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Exploring sequences of behaviour in the wild across scales and species

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2024

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All animals behave. Behaviour allows animals flexibility in dealing with heterogeneous, dynamic environments, and a key goal of the field of animal behaviour is to understand how, when, and why animals do what they do. The expression of behaviour is far from random, believed to be hierarchically organised and driven by processes occurring at multiple time-scales. To better understand behaviour, we can view it as a sequence of discrete behavioural states driven by a behavioural algorithm, a set of principles based on which an animal performs behavioural decision-making. Tremendous advances have been made in understanding animal behaviour by considering different facets of behavioural algorithms and the factors that drive them, but the structure of behaviour itself has received little attention by comparison. In this thesis, I adopt a multi-time-scale perspective to explore behavioural sequences from three species of mammals in the wild: meerkats (Suricata suricatta), white-nosed coatis (Nasua narica), and spotted hyenas (Crocuta crocuta). Individuals from each species were monitored via tracking collars that recorded their locations, movements, and vocalisations. Using data from accelerometer sensors on-board these collars, I developed behaviour classification systems that infer long, continuous, behavioural sequences at high temporal resolution. In chapter 2, I demonstrate long time-scale structure in the behavioural sequences of all collared individuals of all three study species, showing that behaviour depends on past states of the animal much more than expected from any Markovian system. This chapter is likely the most detailed description yet reported in the literature of long time-scale structure in animal behaviour in the wild. In chapter 3, I zoom out to a 24 h time-scale to consider factors affecting daily activity patterns in spotted hyenas. I show that these individuals can synchronise their activity patterns provided that they have spent sufficient time in proximity to each other. In chapter 4, zooming in to consider moment-to-moment dynamics, I examine the social factors affect- ing the spatio-temporal dynamics of vigilance and its effects on movement and collective behaviour in groups of meerkats. I find that vigilance is more likely at the edges of meerkat groups, and is correlated with decreased movement.

Collectively, these chapters illustrate the benefits of studying behaviour with a multi-time-scale perspective, and highlight how analysing behavioural sequences with such a perspective can provide novel insights into the nature of behaviour and the factors that drive it. My work here also demonstrates the efficacy of accelerometer-based inference of behavioural states, and acts as a catalogue of some of the exciting ways to make use of the resulting behavioural sequences. Looking forward, in chapter 5, I highlight possible future directions for this multi-time-scale perspective to better understand the nature of behaviour in animals. I explore two particularly interesting questions in depth: (i) how groups of interacting animals behave, and (ii) how the time-scales of behavioural decision-making are optimised.

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Fachgebiet (DDC)
570 Biowissenschaften, Biologie

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behavioural dynamics, long timescale, behavioural sequences, accelerometer, spotted hyena, meerkat, coati, activity pattern, vigilance

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ISO 690MINASANDRA MADHAVARANGA, Pranav, 2024. Exploring sequences of behaviour in the wild across scales and species [Dissertation]. Konstanz: Universität Konstanz
BibTex
@phdthesis{MinasandraMadhavaranga2024-08-06Explo-70888,
  year={2024},
  title={Exploring sequences of behaviour in the wild across scales and species},
  author={Minasandra Madhavaranga, Pranav},
  address={Konstanz},
  school={Universität Konstanz}
}
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ing the spatio-temporal dynamics of vigilance and its effects on movement and collective behaviour in groups of meerkats. I find that vigilance is more likely at the edges of meerkat groups, and is correlated with decreased movement.

Collectively, these chapters illustrate the benefits of studying behaviour with a multi-time-scale perspective, and highlight how analysing behavioural sequences with such a perspective can provide novel insights into the nature of behaviour and the factors that drive it. My work here also demonstrates the efficacy of accelerometer-based inference of behavioural states, and acts as a catalogue of some of the exciting ways to make use of the resulting behavioural sequences. Looking forward, in chapter 5, I highlight possible future directions for this multi-time-scale perspective to better understand the nature of behaviour in animals. I explore two particularly interesting questions in depth: (i) how groups of interacting animals behave, and (ii) how the time-scales of behavioural decision-making are optimised.</dcterms:abstract>
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Prüfungsdatum der Dissertation

August 6, 2024
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Konstanz, Univ., Diss., 2024
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