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

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Date
2021-09-24
Authors
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-Erik
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10.1111/2041-210X.13728
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Keywords
accuracy
among-individual variation
capture–recapture
GLMMs
individual quality
joint mixed models
multivariate normal distribution
precision
Abstract
1. 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.
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© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Fay, R., Authier, M., Hamel, S., Jenouvrier, S., van de Pol, M., Cam, E., Gaillard, J.-M., Yoccoz, N. G., Acker, P., Allen, A., Aubry, L. M., Bonenfant, C., Caswell, H., Coste, C. F. D., Larue, B., Le Coeur, C., Gamelon, M., Macdonald, K. R., Moiron, M., Nicol-Harper, A., Pelletier, F., Rotella J. J., Teplitsky, C., Touzot, L., Wells, C. P., Saether, B.-E. Quantifying fixed individual heterogeneity in demographic parameters: performance of correlated random effects for Bernoulli variables. Methods in Ecology and Evolution, 00, (2021): 1–14, https://doi.org/10.1111/2041-210X.13728.
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Fay, R., Authier, M., Hamel, S., Jenouvrier, S., van de Pol, M., Cam, E., Gaillard, J.-M., Yoccoz, N. G., Acker, P., Allen, A., Aubry, L. M., Bonenfant, C., Caswell, H., Coste, C. F. D., Larue, B., Le Coeur, C., Gamelon, M., Macdonald, K. R., Moiron, M., Nicol-Harper, A., Pelletier, F., Rotella J. J., Teplitsky, C., Touzot, L., Wells, C. P., Saether, B.-E. (2021). Quantifying fixed individual heterogeneity in demographic parameters: performance of correlated random effects for Bernoulli variables. Methods in Ecology and Evolution, 00, 1–14.
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