Understanding the evolutionary mechanisms underlying the maintenance of individual differences in behavior and physiology is a fundamental goal in ecology and evolution. The pace‐of‐life syndrome hypothesis is often invoked to explain the maintenance of such within‐population variation. This hypothesis predicts that behavioral traits are part of a suite of correlated traits that collectively determine an individual's propensity to prioritize reproduction or survival. A key assumption of this hypothesis is that these traits are underpinned by genetic trade‐offs among life‐history traits: genetic variants that increase fertility, reproduction and growth might also reduce lifespan. We performed a systematic literature review and meta‐analysis to summarize the evidence for the existence of genetic trade‐offs between five key life‐history traits: survival, growth rate, body size, maturation rate, and fertility. Counter to our predictions, we found an overall positive genetic correlation between survival and other life‐history traits and no evidence for any genetic correlations between the non‐survival life‐history traits. This finding was generally consistent across pairs of life‐history traits, sexes, life stages, lab vs. field studies, and narrow‐ vs. broad‐sense correlation estimates. Our study highlights that genetic trade‐offs may not be as common, or at least not as easily quantifiable, in animals as often assumed.
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The study of individual behavioral variation, sometimes called animal personalities or behavioral types, is now a well-established area of research in behavioral ecology and evolution. Considerable theoretical work has developed predictions about its ecological and evolutionary causes and consequences, and studies testing these theories continue to grow. Here, we synthesize the current empirical work to shed light on which theories are well supported and which need further refinement. We find that the major frameworks explaining the existence of individual behavioral variation, the pace-of-life syndrome hypothesis and state-dependent feedbacks models, have mixed support. The consequences of individual behavioral variation are well studied at the individual level but less is known about consequences at higher levels such as among species and communities. The focus of this review is to reevaluate and reestablish the foundation of individual behavioral variation research: What do we know? What questions remain? And where are we going next?more » « less
Behavioral individuality is a ubiquitous phenomenon in animal populations, yet the origins and developmental trajectories of individuality, especially very early in life, are still a black box. Using a high-resolution tracking system, we mapped the behavioral trajectories of genetically identical fish (
Poecilia formosa), separated immediately after birth into identical environments, over the first 10 weeks of their life at 3 s resolution. We find that (i) strong behavioral individuality is present at the very first day after birth, (ii) behavioral differences at day 1 of life predict behavior up to at least 10 weeks later, and (iii) patterns of individuality strengthen gradually over developmental time. Our results establish a null model for how behavioral individuality can develop in the absence of genetic and environmental variation and provide experimental evidence that later-in-life individuality can be strongly shaped by factors pre-dating birth like maternal provisioning, epigenetics and pre-birth developmental stochasticity.
Assessing the biological relevance of variance components estimated using Markov chain Monte Carlo (MCMC)‐based mixed‐effects models is not straightforward. Variance estimates are constrained to be greater than zero and their posterior distributions are often asymmetric. Different measures of central tendency for these distributions can therefore vary widely, and credible intervals cannot overlap zero, making it difficult to assess the size and statistical support for among‐group variance. Statistical support is often assessed through visual inspection of the whole posterior distribution and so relies on subjective decisions for interpretation.
We use simulations to demonstrate the difficulties of summarizing the posterior distributions of variance estimates from MCMC‐based models. We then describe different methods for generating the expected null distribution (i.e. a distribution of effect sizes that would be obtained if there was no among‐group variance) that can be used to aid in the interpretation of variance estimates.
Through comparing commonly used summary statistics of posterior distributions of variance components, we show that the posterior median is predominantly the least biased. We further show how null distributions can be used to derive a
p‐value that provides complementary information to the commonly presented measures of central tendency and uncertainty. Finally, we show how these p‐values facilitate the implementation of power analyses within an MCMC framework.
The use of null distributions for variance components can aid study design and the interpretation of results from MCMC‐based models. We hope that this manuscript will make empiricists using mixed models think more carefully about their results, what descriptive statistics they present and what inference they can make.