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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. [ABSTRACT FROM AUTHOR]

Understanding the processes behind change in reproductive state along life-history trajectories is a salient research program in evolutionary ecology. Two processes, state dependence and heterogeneity, can drive the dynamics of change among states. Both processes can operate simultaneously, begging the difficult question of how to tease them apart in practice. The Neutral Theory for Life Histories ( NTLH) holds that the bulk of variations in life-history trajectories is due to state dependence and is hence neutral: Once previous (breeding) state is taken into account, variations are mostly random. Lifetime reproductive success ( LRS), the number of descendants produced over an individual's reproductive life span, has been used to infer support for NTLH in natura. Support stemmed from accurate prediction of the population-level distribution of LRS with parameters estimated from a state dependence model. We show with Monte Carlo simulations that the current reliance of NTLH on LRS prediction in a null hypothesis framework easily leads to selecting a misspecified model, biased estimates and flawed inferences. Support for the NTLH can be spurious because of a systematic positive bias in estimated state dependence when heterogeneity is present in the data but ignored in the analysis. This bias can lead to spurious positive covariance between fitness components when there is in fact an underlying trade-off. Furthermore, neutrality implied by NTLH needs a clarification because of a probable disjunction between its common understanding by evolutionary ecologists and its translation into statistical models of life-history trajectories. Irrespective of what neutrality entails, testing hypotheses about the dynamics of change among states in life histories requires a multimodel framework because state dependence and heterogeneity can easily be mistaken for each other. [ABSTRACT FROM AUTHOR]

What causes interindividual variation in fitness? Evidence of heritability of latent individual fitness traits has resparked a debate about the causes of variation in life histories in populations: neutralism versus empirical adaptationism. This debate about the processes underlying observed variation pits neutral stochastic demographic processes against evolutionarily relevant differences among individual fitness traits. Advancing this debate requires careful consideration of differences among inference approaches used by proponents of each hypothesis. Here we draw parallels between several disciplines focusing on processes generating variation in individuals’ life-course, and we contrast methodologies to disentangle these processes. We draw on other disciplines to clarify terminology, risks of flawed inference, and expand the panel of hypotheses and formalizations of processes generating variation in life histories. [ABSTRACT FROM AUTHOR]

The founding evolutionary theories of ageing indicate that the force of mortality imposed by environmental factors should influence the strength of natural selection against actuarial senescence and its evolution. To rigorously test this idea, field biologists need methods that yield estimates of age-specific mortality according to cause of death., Here, we present existing methods commonly applied in studies of human health that could be used to accomplish these goals in studies of wild species for which fate can be determined with certainty. We further present a new application of hidden Markov models for capture-reencounter studies of wild animals that can be used to estimate age-specific trajectories of cause-specific mortality when detection is imperfect., By applying our new hidden Markov model with the e-surge and mark softwares to capture-reencounter data sets for long-lived species, we demonstrate that senescence can be severe for natural causes of mortality in the wild, while being largely non-existent for anthropogenic causes., Moreover, we show that conflation of mortality causes in commonly used survival analyses can induce an underestimation of the intensity of senescence and overestimation of mortality for pre-senescent adults. These biases have important implications for both age-structured population modelling used to guide conservation and comparative analyses of senescence across species. Similar to frailty, individual differences in causes of death can generate individual heterogeneity that needs to be accounted for when estimating age-specific mortality patterns., The proposed hidden Markov method and other competing risk estimators can nevertheless be used to formally account for these confounding effects, and we additionally discuss how our new method can be used to gain insight into the mechanisms that drive variation in ageing across the tree of life. [ABSTRACT FROM AUTHOR]

Studies of wild vertebrates have provided evidence of substantial differences in lifetime reproduction among individuals and the sequences of life history 'states' during life (breeding, nonbreeding, etc.). Such differences may reflect 'fixed' differences in fitness components among individuals determined before, or at the onset of reproductive life. Many retrospective life history studies have translated this idea by assuming a 'latent' unobserved heterogeneity resulting in a fixed hierarchy among individuals in fitness components. Alternatively, fixed differences among individuals are not necessarily needed to account for observed levels of individual heterogeneity in life histories. Individuals with identical fitness traits may stochastically experience different outcomes for breeding and survival through life that lead to a diversity of 'state' sequences with some individuals living longer and being more productive than others, by chance alone. The question is whether individuals differ in their underlying fitness components in ways that cannot be explained by observable 'states' such as age, previous breeding success, etc. Here, we compare statistical models that represent these opposing hypotheses, and mixtures of them, using data from kittiwakes. We constructed models that accounted for observed covariates, individual random effects (unobserved heterogeneity), first-order Markovian transitions between observed states, or combinations of these features. We show that individual sequences of states are better accounted for by models incorporating unobserved heterogeneity than by models including first-order Markov processes alone, or a combination of both. If we had not considered individual heterogeneity, models including Markovian transitions would have been the best performing ones. We also show that inference about age-related changes in fitness components is sensitive to incorporation of underlying individual heterogeneity in models. Our approach provides insight into the sources of individual heterogeneity in life histories, and can be applied to other data sets to examine the ubiquity of our results across the tree of life. [ABSTRACT FROM AUTHOR]