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Animal life history is shaped by the pace of life and the distribution of age-specific mortality and reproduction

Abstract

Animals exhibit an extraordinary diversity of life history strategies. These realized combinations of survival, development and reproduction are predicted to be constrained by physiological limitations and by trade-offs in resource allocation. However, our understanding of these patterns is restricted to a few taxonomic groups. Using demographic data from 121 species, ranging from humans to sponges, we test whether such trade-offs universally shape animal life history strategies. We show that, after accounting for body mass and phylogenetic relatedness, 71% of the variation in animal life history strategies can be explained by life history traits associated with the fast–slow continuum (pace of life) and with a second axis defined by the distribution of age-specific mortality hazards and the spread of reproduction. While we found that life history strategies are associated with metabolic rate and ecological modes of life, surprisingly similar life history strategies can be found across the phylogenetic and physiological diversity of animals.

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Data availability

All demography data are available from the COMADRE database (http://www.compadre-db.org). Additional data are available in Supplementary Data.

Code availability

The code used to generate the analysis can be accessed on Github: https://github.com/healyke/Healy_et_al_2019_Animal_Life_History.

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Competing interests

The authors declare no competing interests.

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References

  1. 1.

    Vrtílek, M., Žák, J., Pšenička, M. & Reichard, M. Extremely rapid maturation of a wild African annual fish. Curr. Biol. 28, R822–R824 (2018).

  2. 2.

    Nielsen, J. et al. Eye lens radiocarbon reveals centuries of longevity in the Greenland shark (Somniosus microcephalus). Science 353, 702–704 (2016).

  3. 3.

    Stearns, S. C. Life history evolution: successes, limitations, and prospects. Naturwissenschaften 87, 476–486 (2000).

  4. 4.

    Stearns, S. C. The influence of size and phylogeny on patterns of covariation among life-history traits in the mammals. Oikos 41, 173–187 (1983).

  5. 5.

    De Magalhaes, J. & Costa, J. A database of vertebrate longevity records and their relation to other life‐history traits. J. Evol. Biol. 22, 1770–1774 (2009).

  6. 6.

    Brusca, R., Moore, W. & Shuster, S. Invertebrates 3rd edn (Sinauer Associates, 2016).

  7. 7.

    Gross, M. R. Disruptive selection for alternative life histories in salmon. Nature 313, 47 (1985).

  8. 8.

    Hughes, P. W. Between semelparity and iteroparity: empirical evidence for a continuum of modes of parity. Ecol. Evol. 7, 8232–8261 (2017).

  9. 9.

    Capellini, I., Baker, J., Allen, W. L., Street, S. E. & Venditti, C. The role of life history traits in mammalian invasion success. Ecol. Lett. 18, 1099–1107 (2015).

  10. 10.

    Jones, O. R. et al. Diversity of ageing across the tree of life. Nature 505, 169–173 (2014).

  11. 11.

    Law, R. Optimal life histories under age-specific predation. Am. Nat. 114, 399–417 (1979).

  12. 12.

    Stearns, S. C. The Evolution of Life Histories (Oxford Univ. Press, 1992)

  13. 13.

    Bielby, J. et al. The fast–slow continuum in mammalian life history: an empirical reevaluation. Am. Nat. 169, 748–757 (2007).

  14. 14.

    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).

  15. 15.

    Bauwens, D. & Diaz-Uriarte, R. Covariation of life-history traits in lacertid lizards: a comparative study. Am. Nat. 149, 91–111 (1997).

  16. 16.

    Paniw, M., Ozgul, A. & Salguero-Gómez Interactive life-history traits predict sensitivity of plants and animals to temporal autocorrelation. Ecol. Lett. 21, 275–286 (2017).

  17. 17.

    Bischof, R. et al. Regulated hunting re-shapes the life history of brown bears. Nat. Ecol. Evol. 2, 116 (2018).

  18. 18.

    Winemiller, K. O., Fitzgerald, D. B., Bower, L. M. & Pianka, E. R. Functional traits, convergent evolution, and periodic tables of niches. Ecol. Lett. 18, 737–751 (2015).

  19. 19.

    Gaillard, J.-M. et al. An analysis of demographic tactics in birds and mammals. Oikos 56, 59–76 (1989).

  20. 20.

    Salguero‐Gómez, R. et al. COMADRE: a global data base of animal demography. J. Anim. Ecol. 85, 371–384 (2016).

  21. 21.

    Gaillard, J.-M. et al. Generation time: a reliable metric to measure life-history variation among mammalian populations. Am. Nat. 166, 119–123 (2005).

  22. 22.

    Oli, M. K. The fast–slow continuum and mammalian life-history patterns: an empirical evaluation. Basic Appl. Ecol. 5, 449–463 (2004).

  23. 23.

    Dobson, F. S. & Oli, M. K. Fast and slow life histories of mammals. Ecoscience 14, 292–297 (2007).

  24. 24.

    Read, A. F. & Harvey, P. H. Life history differences among the eutherian radiations. J. Zool. 219, 329–353 (1989).

  25. 25.

    Ricklefs, R. E. Life-history connections to rates of aging in terrestrial vertebrates. Proc. Natl Acad. Sci. USA 107, 10314–10319 (2010).

  26. 26.

    Brown, J. H., Hall, C. A. S. & Sibly, R. M. Equal fitness paradigm explained by a trade-off between generation time and energy production rate. Nat. Ecol. Evol. 2, 262–268 (2018).

  27. 27.

    Colchero, F. et al. The emergence of longevous populations. Proc. Natl Acad. Sci. USA 113, E7681–E7690 (2016).

  28. 28.

    Wrycza, T. F., Missov, T. I. & Baudisch, A. Quantifying the shape of aging. PLoS ONE 10, e0119163 (2015).

  29. 29.

    Cole, L. C. The population consequences of life history phenomena. Q. Rev. Biol. 29, 103–137 (1954).

  30. 30.

    Charnov, E. L. & Schaffer, W. M. Life-history consequences of natural selection: Cole’s result revisited. Am. Nat. 107, 791–793 (1973).

  31. 31.

    Martin, J. G. & Festa‐Bianchet, M. Age‐independent and age‐dependent decreases in reproduction of females. Ecol. Lett. 14, 576–581 (2011).

  32. 32.

    Huber, S. & Fieder, M. Evidence for a maximum ‘shelf-life’ of oocytes in mammals suggests that human menopause may be an implication of meiotic arrest. Sci. Rep. 8, 14099 (2018).

  33. 33.

    Lahdenperä, M., Mar, K. U. & Lummaa, V. Reproductive cessation and post-reproductive lifespan in Asian elephants and pre-industrial humans. Front. Zool. 11, 54 (2014).

  34. 34.

    Congdon, J. D. et al. Testing hypotheses of aging in long-lived painted turtles (Chrysemys picta). Exp. Gerontol. 38, 765–772 (2003).

  35. 35.

    Congdon, J., Nagle, R., Kinney, O. & van Loben Sels, R. Hypotheses of aging in a long-lived vertebrate, Blanding’s turtle (Emydoidea blandingii). Exp. Gerontol. 36, 813–827 (2001).

  36. 36.

    Vaupel, J. W., Baudisch, A., Dölling, M., Roach, D. A. & Gampe, J. The case for negative senescence. Theor. Popul. Biol. 65, 339–351 (2004).

  37. 37.

    Barneche, D. R., Robertson, D. R., White, C. R. & Marshall, D. J. Fish reproductive-energy output increases disproportionately with body size. Science 360, 642–645 (2018).

  38. 38.

    Williams, G. C. Pleiotropy, natural selection, and the evolution of senescence. Evolution 11, 398–411 (1957).

  39. 39.

    Healy, K. et al. Ecology and mode-of-life explain lifespan variation in birds and mammals. Proc. R. Soc. Lond. B 281, 20140298 (2014).

  40. 40.

    Bjørkvoll, E. et al. Stochastic population dynamics and life-history variation in marine fish species. Am. Nat. 180, 372–387 (2012).

  41. 41.

    Nakayama, S., Rapp, T. & Arlinghaus, R. Fast–slow life history is correlated with individual differences in movements and prey selection in an aquatic predator in the wild. J. Anim. Ecol. 86, 192–201 (2017).

  42. 42.

    Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

  43. 43.

    Reznick, D. N., Bryant, M. J., Roff, D., Ghalambor, C. K. & Ghalambor, D. E. Effect of extrinsic mortality on the evolution of senescence in guppies. Nature 431, 1095–1099 (2004).

  44. 44.

    Burns, J. H. et al. Empirical tests of life-history evolution theory using phylogenetic analysis of plant demography. J. Ecol. 98, 334–344 (2010).

  45. 45.

    Auer, S. K., Dick, C. A., Metcalfe, N. B. & Reznick, D. N. Metabolic rate evolves rapidly and in parallel with the pace of life history. Nat. Commun. 9, 14 (2018).

  46. 46.

    Baker, T. R. et al. Fast demographic traits promote high diversification rates of Amazonian trees. Ecol. Lett. 17, 527–536 (2014).

  47. 47.

    Sepp, T., McGraw, K. J., Kaasik, A. & Giraudeau, M. A review of urban impacts on avian life‐history evolution: does city living lead to slower pace of life? Glob. Change Biol. 24, 1452–1469 (2018).

  48. 48.

    Collett, R. A., Baker, A. M. & Fisher, D. O. Prey productivity and predictability drive different axes of life-history variation in carnivorous marsupials. Proc. R. Soc. Lond. B 285, https://doi.org/10.1098/rspb.2018.1291 (2018).

  49. 49.

    Allen, W. L., Street, S. E. & Capellini, I. Fast life history traits promote invasion success in amphibians and reptiles. Ecol. Lett. 20, 222–230 (2017).

  50. 50.

    Salguero-Gómez, R. Implications of clonality for ageing research. Evol. Ecol. 32, 9–28 (2018).

  51. 51.

    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).

  52. 52.

    Keyfitz, K. & Flieger, W. World Population: An Analysis of Vital Data (Univ. of Chicago Press, 1968).

  53. 53.

    Keyfitz, N. & Flieger, W. Population: Facts and Methods of Demography (W. H. Freeman, 1971).

  54. 54.

    Keyfitz, N. & Flieger, W. World Population Growth and Aging: Demographic Trends in the Late Twentieth Century (Univ. of Chicago Press, 1990).

  55. 55.

    Myhrvold, N. P. et al. An amniote life‐history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109–3109 (2015).

  56. 56.

    Froese, R. & Pauly, D. (eds) FishBase (World wide web electronic publication, accessed 13 June 2016); http://www.fishbase.org.

  57. 57.

    White, C. R. & Seymour, R. S. Mammalian basal metabolic rate is proportional to body mass2/3. Proc. Natl Acad. Sci. USA 100, 4046–4049 (2003).

  58. 58.

    White, C. R., Phillips, N. F. & Seymour, R. S. The scaling and temperature dependence of vertebrate metabolism. Biol. Lett. 2, 125–127 (2006).

  59. 59.

    McNab, B. K. Ecological factors affect the level and scaling of avian BMR. Comp. Biochem. Physiol. A 152, 22–45 (2009).

  60. 60.

    Killen, S. S. et al. Ecological influences and morphological correlates of resting and maximal metabolic rates across teleost fish species. Am. Nat. 187, 592–606 (2016).

  61. 61.

    Genoud, M., Isler, K. & Martin, R. D. Comparative analyses of basal rate of metabolism in mammals: data selection does matter. Biol. Rev. 93, 404–438 (2018).

  62. 62.

    Ultsch, G. R. Metabolic scaling in turtles. Comp. Biochem. Physiol. A 164, 590–597 (2013).

  63. 63.

    Fristoe, T. S. et al. Metabolic heat production and thermal conductance are mass-independent adaptations to thermal environment in birds and mammals. Proc. Natl Acad. Sci. USA 112, 15934–15939 (2015).

  64. 64.

    The IUCN Red List of Threatened Species (IUCN, 2019); http://www.iucnredlist.org.

  65. 65.

    Hinchliff, C. E. et al. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proc. Natl Acad. Sci. USA 112, 12764–12769 (2015).

  66. 66.

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

  67. 67.

    Guillerme, T. & Healy, K. mulTree: A Package for Running MCMCglmm Analysis on Multiple Trees (Zenodo, 2014); https://doi.org/10.5281/zenodo.12902

  68. 68.

    Healy, K. Eusociality but not fossoriality drives longevity in small mammals. Proc. R. Soc. Lond. B 282, 20142917 (2015).

  69. 69.

    Salguero-Gomez, R. & Plotkin, J. B. Matrix dimensions bias demographic inferences: implications for comparative plant demography. Am. Nat. 176, 710–722 (2010).

  70. 70.

    Dinno, A. paran: Horn’s Test of Principal Components/Factors R package v.1.5.2 (CRAN, 2012); https://cran.r-project.org/web/packages/paran/index.html.

  71. 71.

    Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER–Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).

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Acknowledgements

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI grant no. 15/ERCD/2803 to Y.M.B.), the Natural Environment Research Council (grant no. NE/M018458/1 to R.S.-G.) and the Australian Research Council (grant no. DE140100505 to R.S.-G.). O.R.J. is supported by the Danish Council for Independent Research (grant no. 6108-00467B). We thank the Laboratory of Evolutionary Biodemography at the Max Planck Institute for Demographic Research for support, development and curation of the COMADRE Animal Matrix Database.

Author information

K.H. designed and conducted the analysis, collated body size, mode of life, metabolic rate and phylogenetic data and wrote the manuscript. T.H.G.E contributed additional human demography data. O.R.J. provided additional code used in the analysis. Y.M.B. and R.S.-G. designed the research. All authors contributed to analysis, design, discussion and interpretation of the results and writing of the manuscript.

Competing interests

The authors declare no competing interests.

Correspondence to Kevin Healy.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Figs. 1–3, and Supplementary Tables 1–5

Reporting Summary

Supplementary Data 1

Species from the COMADRE Animal Matrix Database that meet the selection criteria and corresponding data

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Fig. 1: Life history traits used in our analysis to describe the life history strategies of a given animal population.
Fig. 2: Variation in life history traits across 121 species of vertebrate and invertebrate animals.
Fig. 3: Principal component analysis and the influence of mode of life and metabolic rate on the position of populations along the first principal component axis (PC1).
Fig. 4: Posterior distributions, modes and 95% CI for the effect of log10 body mass on the log10 of each of the life history traits, variance at the population and phylogenetic scales and the principal component loadings of phylogeny- and body mass-corrected life history traits.