, 173–176 (2017).
Kardol, P., Bezemer, T. M. & van der Putten, W. H. Temporal variation in plant–soil feedback controls succession. Ecol. Lett. 9, 1080–1088 (2006).
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).
Bever, J. D. Feedback between plants and their soil communities in an old field community. Ecology 75, 1965–1977 (1994).
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).
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Bever, J. D. Soil community feedback and the coexistence of competitors: conceptual frameworks and empirical tests. New Phytol. 157, 465–473 (2003).
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).
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).
Ke, P. J. & Wan, J. Effects of soil microbes on plant competition: a perspective from modern coexistence theory. Ecol. Monogr. 90, e01391 (2020).
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).
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).
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).
Reinhart, K. O. et al. Globally, plant–soil feedbacks are weak predictors of plant abundance. Ecol. Evol. 11, 1756–1768 (2021).
Casper, B. B. & Castelli, J. P. Evaluating plant–soil feedback together with competition in a serpentine grassland. Ecol. Lett. 10, 394–400 (2007).
Shannon, S., Flory, S. L. & Reynolds, H. Competitive context alters plant–soil feedback in an experimental woodland community. Oecologia 169, 235–243 (2012).
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).
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).
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).
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).
McCarthy-Neumann, S. & Kobe, R. K. Site soil-fertility and light availability influence plant–soil feedback. Front. Ecol. Evol. 7, 383 (2019).
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).
Crawford, K. M. et al. When and where plant–soil feedback may promote plant coexistence: a meta-analysis. Ecol. Lett. 22, 1274–1284 (2019).
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).
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).
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).
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).
Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).
Fierer, N., Schimel, J. P. & Holden, P. A. Influence of drying–rewetting frequency on soil bacterial community structure. Microb. Ecol. 45, 63–71 (2003).
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).
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).
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).
de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).
de Oliveira, T. B. et al. Fungal communities differentially respond to warming and drought in tropical grassland soil. Mol. Ecol. 29, 1550–1559 (2020).
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).
Suzuki, N., Rivero, R. M., Shulaev, V., Blumwald, E. & Mittler, R. Abiotic and biotic stress combinations. New Phytol. 203, 32–43 (2014).
Cavagnaro, T. R. Soil moisture legacy effects: impacts on soil nutrients, plants and mycorrhizal responsiveness. Soil Biol. Biochem. 95, 173–179 (2016).
Crawford, K. M. & Hawkes, C. V. Soil precipitation legacies influence intraspecific plant–soil feedback. Ecology 101, e03142 (2020).
Fry, E. L. et al. Drought neutralises plant–soil feedback of two mesic grassland forbs. Oecologia 186, 1113–1125 (2018).
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).
Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant–soil feedbacks: a meta-analytical review. Ecol. Lett. 11, 980–992 (2008).
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).
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).
Castelli, J. P. & Casper, B. B. Intraspecific AM fungal variation contributes to plant–fungal feedback in a serpentine grassland. Ecology 84, 323–336 (2003).
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).
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).
Gilbert, G. S. & Parker, I. M. The evolutionary ecology of plant disease: a phylogenetic perspective. Annu. Rev. Phytopathol. 54, 549–578 (2016).
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).
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).
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).
Ji, B. & Bever, J. D. Plant preferential allocation and fungal reward decline with soil phosphorus: implications for mycorrhizal mutualism. Ecosphere 7, e01256 (2016).
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).
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).
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).
Chase, J. M. Community assembly: when should history matter? Oecologia 136, 489–498 (2003).
Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).
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).
Mehlich, A. Mehlich-3 soil test extractant: a modification of Mehlich-2 extractant. Commun. Soil Sci. Plant Anal. 15, 1409–1416 (1984).
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).
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).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
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).
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).
Nhu, H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Brooks, M. B. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Lou, J. Entropy and diversity. Oikos 113, 363–375 (2006).
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).
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).
Wickham et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
We thank S. Aziz, K. Boakye, S. Durand-Luecke, J. Nicholas, O. Oladapo and A. Sölter for helping with planting and the biomass harvest. We further thank M. Afkhami and R. Callaway for helpful comments on the manuscript. This study was funded by the grant NSF DEB no. 1754287.
The authors declare no competing interests.
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Pairings of growth form (grass vs. forb) and naturalization status (native vs. exotic) are indicated in the panel headers (n forb/forb = 6, n grass/forb = 16, n grass/grass = 6, n exotic/exotic = 3, n native/exotic = 15, n native/native = 10). Box-whisker plots indicate the median, 25th/75th percentile and 1.5 x IQR.
Bars above and below the zero intercept represent aboveground biomass and belowground biomass of the surviving plants respectively (Mean ± SE). Focal species and watering treatment are indicated in the panel headers and soil conditioning species at the x-axes. Species codes: AT = Asclepias tuberosa, BI = Bothriochloa ischaemum, RC = Ratibida columnifera, RH = Rudbeckia hirta, SH = Sorghum halepense, SN = Sorghastrum nutans, SS = Schizachyrium scoparium, VB = Verbena brasiliensis, ST = sterilized inocculum. n = initially 9 plants per conditioned soil in each watering treatment; note that only surviving plants are represented here.
Bars represent plant mortality rates. Focal species and watering treatment are indicated in the panel headers and soil conditioning species at the x-axes. Species codes: AT = Asclepias tuberosa, BI = Bothriochloa ischaemum, RC = Ratibida columnifera, RH = Rudbeckia hirta, SH = Sorghum halepense, SN = Sorghastrum nutans, SS = Schizachyrium scoparium, VB = Verbena brasiliensis, ST = sterilized inocculum. n = 9 plants per conditioned soil in each water treatment.
Extended Data Fig. 4 Alpha diversity and relative pathogen abundance of fungal communities at the end of the conditioning phase.
(a) Simpson’s diversity of fungal ASVs, and (b) richness of fungal ASVs. Significance of the watering treatment (Pwater) was evaluated using linear models (Methods and Supplementary Table 4). (c) Relative abundance of probable pathogens, and (d) relative abundance of putative pathogens. Box-whisker plots indicate the median, 25th/75th percentile and 1.5 x IQR. Species codes: AT = Asclepias tuberosa, BI = Bothriochloa ischaemum, RC = Ratibida columnifera, RH = Rudbeckia hirta, SH = Sorghum halepense, SN = Sorghastrum nutans, SS = Schizachyrium scoparium, VB = Verbena brasiliensis. n = 71 soil samples; note that for Asclepias tuberosa in the medium watering treatment only two samples were available.
Extended Data Fig. 5 Simulation results under alternative parameterizations of plant mortality and the spatial extent of recruitment under different watering treatments of simulated plant communities.
(a) Alpha diversity (median species richness and average Simpson’s diversity). (b) Average relative abundance of the single species. Species codes: AT = Asclepias tuberosa, BI = Bothriochloa ischaemum, RC = Ratibida columnifera, RH = Rudbeckia hirta, SH = Sorghum halepense, SN = Sorghastrum nutans, SS = Schizachyrium scoparium, VB = Verbena brasiliensis. n = 200 simulated plant communities per watering treatment and parameterization.
Bars above and below the zero intercept represent aboveground biomass and belowground biomass of the surviving plants respectively (Mean ± SE). The focal plant species is indicated in the panel headers; note the different y-axis scaling. Species codes: AT = Asclepias tuberosa, BI = Bothriochloa ischaemum, RC = Ratibida columnifera, RH = Rudbeckia hirta, SH = Sorghum halepense, SN = Sorghastrum nutans, SS = Schizachyrium scoparium, VB = Verbena brasiliensis. n = 9 plants per species in each watering treatment.
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Dudenhöffer, JH., Luecke, N.C. & Crawford, K.M. Changes in precipitation patterns can destabilize plant species coexistence via changes in plant–soil feedback. Nat Ecol Evol 6, 546–554 (2022). https://doi.org/10.1038/s41559-022-01700-7
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