RESUMO
Machine-learning techniques are shifting the boundaries of feasibility in many fields of ethological research. Here, we describe an application of machine learning to the detection/measurement of hygienic behaviour, an important breeding trait in the honey bee (Apis mellifera). Hygienic worker bees are able to detect and destroy diseased brood, thereby reducing the reproduction of economically important pathogens and parasites such as the Varroa mite (Varroa destructor). Video observation of this behaviour on infested combs has many advantages over other methods of measurement, but analysing the recorded material is extremely time-consuming. We approached this problem by combining automatic tracking of bees in the video recordings, extracting relevant features, and training a multi-layer discriminator on positive and negative examples of the behaviour of interest. Including expert knowledge into the design of the features lead to an efficient model for identifying the uninteresting parts of the video which can be safely skipped. This algorithm was then used to semiautomatically identify individual worker bees involved in the behaviour. Application of the machine-learning method allowed to save 70% of the time required for manual analysis, and substantially increased the number of cell openings correctly identified. It thereby turns video-observation of individual cell opening events into an economically competitive method for selecting potentially resistant bees. This method presents an example of how machine learning can be used to boost ethological research, and how it can generate new knowledge by explaining the learned decision rule in form of meaningful parameters.
RESUMO
The mites Varroa destructor Anderson and Trueman and Tropilaelaps mercedesae Anderson and Morgan are both serious threats to the Apis mellifera beekeeping industry. A trait frequently used in selection programs for V. destructor resistance is hygienic behavior, the selective removal of diseased/damaged brood. Here, we measured the level of association of the expression of hygienic behavior against both mites in A. mellifera, by observing whether the same individual bees would carry out the opening and removal of brood infested by the two parasites. The groups of bees showing these behaviors on cells artificially infested by either parasite showed a large overlap, making it appear likely that the two traits are at least closely coupled. Therefore, breeding for V. destructor resistance based on hygienic behavior could prepare A. mellifera populations for dealing with Tropilaelaps sp. mites, and vice versa. Using the same bioassay, we also compared the hygienic behavior of A. mellifera towards T. mercedesae to that of the Asiatic honey bee, Apis cerana. A. cerana workers eliminated a greater proportion of infested cells, which may in part explain the resistance of this bee to Tropilaelaps and the observation that Tropilaelaps reproduction on brood of this species is extremely rare.
