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Mapping Sleeping Bees within Their Nest: Spatial and Temporal Analysis of Worker Honey Bee Sleep
(2014)
Patterns of behavior within societies have long been visualized and interpreted using maps. Mapping the occurrence of sleep across individuals within a society could offer clues as to functional aspects of sleep. In spite of this, a detailed spatial analysis of sleep has never been conducted on an invertebrate society. We introduce the concept of mapping sleep across an insect society, and provide an empirical example, mapping sleep patterns within colonies of European honey bees (Apis mellifera L.). Honey bees face variables such as temperature and position of resources within their colony's nest that may impact their sleep. We mapped sleep behavior and temperature of worker bees and produced maps of their nest's comb contents as the colony grew and contents changed. By following marked bees, we discovered that individuals slept in many locations, but bees of different worker castes slept in different areas of the nest relative to position of the brood and surrounding temperature. Older worker bees generally slept outside cells, closer to the perimeter of the nest, in colder regions, and away from uncapped brood. Younger worker bees generally slept inside cells and closer to the center of the nest, and spent more time asleep than awake when surrounded by uncapped brood. The average surface temperature of sleeping foragers was lower than the surface temperature of their surroundings, offering a possible indicator of sleep for this caste. We propose mechanisms that could generate caste-dependent sleep patterns and discuss functional significance of these patterns.
Questions: What are the relative contributions of kin selection and individual selection to the evolution of dispersal rates in fragmented landscapes? How do environmental parameters influence the relative contributions of both evolutionary forces? Features of the model: Individual-based simulation model of a metapopulation. Logistic local growth dynamics and density-dependent dispersal. An optional shuffling algorithm allows the continuous destruction of any genetic structure in the metapopulation. Ranges of key variables: Depending on dispersal mortality (0.05-0.4) and the strength of environmental fluctuations, mean dispersal probability varied between 0.05 and 0.5. Conclusions: For local population sizes of 100 individuals, kin selection alone could account for dispersal probabilities of up to 0.1. It may result in a ten-fold increase of optimal dispersal rates compared with those predicted on the basis of individual selection alone. Such a substantial contribution of kin selection to dispersal is restricted to cases where the overall dispersal probabilities are small (textless 0.1). In the latter case, as much as 30% of the total fitness of dispersing individuals could arise from the increased reproduction of kin left in the natal patch.