Metabolic rate shapes phenotypic covariance among physiological, behavioral, and life-history traits in honeybees

Brain energy metabolism as an underlying basis of slow and fast cognitive phenotypes in honeybees

In the context of slow-fast behavioral variation, fast individuals are hypothesized to be those who prioritize speed over accuracy while slow individuals are those which do the opposite. Since energy metabolism is a critical component of neural and cognitive functioning, this predicts such differences in cognitive style to be reflected at the level of the brain. We tested this idea in honeybees by first classifying individuals into slow and fast cognitive phenotypes based on a learning assay and then measuring their brain respiration with high-resolution respirometry. Our results broadly show that inter-individual differences in cognition are reflected in differences in brain mass and accompanying energy use at the level of the brain and the whole animal. Larger brains had lower mass-specific energy usage and bees with larger brains had a higher metabolic rate. These differences in brain respiration and brain mass were, in turn, associated with cognitive differences, such that bees with larger brains were fast cognitive phenotypes whereas those with smaller brains were slow cognitive phenotypes. We discuss these results in the context of the role of energy in brain functioning and slow-fast decision making and speed accuracy trade-off.

Metabolic scaling as an emergent outcome of variation in metabolic rate

The allometric scaling of metabolic rate and what drives it are major questions in biology with a long history. Since the metabolic rate at any level of biological organization is an emergent property of its lower-level constituents, it is an outcome of the intrinsic heterogeneity among these units and the interactions among them. However, the influence of lower-level heterogeneity on system-level metabolic rate is difficult to investigate, given the tightly integrated body plan of unitary organisms. In this context, social insects such as honeybees can serve as important model systems because unlike unitary organisms, these superorganisms can be taken apart and reassembled in different configurations to study metabolic rate and its various drivers at different levels of organization. This commentary discusses the background of such an approach and how combining it with artificial selection to generate heterogeneity in metabolic rate with an analytical framework to parse out the different mechanisms that contribute to the effects of heterogeneity can contribute to the various models of metabolic scaling. Finally, the absence of the typical allometric scaling relationship among different species of honeybees is discussed as an important prospect for deciphering the role of top-down ecological factors on metabolic scaling. This article is part of the theme issue 'The evolutionary significance of variation in metabolic rates'.

Metabolic rate shapes differences in foraging efficiency among honeybee foragers

Metabolic rate is the rate at which organisms process energy and is often considered as the fundamental driver of life history processes. The link between metabolic rate and life history is critically mediated via foraging, which shapes the energy acquisition patterns of an individual. This predicts that individuals with different metabolic rates likely vary in their foraging strategies, although such a link has rarely been empirically investigated in the context of optimal foraging theory—a powerful framework for understanding how animals maximize their foraging returns. Many central place foragers such as honeybees maximize their energetic efficiency rather than the rate of energetic gain, given the critical role of energetic costs in foraging decisions. We therefore tested if individuals with low and high metabolic rates differ in efficiency maximization, using genetic lines of honeybees with different metabolic rates. Our results show that low metabolic rate foragers visit more flowers during a foraging trip and have a higher energetic efficiency than high metabolic rate foragers in both low and high resource conditions. We discuss the significance of these results in the context of division of labor and the adaptive role of phenotypic diversity in metabolic rate in a social insect colony.

A fast pace-of-life is traded off against a high thermal performance

The integration of life-history, behavioural and physiological traits into a ‘pace-of-life syndrome’ is a powerful concept in understanding trait variation in nature. Yet, mechanisms maintaining variation in ‘pace-of-life’ are not well understood. We tested whether decreased thermal performance is an energetic cost of a faster pace-of-life. We characterized the pace-of-life of larvae of the damselfly Ischnura elegans from high-latitude and low-latitude regions when reared at 20°C or 24°C in a common-garden experiment, and estimated thermal performance curves for a set of behavioural, physiological and performance traits. Our results confirm a faster pace-of-life (i.e. faster growth and metabolic rate, more active and bold behaviour) in the low-latitude and in warm-reared larvae, and reveal increased maximum performance, R_max, but not thermal optimum T_opt, in low-latitude larvae. Besides a clear pace-of-life syndrome integration at the individual level, larvae also aligned along a ‘cold–hot’ axis. Importantly, a faster pace-of-life correlated negatively with a high thermal performance (i.e. higher T_opt for swimming speed, metabolic rate, activity and boldness), which was consistent across latitudes and rearing temperatures. This trade-off, potentially driven by the energetically costly maintenance of a fast pace-of-life, may be an alternative mechanism contributing to the maintenance of variation in pace-of-life within populations.

Interindividual variation in the use of social information during learning in honeybees

Slow-fast differences in cognition among individuals have been proposed to be an outcome of the speed-accuracy trade-off in decision-making. Based on the different costs associated with acquiring information via individual and social learning, we hypothesized that slow-fast cognitive differences would also be tied to the adoption of these different learning modes. Since foragers in honeybee colonies likely have both these information acquisition modes available to them, we chose to test them for interindividual differences in individual and social learning. By measuring performance on a learning task with and without a social cue and quantifying learning rate and maximum accuracy in these two tasks, our results show the existence of a speed-accuracy trade-off in both the individual and the social learning contexts. However, the trade-off is steeper during individual learning, which was slower than social learning but led to higher accuracy. Most importantly, our results also show that bees that attained high accuracy on the individual learning task had low accuracy on the social learning task and vice versa. We discuss how these two information acquisition strategies tie to slow-fast differences in cognitive phenotypes and how they might contribute to division of labour and social behaviour.

Slow-Fast Cognitive Phenotypes and Their Significance for Social Behavior: What Can We Learn From Honeybees?

Cognitive variation is proposed to be the fundamental underlying factor that drives behavioral variation, yet it is still to be fully integrated with the observed variation at other phenotypic levels that has recently been unified under the common pace-of-life framework. This cognitive and the resulting behavioral diversity is especially significant in the context of a social group, the performance of which is a collective outcome of this diversity. In this review, we argue about the utility of classifying cognitive traits along a slow-fast continuum in the larger context of the pace-of-life framework. Using Tinbergen’s explanatory framework for different levels of analyses and drawing from the large body of knowledge about honeybee behavior, we discuss the observed interindividual variation in cognitive traits and slow-fast cognitive phenotypes from an adaptive, evolutionary, mechanistic and developmental perspective. We discuss the challenges in this endeavor and suggest possible next steps in terms of methodological, statistical and theoretical approaches to move the field forward for an integrative understanding of how slow-fast cognitive differences, by influencing collective behavior, impact social evolution.

Nesting ecology does not explain slow-fast cognitive differences among honeybee species

Slow-fast behavioral and life history differences have been tied to slow-fast variation in cognition that is part of the general speed-accuracy tradeoff. While there is growing evidence for such cognitive variation and its association with behavior and life history at the intraspecific level, it is unknown if a similar relationship extends to the interspecific level. Since interspecific differences in cognition have been shown to be a function of ecology and life history, such differences should be reflected in multiple traits that comprise the slow-fast cognitive axis. In this study, by measuring multiple cognitive traits in individuals, we tested for differences in the cognitive phenotype among four honeybee species, which differ in their behavior and life history in a manner that is associated with differences in their nesting ecology. Our results indicate that a set of cognitive traits consistently covary within each species, resulting in slow and fast cognitive phenotypes that largely meet the predictions of the speed-accuracy tradeoff. We also find that the four species group into two distinct clusters on a slow-fast cognitive axis, although their positions do not align with the known differences in their life history and nesting ecology. We instead find that cognitive differences among the four species are correlated with their brain size. We discuss the possible implications of these results for the role of ecology on slow-fast cognitive differences and the evolution of cognition.