TY - JOUR
T1 - Quantifying fixed individual heterogeneity in demographic parameters
T2 - Performance of correlated random effects for Bernoulli variables
AU - Fay, Rémi
AU - Authier, Matthieu
AU - Hamel, Sandra
AU - Jenouvrier, Stéphanie
AU - van de Pol, Martijn
AU - Cam, Emmanuelle
AU - Gaillard, Jean Michel
AU - Yoccoz, Nigel G.
AU - Acker, Paul
AU - Allen, Andrew
AU - Aubry, Lise M.
AU - Bonenfant, Christophe
AU - Caswell, Hal
AU - Coste, Christophe F.D.
AU - Larue, Benjamin
AU - Le Coeur, Christie
AU - Gamelon, Marlène
AU - Macdonald, Kaitlin R.
AU - Moiron, Maria
AU - Nicol-Harper, Alex
AU - Pelletier, Fanie
AU - Rotella, Jay J.
AU - Teplitsky, Celine
AU - Touzot, Laura
AU - Wells, Caitlin P.
AU - Sæther, Bernt Erik
N1 - 7329, AnE
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - accuracy
KW - among-individual variation
KW - capture–recapture
KW - GLMMs
KW - individual quality
KW - joint mixed models
KW - multivariate normal distribution
KW - precision
KW - international
KW - Plan_S-Compliant-OA
U2 - 10.1111/2041-210X.13728
DO - 10.1111/2041-210X.13728
M3 - Article
AN - SCOPUS:85118220178
SN - 2041-210X
VL - 13
SP - 91
EP - 104
JO - Methods in Ecology and Evolution
JF - Methods in Ecology and Evolution
IS - 1
ER -