From A to B, statistical modelling of the ecology of ants and badgers

Nouvellet, Pierre (2010) From A to B, statistical modelling of the ecology of ants and badgers. Doctoral thesis (DPhil), University of Sussex.

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Abstract

Biological systems involve features/behaviours of individuals and populations that are influenced by a multitude of factors. To explore the dynamics of such systems, a statistical description offers the possibility of testing hypotheses, drawing predictions and more generally, assessing our understanding.

In the work presented, I analyse the properties of various biological systems of two very different organisms: Pharaoh‟s ants (Monomorium pharaonis) and badgers (Meles
meles). The basis of the work, in the two projects on these biological systems, relies heavily on data collection and explaining observations using quantitative methods such
as statistical analysis and simulations.

In the first part of this thesis, I describe animal movement in space and time using data collected on the foraging behaviour of ants. A new model is presented which appears to reflect, with a high degree of accuracy, the behaviour of real organisms. This model constitutes the basis of the second chapter in which the qualities of searching strategies are explored in the context of optimal foraging. The final chapter of first part of this
thesis concludes with a detailed analysis of the rate of exploration of individuals. As an essential part of foraging, the rate of individuals leaving their nest is analysed using collected data, and contrasted with results derived from a mathematical model.

The second part of this thesis focuses on badgers. A first chapter explores the significance of palate maculation that is observed in badgers and relates their symmetry to parasitic infection. I then explore the population dynamics of a population of badgers subject to natural variation in climatic conditions. A first analysis is based on local climatic conditions, while a second analysis focuses on a more general property of climate (i.e. its unpredictability) to infer population dynamics.

Item Type: Thesis (Doctoral)
Schools and Departments: School of Life Sciences > Biology and Environmental Science
Subjects: Q Science > QH Natural history
Q Science > QH Natural history > QH0301 Biology
Depositing User: Library Cataloguing
Date Deposited: 25 Jan 2011 13:57
Last Modified: 14 Aug 2015 10:59
URI: http://sro.sussex.ac.uk/id/eprint/6283

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