Models of aposematism and the role of aversive learning

Teichmann, J. (2015). Models of aposematism and the role of aversive learning. (Unpublished Doctoral thesis, City University London)

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The majority of species are under predatory risk in their natural habitat and targeted by predators as part of the food web. Through the process of evolution by natural selection manifold mechanisms have emerged to avoid predation. As Fisher argued, it is the ubiquitous presence of anti-predator adaptations which shows that predation plays a significant role in the ecology and evolution of ecosystems. These ecosystems are intrinsically complex which derives from the high entanglement of organisms interacting in competitive relationships: the prey is part of the predator’s environment and vice versa. As a result, the evolution of predator and prey is best described as a co-evolutionary process of predator-prey systems. It is common to classify anti-predator adaptations into ‘primary defences’ and ‘secondary defences’. Primary defences operate before an attack by reducing the frequency of detection or encounter with predators. Secondary defences, which are used after a predator has initiated prey-catching behaviour, commonly involve the expression of toxins or deterrent substances which are not observable by the predator. Hence, the possession of such secondary defence in many prey species comes with a specific signal of that defence. This pairing of a toxic secondary defence and a conspicuous primary defence is known as aposematism. Previous models mainly focused on questions of the initial evolution of aposematism in ancestrally cryptic populations. However, the field has a renewed interest in questions beyond the initial evolution of aposematism such as: how conspicuous should a signal be, and how much should be invested into secondary defence? Moreover, which factors influence evolutionary stability of aposematic solutions. Within this context, the role of co-evolution and the mechanisms of aversive learning are at the heart of the current research. On the one hand, to explain stability and persistence of aposematic signals requires a theory of co-evolution of defence and signals. On the other hand, the role of the predator and details of the predator’s aversive learning process gained renewed interest of the field. As the selective agent, aversive learning is an important aspect of predator avoidance and of the co-evolution of predator-prey systems. In the first chapter, this thesis will review the literature on aposematism and introduce the different selective pressures acting on aposematic prey. The thesis will then identify open questions of interest around aposematism. In the second chapter the thesis will focus on the perspective of the prey. The introduction of a game theoretical model of co-evolution of defence and signal will be followed by an adaptation of the model for finite populations. In finite populations, investigating the co-evolution of defence and signalling requires an understanding of natural selection as well as an assessment of the effects of drift as an additional force acting on stability. In the third chapter the thesis will adopt the perspective of the predator. It will introduce reinforcement learning as an normative framework of rational decision making in a changing environment. An analysis of the consequences of aposematism in combination with aversive learning on the predator’s diet and energy intake will be followed by a lifetime model of optimal foraging behaviour in the presence of aposematic prey in the fourth chapter. In the last chapter I will conclude that the predator’s aversive learning process plays a crucial role in the form and stability of aposematism. The introduction of temporal difference learning allows for a better understanding of the specific details of the predator’s role in aposematism and presents a way to take the discipline forward.

Item Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics
Divisions: City University London PhD theses
School of Engineering & Mathematical Sciences > Department of Mathematical Science

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