A quantitative genetics approach to validate lab- versus field-based behavior in novel environments
TL;DR: In great tits, activity expressed in laboratory versus field novel environment assays is modulated by multiple quasi-independent characters, and the lack of cross-context correlation shown here may also apply to other setups, other repeatable behaviors, and other taxa.
Abstract:
Conclusions about the adaptive nature of repeatable variation in behavior (i.e., “personality”) are often derived from laboratory-based assays. However, the expression of genetic variation differs between laboratory and field. Laboratory-based behavior might not predict field-based behavior thus, cross-context validation is required. We estimated the cross-context correlation between behavior expressed by wild great tits (Parus major) in established laboratory versus field novel environment assays. Both assays have been used as proxies for “exploration tendency.” Behavior in both contexts had similar repeatability (R = 0.35 vs. 0.37) but differed in heritability (h2 = 0.06 vs. 0.23), implying differences in selection pressures. Unexpectedly, there was no cross-context correlation. Laboratory- and field-based behavior thus reflected expressions of two distinct underlying characters. Post hoc simulations revealed that sampling bias did not explain the lack of correlation. Laboratory-based behavior may reflect fear and exploration, but field-based behavior may reflect escape behavior instead, though other functional interpretations cannot be excluded. Thus, in great tits, activity expressed in laboratory versus field novel environment assays is modulated by multiple quasi-independent characters. The lack of cross-context correlation shown here may also apply to other setups, other repeatable behaviors, and other taxa. Our study thus implies care should be taken in labeling behaviors prior to firm validation studies.
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TL;DR: In this paper , the authors conducted meta-analyses of the literature to establish, firstly, whether physiological traits underlie activity, exploration, and dispersal by individuals, and secondly whether physiological characteristics differed between range core and edges of distributions.
Abstract: Physiology can underlie movement, including short-term activity, exploration of unfamiliar environments, and larger scale dispersal, and thereby influence species distributions in an environmentally sensitive manner. We conducted meta-analyses of the literature to establish, firstly, whether physiological traits underlie activity, exploration, and dispersal by individuals (88 studies), and secondly whether physiological characteristics differed between range core and edges of distributions (43 studies). We show that locomotor performance and metabolism influenced individual movement with varying levels of confidence. Range edges differed from cores in traits that may be associated with dispersal success, including metabolism, locomotor performance, corticosterone levels, and immunity, and differences increased with increasing time since separation. Physiological effects were particularly pronounced in birds and amphibians, but taxon-specific differences may reflect biased sampling in the literature, which also focussed primarily on North America, Europe, and Australia. Hence, physiology can influence movement, but undersampling and bias currently limits general conclusions.
4 citations
TL;DR: The authors designed a variant of the detour task for wild great tits, Parus major, and deployed it at the nesting site across two spring seasons, and compared task performance of the same individuals in the wild across 2 years, and with their performance in captivity when tested using the classical cylinder detour tasks during the nonbreeding season.
2 citations
TL;DR: For example, the authors designed a variant of the detour task for wild great tits (Parus major) and deployed it at the nesting site of the same individuals across two spring seasons.
Abstract: Inhibitory control is one of several cognitive mechanisms required for self-regulation, decision making and attention towards tasks. Linked to a variety of maladaptive behaviours in humans, inhibitory control is expected to influence behavioural plasticity in animals in the context of foraging, social interaction, or responses to sudden changes in the environment. One widely used cognitive assay, the detour task, putatively tests inhibitory control. In this task, subjects must avoid impulsively touching transparent barriers positioned in front of food, and instead access the food by an alternative but known route. Recently it has been suggested that the detour task is unreliable and measures factors unrelated to inhibitory control, including motivation, previous experience and persistence. Consequently, there is growing uncertainty as to whether this task leads to erroneous interpretations about animal cognition and its links with socio-ecological traits. To address these outstanding concerns, we designed a variant of the detour task for wild great tits (Parus major) and deployed it at the nesting site of the same individuals across two spring seasons. This approach eliminated the use of food rewards, limited social confounds, and maximised motivation. We compared task performance in the wild with their performance in captivity when tested using the classical cylinder detour task during the non-breeding season. Task performance was temporally and contextually repeatable, and none of the confounds had any significant effect on performance, nor did they drive any of the observed repeatable differences among individuals. These results support the hypothesis that our assays captured intrinsic differences in inhibitory control. Instead of throwing the detour task out with the bathwater, we suggest confounds are likely system and experimental-design specific, and that assays for this potentially fundamental but largely overlooked source of behavioural plasticity in animal populations, should be validated and refined for each study system.
1 citations
TL;DR: In this paper , a social evolutionary approach to personality, synthesizing theory, methods, and organizing questions in the study of individuality and sociality in behavior, is presented, which is essential for understanding the evolution of cooperation and conflict in behavior.
1 citations
TL;DR: The authors found that latency to explore, as a proxy for boldness, correlated with values of δ13C with bolder fish having lower δ 13C values and also explained variation in the change in individual stable isotope niche over time.
Abstract: Research on among individual variation in behavior has increased rapidly
in recent years. It is intuitively appealing that among individual
variation in behavior has ecological consequences and among the most
likely to be affected is trophic niche. Bold individuals, with the
tendency to be explorative and risk tolerant, can be less likely to
alter their foraging behavior across contexts and therefore forage more
consistently. Stable isotopes are a useful tool to retrospectively
estimate ecological niche and have been found to correlate to foraging
behavior in the wild. It is now pressing to extent studies to further
examine the ecological or evolutionary relevance of personality. We
examined if common behavioral traits were correlated to ecological niche
in the wild using a rapid behavioral assay and δ13C and δ15N stable
isotopes from fin and muscle reflecting ecological niche for the
previous weeks and months. We found that latency to explore, as a proxy
for boldness, correlated to values of δ13C with bolder fish having lower
δ13C values. Moreover, latency to explore also explained variation in
the change in individual stable isotope niche over time. These results
highlight the long-term ecological importance of among individual
variation in behavior and are among the first to support a correlation
of laboratory measures of behavior and ecological niche in the wild.
References
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TL;DR: It is proposed that temperament can and should be studied within an evolutionary ecology framework and provided a terminology that could be used as a working tool for ecological studies of temperament, which includes five major temperament trait categories: shyness‐boldness, exploration‐avoidance, activity, sociability and aggressiveness.
Abstract: Temperament describes the idea that individual behavioural differences are repeatable over time and across situations. This common phenomenon covers numerous traits, such as aggressiveness, avoidance of novelty, willingness to take risks, exploration, and sociality. The study of temperament is central to animal psychology, behavioural genetics, pharmacology, and animal husbandry, but relatively few studies have examined the ecology and evolution of temperament traits. This situation is surprising, given that temperament is likely to exert an important influence on many aspects of animal ecology and evolution, and that individual variation in temperament appears to be pervasive amongst animal species. Possible explanations for this neglect of temperament include a perceived irrelevance, an insufficient understanding of the link between temperament traits and fitness, and a lack of coherence in terminology with similar traits often given different names, or different traits given the same name. We propose that temperament can and should be studied within an evolutionary ecology framework and provide a terminology that could be used as a working tool for ecological studies of temperament. Our terminology includes five major temperament trait categories: shyness-boldness, exploration-avoidance, activity, sociability and aggressiveness. This terminology does not make inferences regarding underlying dispositions or psychological processes, which may have restrained ecologists and evolutionary biologists from working on these traits. We present extensive literature reviews that demonstrate that temperament traits are heritable, and linked to fitness and to several other traits of importance to ecology and evolution. Furthermore, we describe ecologically relevant measurement methods and point to several ecological and evolutionary topics that would benefit from considering temperament, such as phenotypic plasticity, conservation biology, population sampling, and invasion biology.
2,860 citations
TL;DR: Two types of repeatability (ordinary repeatability and extrapolated repeatability) are compared in relation to narrow‐sense heritability and two methods for calculating standard errors, confidence intervals and statistical significance are addressed.
Abstract: Repeatability (more precisely the common measure of repeatability, the intra-class correlation coefficient, ICC) is an important index for quantifying the accuracy of measurements and the constancy of phenotypes. It is the proportion of phenotypic variation that can be attributed to between-subject (or between-group) variation. As a consequence, the non-repeatable fraction of phenotypic variation is the sum of measurement error and phenotypic flexibility. There are several ways to estimate repeatability for Gaussian data, but there are no formal agreements on how repeatability should be calculated for non-Gaussian data (e.g. binary, proportion and count data). In addition to point estimates, appropriate uncertainty estimates (standard errors and confidence intervals) and statistical significance for repeatability estimates are required regardless of the types of data. We review the methods for calculating repeatability and the associated statistics for Gaussian and non-Gaussian data. For Gaussian data, we present three common approaches for estimating repeatability: correlation-based, analysis of variance (ANOVA)-based and linear mixed-effects model (LMM)-based methods, while for non-Gaussian data, we focus on generalised linear mixed-effects models (GLMM) that allow the estimation of repeatability on the original and on the underlying latent scale. We also address a number of methods for calculating standard errors, confidence intervals and statistical significance; the most accurate and recommended methods are parametric bootstrapping, randomisation tests and Bayesian approaches. We advocate the use of LMM- and GLMM-based approaches mainly because of the ease with which confounding variables can be controlled for. Furthermore, we compare two types of repeatability (ordinary repeatability and extrapolated repeatability) in relation to narrow-sense heritability. This review serves as a collection of guidelines and recommendations for biologists to calculate repeatability and heritability from both Gaussian and non-Gaussian data.
2,104 citations
2,076 citations
TL;DR: This work recommends rescaling as a default option--an improvement upon the usual approach of including variables in whatever way they are coded in the data file--so that the magnitudes of coefficients can be directly compared as a matter of routine statistical practice.
Abstract: Interpretation of regression coefficients is sensitive to the scale of the inputs. One method often used to place input variables on a common scale is to divide each numeric variable by its standard deviation. Here we propose dividing each numeric variable by two times its standard deviation, so that the generic comparison is with inputs equal to the mean ±1 standard deviation. The resulting coefficients are then directly comparable for untransformed binary predictors. We have implemented the procedure as a function in R. We illustrate the method with two simple analyses that are typical of applied modeling: a linear regression of data from the National Election Study and a multilevel logistic regression of data on the prevalence of rodents in New York City apartments. We recommend our rescaling as a default option—an improvement upon the usual approach of including variables in whatever way they are coded in the data file—so that the magnitudes of coefficients can be directly compared as a matter of routine statistical practice. Copyright © 2007 John Wiley & Sons, Ltd.
1,894 citations
TL;DR: Meta-analysis is used to ask whether different types of behaviours were more repeatable than others, and if repeatability estimates depended on taxa, sex, age, field versus laboratory, the number of measures and the interval between measures.
1,671 citations