Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Plant–soil feedback effects on conspecific and heterospecific successors of annual and perennial Central European grassland plants are correlated

Nature Plants volume 9, pages 1057–1066 (2023)Cite this article

Subjects

Abstract

Plant–soil feedbacks (PSFs), soil-mediated plant effects on conspecific or heterospecific successors, are a major driver of vegetation development. It has been proposed that specialist plant antagonists drive differences in PSF responses between conspecific and heterospecific plants, whereas contributions of generalist plant antagonists to PSFs remain understudied. Here we examined PSFs among nine annual and nine perennial grassland species to test whether poorly defended annuals accumulate generalist-dominated plant antagonist communities, causing equally negative PSFs on conspecific and heterospecific annuals, whereas well-defended perennial species accumulate specialist-dominated antagonist communities, predominantly causing negative conspecific PSFs. Annuals exhibited more negative PSFs than perennials, corresponding to differences in root–tissue investments, but this was independent of conditioning plant group. Overall, conspecific and heterospecific PSFs did not differ. Instead, conspecific and heterospecific PSF responses in individual species’ soils were correlated. Soil fungal communities were generalist dominated but could not robustly explain PSF variation. Our study nevertheless suggests an important role for host generalists as drivers of PSFs.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Annual plants experience more negative PSFs than perennial plants.
Fig. 2: Species-level conditioning effects and PSF responses are correlated.
Fig. 3: Root trait variation correlates to PSF responses.
Fig. 4: Average PSF responses vary with fungal community composition.
Fig. 5: Generalist pathogen abundances correlate to soil conditioning effects.

Similar content being viewed by others

Data availability

Fungal ITS2 sequence data are uploaded to NCBI under project number PRJNA952944. Plant biomass data of the main experiment as well as root trait data are available on figshare under: https://doi.org/10.6084/m9.figshare.22740974 ref. 69. The FUNGUILD database46 (http://www.funguild.org) was used for fungal ASV annotation to ecological guilds. Source data are provided with this paper.

References

  1. 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 

  2. De Deyn, G. B. et al. Soil invertebrate fauna enhances grassland succession and diversity. Nature 422, 711–713 (2003).

    PubMed  Google Scholar 

  3. Liu, S. et al. Phylotype diversity within soil fungal functional groups drives ecosystem stability. Nat. Ecol. Evol. 6, 900–909 (2022).

    PubMed  Google Scholar 

  4. Wilschut, R. A. et al. Root traits and belowground herbivores relate to plant–soil feedback variation among congeners. Nat. Commun. 10, 1564 (2019).

    PubMed  PubMed Central  Google Scholar 

  5. Philippot, L., Raaijmakers, J. M., Lemanceau, P. & van der Putten, W. H. Going back to the roots: the microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799 (2013).

    CAS  PubMed  Google Scholar 

  6. Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, eaba3756 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Bezemer, T. M. et al. Divergent composition but similar function of soil food webs of individual plants: plant species and community effects. Ecology 91, 3027–3036 (2010).

    CAS  PubMed  Google Scholar 

  8. van der Putten, W. H. et al. Plant–soil feedbacks: the past, the present and future challenges. J. Ecol. 101, 265–276 (2013).

    Google Scholar 

  9. 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 

  10. Klironomos, J. N. Feedback with soil biota contributes to plant rarity and invasiveness in communities. Nature 417, 67–70 (2002).

    CAS  PubMed  Google Scholar 

  11. Kempel, A., Rindisbacher, A., Fischer, M. & Allan, E. Plant soil feedback strength in relation to large-scale plant rarity and phylogenetic relatedness. Ecology 99, 597–606 (2018).

    PubMed  Google Scholar 

  12. 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 

  13. 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 

  14. 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 

  15. Thakur, M. P. et al. Plant–soil feedbacks and temporal dynamics of plant diversity–productivity relationships. Trends Ecol. Evol. 36, 651–661 (2021).

    PubMed  Google Scholar 

  16. van de Voorde, T. F. J., van der Putten, W. H. & Martijn Bezemer, T. Intra- and interspecific plant–soil interactions, soil legacies and priority effects during old-field succession. J. Ecol. 99, 945–953 (2011).

    Google Scholar 

  17. Kardol, P., Cornips, N. J., van Kempen, M. M. L., Bakx-Schotman, J. M. T. & van der Putten, W. H. Microbe-mediated plant-soil feedback causes historical contingency effects in plant community assembly. Ecol. Monogr. 77, 147–162 (2007).

    Google Scholar 

  18. Callaway, R. M., Montesinos, D., Williams, K. & Maron, J. L. Native congeners provide biotic resistance to invasive Potentilla through soil biota. Ecology 94, 1223–1229 (2013).

    PubMed  Google Scholar 

  19. 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 

  20. Semchenko, M. et al. Deciphering the role of specialist and generalist plant–microbial interactions as drivers of plant-soil feedback. New Phytol. 234, 1929–1944 (2022).

    CAS  PubMed  Google Scholar 

  21. Spear, E. R. & Broders, K. D. Host-generalist fungal pathogens of seedlings may maintain forest diversity via host-specific impacts and differential susceptibility among tree species. New Phytol. 231, 460–474 (2021).

    PubMed  Google Scholar 

  22. Van der Putten, W. H. Plant defense belowground and spatiotemporal processes in natural vegetation. Ecology 84, 2269–2280 (2003).

    Google Scholar 

  23. Mommer, L. et al. Lost in diversity: the interactions between soil-borne fungi, biodiversity and plant productivity. New Phytol. 218, 542–553 (2018).

    PubMed  PubMed Central  Google Scholar 

  24. Ruijven, J., Ampt, E., Francioli, D., Mommer, L. & Fridley, J. Do soil‐borne fungal pathogens mediate plant diversity–productivity relationships? Evidence and future opportunities. J. Ecol. 108, 1810–1821 (2020).

    Google Scholar 

  25. Wilschut, R. A. & Geisen, S. Nematodes as drivers of plant performance in natural systems. Trends Plant Sci. 26, 237–247 (2021).

    CAS  PubMed  Google Scholar 

  26. Cortois, R. et al. Plant–soil feedbacks: role of plant functional group and plant traits. J. Ecol. 104, 1608–1617 (2016).

    Google Scholar 

  27. Semchenko, M. et al. Fungal diversity regulates plant-soil feedbacks in temperate grassland. Sci. Adv. 4, eaau4578 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Lemmermeyer, S., Lorcher, L., van Kleunen, M. & Dawson, W. Testing the plant growth–defense hypothesis belowground: do faster-growing herbaceous plant species suffer more negative effects from soil biota than slower-growing ones? Am. Nat. 186, 264–271 (2015).

    PubMed  Google Scholar 

  29. Xi, N. et al. Relationships between plant–soil feedbacks and functional traits. J. Ecol. 109, 3411–3423 (2021).

    Google Scholar 

  30. Dowarah, B., Gill, S. S. & Agarwala, N. Arbuscular mycorrhizal fungi in conferring tolerance to biotic stresses in plants. J. Plant Growth Regul. 41, 1429–1444 (2021).

    Google Scholar 

  31. Jarosz, A. M. & Davelos, A. L. Effects of disease in wild plant populations and the evolution of pathogen aggressiveness. New Phytol. 129, 371–387 (2006).

    Google Scholar 

  32. Spitzer, C. M. et al. Root traits and soil micro‐organisms as drivers of plant–soil feedbacks within the sub‐arctic tundra meadow. J. Ecol. 110, 466–478 (2021).

    Google Scholar 

  33. Grime, J. P. Plant Strategies, Vegetation Processes, and Ecosystem Properties (Wiley, 2006).

  34. Bennett, J. A. & Klironomos, J. Mechanisms of plant–soil feedback: interactions among biotic and abiotic drivers. New Phytol. 222, 91–96 (2019).

    PubMed  Google Scholar 

  35. De Long, J. R. et al. Contrasting responses of soil microbial and nematode communities to warming and plant functional group removal across a post-fire boreal forest successional gradient. Ecosystems 19, 339–355 (2015).

    Google Scholar 

  36. Olff, H., Hoorens, B., de Goede, R. G. M., van der Putten, W. H. & Gleichman, J. M. Small-scale shifting mosaics of two dominant grassland species: the possible role of soil-borne pathogens. Oecologia 125, 45–54 (2000).

    CAS  PubMed  Google Scholar 

  37. Vincenot, C. E., Cartenì, F., Bonanomi, G., Mazzoleni, S. & Giannino, F. Plant–soil negative feedback explains vegetation dynamics and patterns at multiple scales. Oikos 126, 1319–1328 (2017).

    Google Scholar 

  38. in 't Zandt, D. et al. Species abundance fluctuations over 31 years are associated with plant–soil feedback in a species‐rich mountain meadow. J. Ecol. 109, 1511–1523 (2020).

    Google Scholar 

  39. Mordecai, E. A. Pathogen impacts on plant diversity in variable environments. Oikos 124, 414–420 (2015).

    Google Scholar 

  40. Lepinay, C., Vondrakova, Z., Dostalek, T. & Munzbergova, Z. Duration of the conditioning phase affects the results of plant–soil feedback experiments via soil chemical properties. Oecologia 186, 459–470 (2018).

    PubMed  Google Scholar 

  41. Maron, J. L., Marler, M., Klironomos, J. N. & Cleveland, C. C. Soil fungal pathogens and the relationship between plant diversity and productivity. Ecol. Lett. 14, 36–41 (2011).

    PubMed  Google Scholar 

  42. Veen, G. F. et al. The role of plant litter in driving plant–soil feedbacks. Front. Environ. Sci. 7, 168 (2019).

    Google Scholar 

  43. Lekberg, Y. et al. More bang for the buck? Can arbuscular mycorrhizal fungal communities be characterized adequately alongside other fungi using general fungal primers? New Phytol. 220, 971–976 (2018).

    PubMed  Google Scholar 

  44. Benitez, M. S., Hersh, M. H., Vilgalys, R. & Clark, J. S. Pathogen regulation of plant diversity via effective specialization. Trends Ecol. Evol. 28, 705–711 (2013).

    PubMed  Google Scholar 

  45. Nilsson, R. H. et al. Mycobiome diversity: high-throughput sequencing and identification of fungi. Nat. Rev. Microbiol. 17, 95–109 (2019).

    CAS  PubMed  Google Scholar 

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

    Google Scholar 

  47. Tedersoo, L. & Anslan, S. Towards PacBio-based pan-eukaryote metabarcoding using full-length ITS sequences. Environ. Microbiol. Rep. 11, 659–668 (2019).

    CAS  PubMed  Google Scholar 

  48. Eck, J. L., Stump, S. M., Delavaux, C. S., Mangan, S. A. & Comita, L. S. Evidence of within-species specialization by soil microbes and the implications for plant community diversity. Proc. Natl Acad. Sci. USA 116, 7371–7376 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).

    CAS  Google Scholar 

  50. Das, K., Prasanna, R. & Saxena, A. K. Rhizobia: a potential biocontrol agent for soilborne fungal pathogens. Folia Microbiol. 62, 425–435 (2017).

    CAS  Google Scholar 

  51. Reinhart, K. O., Tytgat, T., Van der Putten, W. H. & Clay, K. Virulence of soil-borne pathogens and invasion by Prunus serotina. New Phytol. 186, 484–495 (2010).

    PubMed  Google Scholar 

  52. Hannula, S. E. et al. Shifts in rhizosphere fungal community during secondary succession following abandonment from agriculture. ISME J. 11, 2294–2304 (2017).

    PubMed  PubMed Central  Google Scholar 

  53. Heinen, R. et al. Plant community composition steers grassland vegetation via soil legacy effects. Ecol. Lett. 23, 973–982 (2020).

    PubMed  PubMed Central  Google Scholar 

  54. Forero, L. E., Grenzer, J., Heinze, J., Schittko, C. & Kulmatiski, A. Greenhouse- and field-measured plant–soil feedbacks are not correlated. Front. Environ. Sci. 7, 184 (2019).

    Google Scholar 

  55. Bagchi, R. et al. Pathogens and insect herbivores drive rainforest plant diversity and composition. Nature 506, 85–88 (2014).

    CAS  PubMed  Google Scholar 

  56. Parker, I. M. et al. Phylogenetic structure and host abundance drive disease pressure in communities. Nature 520, 542–544 (2015).

    CAS  PubMed  Google Scholar 

  57. Comita, L. S., Muller-Landau, H. C., Aguilar, S. & Hubbell, S. P. Asymmetric density dependence shapes species abundances in a tropical tree community. Science 329, 330–332 (2010).

    CAS  PubMed  Google Scholar 

  58. Johnson, D. J., Beaulieu, W. T., Bever, J. D. & Clay, K. Conspecific negative density dependence and forest diversity. Science 336, 904–907 (2012).

    CAS  PubMed  Google Scholar 

  59. Bellemain, E. et al. ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC Microbiol. 10, 189 (2010).

    PubMed  PubMed Central  Google Scholar 

  60. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  61. Koljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).

    CAS  PubMed  Google Scholar 

  62. Abarenkov, K. et al. The UNITE database for molecular identification of fungi–recent updates and future perspectives. New Phytol. 186, 281–285 (2010).

    PubMed  Google Scholar 

  63. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

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

    Google Scholar 

  65. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3.1-117 (2014); https://cran.r-project.org/web/packages/nlme/

  66. Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).

    PubMed  PubMed Central  Google Scholar 

  67. Oksanen, J. et al. The Vegan package. Community Ecol. 10, 631–637 (2007).

    Google Scholar 

  68. van Kleunen, M. et al. Economic use of plants is key to their naturalization success. Nat. Commun. 11, 3201 (2020).

    PubMed  PubMed Central  Google Scholar 

  69. Wilschut, R. A. & van Kleunen, M. Conspecific and heterospecific plant-soil feedback data and root trait measurements of 18 annual and perennial plant species. figshare https://doi.org/10.6084/m9.figshare.22740974 (2023).

Download references

Acknowledgements

We acknowledge Z. Zhang, E. Hannula, S. Geisen, R. Reuter and M. Stift for advice on statistical, molecular and root trait analyses, Q. Yang for the phylogenetic data and O. Ficht, M. Fuchs, H. Vahlenkamp, B. Speißer, B. Rüter, P. Kukofka, T. Voortman, N. Buchenau and student helpers from the University of Konstanz for practical assistance. Moreover, we acknowledge the University of Konstanz Sequencing Analysis Core Facility for assistance in analysing the fungal ITS2 sequencing data, and three reviewers for their valuable comments on the paper. R.A.W. acknowledges funding from the Wageningen Graduate Schools (WGS Postdoc Talent Grant to R.A.W.).

Author information

Authors and Affiliations

  1. Ecology Group, Department of Biology, University of Konstanz, Konstanz, Germany

    Rutger A. Wilschut, Ekaterina Mamonova & Mark van Kleunen

  2. Department of Nematology, Wageningen University and Research, Wageningen, the Netherlands

    Rutger A. Wilschut

  3. SequAna, University of Konstanz, Konstanz, Germany

    Benjamin C. C. Hume

  4. Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou University, Taizhou, China

    Mark van Kleunen

Authors
  1. Rutger A. Wilschut
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benjamin C. C. Hume
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ekaterina Mamonova
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mark van Kleunen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.A.W. and M.v.K. designed the study. R.A.W. and E.M. performed, respectively, the greenhouse experiments and molecular laboratory work. B.C.C.H. processed the raw sequencing data. Data analyses were done by R.A.W. with inputs from M.v.K. The paper was written by R.A.W. with considerable inputs from M.v.K. and was reviewed by all authors.

Corresponding author

Correspondence to Rutger A. Wilschut.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Plants thanks Brenda Casper and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Plant biomass in the conditioning phase does not explain average plant-soil-feedback responses.

Both panels show the average plant-soil-feedback response (Ln(biomassconditioned/biomasscontrol) of 18 responding plant species in relation to the average plant biomass (Ln-transformed) in pooled conditioning phase soils (see Methods). (A) Average feedback responses to all conditioned soils (N = 90; 18 conditioning species × 5 independent biological replicates). (B) Average feedback responses to all plant species, except legumes (N = 70; 14 conditioning species × 5 independent biological replicates). Results of two-sided Pearson’s correlation tests between conditioning phase biomass and average feedback responses are shown.

Source data

Extended Data Fig. 2 Root-trait variation does not predict soil-conditioning effects.

Species-level soil conditioning effects ln(biomassconditioned/biomasscontrol), averaged across 18 responding plant species, were not correlated with specific root length (A; mm/g, log-transformed), marginally significantly correlated with relative root weight (B), and not significantly correlated with average root diameter (C; mm × 10). Average trait values were obtained from a separate root-trait experiment (see Methods). Results of two-sided Pearson’s correlation tests between trait average and average feedback responses in conditioned soils are shown. In panel B, trend line and shading represent the correlation coefficient (±95% CI) between relative root weight and average PSF effect.

Source data

Extended Data Fig. 3 Fungal communities become more dissimilar with increasing phylogenetic distance.

Average pairwise Bray-Curtis dissimilarities of complete fungal communities among all 18 conditioning plant species (A), among the nine annual species (B) and among the nine perennial species (C) correlate to pairwise phylogenetic distances (ln transformed; see Methods). Results of Mantel tests between pairwise phylogenetic distances and pairwise Bray-Curtis dissimilarities are shown in each panel, while trend lines and shading represent correlation coefficients (±95% CI’s).

Source data

Extended Data Fig. 4 Putative fungal pathogen-community composition and soil-conditioning effects.

(A) NMDS-ordination showing Bray-Curtis dissimilarity-based composition of putative fungal pathogen communities, based on fungal amplicon sequence variant (ASV) abundances. PERMANOVA analysis revealed significant variation in of putative fungal pathogen community composition among conditioning plant species (see methods; full species names are listed in Supplementary Table 1). (B) Average plant-soil-feedback responses to individual conditioned soils marginally significantly vary with putative fungal pathogen community composition (NMDS-axis 2), as indicated by a linear mixed effect model and log-likelihood tests (see methods and Supplementary Table 7). In panel B, trend line and shading represent the predicted linear relationship (±95% CI) between NMDS2 and average PSF response.

Source data

Extended Data Fig. 5 Arbuscular mycorrhizal fungal community composition.

NMDS-ordination showing Bray-Curtis dissimilarity-based composition of arbuscular mycorrhizal fungal communities, based on of fungal amplicon sequence variant (ASV) abundances. PERMANOVA analysis revealed significant variation in composition of arbuscular mycorrhizal fungal communities among conditioning plant species (see methods; full species names are listed in Supplementary Table 1).

Source data

Extended Data Fig. 6 Total pathogen abundances explain soil-conditioning effects.

(A) Species-level variation in logit-transformed total pathogen abundances (means ± SEM, N = 5 independent biological replicates per plant species; full species names are listed in Supplementary Table 1). Abundances are based on all ASVs assigned as putative pathogens. (B) Average plant-soil-feedback responses to individual conditioned soils significantly vary with total pathogen abundances. In both panels, Log-likelihood ratio test results are based on linear mixed effect model (see methods and Supplementary Tables 11 & 12). In panel B, trend line and shading represent the predicted linear relationship (±95% CI) between pathogen abundance and average PSF response.

Source data

Extended Data Fig. 7 Relative abundances of generalist and specialist putative pathogens and arbuscular mycorrhizal fungi in soils conditioned by 18 annual and perennial plant species.

Fungal ASVs were manually assigned as generalists and specialists based on their occurrence in at least 2/3 or maximally 1/3 of the plant species in this study (full species names are listed in Supplementary Table 1). Relative abundances were calculated based on untransformed read counts.

Source data

Extended Data Fig. 8 Specialist-pathogen accumulation in soils of annual and perennial plant species.

Dots and bars represent means ± SEM, calculated based on average logit-transformed relative putative specialist pathogen abundances in rhizosphere soil of single plant species (N = 9 plant species per plant group (annuals/perennials)).

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1–15.

Reporting Summary

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wilschut, R.A., Hume, B.C.C., Mamonova, E. et al. Plant–soil feedback effects on conspecific and heterospecific successors of annual and perennial Central European grassland plants are correlated. Nat. Plants 9, 1057–1066 (2023). https://doi.org/10.1038/s41477-023-01433-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-023-01433-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing