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:

Divergent demographic strategies of plants in variable environments

Abstract

One of the best-supported patterns in life history evolution is that organisms cope with environmental fluctuations by buffering their most important vital rates against them. This demographic buffering hypothesis is evidenced by a tendency for temporal variation in rates of survival and reproduction to correlate negatively with their contribution to fitness. Here, we show that widespread evidence for demographic buffering can be artefactual, resulting from natural relationships between the mean and variance of vital rates. Following statistical scaling, we find no significant tendency for plant life histories to be buffered demographically. Instead, some species are buffered, whereas others have labile life histories with higher temporal variation in their more important vital rates. We find phylogenetic signal in the strength and direction of variance–importance correlations, suggesting that clades of plants are prone to being either buffered or labile. Species with simple life histories are more likely to be demographically labile. Our results suggest important evolutionary nuances in how species deal with environmental fluctuations.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: Evidence for buffering and lability.
Figure 2: Posterior distributions of the correlation coefficient (at average number of stage classes) between sensitivity and standard deviation of vital rates, as predicted from MCMCglmm models that account for species identity and the number of stage classes.
Figure 3: Maximum likelihood ancestral state reconstruction of demographic strategy onto the phylogeny.

Similar content being viewed by others

References

  1. Boyce, M. S., Haridas, C. V., Lee, C. T. & Group, N. S. D. W. Demography in an increasingly variable world. Trends Ecol. Evol. 21, 141–148 (2006).

    Article  Google Scholar 

  2. Gillespie, J. H. Natural selection for variances in offspring numbers: a new evolutionary principle. Am. Nat. 111, 1010–1014 (1977).

    Article  Google Scholar 

  3. Lewontin, R. C. & Cohen, D. On population growth in a randomly varying environment. Proc. Natl Acad. Sci. USA 62, 1056–1060 (1969).

    Article  CAS  Google Scholar 

  4. Gaillard, J.-M. & Yoccoz, N. G. Temporal variation in survival of mammals: a case of environmental canalization? Ecology 84, 3294–3306 (2003).

    Article  Google Scholar 

  5. Sæther, B.-E. & Bakke, Ø. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).

    Article  Google Scholar 

  6. Chantepie, S. et al. Age-related variation and temporal patterns in the survival of a long-lived scavenger. Oikos 125, 167–178 (2016).

    Article  Google Scholar 

  7. Pfister, C. A. Patterns of variance in stage-structured populations: evolutionary predictions and ecological implications. Proc. Natl Acad. Sci. USA 95, 213–218 (1998).

    Article  CAS  Google Scholar 

  8. Gaillard, J.-M., Festa-Bianchet, M. & Yoccoz, N. G. Population dynamics of large herbivores: variable recruitment with constant adult survival. Trends Ecol. Evol. 13, 58–63 (1998).

    Article  CAS  Google Scholar 

  9. Gaillard, J.-M., Festa-Bianchet, M., Yoccoz, N., Loison, A. & Toigo, C. Temporal variation in fitness components and population dynamics of large herbivores. Annu. Rev. Ecol. Syst. 31, 367–393 (2000).

    Article  Google Scholar 

  10. Heppell, S. S. Application of life-history theory and population model analysis to turtle conservation. Copeia 1998, 367–375 (1998).

    Article  Google Scholar 

  11. Koons, D. N., Pavard, S., Baudisch, A. & Metcalf, J. E. Is life‐history buffering or lability adaptive in stochastic environments? Oikos 118, 972–980 (2009).

    Article  Google Scholar 

  12. Morris, W. F. & Doak, D. F. Buffering of life histories against environmental stochasticity: accounting for a spurious correlation between the variabilities of vital rates and their contributions to fitness. Am. Nat. 163, 579–590 (2004).

    Article  Google Scholar 

  13. Bjørkvoll, E. et al. Demographic buffering of life histories? Implications of the choice of measurement scale. Ecology 97, 40–47 (2016).

    Article  Google Scholar 

  14. Link, W. A. & Doherty, P. F. Jr Scaling in sensitivity analysis. Ecology 83, 3299–3305 (2002).

    Article  Google Scholar 

  15. Jongejans, E., De Kroon, H., Tuljapurkar, S. & Shea, K. Plant populations track rather than buffer climate fluctuations. Ecol. Lett. 13, 736–743 (2010).

    Article  Google Scholar 

  16. Levine, J. M. & Rees, M. Effects of temporal variability on rare plant persistence in annual systems. Am. Nat. 164, 350–363 (2004).

    Article  Google Scholar 

  17. Jensen, J. L. W. V. Sur les fonctions convexes et les inégalités entre les valeurs moyennes. Acta Math. 30, 175–193 (1906).

    Article  Google Scholar 

  18. Ruel, J. J. & Ayres, M. P. Jensen’s inequality predicts effects of environmental variation. Trends Ecol. Evol. 14, 361–366 (1999).

    Article  CAS  Google Scholar 

  19. Li, S. L. & Ramula, S. Demographic strategies of plant invaders in temporally varying environments. Popul. Ecol. 57, 373–380 (2015).

    Article  Google Scholar 

  20. Caswell, H. Matrix Population Models (Wiley, 2001).

    Google Scholar 

  21. Salguero-Gomez, R. et al. The COMPADRE plant matrix database: an open online repository for plant demography. J. Ecol. 103, 202–218 (2015).

    Article  Google Scholar 

  22. Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).

    Article  Google Scholar 

  23. Chevin, L. M. & Lande, R. Adaptation to marginal habitats by evolution of increased phenotypic plasticity. J. Evol. Biol. 24, 1462–1476 (2011).

    Article  Google Scholar 

  24. Lande, R. Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation. J. Evol. Biol. 22, 1435–1446 (2009).

    Article  Google Scholar 

  25. Melbourne, B. A. & Hastings, A. Extinction risk depends strongly on factors contributing to stochasticity. Nature 454, 100–103 (2008).

    Article  CAS  Google Scholar 

  26. McDonald, J. L., Stott, I., Townley, S. & Hodgson, D. J. Transients drive the demographic dynamics of plant populations in variable environments. J. Ecol. 104, 306–314 (2016).

    Article  Google Scholar 

  27. Childs, D. Z., Metcalf, C. & Rees, M. Evolutionary bet-hedging in the real world: empirical evidence and challenges revealed by plants. Proc. R. Soc. B 277, rspb20100707 (2010).

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Stott, I., Townley, S., Carslake, D. & Hodgson, D. J. On reducibility and ergodicity of population projection matrix models. Methods Ecol. Evol. 1, 242–252 (2010).

    Google Scholar 

  30. Lefkovitch, L. The study of population growth in organisms grouped by stages. Biometrics 21, 1–18 (1965).

    Article  Google Scholar 

  31. Stott, I., Hodgson, D. J. & Townley, S. popdemo: an R package for population demography using projection matrix analysis. Methods Ecol. Evol. 3, 797–802 (2012).

    Article  Google Scholar 

  32. R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2016).

  33. Salguero-Gómez, R. et al. Fast–slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc. Natl Acad. Sci. USA 113, 230–235 (2016).

    Article  Google Scholar 

  34. The Global Invasive Species Database (ISSG, 2014); http://www.issg.org/database/welcome/

  35. Invasive Species Compendium (CABI, 2014); http://www.cabi.org/isc/

  36. Australian Weed List (Australian government, 2014); http://www.environment.gov.au/biodiversity/invasive/weeds/index.html

  37. Australian Plant Census (CHAH, 2006); http://biodiversity.org.au/apni.reference/181584

  38. European and Mediterranean Plant Protection Organization Database (EPPO, 2014); http://www.eppo.int/DATABASES/databases.htm

  39. Plants Database (USDA, 2014); http://plants.usda.gov/checklist.html

  40. Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2006).

    Google Scholar 

  41. Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Van Der Linde, A. Bayesian measures of model complexity and fit. J. R. Stat. Soc. B 64, 583–639 (2002).

    Article  Google Scholar 

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

    Article  Google Scholar 

  43. Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4, 754–759 (2013).

    Article  Google Scholar 

  44. Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 124,1–15 (1985).

    Article  Google Scholar 

  45. Bremer, B. et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Bot. J. Linn. Soc. 141, 399–436 (2009) .

    Google Scholar 

  46. Stevens, P. F. & Davis, H. & Garden, M. B. Angiosperm Phylogeny Website (Missouri Botanical Garden, 2001).

  47. Webb, C. O. & Donoghue, M. J. Phylomatic: tree assembly for applied phylogenetics. Mol. Ecol. Notes 5, 181–183 (2005).

    Article  Google Scholar 

  48. Maddison, W. P. & Maddison, D. R. Mesquite: A Modular System for Evolutionary Analysis (Mesquite, 2001).

  49. Federhen, S. The NCBI taxonomy database. Nucleic Acids Res. 40, D136–D143 (2012).

    Article  CAS  Google Scholar 

  50. Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).

    Article  CAS  Google Scholar 

  51. Wikström, N., Savolainen, V. & Chase, M. W. Evolution of the angiosperms: calibrating the family tree. Proc. R. Soc. B 268, 2211–2220 (2001).

    Article  Google Scholar 

  52. Hadfield, J. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi‐trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was funded by the Natural Environment Research Council (NERC) UK, grant NE/L007770/1, and supported by NERC IOF grant NE/N006798/1. T.H.G.E. is funded by NERC Fellowship NE/J018163/1.

Author information

Authors and Affiliations

Authors

Contributions

Research was conceived by D.J.H., T.E., M.F., S.T. and J.L.M. Analyses were carried out by J.L.M. with advice from D.J.H. Phylogeny was provided by M.F. Results were interpreted by J.L.M. and D.J.H. Invasiveness data were provided by K.J. The manuscript was written by J.L.M. and D.J.H. with advice and revisions from all authors.

Corresponding author

Correspondence to Dave J. Hodgson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Supplementary information

Supplementary information

Supplementary Results, Supplementary Figs 1–7, Supplementary Table 1 (PDF 335 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McDonald, J., Franco, M., Townley, S. et al. Divergent demographic strategies of plants in variable environments. Nat Ecol Evol 1, 0029 (2017). https://doi.org/10.1038/s41559-016-0029

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41559-016-0029

This article is cited by

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