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

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

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.

accuracy, among-individual variation, capture–recapture, GLMMs, individual quality, joint mixed models, multivariate normal distribution, precision
2041-210X
91-104
Fay, Rémi
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Authier, Matthieu
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Hamel, Sandra
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Jenouvrier, Stéphanie
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van de Pol, Martijn
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Cam, Emmanuelle
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Gaillard, Jean Michel
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Yoccoz, Nigel G.
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Acker, Paul
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Allen, Andrew
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Aubry, Lise M.
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Bonenfant, Christophe
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Caswell, Hal
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Coste, Christophe F.D.
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Larue, Benjamin
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Le Coeur, Christie
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Gamelon, Marlène
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Macdonald, Kaitlin R.
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Moiron, Maria
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Nicol-Harper, Alex
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Pelletier, Fanie
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Rotella, Jay J.
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Teplitsky, Celine
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Touzot, Laura
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Wells, Caitlin P.
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Sæther, Bernt Erik
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Fay, Rémi
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Authier, Matthieu
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Hamel, Sandra
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Jenouvrier, Stéphanie
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van de Pol, Martijn
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Cam, Emmanuelle
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Gaillard, Jean Michel
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Yoccoz, Nigel G.
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Acker, Paul
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Allen, Andrew
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Aubry, Lise M.
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Bonenfant, Christophe
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Caswell, Hal
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Coste, Christophe F.D.
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Larue, Benjamin
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Le Coeur, Christie
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Gamelon, Marlène
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Macdonald, Kaitlin R.
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Moiron, Maria
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Nicol-Harper, Alex
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Pelletier, Fanie
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Rotella, Jay J.
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Teplitsky, Celine
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Touzot, Laura
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Wells, Caitlin P.
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Sæther, Bernt Erik
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Fay, Rémi, Authier, Matthieu, Hamel, Sandra, Jenouvrier, Stéphanie, 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. and Sæther, Bernt Erik (2022) Quantifying fixed individual heterogeneity in demographic parameters: performance of correlated random effects for Bernoulli variables. Methods in Ecology and Evolution, 13 (1), 91-104. (doi:10.1111/2041-210X.13728).

Record type: Article

Abstract

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.

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Methods Ecol Evol - 2021 - Fay - Quantifying fixed individual heterogeneity in demographic parameters Performance of - Version of Record
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Accepted/In Press date: 31 August 2021
Published date: January 2022
Additional Information: Funding Information: The idea for this paper was developed during a workshop on ‘Individual heterogeneity in animal's life histories—more than meets the eye’ and the authors acknowledge the Lorentz Centre of Leiden University for their support and facilitating this meeting. The analyses presented in this paper were run on two supercomputers located in Canada (Beluga & Cedar). They thank the support provided by Compute Canada ( www.computecanada.ca ). They also acknowledge the support of NSF OPP 1640481 and 1840058 to J.R. and S.J. respectively. Finally, they thank Julien Martin and two anonymous reviewers for providing useful comments on this manuscript. Publisher Copyright: © 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society
Keywords: accuracy, among-individual variation, capture–recapture, GLMMs, individual quality, joint mixed models, multivariate normal distribution, precision

Identifiers

Local EPrints ID: 453354
URI: http://eprints.soton.ac.uk/id/eprint/453354
ISSN: 2041-210X
PURE UUID: a7fae749-4263-4820-b4c5-f1fdd482e945
ORCID for Alex Nicol-Harper: ORCID iD orcid.org/0000-0002-8684-9333

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Date deposited: 13 Jan 2022 18:12
Last modified: 17 Dec 2022 02:56

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Contributors

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

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