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Title: Unifying food web structure and dynamics
Author: Hudson, Lawrence Nicholas Thomas
ISNI:       0000 0004 2729 1290
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2012
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A major goal of ecology is to discover how the dynamics and structure of multi-trophic ecological communities are related. It is difficult to understand links between dynamics and structure because mathematical models of the dynamics of systems of realistic complexity have a large number of unmeasured parameters, and whole-community data are limited and typically comprise only a snapshot or time-averaged picture. The resulting 'plague of parameters' means most studies of multi-species population dynamics have been very theoretical. Dynamical models parameterised using physiological allometries suggest a solution to the plague of parameters. These models are a synthesis of allometric scaling and Lotka-Volterra style dynamical models (Yodzis & Innes, 1992): model parameters are computed from empirically-observed inter-specific power-law relationships between physiological rates and body masses. This approach avoids the need to derive species- or population-specific parameters, sacrificing some accuracy for generality and making it possible to investigate the dynamics of complex communities. These models have been used in a large number of theoretical studies that have drawn conclusions on a wide range of topics. Despite their increasing use, this class of dynamical models are rarely tested against empirical data. This PhD examined this modelling approach and some of its assumptions. Outcomes of this work are 1) publication of a new dataset of field metabolic rate data of individual birds and mammals together with an analysis of this data using linear mixed-effects models, leading to a better understanding of one of the model's principal assumptions, 2) an open-source R package for analysing and visualising empirical food-web data, 3) an open-source R package for simulating community dynamics using the model of interest and 4) validation of the model's ability to recreate static patterns seen in empirical community data.
Supervisor: Reuman, Daniel Sponsor: Microsoft Research
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral