Evolutionary demographic models reveal the strength of purifying selection on susceptibility alleles to late-onset diseases

Assessing the role played by purifying selection on a Susceptibility Allele to Late-Onset Disease (SALOD) is crucial to understand the puzzling allelic spectrum of a disease: most alleles are recent and rare. This fact is surprising, as it suggests that alleles are under purifying selection, while alleles that are involved in post-menopause mortality are often considered neutral in the genetic literature. The aim of this presentation is to use an evolutionary demography model in order to asse..

Evolution of the Human Life Cycle

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Analysis of a multitrait population projection matrix reveals the evolutionary and demographic effects of a life history trade-off

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Trait level analysis of multitrait population projection matrices

International audienceIn 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-fertility trade-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

Data from: Density-dependent population dynamics of a high Arctic capital breeder, the barnacle goose

Density regulation of the population growth rate occurs through negative feedbacks on underlying vital rates, in response to increasing population size. Here, we examine in a capital breeder how vital rates of different life‐history stages, their elasticities and population growth rates are affected by changes in population size. We developed an integrated population model for a local population of Svalbard barnacle geese, Branta leucopsis, using counts, reproductive data and individual‐based mark–recapture data (1990–2017) to model age class‐specific survival, reproduction and number of individuals. Based on these estimates, we quantified the changes in demographic structure and the effect of population size on age class‐specific vital rates and elasticities, as well as the population growth rate. Count data of the number of yearlings and adults in Kongsfjorden came from two sources; from 1990 to 1996, total population size was estimated from the number of marked individuals observed, divided by the average proportion of marked geese in catches, known as a Petersen estimate (Begon, 1979). After 1996, counts of the number of yearlings and adults occurred during the moulting phase (end of July). Only counts of yearlings and adults (combined) were included since the timing of counts was often before first‐year birds fledged and pre‐fledging mortality is high (Loonen et al.,1998 ). R-script included in the package

Quantifying fixed individual heterogeneity in demographic parameters: Performance of correlated random effects for Bernoulli variables

An increasing number of empirical studies aim to quantify individual variation in demographic parameters because these patterns are key for evolutionary and ecological processes. Advanced approaches to estimate individual heterogeneity are now using a multivariate normal distribution with correlated individual random effects to account for the latent correlations among different demographic parameters occurring within individuals. Despite the frequent use of multivariate mixed models, we lack an assessment of their reliability when applied to Bernoulli variables. Using simulations, we estimated the reliability of multivariate mixed effect models for estimating correlated fixed individual heterogeneity in demographic parameters modelled with a Bernoulli distribution. We evaluated both bias and precision of the estimates across a range of scenarios that investigate the effects of life-history strategy, levels of individual heterogeneity and presence of temporal variation and state dependence. We also compared estimates across different sampling designs to assess the importance of study duration, number of individuals monitored and detection probability. In many simulated scenarios, the estimates for the correlated random effects were biased and imprecise, which highlight the challenge in estimating correlated random effects for Bernoulli variables. The amount of fixed among-individual heterogeneity was frequently overestimated, and the absolute value of the correlation between random effects was almost always underestimated. Simulations also showed contrasting performances of mixed models depending on the scenario considered. Generally, estimation bias decreases and precision increases with slower pace of life, large fixed individual heterogeneity and large sample size. We provide guidelines for the empirical investigation of individual heterogeneity using correlated random effects according to the life-history strategy of the species, as well as, the volume and structure of the data available to the researcher. Caution is warranted when interpreting results regarding correlated individual random effects in demographic parameters modelled with a Bernoulli distribution. Because bias varies with sampling design and life history, comparisons of individual heterogeneity among species is challenging. The issue addressed here is not specific to demography, making this warning relevant for all research areas, including behavioural and evolutionary studies