Insect flight is a complex behaviour that goes beyond pure mechanism. Integrating genetics, morphology, physiology, sensorimotor control, kinematics and aerodynamics, researching the multiform nature of insect flight is of great scientific interest and also a challenge. Consequently, exploring different strategies for reducing the dimensionality of large flight datasets to uncover meaningful relationships is critical to furthering our understanding of insect flight biomechanics. The aim of my DPhil is to explore different strategies for analysing large datasets of free-flight kinematics in three Dipteran species: Drosophila melanogaster (fruit fly), Calliphora vicina (blowfly) and Eristalis tenax (drone fly). In Chapter 2, I critically evaluate the k-means clustering algorithm as a means of partitioning dipteran body kinematics, and show that it is an unsuitable target for body kinematic data that shifts continuously. In Chapter 3, I show that sexual dimorphism in fruit flies affects their wing beat kinematics beyond size dimorphism. In Chapter 4, I used the k-nearest neighbour clustering algorithm to develop a way of predicting the gear stops that blowflies engage in based on their wing beat kinematics. In Chapter 5, I contrast the wing beat kinematics and aerodynamic profile of all three species. Overall, my thesis shows firstly, that fruit flies, blowflies and drone flies occupy distinct kinematic morphospaces, and secondly, that careful consideration should be given to the choice of technique used to reduce the dimensionality of the data.


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    Titel :

    Free-flight kinematics of diptera


    Beteiligte:

    Erscheinungsdatum :

    2019-07-24


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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