Low investment in sexual reproduction threatens plants adapted to phosphorus limitation


Plant species diversity in Eurasian wetlands and grasslands depends not only on productivity but also on the relative availability of nutrients, particularly of nitrogen and phosphorus1,2,3,4. Here we show that the impacts of nitrogen:phosphorus stoichiometry on plant species richness can be explained by selected plant life-history traits, notably by plant investments in growth versus reproduction. In 599 Eurasian sites with herbaceous vegetation we examined the relationship between the local nutrient conditions and community-mean life-history traits. We found that compared with plants in nitrogen-limited communities, plants in phosphorus-limited communities invest little in sexual reproduction (for example, less investment in seed, shorter flowering period, longer lifespan) and have conservative leaf economy traits (that is, a low specific leaf area and a high leaf dry-matter content). Endangered species were more frequent in phosphorus-limited ecosystems and they too invested little in sexual reproduction. The results provide new insight into how plant adaptations to nutrient conditions can drive the distribution of plant species in natural ecosystems and can account for the vulnerability of endangered species.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Relationship between biodiversity indices of vascular plants and N:P ratio corrected for productivity effects.
Figure 2: Relationship between N:P ratios and community-mean trait values of herbaceous vascular plant species, after removing confounding effects of productivity.
Figure 3: Relationship between N:P ratios and community-mean values for C, S and R scores of CSR strategy after removing confounding effects of productivity.
Figure 4: Difference in trait values between endangered and non-endangered herbaceous vascular plant species.


  1. 1

    Olde Venterink, H., Wassen, M. J., Verkroost, A. W. M. & de Ruiter, P. C. Species richness–productivity patterns differ between N-, P-, and K- limited wetlands. Ecology 84, 2191–2199 (2003)

    Article  Google Scholar 

  2. 2

    Wassen, M. J., Olde Venterink, H., Lapshina, E. D. & Tanneberger, F. Endangered plants persist under phosphorus limitation. Nature 437, 547–550 (2005)

    CAS  Article  ADS  Google Scholar 

  3. 3

    Ceulemans, T., Merckx, R., Hens, M. & Honnay, O. A trait-based analysis of the role of phosphorus vs. nitrogen enrichment in plant species loss across North-west European grasslands. J. Appl. Ecol. 48, 1155–1163 (2011)

    CAS  Article  Google Scholar 

  4. 4

    Klaus, V. H. et al. Nutrient concentrations and fibre contents of plant community biomass reflect species richness patterns along a broad range of land-use intensities among agricultural grasslands. Persp. Plant Ecol. Evol. System. 13, 287–295 (2011)

    Article  Google Scholar 

  5. 5

    Cardinale, B. J., Hillebrand, H., Harpole, W. S., Gross, K. & Ptacnik, R. Separating the influence of resource ‘availability' from resource ‘imbalance' on productivity–diversity relationships. Ecol. Lett. 12, 475–487 (2009)

    PubMed  Article  Google Scholar 

  6. 6

    Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007)

    PubMed  Article  Google Scholar 

  7. 7

    Olde Venterink, H., Wassen, M. J., Belgers, D. M. & Verhoeven, J. T. A. Control of environmental variables on species density in fens and meadows: importance of direct effects and effects through community biomass. J. Ecol. 89, 1033–1040 (2001)

    CAS  Article  Google Scholar 

  8. 8

    Lannes, L. S., Bustamante, M. M. C., Edwards, P. J. & Olde Venterink, H. Alien and endangered plants in the Brazilian Cerrado exhibit contrasting relationships with vegetation biomass and N:P stoichiometry. New Phytol. 196, 816–823 (2012)

    CAS  PubMed  Article  Google Scholar 

  9. 9

    Lambers, H. & Poorter, H. Inherent variation in growth rate between higher plants: a search for physiological causes and ecological consequences. Adv. Ecol. Res. 23, 187–261 (1992)

    CAS  Article  Google Scholar 

  10. 10

    Aerts, R. & Chapin III, F. S. The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns. Adv. Ecol. Res. 30, 1–67 (2000)

    CAS  Google Scholar 

  11. 11

    Güsewell, S. Nutrient resorption of wetland graminoids is related to the type of nutrient limitation. Funct. Ecol. 19, 344–354 (2005)

    Article  Google Scholar 

  12. 12

    Güsewell, S. High nitrogen: phosphorus ratios reduce nutrient retention and second-year growth of wetland sedges. New Phytol. 166, 537–550 (2005)

    PubMed  Article  CAS  Google Scholar 

  13. 13

    Lambers, H., Raven, J. A., Shaver, G. R. & Smith, S. E. Plant nutrient-acquisition strategies change with soil age. Trends Ecol. Evol. 23, 95–103 (2008)

    PubMed  Article  Google Scholar 

  14. 14

    Olde Venterink, H. & Güsewell, S. Competitive interactions between two meadow grasses under nitrogen and phosphorus limitation. Funct. Ecol. 24, 877–886 (2010)

    Article  Google Scholar 

  15. 15

    Fujita, Y., Robroek, B. J. M., de Ruiter, P. C., Heil, G. W. & Wassen, M. J. Increased N affects P uptake of eight grassland species: the role of root surface phosphatase activity. Oikos 119, 1665–1673 (2010)

    CAS  Article  Google Scholar 

  16. 16

    Olde Venterink, H. Does phosphorus limitation promote species-rich plant communities? Plant Soil 345, 1–9 (2011)

    CAS  Article  Google Scholar 

  17. 17

    Elser, J. J. et al. Biological stoichiometry from genes to ecosystems. Ecol. Lett. 3, 540–550 (2000)

    Article  Google Scholar 

  18. 18

    Grime, J. P., Hodgson, J. G. & Hunt, R. Comparative Plant Ecology: a Functional Approach to Common British Species. (Castlepoint Press, 2007)

    Google Scholar 

  19. 19

    Ordoñez, J. C. et al. A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 18, 137–149 (2009)

    Article  Google Scholar 

  20. 20

    Kerkhoff, A. J., Fagan, W. F., Elser, J. J. & Enquist, B. J. Phylogenetic and growth form variation in the scaling of nitrogen and phosphorus in the seed plants. Am. Nat. 168, E103–E122 (2006)

    PubMed  Article  Google Scholar 

  21. 21

    Fenner, M. The allocation of minerals to seeds in Senecio vulgaris plants subjected to nutrient shortage. J. Ecol. 74, 385–392 (1986)

    CAS  Article  Google Scholar 

  22. 22

    Van Andel, J. & Vera, F. Reproductive allocation in Senecio Sylvaticus and Chamaenerion Angustifolium in relation to mineral nutrition. J. Ecol. 65, 747–758 (1977)

    CAS  Article  Google Scholar 

  23. 23

    Vergeer, P., Rengelink, R., Joop Ouborg, N. & Roelofs, J. G. M. Effects of population size and genetic variation on the response of Succisa pratensis to eutrophication and acidification. J. Ecol. 91, 600–609 (2003)

    Article  Google Scholar 

  24. 24

    Lambers, H., Brundrett, M. C., Raven, J. A. & Hopper, S. D. Plant mineral nutrition in ancient landscapes: high plant species diversity on infertile soils is linked to functional diversity for nutritional strategies. Plant Soil 334, 11–31 (2010)

    CAS  Article  Google Scholar 

  25. 25

    Lahti, T., Kemppainen, E., Kurtto, A. & Uotila, P. Distribution and biological characteristics of threatened vascular plants in Finland. Biol. Conserv. 55, 299–314 (1991)

    Article  Google Scholar 

  26. 26

    Murray, B. R., Thrall, P. H., Gill, A. M. & Nicotra, A. B. How plant life-history and ecological traits relate to species rarity and commonness at varying spatial scales. Austral Ecol. 27, 291–310 (2002)

    Article  Google Scholar 

  27. 27

    Kunin, W. E. & Gaston, K. J. The biology of rarity—patterns, causes and consequences. Trends Ecol. Evol. 8, 298–301 (1993)

    CAS  PubMed  Article  Google Scholar 

  28. 28

    Farnsworth, E. J. & Ogurcak, D. E. Functional groups of rare plants differ in levels of imperilment. Am. J. Bot. 95, 943–953 (2008)

    PubMed  Article  Google Scholar 

  29. 29

    Elser, J. J., Fagan, W. F., Kerkhoff, A. J., Swenson, N. G. & Enquist, B. J. Biological stoichiometry of plant production: metabolism, scaling and ecological response to global change. New Phytol. 186, 593–608 (2010)

    CAS  PubMed  Article  Google Scholar 

  30. 30

    Vance, C. P., Uhde-Stone, C. & Allan, D. L. Phosphorus acquisition and use: critical adaptations by plants for securing a nonrenewable resource. New Phytol. 157, 423–447 (2003)

    CAS  Article  Google Scholar 

  31. 31

    Bremner, J. M., Mulvaney, C. S., Page, A. C., Miller, R. H. & Keeney, D. R. In Methods of Soil Analysis; Part 2, Chemical and Microbiological Properties 621–622 (American society of Agronomy, 1982)

    Google Scholar 

  32. 32

    Centraal. Bureau voor Statistiek. Dutch Botanical Database, BioBase 2003 http://www.cbs.nl/nl-NL/menu/methoden/classificaties/overzicht/namenlijsten-planten-dieren/2003-biobase.htm (accessed 18 May 2009)

  33. 33

    Kleyer, M. et al. The LEDA Traitbase: a database of life-history traits of the Northwest European flora. J. Ecol. 96, 1266–1274 (2008)

    Article  Google Scholar 

  34. 34

    Klimešová, J. & de Bello, F. CLO-PLA: the database of clonal and bud bank traits of Central European flora. J. Veg. Sci. 20, 511–516 (2009)

    Article  Google Scholar 

  35. 35

    Hodgson, J. G., Grime, J. P., Hunt, R. & Thompson, K. The Electronic Comparative Plant Ecology. (Chapman & Hall, 1995)

    Google Scholar 

  36. 36

    Klotz, S., Kühn, I. & Durka, W. In BIOLFLOR — Eine Datenbank zu biologisch-ökologischen Merkmalen der Gefäßpflanzen in Deutschland. Schriftenreihe für Vegetationskunde 38, (Bundesamt für Naturschutz, 2002)

    Google Scholar 

  37. 37

    Sprent, J. I. Nodulation in Legumes. (Royal Botanic Gardens, 2001)

    Google Scholar 

  38. 38

    Royal Botanic Gardens Kew. Seed Information Database (SID). Version 7.1http://data.kew.org/sid (09/09/, 2011)

  39. 39

    Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33, 125–159 (2002)

    Article  Google Scholar 

  40. 40

    Hodgson, J. G., Wilson, P. J., Hunt, R., Grime, J. P. & Thompson, K. Allocating C-S-R plant functional types: a soft approach to hard problem. Oikos 85, 282–296 (1999)

    Article  Google Scholar 

  41. 41

    Willby, N. J., Pulford, I. D. & Flowers, T. H. Tissue nutrient signatures predict herbaceous-wetland community responses to nutrient availability. New Phytol. 152, 463–481 (2001)

    CAS  Article  Google Scholar 

  42. 42

    Tamis, W. L. M. et al. Standaardlijst van de Nederlandse flora 2003. Gorteria 60, 101–195 (2004)

    Google Scholar 

  43. 43

    Schnittler, M. & Ludwig, G. In Rote Listen und Florenlisten gefährdeter Pflanzen in Deutschland. Schriftenreihe für Vegetationskunde 28, 709–739 (Bundesamt für Naturschutz, 1996)

    Google Scholar 

  44. 44

    Mirek, Z., Zarzycki, K., Wojewoda, W. & Szelag, Z. Red List of Plants and Fungi in Poland (Bundesamt für Naturschutz, 2006)

    Google Scholar 

  45. 45

    Swedish Species Information Centre. ArtDatabankenhttp://www.artfakta.se (accessed 15 July 2011)

  46. 46

    JNCC. The Vascular Plant Red Data List for Great Britainhttp://jncc.defra.gov.uk (accessed 15 July 2011)

  47. 47

    Icelandic Institute of Natural History. Válisti háplantnahttp://www.ni.is/grodur/valisti (accessed 18 July 2011)

  48. 48

    State Committee for Environmental Protection of the Novosibirsk region. The Red List of Plants of Novosibirsk Regionhttp://edu.nstu.ru/frc/konkurs/kkrno/spisok.htm (accessed 30 June 2011)

  49. 49

    Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: the Biology of Elements from Molecules to the Biosphere. (Princeton Univ. Press, 2002)

    Google Scholar 

  50. 50

    Ågren, G. I. The C: N: P stoichiometry of autotrophs — theory and observations. Ecol. Lett. 7, 185–191 (2004)

    Article  Google Scholar 

  51. 51

    Niklas, K. J., Owens, T., Reich, P. B. & Cobb, E. D. Nitrogen/phosphorus leaf stoichiometry and the scaling of plant growth. Ecol. Lett. 8, 636–642 (2005)

    Article  Google Scholar 

  52. 52

    R:. A language and environment for statistical computing. Version R-3.0.1. http://www.R-project.org (16/05/, 2013)

  53. 53

    Cade, B. S., Terrell, J. W. & Schroeder, R. L. Estimating effects of limiting factors with regression quantiles. Ecology 80, 311–323 (1999)

    Article  Google Scholar 

  54. 54

    Sokal, R. R. & Rohlf, F. J. Biometry: the Principles and Practice of Statistics in Biological Research (W. H. Freeman and Company, 1995)

    Google Scholar 

  55. 55

    quantreg:. Quantile Regression. R package version 4.76. http://CRAN.R-project.org/package=quantreg (02/12/, 2011)

  56. 56

    Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009)

    PubMed  PubMed Central  Article  Google Scholar 

  57. 57

    Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge Univ. Press, 2006)

    Google Scholar 

  58. 58

    Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992)

    Article  Google Scholar 

  59. 59

    Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003)

    Article  Google Scholar 

  60. 60

    Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001)

    Google Scholar 

  61. 61

    vegan:. Community Ecology Package. R package version 1.17-9. http://CRAN.R-project.org/package=vegan (released on 31/03/, 2011)

  62. 62

    Howell, D. C. Statistical Methods for Psychology 8th edn (Wadsworth Publishing, 2012)

    Google Scholar 

Download references


We would like to thank N. A. Soudzilovskaia for obtaining the Russian Red List of plant species, M. Soons, D. Ertsen and D. van der Goes for permission to use their vegetation records, T. Markus and M. Stoete for drawing figures and J. Burrough for editing the near-final draft. Y.F. was funded by the Utrecht Centre of Geosciences, and the research in the Rospuda river valley was financed by the Polish Ministry of Science and Higher Education Grant no. N304 010 31/0414.

Author information




Y.F., H.O.V., N.H., E.J., W.K., P.P., T.O. and M.J.W. collected data; Y.F., H.O.V., P.M.v.B., J.C.D. and M.J.W analysed data; Y.F., H.O.V., P.M.v.B., P.C.d.R. and M.J.W. wrote the manuscript; J.C.D., G.W.H., N.H., E.J., W.K., P.P. and T.O. commented on the manuscript; P.C.d.R., G.W.H. and M.J.W. were project leaders.

Corresponding author

Correspondence to Martin J. Wassen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Data analysis approach.

a, b, Schematic proposed relationships between site productivity (that is, aboveground biomass of vascular plants; X2), N:P ratio in aboveground plant biomass (X1), and species diversity (a; X3) or community-mean traits (b; X4). Solid arrows are relationships in which the explanatory variable is constrained by the response variable (direct causality); dashed arrows are relationships in which upper bound of the explanatory variable is constrained by the response variable (limitation). Arrow a represents the pattern predicted by the growth rate hypothesis (see Supplementary Discussion 1 for details). The effect of N:P ratio on species diversity (arrow b) was tested by quantile regression analysis (thus treating arrow c as another limiting factor) with the residual values of X1 versus X2 as an explanatory variable (thus removing the effect illustrated by arrow a). The effect of N:P ratio on a community-mean trait (arrow d) was tested by comparing the residual values of X1 versus X2 (thus removing the effect illustrated by arrow a) with the residual values of X4 versus X2 (thus removing the effect illustrated by arrow e), using concepts of path analysis.

Extended Data Figure 2 Ninety-five per cent confidence intervals of the quantile regression coefficients.

ac, Estimates (dots) and 95% confidence intervals (bars) of quadratic and linear coefficients (b2 and b1, respectively) of quantile regression models are shown for the number of vascular plant species (a), the number of endangered species (b), and the percentage of endangered species (c) regressed by N:P ratio corrected for productivity effects. The fitted models were (y1): ln(y1)  = b0 + b1x + b2x2 for number of species; (y2): ln(y2 + 1) = b0 + b1x + b2x2for number of endangered species; and (y3 = 100*y2/y1): ln((y2 + 0.5)/(y1 − y2 + 0.5)) = b0 + b1x for percentage of endangered species, where x is the residuals of plant N:P ratio regressed by productivity. Models were examined for 50% (τ = 0.50) to 95% (τ = 0.95) quantiles. See Fig. 1 for the shape of the quantile regression models for τ = 0.50, 0.75, 0.90, 0.95.

Extended Data Figure 3 Effects of habitat types on relationships between residual N:P ratio and biodiversity indices.

Relationships between N:P ratio corrected for productivity effects and the number of endangered species (a) and percentage of endangered species (b) are shown for different habitat types (left, 187 fens; middle, 56 bogs; and right, 296 other habitat types). Linear, rather than quadratic, quantile regression models were applied because for most quantiles the quadratic coefficients did not differ significantly from zero. τth linear quantile regression models (τ = 0.50, 0.75, 0.90, 0.95) are shown only when the 95% confidence intervals of the linear coefficients of the regression models were above or below zero for the majority of the quantiles. Number and percentage of endangered species increased concomitantly with increasing N:P ratio (corrected for productivity) even in plots that are not fens and bogs, indicating that our findings on the relationship between N:P ratio and endangered species were not an artefact resulting from the stratified sampling of habitat types.

Extended Data Figure 4 Relationships between community-mean trait values and plant N:P ratio.

ar, The tested traits are canopy height (a, number of sites (n) = 530), leaf mass (b, n = 525), specific leaf area (c, n = 529), leaf dry-matter content (d, n = 525), seed mass (e, n = 533), seed number per shoot (f, n = 524), seed investment (g, n = 523), starting month of flowering (h, n = 528), flowering period (i, n = 528), lateral spread (j, n = 526), reproduction by seeds (k, n = 528), vegetative reproduction (l, n = 528), life span (m, n = 531), plant architecture (n, n = 533), N fixation (o, n = 502), C score (p, n = 528), S score (q, n = 528) and R score (r, n = 528). See Extended Data Table 1 for abbreviations and units of the traits. Canopy height, leaf mass, specific leaf area, seed mass, number of seeds, seed investment, and flowering period were log-transformed before the calculation of community-mean values. For binary traits, plot mean values were shown as a fraction of species with 1s over total species (that is, sum of 1s and 0s) to allow graphical presentation. Standardized regression coefficients (β) of community-mean trait regressed by N:P ratio using GLM and their two-tailed p-values (***P < 0.001, **P < 0.01, *P < 0.05) are shown.

Extended Data Table 1 List of functional traits of herbaceous vascular plant species.

Related audio

Crystallographer Ilme Schlichting outlines a new way of figuring out the structure of large, difficult-to-crystallise molecules.

Supplementary information

Supplementary Information

This file contains Supplementary Discussions 1-5. Discussion 1 justifies the use of plant N:P ratio as a proxy for N:P stoichiometry of a site. We summarize the regulation mechanisms of plant N:P ratio and explain how our analysis took into account the confounding effects of productivity on plant N:P ratio. Discussion 2 shows the results of a Principal Component Analysis of plant functional traits. The relationships between community-mean PCA axis scores and N:P ratio are shown, as well as the difference between the PCA axis scores of endangered and non-endangered species. In discussion 3 we discuss the potential consequences of the different plot sizes in our dataset on the observed pattern of species diversity. Furthermore, we explain how we tested if the observed relationship between P limitation and endangered species holds when we use a subset of data from the most common plot size (10 m2). Discussion 4 tests if the relationship between seed investment and N:P ratio still holds after correcting seed investment for plant size and Finally in Discussion 5 we summarize the existing knowledge about environmental factors potentially related to P limitation. Furthermore, we examine the potential confounding effects of several environmental factors on the relationship between P limitation and richness of endangered species. (PDF 583 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Fujita, Y., Venterink, H., van Bodegom, P. et al. Low investment in sexual reproduction threatens plants adapted to phosphorus limitation. Nature 505, 82–86 (2014). https://doi.org/10.1038/nature12733

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing