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Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components


Bonnet, Timothée; Postma, Erik (2016). Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components. The American Naturalist, 187(1):60-74.

Abstract

Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g., genetic variation) and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods and apply them to empirical data from a wild rodent population. While MM show high false-positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false-positive rates but always have low power. Indeed, MM analyses of the rodent population data show significant fixed heterogeneity in reproductive success, whereas NS analyses do not. We suggest that fixed heterogeneity may be more common than is suggested by NS and that NS are useful only if more powerful methods are not applicable and if they are complemented by a power analysis.

Abstract

Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g., genetic variation) and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods and apply them to empirical data from a wild rodent population. While MM show high false-positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false-positive rates but always have low power. Indeed, MM analyses of the rodent population data show significant fixed heterogeneity in reproductive success, whereas NS analyses do not. We suggest that fixed heterogeneity may be more common than is suggested by NS and that NS are useful only if more powerful methods are not applicable and if they are complemented by a power analysis.

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1 citation in Web of Science®
2 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Uncontrolled Keywords:Chionomys nivalis, individual-based model, generalized linear mixed model, simulations, snow vole, statistical power.
Language:English
Date:2016
Deposited On:12 Jan 2016 15:00
Last Modified:13 Apr 2016 19:06
Publisher:University of Chicago Press
ISSN:0003-0147
Additional Information:© 2015 by The University of Chicago
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1086/684158

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