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Stochastic models of plant growth and competition

Croft, Simon Antony (2012) Stochastic models of plant growth and competition. PhD thesis, University of York.

Available under License Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales.

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Plants have been observed to show a range of plastic responses to environmental conditions. For example, the abundance and distribution of nutrients, as well as the presence and proximity of local competition, have been seen to result in changes in root proliferation and architecture. However, whilst some species have been witnessed displaying certain responses under given circumstances, experimental evidence suggests that responses to environmental factors can be far from simple, and sometimes counter-intuitive. Plant responses to components of the environment, and the benefit of such responses, are highly context sensitive. This thesis explores some of the real world complexities that result in the observed responses to hierarchical sets of environmental factors, and presents a theoretical model that seeks to elucidate the interplay between different factors and their effects on “optimal” behaviour by both individuals and populations. Starting with a simple one-dimensional model comprising a linearised approximation of a Gompertz growth function with nutrient patch dependent growth, the individual and combined effects of stochasticity in resource and competitor distribution are investigated. Complexity and functionality are progressively built up, with a resource dependent proliferation response, a scaling up into two-dimensions, and finally different intrinsic plant growth strategies trading growth rate against root system efficiency all introduced and investigated. Throughout the work presented in this thesis, complex and subtle behavioural responses and patterns emerge from seemingly simple models. The importance of stochasticity on individual and population level performance is also highlighted, and the results demonstrate the inability for mean-field approximations and expected results to capture the emergent behaviour.

Item Type: Thesis (PhD)
Academic Units: The University of York > Biology (York)
Identification Number/EthosID: uk.bl.ethos.581611
Depositing User: Mr Simon Antony Croft
Date Deposited: 25 Nov 2013 10:29
Last Modified: 08 Sep 2016 13:02
URI: http://etheses.whiterose.ac.uk/id/eprint/4674

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