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

Rémi Fay* (Corresponding author), Matthieu Authier, Sandra Hamel, Stéphanie Jenouvrier, Martijn van de Pol, Emmanuelle Cam, Jean Michel Gaillard, Nigel G. Yoccoz, Paul Acker, Andrew Allen, Lise M. Aubry, Christophe Bonenfant, Hal Caswell, Christophe F.D. Coste, Benjamin Larue, Christie Le Coeur, Marlène Gamelon, Kaitlin R. Macdonald, Maria Moiron, Alex Nicol-HarperFanie Pelletier, Jay J. Rotella, Celine Teplitsky, Laura Touzot, Caitlin P. Wells, Bernt Erik Sæther

*Corresponding author for this work

Research output: Contribution to journal/periodicalArticleScientificpeer-review

4 Citations (Scopus)
41 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)91-104
Number of pages14
JournalMethods in Ecology and Evolution
Issue number1
Early online date2021
Publication statusPublished - 2022


  • accuracy
  • among-individual variation
  • capture–recapture
  • GLMMs
  • individual quality
  • joint mixed models
  • multivariate normal distribution
  • precision
  • international
  • Plan_S-Compliant-OA


Dive into the research topics of 'Quantifying fixed individual heterogeneity in demographic parameters: Performance of correlated random effects for Bernoulli variables'. Together they form a unique fingerprint.

Cite this