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.