Hanif Kawousi, (2024) Population effects of fear-inducing signals in digital songbirds
Abstract Anthropogenic environments are rapidly changing. Reactions to these changes within prey populations can become detrimental since behavioral strategies for foraging and vigilance are adapted to the ancestral environment. Ecology of Fear studies show that fear alone can halve songbird populations in five years, not considering lasting transgenerational impacts. Ecologists point towards the need for explanations for this phenomenon, but theoretical models have yet to tackle the challenge of predicting how rapid environmental changes affect prey behavior and survival.
This thesis presents a new, object-oriented model that bridges ecology and subjective cognition. It simulates how evolved emotions governed by two genes related to fear and hunger shape the behavioral patterns of digital songbirds experiencing a sudden increase in perceived risk. The thesis model consists of two phases: First, it evolves digital songbirds equipped with genes for fear and hunger through a genetic algorithm. Then, it exposes these evolved digital songbirds to greater levels of predator presence, exceeding that of their ancestral environment. By disabling the predator attack, the model predicts how fear alone changes evolved behavioral patterns in the simulated population.
The model results show that as fear levels increase, all simulated populations experience a decrease in average body mass and, consequently, in expected fecundity. Additionally, the simulated populations with unique evolved gene pools each exhibited distinct patterns of body mass distribution as fear levels increased, indicating altered foraging strategies under heightened fear conditions. However, due to its current simplicity, the model is not yet suitable for real-world application.
Lars Kristian Landsrød, (2017). Decision-making in a proximate model framework: How behaviour flexibility is generated by arousal and attention.
Abstract Animals must make decisions based on limited information and during a limited amount of time. The time spent exploring possibilities and sampling environmental information, means less time spent at actually gathering and securing recourses. A realistic modelling of animals and their behaviour must include organisms that do not make optimal decisions. Instead, they are constrained by local factors like illumination and conspecifics, as well as the animal’s own state like hungry or afraid.
The animal’s personality must also be taken into account when discussing decision-making. Relative stabile traits have been observed in many species, and allow us to predict to a certain extent, their behaviour in the future. In the context of coping with stressful situations, behaviour flexibility (or to what degree the animals react to environmental information) seems to be an important trait.
In this thesis, I have explored a computer model for decision-making in fish and studied how behaviour flexibility can be generated in the agents. Behaviour flexibility was measured as the fish’ propensity to change their internal state, called the global organismic state (GOS). The study was done by adjusting two parameters. The first of these control the rate at which motivation declines, after first being elevated (e.g. by seeing a predator). The second parameter controls the filtration of irrelevant information, when the agent is highly motivated. These are called arousal dissipation factor (ADF) and attention modulation factor (AMF), respectively.
The results show that both factors affects behaviour flexibility in the fish. ADF influences how often the fish re-evaluate their current state, in light of the available information. Fish that re- evaluates more often were more likely to change their GOS. Even though the ADF was sufficient to generate variation in flexibility, information filtering (AMF) was required to generate particularly rigid behaviour, i.e. rarely changing their internal state.
The project was supervised by Sergey Budaev and Sigrunn Eliassen.
Get full text at BORA.