Variance in animal longevity: contributions of heterogeneity and stochasticity

Abstract

Variance in longevity among individuals may arise as an effect of heterogeneity (differences in mortality rates experienced at the same age or stage) or as an effect of individual stochasticity (the outcome of random demographic events during the life cycle). Decomposing the variance into components due to heterogeneity and stochasticity is crucial for evolutionary analyses.In this study, we analyze longevity from ten studies of invertebrates in the laboratory, and use the results to partition the variance in longevity into its components. To do so, we fit finite mixtures of Weibull survival functions to each data set by maximum likelihood, using the EM algorithm. We used the Bayesian Information Criterion to select the most well supported model. The results of the mixture analysis were used to construct an age × stage-classified matrix model, with heterogeneity groups as stages, from which we calculated the variance in longevity and its components. Almost all data sets revealed evidence of some degree of heterogeneity. The median contribution of unobserved heterogeneity to the total variance was 35%, with the remaining 65% due to stochasticity. The differences among groups in mean longevity were typically on the order of 30% of the overall life expectancy. There was considerable variation among data sets in both the magnitude of heterogeneity and the proportion of variance due to heterogeneity, but no clear patterns were apparent in relation to sex, taxon, or environmental conditions.

General conclusion to the special issue Moving forward on individual heterogeneity

“…The enthusiasm for such models is growing in ecology. For instance, mixture models are increasingly used to uncover life-history tactics that may occur within a population (Authier et al 2012, Chevallier et al 2013, Fay et al 2016, Hamel et al 2017, 2018 and to partition the variance in lifetime demographic traits into components due to heterogeneity fixed at birth and heterogeneity generated by stochasticity during a life course (Hartemink et al 2017, Jenouvrier et al 2017, Hartemink and Caswell 2018. Joint models are used to assess the full co-variation among traits, thereby improving our understanding of positive and negative relationships among numerous traits (Cam et al 2002, Pavitt et al 2016, Careau and Wilson 2017, Hamel et al 2017.…”

Highlight in this issue

“…We are pleased to announce that we publish the special feature "Evolutionary demography: the dynamic and broad intersection of ecology and evolution" that consists of 14 papers (Anderson 2018;Colleran and Snopkowski 2018;DeLong and Luhring 2018;Gimenez and Gaillard 2018;Hartemink and Caswell 2018;Horvitz et al 2018;Kellett and Shefferson 2018;Morita 2018;Nakahashi et al 2018;Nicol-Harper et al 2018;Shefferson et al 2018;Shyu and Caswell 2018;Tai and Noymer 2018;. Although a special feature usually consists of 4-8 papers, either original or review articles, if the number of collected articles is large enough, an entire special issue, comprised of only those articles, would be considered (this is the case of this issue).…”

The long and winding road of evolutionary demography: preface

“…The seventh to tenth papers deal with invertebrates and mammals, including hominids (Anderson 2018;Gimenez and Gaillard 2018;Hartemink and Caswell 2018;Nakahashi et al 2018). Hartemink and Caswell (2018) construct an age-stage classified projection matrix model to detect variance in longevity among individuals from 10 studies of invertebrates.…”

“…Hartemink and Caswell (2018) construct an age-stage classified projection matrix model to detect variance in longevity among individuals from 10 studies of invertebrates. They decompose the variation into the effects of heterogeneity among stage groups and stochasticity, and show that the differences in longevity among heterogeneity groups are on the order of 30% of the overall life expectancy.…”