Interactions between structure and stochasticity in demogaphic models
Author(s)
Ezard, Thomas Harold George
Type
Thesis
Abstract
Demography is the study of population dynamics. Populations can be considered as
groups of individuals living within a given region. These simple statements
encompass highly disparate systems, which respond to demographic and
environmental stochasticity in predictable and unpredictable ways. The responses
depend on the structure of the population, since individuals can have vastly different
survival and recruitment, which, with dispersal, determine population abundance.
Whilst some variation is inter-st(age) – increases in reproductive performance with
age, for example – substantial intra-st(age) variation is not uncommon. Using longterm
individual-based data on two disparate vertebrate populations, the focus of this
thesis is the interaction between structure and stochasticity in demographic models,
and consequences on resultant aspects of population growth.
Structured models that incorporated variation in demographic rates detected marked
differences within, between and across diverse habitats for different age-classes in
both populations. These results were consistent for a wide range of scaling and
definition to account for mathematical dependence. Spatial structure was more
influential than age-structure in responses to stochastic predation. Despite significant
changes in performance and phenotype with age, individual heterogeneity within ageclasses
was vast.
These results are of importance for conservation and management action, as well as
predictors of evolutionary change. The population is a fundamental force in ecology
and evolution. This work adds weight to the argument that characteristics of
individual performance in response to variability in their environments are pivotal to
increased understanding of changes in population abundance. These individual
responses are dependent upon the opportunity generated by population structure. A
failure to incorporate either structure or stochasticity neglects crucial aspects in
population regulation, and therefore ecological and evolutionary change.
groups of individuals living within a given region. These simple statements
encompass highly disparate systems, which respond to demographic and
environmental stochasticity in predictable and unpredictable ways. The responses
depend on the structure of the population, since individuals can have vastly different
survival and recruitment, which, with dispersal, determine population abundance.
Whilst some variation is inter-st(age) – increases in reproductive performance with
age, for example – substantial intra-st(age) variation is not uncommon. Using longterm
individual-based data on two disparate vertebrate populations, the focus of this
thesis is the interaction between structure and stochasticity in demographic models,
and consequences on resultant aspects of population growth.
Structured models that incorporated variation in demographic rates detected marked
differences within, between and across diverse habitats for different age-classes in
both populations. These results were consistent for a wide range of scaling and
definition to account for mathematical dependence. Spatial structure was more
influential than age-structure in responses to stochastic predation. Despite significant
changes in performance and phenotype with age, individual heterogeneity within ageclasses
was vast.
These results are of importance for conservation and management action, as well as
predictors of evolutionary change. The population is a fundamental force in ecology
and evolution. This work adds weight to the argument that characteristics of
individual performance in response to variability in their environments are pivotal to
increased understanding of changes in population abundance. These individual
responses are dependent upon the opportunity generated by population structure. A
failure to incorporate either structure or stochasticity neglects crucial aspects in
population regulation, and therefore ecological and evolutionary change.
Date Issued
2008-03-31T12:21:23Z
Date Awarded
2008-02
Format Extent
5783017 bytes
Advisor
Coulson, Tim
Sponsor
NERC
Creator
Ezard, Thomas Harold George
Publisher Department
Biological Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)