Aubry
Lise M.
Aubry
Lise M.
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PreprintInteracting effects of unobserved heterogeneity and individual stochasticity in the life-history of the Southern fulmar( 2017-09-19) Jenouvrier, Stephanie ; Aubry, Lise M. ; Barbraud, Christophe ; Weimerskirch, Henri ; Caswell, HalIndividuals 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 genetic, maternal, or environmental factors. Such unobserved heterogeneity (UH) affects the behavior 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 unobserved heterogeneity 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 multi-event, multi-state markrecapture 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 unobserved heterogeneity 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 lifespan. 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 lifespan. 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 unobserved heterogeneity versus 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 always be accounted for in demographic studies of wild populations.
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ArticleWhen the going gets tough, the tough get going: effect of extreme climate on an Antarctic seabird’s life history(Wiley, 2022-08-18) Jenouvrier, Stephanie ; Aubry, Lise M. ; van Daalen, Silke F. ; Barbraud, Christophe ; Weimerskirch, Henri ; Caswell, HalIndividuals differ in many ways. Most produce few offspring; a handful produce many. Some die early; others live to old age. It is tempting to attribute these differences in outcomes to differences in individual traits, and thus in the demographic rates experienced. However, there is more to individual variation than meets the eye of the biologist. Even among individuals sharing identical traits, life history outcomes (life expectancy and lifetime reproduction) will vary due to individual stochasticity, that is to chance. Quantifying the contributions of heterogeneity and chance is essential to understand natural variability. Interindividual differences vary across environmental conditions, hence heterogeneity and stochasticity depend on environmental conditions. We show that favourable conditions increase the contributions of individual stochasticity, and reduce the contributions of heterogeneity, to variance in demographic outcomes in a seabird population. The opposite is true under poor conditions. This result has important consequence for understanding the ecology and evolution of life history strategies.
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ArticleQuantifying fixed individual heterogeneity in demographic parameters: performance of correlated random effects for Bernoulli variables(British Ecological Society, 2021-09-24) Fay, Remi ; Authier, Matthieu ; Hamel, Sandra ; Jenouvrier, Stephanie ; van de Pol, Martijn ; Cam, Emmanuelle ; Gaillard, Jean-Michel ; Yoccoz, Nigel G. ; Acker, Paul ; Allen, Andrew ; Aubry, Lise M. ; Bonenfant, Christophe ; Caswell, Hal ; Coste, Christophe F. D. ; Larue, Benjamin ; Le Coeur, Christie ; Gamelon, Marlène ; Macdonald, Kaitlin R. ; Moiron, Maria ; Nicol-Harper, Alex ; Pelletier, Fanie ; Rotella, Jay J. ; Teplitsky, Celine ; Touzot, Laura ; Wells, Caitlin P. ; Saether, Bernt-Erik1. 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. 2. 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. 3. 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. 4. 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.