Evolution and Learning in Heterogeneous Environments

Jones, Daniel. 2015. Evolution and Learning in Heterogeneous Environments. Doctoral thesis, Goldsmiths, University of London [Thesis]

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Abstract or Description

A real-world environment is complex and non-uniform, varying over space and time. This thesis demonstrates the impact of such environmental heterogeneity upon the ways in which organisms acquire information about the world, via a series of individual-based computational models that apply progressively more detailed forms of environmental structure to understand the causal impact of four distinct environmental factors: temporal variability; task complexity; population structure; and spatial heterogeneity.

We define a baseline model, comprised of an evolving population of polygenic individuals that can follow three learning modes: innate behaviour, in which an organism acts according to its genetically-encoded traits; individual learning, in which an organism engages in trial-and-error to modify its inherited behaviours; and social learning, in which an individual mimics the behaviours of its peers.

This model is used to show that environmental variability and task complexity affect the adaptive success of each learning mode, with social learning only arising as a dominant strategy in environments of median variability and complexity. Beyond a certain complexity threshold, individual learning is shown to be the sole dominant strategy. Social learning is shown to play a beneficial role following a sudden environmental change, contributing to the dissemination of novel traits in a population of poorly-adapted individuals.

Introducing population structure in the form of a k-regular graph, we show that bounded and rigid neighbourhood relationships can have deleterious effects on a population, diminishing its evolutionary rate and equilibrium fitness, and, in some cases, preventing the population from crossing a fitness valley to a global optimum. A larger neighbourhood size is shown to increase the effectiveness of social learning, and results in a more rapid evolutionary convergence rate.

The research subsequently focuses on spatially heterogeneous environments, proposing a new method of constructing an environment characterised by two key metrics derived from landscape ecology, “patchiness” and “gradient”. We show that spatial complexity slows the rate of genetic adaptation when movement is restricted, but can increase the rate of evolution for mobile individuals. Social learning is shown to be particularly beneficial within heterogeneous environments, particularly when mobility is restricted, suggesting that phenotypic plasticity may act as a substitute for mobility.

Item Type:

Thesis (Doctoral)

Identification Number (DOI):



Behavioural ecology, landscape ecology, social learning, Baldwin effect, agent-based modelling, social simulation

Departments, Centres and Research Units:



31 October 2015

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Date Deposited:

23 Nov 2015 11:45

Last Modified:

08 Sep 2022 15:23



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