Demographic measures of an individual’s “pace of life”: fecundity rate, lifespan, generation time, or a composite variable?

  • Yimen G. Araya-AjoyEmail author
  • Geir H. Bolstad
  • Jon Brommer
  • Vincent Careau
  • Niels J. Dingemanse
  • Jonathan Wright
Original Article
Part of the following topical collections:
  1. Pace-of-life syndromes: a framework for the adaptive integration of behaviour, physiology and life-history


Comparative analyses have demonstrated the existence of a ”pace-of-life” (POL) continuum of life-history strategies, from fast-reproducing short-lived species to slow-reproducing long-lived species. This idea has been extended to the concept of a ”pace-of-life syndrome” (POLS), an axis of phenotypic covariation among individuals within species, concerning morphological, physiological, behavioral and life-history traits. Several life-history metrics can be used to place species in the fast-slow continuum; here, we asked whether individual variation in POL can also be studied using similar life-history measures. We therefore translated measures commonly used in demographic studies into individual-level estimates. We studied fecundity rate, generation time, lifespan, age at first reproduction, fecundity at first reproduction, and principal component scores integrating these different metrics. Using simulations, we show how demographic stochasticity and individual variation in resources affect the ability to predict an individual’s POL using these individual-level parameters. We found that their accuracy depends on how environmental stochasticity varies with the species’ position on the fast-slow continuum and with the amount of (co)variation in life-history traits caused by individual differences in resources. These results highlight the importance of studying the sources of life-history covariation to determine whether POL explains the covariation between morphological, physiological, and behavioral traits within species. Our simulations also show that quantifying not only among-individual but also among-population patterns of life-history covariation helps in interpreting demographic estimates in the study of POLSs within species.

Significance statement

It has been demonstrated that there is a continuum of life-history strategies, from fast-reproducing short-lived species to slow-reproducing long-lived species. This pattern of variation in the tempo of life-history strategies has been named the pace-of-life continuum. Recently, it has been suggested that within a population, variation in pace of life explains differences between individuals in their morphological, behavioral, and physiological traits. This paper provides guidelines on how to quantify the pace of life of individuals using demographic approaches that have been developed to study the pace of life of species.


Fast-slow continuum Individual variation Life history trade-offs Multi-level variation 



We are grateful to the sponsors, organizers, and participants of the VW-funded workshops ‘Towards a general theory of POLS,” Hannover 2015-6, which inspired this journal topical collection and provided feedback during discussions of earlier versions of the ideas presented here. We also thank Jean-Michel Gaillard, Melanie Dammhahn, Denis Réale, and one anonymous reviewer for the insightful comments during the reviewing process. This work was supported by the European Research Council (ERC-2010-AdG 268,562) and the Research Council of Norway (SFF-III 223257/F50).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yimen G. Araya-Ajoy
    • 1
    Email author
  • Geir H. Bolstad
    • 2
  • Jon Brommer
    • 3
  • Vincent Careau
    • 4
  • Niels J. Dingemanse
    • 5
  • Jonathan Wright
    • 1
  1. 1.Centre for Biodiversity Dynamics (CBD), Department of BiologyNorwegian University of Science and Technology (NTNU)TrondheimNorway
  2. 2.Norwegian Institute for Nature Research (NINA)TrondheimNorway
  3. 3.Department of BiologyUniversity of Turku, University HillTurkuFinland
  4. 4.Canada Research Chair in Functional Ecology, Department of BiologyUniversity of OttawaOttawaCanada
  5. 5.Behavioural Ecology, Department of BiologyLudwig Maximilian University of Munich (LMU)MartinsriedGermany

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