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:

Nutrient scarcity as a selective pressure for mast seeding

Nature Plants volume 5, pages 1222–1228 (2019)Cite this article

Subjects

Matters Arising to this article was published on 22 June 2020

Abstract

Mast seeding is one of the most intriguing reproductive traits in nature. Despite its potential drawbacks in terms of fitness, the widespread existence of this phenomenon suggests that it should have evolutionary advantages under certain circumstances. Using a global dataset of seed production time series for 219 plant species from all of the continents, we tested whether masting behaviour appears predominantly in species with low foliar nitrogen and phosphorus concentrations when controlling for local climate and productivity. Here, we show that masting intensity is higher in species with low foliar N and P concentrations, and especially in those with imbalanced N/P ratios, and that the evolutionary history of masting behaviour has been linked to that of nutrient economy. Our results support the hypothesis that masting is stronger in species growing under limiting conditions and suggest that this reproductive behaviour might have evolved as an adaptation to nutrient limitations and imbalances.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Masting behaviour intensity per species and its relationship with potential resource depletion (negative AR1) and temporal variability (PV) of reproductive effort.
Fig. 2: 3D graph showing the interaction between foliar N and P and masting intensity.
Fig. 3: Different optimum values of foliar N and P for subsets of masting and non-masting species.

Similar content being viewed by others

Data availability

Data supporting the findings of this study can be found at https://doi.org/10.6084/m9.figshare.9863006.v1 and https://www.sciencebase.gov/catalog/item/5d9f7f49e4b036616294495d. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government.

References

  1. Salisbury, E. The Reproductive Capacity of Plants (Bell, 1942).

  2. Silvertown, J. W. The evolutionary ecology of mast seeding in trees. Biol. J. Linn. Soc. 14, 235–250 (1980).

    Article  Google Scholar 

  3. Kelly, D. & Sork, V. L. Mast seeding in perennial plants: why, how, where? Annu. Rev. Ecol. Syst. 33, 427–447 (2002).

    Article  Google Scholar 

  4. Waller, D. M. Models of mast fruiting in trees. J. Theor. Biol. 80, 223–232 (1979).

    Article  CAS  PubMed  Google Scholar 

  5. Norton, D. A. & Kelly, D. Mast seeding over 33 years by Dacrydium cupressinum Lamb. (rimu) (Podocarpaceae) in New Zealand: the importance of economies of scale. Funct. Ecol. 2, 399–408 (1988).

    Article  Google Scholar 

  6. Pearse, I. S., Koenig, W. D. & Kelly, D. Mechanisms of mast seeding: resources, weather, cues, and selection. New Phytol. 212, 546–562 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Espelta, J. M., Cortés, P., Molowny-horas, R., Sánchez-Humanes, B. & Retana, J. Masting mediated by summer drought reduces acorn predation in Mediterranean oak forests. Ecology 89, 805–817 (2008).

    Article  PubMed  Google Scholar 

  8. Espelta, J. M. et al. Beyond predator satiation: masting but also the effects of rainfall stochasticity on weevils drive acorn predation. Ecosphere 8, e01836 (2017).

    Article  Google Scholar 

  9. Donaldson, J. S. Mast-seeding in the cycad genus Encephalartos: a test of the predator satiation hypothesis. Oecologia 94, 262–271 (1993).

    Article  PubMed  Google Scholar 

  10. Smith, C. C., Hamrick, J. L. & Kramer, C. L. The advantage of mast years for wind pollination. Am. Nat. 136, 154–166 (1990).

    Article  Google Scholar 

  11. Fernández-Martínez, M., Belmonte, J., Maria Espelta, J. & Espelta, J. M. Masting in oaks: disentangling the effect of flowering phenology, airborne pollen load and drought. Acta Oecologica 43, 51–59 (2012).

    Article  Google Scholar 

  12. Herrera, C., Jordano, P., Guitián, J. & Traveset, A. Annual variability in seed production by woody plants and the masting concept: reassessment of principles and relationship to pollination and seed dispersal. Am. Nat. 152, 576–594 (1998).

    Article  CAS  PubMed  Google Scholar 

  13. Pearse, I. S., LaMontagne, J. M. & Koenig, W. D. Inter-annual variation in seed production has increased over time (1900–2014). Proc. R. Soc. B Biol. Sci. 284, 20171666 (2017).

    Article  Google Scholar 

  14. Fernández-Martínez, M., Vicca, S., Janssens, I. A., Espelta, J. M. & Peñuelas, J. The role of nutrients, productivity and climate in determining tree fruit production in European forests. New Phytol. 213, 669–679 (2016).

    Article  PubMed  CAS  Google Scholar 

  15. Isagi, Y., Sugimura, K., Sumida, A. & Ito, H. How does masting happen and synchronize? J. Theor. Biol. 187, 231–239 (1997).

    Article  Google Scholar 

  16. Ye, X. & Sakai, K. A new modified resource budget model for nonlinear dynamics in citrus production. Chaos Solitons Fract. 87, 51–60 (2016).

    Article  Google Scholar 

  17. Pérez-Ramos, I. M., Ourcival, J. M., Limousin, J. M. & Rambal, S. Mast seeding under increasing drought: results from a long-term data set and from a rainfall exclusion experiment. Ecology 91, 3057–3068 (2010).

    Article  PubMed  Google Scholar 

  18. Fernández-Martínez, M., Vicca, S., Janssens, I. A., Espelta, J. M. & Peñuelas, J. The North Atlantic Oscillation synchronises fruit production in western European forests. Ecography 39, 864–874 (2016).

    Google Scholar 

  19. Sork, V. L., Bramble, J. & Sexton, O. Ecology of mast-fruiting in three species of North American deciduous oaks. Ecology 74, 528–541 (1993).

    Article  Google Scholar 

  20. Perez-Ramos, I. M., Padilla-Díaz, C. M. & Koenig, W. D. & Marañón, T. Environmental drivers of mast-seeding in Mediterranean oak species: does leaf habit matter? J. Ecol. 103, 691–700 (2015).

    Article  Google Scholar 

  21. Fernández-Martínez, M., Bogdziewicz, M., Espelta, J. M. & Peñuelas, J. Nature beyond linearity: meteorological variability and Jensen’s inequality can explain mast seeding behavior. Front. Ecol. Evol. 5, 134 (2017).

    Article  Google Scholar 

  22. Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).

    Article  CAS  PubMed  Google Scholar 

  23. Domingues, T. F. et al. Co-limitation of photosynthetic capacity by nitrogen and phosphorus in West Africa woodlands. Plant Cell Environ. 33, 959–980 (2010).

    Article  CAS  PubMed  Google Scholar 

  24. Field, C., Merino, J. & Mooney, H. A. Compromises between water-use efficiency and nitrogen-use efficiency in five species of California evergreens. Oecologia 60, 384–389 (1983).

    Article  CAS  PubMed  Google Scholar 

  25. Elser, J. J. et al. Nutritional constraints in terrestrial and freshwater food webs. Nature 408, 578–580 (2000).

    Article  CAS  PubMed  Google Scholar 

  26. Sardans, J. et al. Factors influencing the foliar elemental composition and stoichiometry in forest trees in Spain. Perspect. Plant Ecol. Evol. Syst. 18, 52–69 (2016).

    Article  Google Scholar 

  27. Peñuelas, J. et al. The bioelements, the elementome, and the biogeochemical niche. Ecology 100, e02652 (2019).

    Article  PubMed  Google Scholar 

  28. Fernández-Martínez, M. et al. Nutrient-rich plants emit a less intense blend of volatile isoprenoids. New Phytol. 220, 773–784 (2018).

    Article  PubMed  CAS  Google Scholar 

  29. Camarero, J. J., Albuixech, J., López-Lozano, R., Casterad, M. A. & Montserrat-Martí, G. An increase in canopy cover leads to masting in Quercus ilex. Trees 24, 909–918 (2010).

    Article  Google Scholar 

  30. Fernández-Martínez, M., Garbulsky, M., Peñuelas, J., Peguero, G. & Espelta, J. M. Temporal trends in the enhanced vegetation index and spring weather predict seed production in Mediterranean oaks. Plant Ecol. 216, 1061–1072 (2015).

    Article  Google Scholar 

  31. Sala, A., Hopping, K., McIntire, E. J. B., Delzon, S. & Crone, E. E. Masting in whitebark pine (Pinus albicaulis) depletes stored nutrients. New Phytol. 196, 189–199 (2012).

    Article  CAS  PubMed  Google Scholar 

  32. Reekie, E. G. & Bazzaz, F. A. Reproductive effort in plants. 2. Does carbon reflect the allocation of other resources? Am. Nat. 129, 897–906 (1987).

    Article  Google Scholar 

  33. Miyazaki, Y. et al. Nitrogen as a key regulator of flowering in Fagus crenata: understanding the physiological mechanism of masting by gene expression analysis. Ecol. Lett. 17, 1299–1309 (2014).

    Article  PubMed  Google Scholar 

  34. Han, Q., Kabeya, D., Iio, A., Inagaki, Y. & Kakubari, Y. Nitrogen storage dynamics are affected by masting events in Fagus crenata. Oecologia 174, 679–687 (2013).

    Article  PubMed  Google Scholar 

  35. Fujita, Y. et al. Low investment in sexual reproduction threatens plants adapted to phosphorus limitation. Nature 505, 82–86 (2014).

    Article  PubMed  CAS  Google Scholar 

  36. Sardans, J., Rivas-Ubach, A. & Peñuelas, J. The C:N:P stoichiometry of organisms and ecosystems in a changing world: a review and perspectives. Perspect. Plant Ecol. Evol. Syst. 14, 33–47 (2012).

    Article  Google Scholar 

  37. Güsewell, S. N. P ratios in terrestrial plants: variation and functional significance. New Phytol. 164, 243–266 (2004).

    Article  PubMed  Google Scholar 

  38. McGroddy, M. E., Daufresne, T. & Hedin, O. L. Scaling of C:N:P stoichiometry in forests worldwide: implications of terrestrial redfield-type ratios. Ecology 85, 2390–2401 (2004).

    Article  Google Scholar 

  39. Sardans, J. et al. Foliar and soil concentrations and stoichiometry of nitrogen and phosphorous across European Pinus sylvestris forests: relationships with climate, N deposition and tree growth. Funct. Ecol. 30, 676–689 (2016).

    Article  Google Scholar 

  40. Eckstein, R. L. & Karlsson, P. S. Above-ground growth and nutrient use by plants in a subarctic environment: effects of habitat, life-form and species. Oikos 79, 311–324 (1997).

    Article  Google Scholar 

  41. Fernández-Martínez, M., Vicca, S., Janssens, I. A., Martín-Vide, J. & Peñuelas, J. The consecutive disparity index, D, as measure of temporal variability in ecological studies. Ecosphere 9, e02527 (2018).

    Article  Google Scholar 

  42. Martín-Vide, J. Notes per a la definició d’un índex de “desordre“ en pluviometria. Soc. Catalana Geogr. 7, 89–96 (1986).

    Google Scholar 

  43. Heath, J. P. Quantifying temporal variability in population abundances. Oikos 115, 573–581 (2006).

    Article  Google Scholar 

  44. Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11, 995–1003 (2008).

    Article  PubMed  Google Scholar 

  45. 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).

    Article  PubMed  Google Scholar 

  46. LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).

    Article  PubMed  Google Scholar 

  47. Erel, R. Flowering and fruit set of olive trees. Am. Soc. 133, 639–647 (2008).

    Google Scholar 

  48. Sardans, J. & Peñuelas, J. Drought changes phosphorus and potassium accumulation patterns in an evergreen Mediterranean forest. Funct. Ecol. 21, 191–201 (2007).

    Article  Google Scholar 

  49. Elser, J. J. et al. Growth rate–stoichiometry couplings in diverse biota. Ecol. Lett. 6, 936–943 (2003).

    Article  Google Scholar 

  50. Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Change 4, 471–476 (2014).

    Article  CAS  Google Scholar 

  51. Nogueira, C. et al. Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs. Nat. Ecol. Evol. 3, 400–406 (2019).

    Article  PubMed  Google Scholar 

  52. Smaill, S. J., Clinton, P. W., Allen, R. B. & Davis, M. R. Climate cues and resources interact to determine seed production by a masting species. J. Ecol. 99, 870–877 (2011).

    Article  Google Scholar 

  53. Saura-Mas, S. & Lloret, F. Linking post-fire regenerative strategy and leaf nutrient content in Mediterranean woody plants. Perspect. Plant Ecol. Evol. Syst. 11, 219–229 (2009).

    Article  Google Scholar 

  54. Kelly, D., Hart, D. & Allen, R. B. Evaluating the wind pollination benefits of mast seeding. Ecology 82, 117–126 (2001).

    Article  Google Scholar 

  55. Fernández‐Martínez, M. et al. Towards a moss sclerophylly continuum: evolutionary history, water chemistry and climate control traits of hygrophytic mosses. Funct. Ecol. https://doi.org/10.1111/1365-2435.13443 (2019).

  56. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  57. Harris, I., Jones, P. D. D., Osborn, T. J. J. & Lister, D. H. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2013).

    Article  Google Scholar 

  58. Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000–2016. Sci. Data 4, 170165 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Kattge, J. et al. TRY—a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).

    Article  Google Scholar 

  60. Maitner, B. S. et al. The bien r package: a tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2017).

    Article  Google Scholar 

  61. Rautio, P., Fürst, A., Stefan, K., Raitio, H. & Bartels, U. Sampling and analysis of needles and leaves (ICP Forests, 2010).

  62. Cayuela, L. & Oksanen, J. Taxonstand: Taxonomic Standardization of Plant Species Names (2016).

  63. Qian, H. & Jin, Y. An updated megaphylogeny of plants, a tool for generating plant phylogenies and an analysis of phylogenetic community structure. J. Plant Ecol. 9, 233–239 (2016).

    Article  Google Scholar 

  64. R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  65. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

  66. Tung Ho, L. S. & Ané, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).

    Article  Google Scholar 

  67. Breheny, P. & Burchett, W. Visualization of regression models using visreg. R. J. 9:2, 56–71 (2017).

    Article  Google Scholar 

  68. Bollback, J. P. SIMMAP: stochastic character mapping of discrete traits on phylogenies. BMC Bioinformatics 7, 88 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. O’Meara, B. C., Ané, C., Sanderson, M. J. & Wainwright, P. C. Testing for different rates of continuous trait evolution using likelihood. Evolution 60, 922–933 (2006).

    Article  PubMed  Google Scholar 

  70. Thomas, G. H., Freckleton, R. P. & Székely, T. Comparative analyses of the influence of developmental mode on phenotypic diversification rates in shorebirds. Proc. R. Soc. Lond. B 273, 1619–1624 (2006).

    Article  Google Scholar 

  71. Beaulieu, J. M., Jhwueng, D. C., Boettiger, C. & O’Meara, B. C. Modeling stabilizing selection: expanding the Ornstein–Uhlenbeck model of adaptive evolution. Evolution 66, 2369–2383 (2012).

    Article  PubMed  Google Scholar 

  72. Beaulieu, J. M. & O’Meara, B. OUwie: Analysis of Evolutionary Rates in an OU Framework (2016).

Download references

Acknowledgements

This research was supported by the Spanish Government project CGL2016-79835-P (FERTWARM), European Research Council Synergy Grant ERC-2013-726 SyG-610028 IMBALANCE-P and Catalan Government project SGR 2017-1005. M.F.-M. is a postdoctoral fellow of the Research Foundation – Flanders (FWO). M.B. was supported by (Polish) NSF grants Sonatina 2017/24/C/NZ8/00151 and Uwertura 2018/28/U/NZ8/00003. This research was also supported by NSF grants DEB-1745496 630 to J.M.L. and DEB-1256394 to W.D.K.

Author information

Authors and Affiliations

  1. PLECO (Plants and Ecosystems), Department of Biology, University of Antwerp, Antwerp, Belgium

    M. Fernández-Martínez & I. A. Janssens

  2. Global Ecology Unit, CREAF–CSIC–UAB, Barcelona, Spain

    M. Fernández-Martínez, J. Sardans & J. Peñuelas

  3. US Geological Survey, Fort Collins Science Center, Fort Collins, CO, USA

    I. Pearse

  4. CREAF, Barcelona, Spain

    J. Sardans, J. M. Espelta & J. Peñuelas

  5. Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden

    F. Sayol

  6. Gothenburg Global Biodiversity Centre, Gothenburg, Sweden

    F. Sayol

  7. Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA

    W. D. Koenig

  8. Department of Biological Sciences, DePaul University, Chicago, IL, USA

    J. M. LaMontagne

  9. Department of Systematic Zoology, Faculty of Biology, Adam Mickiewicz University in Poznań, Poznań, Poland

    M. Bogdziewicz

  10. Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR-ISAFOM), Rende, Italy

    A. Collalti

  11. Department of Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, Viterbo, Italy

    A. Collalti

  12. Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, UK

    A. Hacket-Pain

  13. DISAA, Università di Milano, Milan, Italy

    G. Vacchiano

Authors
  1. M. Fernández-Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. Pearse
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Sardans
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. Sayol
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. W. D. Koenig
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J. M. LaMontagne
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. M. Bogdziewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. A. Collalti
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. A. Hacket-Pain
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. G. Vacchiano
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. J. M. Espelta
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. J. Peñuelas
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. I. A. Janssens
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.F.-M., I.P. and I.A.J. conceived the paper. M.F.-M and F.S. analysed the data. M.F.-M., J.S., J.P., I.P., W.D.K. and J.M.L. provided the data. All authors, including M.B., A.C., A.H.-P., G.V. and J.M.E. contributed substantially to the writing and discussion of the paper.

Corresponding author

Correspondence to M. Fernández-Martínez.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Shuli Niu, Ignacio Perez Ramos and the other, anonymous, reviewer for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Evolutionary relationship between potential resource depletion coefficient (AR1) and temporal variability (PV) in seed production.

Evolutionary relationship between potential resource depletion coefficient (AR1) and temporal variability (PV) in seed production shown in a continuous trait phylogenetic reconstruction (a) and a phylomorphospace plot (b). Phylogenetic signal was estimated using Pagel’s lambda (λ). Potential resource depletion and variability in seed production were not evolutionary correlated. Negative values of AR1 indicate that potential resource depletion may happen, see Methods. N=219 species. t- value of the Pearson’s correlation was 1.95 (218 DF).

Extended Data Fig. 2 Mean differences (ΔAICc, second-order Akaike information criterion) between each of the model’s AICc and the model with the lowest AICc.

Mean differences (ΔAICc, second-order Akaike information criterion) between each of the model’s AICc and the model with the lowest AICc. Evolutionary models were Brownian motion (BM1, BMS) and generalised Ornstein-Uhlenbeck-based Hansen (OU1, OUM, OUMV), fitting “masting” and “non-masting” species-state and foliar nutrient concentrations (N: nitrogen, P: phosphorus, N:P: ratio N-to-P and, N×P: N times P (overall nutrient availability). Average AICc values were calculated using the subset of models in which none of them presented negative eigenvalues (sound models, n column: samples, independent simulations). Non-masting and masting columns indicate the number of species used in each category depending on the percentile of masting intensity used to classify species as non-masting (that is, higher than for example, 33%) and masting (that is, lower than for example, 66%). Models with ΔAICc lower than 2 (indicating equal performance) were highlighted. See Methods for further information.

Extended Data Fig. 3 Phylogenetic tree including the subset of low (non-masting) and high masting intensity (masting) species used to perform the generalised Ornstein-Uhlenbeck model results.

Phylogenetic tree including the subset of low (non- masting) and high masting intensity (masting) species used to perform the generalised Ornstein-Uhlenbeck model results presented in the main text (20th – 80th percentile thresholds for non-masting and masting species, Fig. 3, Extended Data Fig. 2 and Extended Data Fig. 4). The phylogenetic tree includes the estimated probability that ancestor nodes were masting or non-masting species (large circles) as pie charts. Small circles indicate the current category of the species. The ancestral character reconstruction was performed using 1000 stochastic character-mapped trees (see Methods for further information).

Extended Data Fig. 4 Estimated foliar nitrogen (N) and phosphorus (P) concentrations, N:P and N×P (overall nutrient availability) optimal values for masting and non-masting species.

Estimated foliar nitrogen (N) and phosphorus (P) concentrations, N:P and N×P (overall nutrient availability) optimal values for masting and non-masting species using OUMV and OUM models (see Methods for further information about the models), chosen based on the lowest ΔAICc estimating different state means for masting and non-masting species (Extended Data Fig. 2). Masting and non-masting species were classified depending on the percentile of masting intensity (for example, masting for higher than 66%, non-masting for lower than 33%, see subheaders within the table). Columns 2.5%, 50 and 97.5% indicate, for masting and non-masting species, the percentiles of the optimal values based on the sound models (without negative eigenvalues, n column: samples, independent simulations) used. M>N% indicate the percentage of models in which masting species presented average higher N, P, N:P or N×P optimal values than non-masting species. ΔM-N, followed by s.e.m (standard error of the mean), indicate the paired (across simulations) difference between optimal values in masting and non-masting species. P (two-sided t-test) shows the P-value of the paired t-test testing for differences in the mean optimal values of masting and non-masting species. ΔM-N%, followed by s.e.m., indicates the average percentual difference (geometric, paired differences) in mean optimal values between masting and non-masting species.

Extended Data Fig. 5 Evolutionary relationship between foliar N and P shown in a continuous trait phylogenetic reconstruction (a) and a phylomorphospace plot (b).

Evolutionary relationship between foliar N and P shown in a continuous trait phylogenetic reconstruction (a) and a phylomorphospace plot (b). Phylogenetic signal was estimated using Pagel’s lambda (λ). Foliar N and P concentrations were evolutionary correlated. N=168 species. t-value of the Pearson’s correlation was 5.38 (166 DF).

Extended Data Fig. 6 Map showing interannual variability (PV index) in mean annual precipitation (MAP) and site of origin of our fruit production data (blue dots).

Map showing interannual variability (PV index) in mean annual precipitation (MAP) and site of origin of our fruit production data (blue dots).

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fernández-Martínez, M., Pearse, I., Sardans, J. et al. Nutrient scarcity as a selective pressure for mast seeding. Nat. Plants 5, 1222–1228 (2019). https://doi.org/10.1038/s41477-019-0549-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-019-0549-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

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