Dynamic heterogeneity and life histories

Population Dynamic Consequences of Context-Dependent Trade-Offs across Life Histories

AbstractTrade-offs are central to life history theory and play a role in driving life history diversity. They arise from a finite amount of resources that need to be allocated among different functions by an organism. Yet covariation of demographic rates among individuals frequently do not reflect allocation trade-offs because of variation in resource acquisition. The covariation of traits among individuals can thus vary with the environment and often increases in benign environments. Surprisingly, little is known about how such context-dependent expression of trade-offs among individuals affect population dynamics across species with different life histories. To study their influence on population stability, we develop an individual-based simulation where covariation in demographic rates varies with the environment. We use it to simulate population dynamics for various life histories across the slow-fast pace-of-life continuum. We found that the population dynamics of slower life histories are relatively more sensitive to changes in covariation, regardless of the trade-off considered. Additionally, we found that the impact on population stability depends on which trade-off is considered, with opposite effects of intraindividual and intergenerational trade-offs. Last, the expression of different trade-offs can feed back to influence generation time through selection acting on individual heterogeneity within cohorts, ultimately affecting population dynamics.

Demystifying individual heterogeneity

Among-individual variation in vital rates, such as mortality and birth rates, exists in nearly all populations. Recent studies suggest that this individual heterogeneity produces substantial life-history and fitness differences among individuals, which in turn scale up to influence population dynamics. However, our ability to understand the consequences of individual heterogeneity is limited by inconsistencies across conceptual frameworks in the field. Studies of individual heterogeneity remain filled with contradicting and ambiguous terminology that introduces risks of misunderstandings, conflicting models and unreliable conclusions. Here, we synthesise the existing literature into a single and comparatively straightforward framework with explicit terminology and definitions. This work introduces a distinction between potential vital rates and realised vital rates to develop a coherent framework that maps directly onto mathematical models of individual heterogeneity. We suggest the terms "fixed condition" and "dynamic condition" be used to distinguish potential vital rates that are permanent from those that can change throughout an individual's life. To illustrate, we connect the framework to quantitative genetics models and to common classes of statistical models used to infer individual heterogeneity. We also develop a population projection matrix model that provides an example of how our definitions are translated into precise quantitative terms.

Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model

Recent studies have examined the risk of poverty throughout the life course, but few have considered how transitioning in and out of poverty shape the dynamic heterogeneity and mortality disparities of a cohort at each age. Here we use state-by-age modeling to capture individual heterogeneity in crossing one of three different poverty thresholds (defined as 1×, 2× or 3× the “official” poverty threshold) at each age. We examine age-specific state structure, the remaining life expectancy, its variance, and cohort simulations for those above and below each threshold. Survival and transitioning probabilities are statistically estimated by regression analyses of data from the Health and Retirement Survey RAND data-set, and the National Longitudinal Survey of Youth. Using the results of these regression analyses, we parameterize discrete state, discrete age matrix models. We found that individuals above all three thresholds have higher annual survival than those in poverty, especially for mid-ages to about age 80. The advantage is greatest when we classify individuals based on 1× the “official” poverty threshold. The greatest discrepancy in average remaining life expectancy and its variance between those above and in poverty occurs at mid-ages for all three thresholds. And fewer individuals are in poverty between ages 40-60 for all three thresholds. Our findings are consistent with results based on other data sets, but also suggest that dynamic heterogeneity in poverty and the transience of the poverty state is associated with income-related mortality disparities (less transience, especially of those above poverty, more disparities). This paper applies the approach of age-by-stage matrix models to human demography and individual poverty dynamics. In so doing we extend the literature on individual poverty dynamics across the life course.

Interacting effects of unobserved heterogeneity and individual stochasticity in the life history of the southern fulmar

Individuals are heterogeneous in many ways. Some of these differences are incorporated as individual states (e.g. age, size, breeding status) in population models. However, substantial amounts of heterogeneity may remain unaccounted for, due to unmeasurable genetic, maternal or environmental factors. Such unobserved heterogeneity (UH) affects the behaviour of heterogeneous cohorts via intra-cohort selection and contributes to inter-individual variance in demographic outcomes such as longevity and lifetime reproduction. Variance is also produced by individual stochasticity, due to random events in the life cycle of wild organisms, yet no study thus far has attempted to decompose the variance in demographic outcomes into contributions from UH and individual stochasticity for an animal population in the wild. We developed a stage-classified matrix population model for the southern fulmar breeding on Ile des Pétrels, Antarctica. We applied multievent, multistate mark-recapture methods to estimate a finite mixture model accounting for UH in all vital rates and Markov chain methods to calculate demographic outcomes. Finally, we partitioned the variance in demographic outcomes into contributions from UH and individual stochasticity. We identify three UH groups, differing substantially in longevity, lifetime reproductive output, age at first reproduction and in the proportion of the life spent in each reproductive state. -14% of individuals at fledging have a delayed but high probability of recruitment and extended reproductive life span. -67% of individuals are less likely to reach adulthood, recruit late and skip breeding often but have the highest adult survival rate. -19% of individuals recruit early and attempt to breed often. They are likely to raise their offspring successfully, but experience a relatively short life span. Unobserved heterogeneity only explains a small fraction of the variances in longevity (5.9%), age at first reproduction (3.7%) and lifetime reproduction (22%). UH can affect the entire life cycle, including survival, development and reproductive rates, with consequences over the lifetime of individuals and impacts on cohort dynamics. The respective role of UH vs. individual stochasticity varies greatly among demographic outcomes. We discuss the implication of our finding for the gradient of life-history strategies observed among species and argue that individual differences should be accounted for in demographic studies of wild populations.

Fitness consequences of peak reproductive effort in a resource pulse system

The age trajectory of reproductive performance of many iteroparous species features an early - life increase in performance followed by a late - life senescent decline. The largest contribution of lifetime reproductive success is therefore gained at the age at which reproductive performance peaks. Using long term data on North American red squirrels we show that the environmental conditions individuals encountered could cause variation among individuals in the “height” and timing of this peak, contributing to life history variation and fitness in this population that experiences irregular resource pulses. As expected, high peak effort was positively associated with lifetime reproductive output up to a high level of annual effort. Furthermore, individuals that matched their peak reproductive effort to an anticipated resource pulse gained substantial fitness benefits through recruiting more offspring over their lifetime. Individual variation in peak reproductive effort thus has strong potential to shape life history evolution by facilitating adaptation to fluctuating environments.

Trait level analysis of multitrait population projection matrices

In most matrix population projection models, individuals are characterized according to, usually, one or two traits such as age, stage, size or location. A broad theory of multitrait population projection matrices (MPPMs) incorporating larger number of traits was long held back by time and space computational complexity issues. As a consequence, no study has yet focused on the influence of the structure of traits describing a life-cycle on population dynamics and life-history evolution. We present here a novel vector-based MPPM building methodology that allows to computationally-efficiently model populations characterized by numerous traits with large distributions, and extend sensitivity analyses for these models. We then present a new method, the trait level analysis consisting in folding an MPPM on any of its traits to create a matrix with alternative trait structure (the number of traits and their characteristics) but similar asymptotic properties. Adding or removing one or several traits to/from the MPPM and analyzing the resulting changes in spectral properties, allows investigating the influence of the trait structure on the evolution of traits. We illustrate this by modeling a 3-trait (age, parity and fecundity) population designed to investigate the implications of parity-fertilitytrade-offs in a context of fecundity heterogeneity in humans. The trait level analysis, comparing models of the same population differing in trait structures, demonstrates that fertility selection gradients differ between cases with or without parity-fertility trade-offs. Moreover it shows that age-specific fertility has seemingly very different evolutionary significance depending on whether heterogeneity is accounted for. This is because trade-offs can vary strongly in strength and even direction depending on the trait structure used to model the population.

Life History and Risk of Death after 50: A Survival Analysis for Europe

Dans notre étude nous avons examiné l’influence des événements du passé des individus sur le risque de décès des Européens âgés de plus de 50 ans, en contrôlant autres variables pertinentes. Notre analyse était basée sur les données d’entretiens biographiques rétrospectifs, les données d’entretiens réguliers au suivi longitudinal et celles d’entretiens de fin de vie de l’Enquête européenne SHARE sur la santé, le vieillissement et la retraite en Europe. En particulier, nous relevons l’état de santé auto-déclaré pendant l’enfance; les périodes de pauvreté, de faim et de mauvaise santé éprouvées dans le passé; et aussi l’histoire des soins de santé, y compris les soins dentaires, les analyses de sang et les mesures de pression artérielle. Ces informations, avec l’age, le sexe, l’état de santé subjectif et objectif, et d’autres facteurs socio-démographiques, permettent d’expliquer le risque de décès. L’analyse de survie, en contrôlant des variables pertinentes, montre que les événements du passé ont un impact significatif sur le risque de décès des Européens âgés de plus de 50 ans.

Reproductive aging: theoretical perspectives, mechanisms, nonhuman models, and health correlates

Every 365.2421999 days--more or less--the earth rotates, revolves, wobbles, and precesses along an elliptical path around the sun. Those of us who survive the journey are a year older: we have aged one solar year. Some years we seem to age faster than other years; some people seem to age faster than other people; some systems seem to age faster than other systems. As we begin to mature, reach our middle years, and become elderly, reproductive changes are among the markers of aging that are most notable, particularly among women. What--if anything--can we learn about more general processes of aging from reproductive aging? Does our postreproductive survival contribute to our fitness, or is it just a chance event, a result of selection on other characteristics? Can our insights and research be translated into improved clinical practice? We explore reproductive aging with a wide-angle multidisciplinary lens that we use to focus on four articulating areas: theoretical perspectives, mechanisms, nonhuman models, and health correlates. We propose directions for future work.