Metcalf, C. J. E. & Pavard, S. Why evolutionary biologists should be demographers. Trends Ecol. Evol. 22, 205–212 (2007).
Google Scholar
Lande, R., Engen, S. & Saether, B. Stochastic population dynamics in ecology and conservation. (Oxfor University Press, 2003).
Roughgarden, J. A simple model for population dynamics in stochastic environments. Am. Nat. 109, 713–736 (1975).
May, R. M. Stability and complexity in model ecosystems (Princeton Univ, 2001).
Google Scholar
Engen, S., Bakke, Ø. & Islam, A. Demographic and Environmental Stochasticity-Concepts and Definitions on JSTOR. Biometrics 54, 840–846 (1998).
Google Scholar
Melbourne, B. a & Hastings, A. Extinction risk depends strongly on factors contributing to stochasticity. Nature 454, 100–3 (2008).
Tuljapurkar, S., Steiner, U. K. & Orzack, S. H. Dynamic heterogeneity in life histories. Ecol. Lett. 12, 93–106 (2009).
Google Scholar
Vindenes, Y. & Engen, S. Demographic stochasticity and temporal autocorrelation in the dynamics of structured populations. Oikos https://doi.org/10.1111/oik.03958 (2017).
Google Scholar
Caswell, H. Stage, age and individual stochasticity in demography. Oikos 118, 1763–1782 (2009).
Steiner, U. K. & Tuljapurkar, S. Neutral theory for life histories and individual variability in fitness components. Proc. Natl. Acad. Sci. USA 109, 4684–4689 (2012).
Google Scholar
Vindenes, Y. & Langangen, Ø. Individual heterogeneity in life histories and eco-evolutionary dynamics. Ecol. Lett. 18, 417–432 (2015).
Google Scholar
Snyder, R. E. & Ellner, S. P. Pluck or Luck: Does Trait Variation or Chance Drive Variation in Lifetime Reproductive Success?. Am. Nat. 191, E90–E107 (2018).
Google Scholar
Steiner, U. K., Tuljapurkar, S. & Orzack, S. H. Dynamic heterogeneity and life history variability in the kittiwake. J. Anim. Ecol. 79, 436–444 (2010).
Google Scholar
Pennisi, E. The Great Guppy Experiment. Science (80-. ). 337, 904–908 (2012).
Pajunen, V. I. & Pajunen, I. Long-term dynamics in rock pool Daphnia metapopulations. Ecography (Cop.) 26, 731–738 (2003).
Ozgul, A. et al. The dynamics of phenotypic change and the shrinking sheep of St. Kilda.. Science 325, 464–467 (2009).
Google Scholar
Roach, D. A. & Gampe, J. Age-specific demography in Plantago: uncovering age-dependent mortality in a natural population. Am. Nat. 164, 60–69 (2004).
Google Scholar
Reid, J. M., Nietlisbach, P., Wolak, M. E., Keller, L. F. & Arcese, P. Individuals’ expected genetic contributions to future generations, reproductive value, and short-term metrics of fitness in free-living song sparrows ( Melospiza melodia ). Evol. Lett. 3, 271–285 (2019).
Google Scholar
Endler, J. A. Natural selection in the wild. Monographs in Population Biology vol. 21 (Princeton University Press, 1986).
Hadfield, J. D., Wilson, A. J., Garant, D., Sheldon, B. C. & Kruuk, L. E. B. The misuse of BLUP in ecology and evolution. Am. Nat. 175, 116–125 (2010).
Google Scholar
Steiner, U. K., Tuljapurkar, S. & Coulson, T. Generation time, net reproductive rate, and growth in stage-age-structured populations. Am. Nat. 183, 771–783 (2014).
Google Scholar
Roach, D. A., Ridley, C. E. & Dudycha, J. L. Longitudinal analysis of Plantago : Age-by-environment interactions reveal aging. Ecology 90, 1427–1433 (2009).
Google Scholar
Roach, D. A. Age, growth and size interact with stress to determine life span and mortality. Exp. Gerontol. 47, 782–786 (2012).
Google Scholar
Shefferson, R. P. & Roach, D. A. The triple helix of Plantago lanceolata: Genetics and the environment interact to determine population dynamics. Ecology 93, 793–802 (2012).
Google Scholar
Coulson, T., Tuljapurkar, S. & Step, T. The dynamics of a quantitative trait in an age-structured population living in a variable environment. Am. Nat. 172, 599–612 (2008).
Google Scholar
Coulson, T., Tuljapurkar, S. & Childs, D. Z. Using evolutionary demography to link life history theory, quantitative genetics and population ecology. J. Anim. Ecol. 79, 1226–1240 (2010).
Google Scholar
Lacey, E. P. et al. Multigenerational effects of flowering and fruiting phenology in Plantago lanceolata. Ecology 84, 2462–2475 (2003).
Jones, O. R. et al. Senescence rates are determined by ranking on the fast-slow life-history continuum. Ecol. Lett. 11, 664–673 (2008).
Google Scholar
Fisher, R. The genetical theory of natural selection. (Clarendon, 1930).
Wright, S. Evolution in Mendelian populations. Genetics 16, 0097–0159 (1931).
Google Scholar
Crow, J. F. & Kimura, M. An introduction to population genetics theory. (1970).
Merilä, J. & Sheldon, B. Lifetime Reproductive Success and Heritability in Nature. Am. Nat. 155, 301–310 (2000).
Google Scholar
Kruuk, L. E. et al. Heritability of fitness in a wild mammal population. Proc. Natl. Acad. Sci. U. S. A. 97, 698–703 (2000).
Google Scholar
Teplitsky, C., Mills, J. a, Yarrall, J. W. & Merilä, J. Heritability of fitness components in a wild bird population. Evolution 63, 716–26 (2009).
Kruuk, L. E., Merilä, J. & Sheldon, B. C. Phenotypic selection on a heritable size trait revisited. Am. Nat. 158, 557–571 (2001).
Google Scholar
Sheldon, B. C., Kruuk, L. E. B. & Merilä, J. Natural selection and inheritance of breeding time and clutch size in the collared flycatcher. Evolution 57, 406–420 (2003).
Google Scholar
Merilä, J. & Sheldon, B. C. Short Review Genetic architecture of fitness and non fitness traits : empirical patterns and development of ideas. Heredity (Edinb). 83, (1999).
Hartl, D. J. & Clark, A. G. Principles of population genetics. (Sinauer, 2007).
Charlesworth, B. Evolution in age-structured populations. (Cambridge University Press, 1994).
Kirkwood, T. B. L. et al. What accounts for the wide variation in life span of genetically identical organisms reared in a constant environment?. Mech. Ageing Dev. 126, 439–443 (2005).
Google Scholar
Finch, C. & Kirkwood, T. B. Chance, Development, and Aging. (Oxford University Press, 2000).
Schiemer, F. Food Dependence and Energetics of Freeliving Nematodes. II. Life History Parameters of Caenorhabditis briggsae (Nematoda) at Different Levels of Food Supply. Oecologia 54, 122–128 (1982).
Kennedy, B. K. Daughter cells of Saccharomyces cerevisiae from old mothers display a reduced life span. J. Cell Biol. 127, 1985–1993 (1994).
Google Scholar
Steiner, U. K. et al. Two stochastic processes shape diverse senescence patterns in a single-cell organism. Evolution (N. Y). 73, 847–857 (2019).
Jouvet, L., Rodríguez-Rojas, A. & Steiner, U. K. Demographic variability and heterogeneity among individuals within and among clonal bacteria strains. Oikos 127, 728–737 (2018).
Curtsinger, J., Fukui, H., Townsend, D. & Vaupel, J. Demography of genotypes: failure of the limited life-span paradigm in Drosophila melanogaster. Science (80-. ). 258, 461–463 (1992).
Roach, D. A. & Smith, E. F. Life-history trade-offs and senescence in plants. Funct. Ecol. 34, 17–25 (2020).
Edelfeldt, S., Bengtsson, K. & Dahlgren, J. P. Demographic senescence and effects on population dynamics of a perennial plant. Ecology 100, e02742 (2019).
van Daalen, S. F. & Caswell, H. Variance as a life history outcome: Sensitivity analysis of the contributions of stochasticity and heterogeneity. Ecol. Modell. 417, (2020).
Caswell, H. & Vindenes, Y. Demographic variance in heterogeneous populations: matrix models and sensitivity analysis. Oikos 127, 648–663 (2018).
Jenouvrier, S., Aubry, L. M., Barbraud, C., Weimerskirch, H. & Caswell, H. Interacting effects of unobserved heterogeneity and individual stochasticity in the life history of the southern fulmar. J. Anim. Ecol. 87, 212–222 (2018).
Google Scholar
Balázsi, G., Van Oudenaarden, A. & Collins, J. J. Cellular decision making and biological noise: from microbes to mammals. Cell 144, 910–925 (2011).
Google Scholar
Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science (80-. ). 297, 1183–1186 (2002).
Kærn, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).
Google Scholar
Vera, M., Biswas, J., Senecal, A., Singer, R. H. & Park, H. Y. Single-Cell and Single-Molecule Analysis of Gene Expression Regulation. Annu. Rev. Genet. 50, 267–291 (2016).
Google Scholar
Norman, T. M., Lord, N. D., Paulsson, J. & Losick, R. Stochastic Switching of Cell Fate in Microbes. Annu. Rev. Microbiol. 69, 381–403 (2015).
Google Scholar
Ballouz, S., Pena, M., Knight, F., Adams, L. & Gillis, J. The transcriptional legacy of developmental stochasticity. bioRxiv 2019.12.11.873265 (2019) https://doi.org/10.1101/2019.12.11.873265.
Vogt, G. Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences. J. Biosci. 40, 159–204 (2015).
Google Scholar
Hill, W. G. Effective size of populations with overlapping generations. Theor. Popul. Biol. 3, 278–289 (1972).
Google Scholar
Engen, S., Lande, R. & Saether, B.-E. Effective Size of a Fluctuating Age-Structured Population. Genetics 170, 941–954 (2005).
Google Scholar
Vindenes, Y., Engen, S. & Saether, B.-E. Individual heterogeneity in vital parameters and demographic stochasticity. Am. Nat. 171, 455–467 (2008).
Google Scholar
Engen, S., Lande, R., aether, B.-E. & Weimerskirch, H. Extinction in relation to demographic and environmental stochasticity in age-structured models. Math. Biosci. 195, 210–27 (2005).
Stearns, S. C. The evolution of life-histories. (Oxford University Press, 1992).
Kendall, B. E. & Fox, G. a. Variation among Individuals and Reduced Demographic Stochasticity. Conserv. Biol. 16, 109–116 (2002).
Fox, G. A. & Kendall, B. E. Demographic stochasticity and the variance reduction effect. Ecology 83, 1928–1934 (2002).
Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).
Google Scholar
Hartemink, N. & Caswell, H. Variance in animal longevity: contributions of heterogeneity and stochasticity. Popul. Ecol. 60, 89–99 (2018).
Google Scholar
Alonso, D., Etienne, R. S. & McKane, A. J. The merits of neutral theory. Trends Ecol. Evol. 21, 451–457 (2006).
Google Scholar
Ohta, T. & Gillespie, J. Development of Neutral and Nearly Neutral Theories. Theor. Popul. Biol. 49, 128–142 (1996).
Google Scholar
Hughes, A. L. Near neutrality: leading edge of the neutral theory of molecular evolution. Ann. N. Y. Acad. Sci. 1133, 162–179 (2008).
Google Scholar
Comstock, R. E. & Robinson, H. F. The components of genetic variance in populations of biparental progenies and their use in estimating the average degree of dominance. Biometrics 254–266 (1948).
Ellner, S. P. & Rees, M. Integral projection models for species with complex demography. Am. Nat. 167, 410–428 (2006).
Google Scholar
Steiner, U. K., Tuljapurkar, S., Coulson, T. & Horvitz, C. Trading stages: life expectancies in structured populations. Exp. Gerontol. 47, 773–781 (2012).
Google Scholar
R Core Team, R. A. language and environment for statistical computing. R: A language and environment for statistical computing. R Foundation for Statistical Computing vol. 1 409 (2016).
van de Pol, M. & Wright, J. A simple method for distinguishing within- versus between-subject effects using mixed models. Anim. Behav. 77, 753–758 (2009).
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
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