Remarkable Sensitivity of Young Honey Bee Workers to Multiple Non-photic, Non-thermal, Forager Cues That Synchronize Their Daily Activity Rhythms

Time memory in social insects with a special focus on honey bees

The ability to associate time and location with food sources is an evolutionary advantage for foraging animals. We find highly sophisticated time memory capabilities especially in social insects, which require efficient foraging capabilities for colony provisioning. Honey bees are perfectly suitable to study time memory mechanisms: they possess an elaborated time memory combined with a relatively simple neuronal clock network and a smaller gene set compared with the mouse model organism. This review provides a short overview majorly across insects, which have demonstrated time memory capabilities, with a focus on time-place learning, and describes basic properties as well as state-of-the-art research connecting time memory with the circadian clock at the behavioral, molecular, and neuroanatomical levels. Despite a long history of research on time memory of honey bees, putative connections between clock and time memory have only recently been identified and imply a rather complex regulation mechanism with multiple signaling pathways.

Regulation of division of labor in insects: a colony-level perspective

Studies of division of labor have focused mainly on individual workers performing tasks. Here I propose a shift in perspective: colonies perform tasks, and task performance should be evaluated at the colony level. I then review studies from the recent literature from this perspective, on topics including evaluating task performance; specialization and efficiency; flexibility and task performance; response threshold models; and variation in behavior arising from diverse sensory experiences and learning. The use of specialized workers is only one of a variety of strategies that colonies may follow in performing tasks. The ability of colonies to produce consistent responses and to compensate for changes in the labor pool supports the idea of a task allocation system that precedes specialization. The colony-level perspective raises new questions about how tasks are done and the strategies used to improve colony task performance.

Estimation of Circular Statistics in the Presence of Measurement Bias

Circular statistics and Rayleigh tests are important tools for analyzing cyclic events. However, current methods are not robust to significant measurement bias, especially incomplete or otherwise non-uniform sampling. One example is studying 24-cyclicity but having data not recorded uniformly over the full 24-hour cycle. Our objective is to present a robust method to estimate circular statistics and their statistical significance in the presence of incomplete or otherwise non-uniform sampling. Our method is to solve the underlying Fredholm Integral Equation for the more general problem, estimating probability distributions in the context of imperfect measurements, with our circular statistics in the presence of incomplete/non-uniform sampling being one special case. The method is based on linear parameterizations of the underlying distributions. We simulated the estimation error of our approach for several toy examples as well as for a real-world example: analyzing the 24-hour cyclicity of an electrographic biomarker of epileptic tissue controlled for states of vigilance. We also evaluated the accuracy of the Rayleigh test statistic versus the direct simulation of statistical significance. Our method shows a very low estimation error. In the real-world example, the corrected moments had a root mean square error of [Formula: see text]. In contrast, the Rayleigh test statistic overestimated the statistical significance and was thus not reliable. The presented methods thus provide a robust solution to computing circular moments even with incomplete or otherwise non-uniform sampling. Since Rayleigh test statistics cannot be used in this circumstance, direct estimation of significance is the preferable option for estimating statistical significance.