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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of comparative physiology 172 (1993), S. 693-706 
    ISSN: 1432-1351
    Keywords: Bees ; Routes ; Sequence learning ; Memory retrieval
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Medicine
    Notes: Abstract Bees of several genera make foraging trips on which they visit a series of plants in a fixed order. To help understand how honeybees might acquire such routes, we examined whether (1) bees learn motor sequences, (2) they link motor instructions to visual stimuli, (3) their visual memories are triggered by contextual cues associated with the bees' position in a sequence. 1. Bees were trained to follow a complex route through a series of obstacles inside a large, 250 cm by 250 cm box. In tests, the obstacles were briefly removed and the bees continued to fly the same zig-zag trajectory that they had when the obstacles were present. The bees' complex trajectory could reflect either the performance of a sequence of motor instructions or their attempt to reach fixed points in their environment. When the point of entry to the box was shifted, the bees' trajectory with respect to the new point of entry was relatively unchanged, suggesting that bees have learnt a motor sequence. 2. Bees were trained along an obstacle course in which different flight directions were associated with the presence of different large patches of colour. In tests, the order of coloured patches was reversed, the trajectory followed by the bees was determined by the order of colours rather than by the learnt motor sequence suggesting that bees will readily link the performance of a particular trajectory to an arbitrary visual stimulus. 3. Bees flew through a series of 3 similar compartments to reach a food reward. Passage from one compartment to the next was only possible through the centre of one of a pair of patterns, e.g. white + ve vs. black — ve in the first box, blue + ve vs. yellow -ve in the second, vertical + ve vs. horizontal — ve in the last. In some tests, bees were presented with a white vs. a vertical stimulus in the front compartment, while, in other tests, the same pair of stimuli was presented in the rear compartment. Bees preferred the white stimulus when tested in the first compartment, but chose the vertical stimulus in the last compartment. Bees reaching a compartment are thus primed to recall the stimulus which they normally encounter there. We argue that the elements which are linked together to form a route are “path-segments”, each of which takes a bee for a given distance in a given direction.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2015-03-04
    Description: Experimental evolution is a powerful tool for investigating complex traits. Artificial selection can be applied for a specific trait and the resulting phenotypically divergent populations pool-sequenced to identify alleles that occur at substantially different frequencies in the extreme populations. To maximize the proportion of loci that are causal to the phenotype among all enriched loci, population size and number of replicates need to be high. These requirements have, in fact, limited evolution studies in higher organisms, where the time investment required for phenotyping is often prohibitive for large-scale studies. Animal size is a highly multigenic trait that remains poorly understood, and an experimental evolution approach may thus aid in gaining new insights into the genetic basis of this trait. To this end, we developed the FlyCatwalk, a fully automated, high-throughput system to sort live fruit flies ( Drosophila melanogaster ) based on morphometric traits. With the FlyCatwalk, we can detect gender and quantify body and wing morphology parameters at a four-old higher throughput compared with manual processing. The phenotyping results acquired using the FlyCatwalk correlate well with those obtained using the standard manual procedure. We demonstrate that an automated, high-throughput, feature-based sorting system is able to avoid previous limitations in population size and replicate numbers. Our approach can likewise be applied for a variety of traits and experimental settings that require high-throughput phenotyping.
    Electronic ISSN: 2160-1836
    Topics: Biology
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