Environmental stochasticity is a key determinant of population viability. Decades of work exploring how environmental stochasticity influences population dynamics have highlighted the ability of some natural populations to limit the negative effects of environmental stochasticity, one of these strategies being demographic buffering. Whilst various methods exist to quantify demographic buffering, we still do not know which environment factors and demographic characteristics are most responsible for the demographic buffering observed in natural populations. Here, we introduce a framework to quantify the relative effects of three key drivers of demographic buffering: environment components (e.g., temporal autocorrelation and variance), population structure, and demographic rates (e.g., progression and fertility). Using Integral Projection Models, we explore how these drivers impact the demographic buffering abilities of three plant species with different life histories and demonstrate how our approach successfully characterises a population’s capacity to demographically buffer against environmental stochasticity in a changing world.

Extreme climatic events may influence individual-level variability in phenotypes, survival, and reproduction, and thereby drive the pace of evolution. Here, we quantify how experiencing major hurricanes influences individual life courses in the Cayo Santiago rhesus macaques. Our results show that major hurricanes increase heterogeneity in reproductive life courses despite an average reduction in mean fertility and survival, i.e. shortened life courses. In agreement with this, the population is expected to achieve stable population dynamics faster after a hurricane. Our work suggests that natural disasters force individuals into new niches to potentially reduce strong competition during poor environments where mean reproduction and survival are compromised. We also demonstrate that variance in lifetime reproductive success and longevity are differently affected by hurricanes, and such variability is mostly driven by survival.

Raisa Hernández-Pachecoa*, Ulrich K. Steinerb, Alexandra G. Rosatic, Shripad TuljapurkardaCalifornia State University-Long Beach, Department of Biological Sciences, 1250 N Bellflower Blvd, Long Beach, CA, USA 90840-0004bFreie Universität Berlin, Biological Institute, Königin-Luise Str. 1-3,14195 Berlin, Germany; cDepartments of Psychology and Anthropology, University of Michigan, 530 Church St, Ann Arbor, MI, USA 48109 d327 Campus Dr., Rm 233, Stanford University, Stanford, CA, USA 94305*Corresponding author:[email protected] interests statement : None.Abstract . Several social traits including status, integration, early-life adversity, and their interactions across the life course can predict health, reproduction, and mortality in humans. Accordingly, individual sociality plays a fundamental role in the emergence of phenotypes driving the evolution of aging. Recent work placing human social gradients on a biological continuum with other species provides a useful evolutionary context for aging questions, but there is still a need for a unified evolutionary framework for sociality, health, and aging. Here, we first summarize current challenges to disentangle the effects of the social environment on human life courses. Next, we review recent advances in comparative biodemography and propose a biodemographic perspective to address socially driven health phenotype distributions and their evolutionary consequences using a nonhuman primate population. This new comparative approach uses evolutionary demography to address the joint dynamics of populations, sociality, phenotypes, and life history parameters. The long-term goal is to advance our understanding of the link between individual sociality, population-level outcomes, and the evolution of aging.

Heterogeneity among individuals in fitness components is what selection acts upon. Evolutionary theories predict that selection in constant environments acts against such heterogeneity. But observations reveal substantial non-genetic and also non-environmental variability in phenotypes. Here we examine whether there is a relationship between selection pressure and phenotypic variability by analysing structured population models based on data from a large and diverse set of species. Our findings suggest that non-genetic, non-environmental variation is in general neither truly neutral, selected for, or selected against. We find much variation among species and populations within species, with mean patterns suggesting nearly neutral evolution of life course variability. Populations that show greater diversity of life courses do not show, in general, increased or decreased population growth rates. Our analysis suggests we are only at the beginning in understanding the evolution and maintenance of non-genetic non environmental variation.

Generation time has previously been the focus of comparative life history analyses. Here we examine three metrics: generation time Tc, reproductive dispersion S (the distribution of ages of reproduction), and damping time τ (time to converge to stable (st)age distribution). We use data on 633 species of animals and plants, and perform phylogenetically corrected analyses. First we find that S varies allometrically and isometrically with Tc. As a result, τ varies allometrically with either Tc or S but not both. Second, we find a trade-off between τ and S, so that τ does not vary isometrically with Tc. This trade-off is a novel demographic component to the relationship between τ, Tc and S that is otherwise partly determined by their similarity as biological times. Our results indicate that species at the slow end of the slow-fast continuum take longer to converge to stable distribution than species with fast life-histories.

The lifetime reproductive success (LRS) of individuals is affected by random events such as death, realized growth, or realized reproduction, and the outcomes of these events can differ even when individuals have identical probabilities. Another source of randomness arises when these probabilities also change over time in variable environments. For structured populations in stochastic environments, we extend our recent method to determine how birth environment and birth stage determine the random distribution of the LRS. Our results provide a null model that quantifies effects on LRS of just the birth size or stage. Using Roe deer Capreolus capreolus as a case study, we show that the effect of an individual’s birth environment on LRS varies with the frequency of environments and their temporal autocorrelation, and that lifetime performance is affected by changes in the pattern of environmental states expected as a result of climate change.