Pseudoproxy modelling to explore ecosystem dynamics and assess uncertainties in palaeoecology
type: CreativeWork
description: Contemporary ecosystems are experiencing increasing anthropogenic pressure and there is growing concern over how they will respond to such novel pressures. Palaeoecology offers crucial understanding of how ecosystems have responded to environmental change in the past, and provides valuable insight into how ecosystems will change in the future. However, palaeo-proxy records are associated with many uncertainties from environmental processes and process and observer error that may limit the inferences drawn from them. There is a call for palaeoecology to move from a descriptive to a quantitative discipline by quantifying uncertainties and employing rigorous statistical data analysis methods. Thus, the sources of uncertainty in the data must be assessed for their influence on statistical analyses, in terms of false positive and false negative pattern identification (e.g. of regime shifts or indicators of resilience). Virtual assessments of how uncertainty in palaeoecological data affect statistical analyses are few and do not adequately represent the underlying ecological dynamics or the multivariate data typical of proxy records. This lack of understanding of uncertainties is a key knowledge gap in the scientific literature that I seek to address in this thesis. First, I develop a model for generating multivariate proxy data based on underlying ecological dynamics. Second, I apply multivariate statistics to the simulated data under increasing levels of uncertainty introduced from core mixing, sub-sampling and proxy counting to determine which individual and combined sources of uncertainty have the greatest influence on the analyses. Results suggest that individual sources of uncertainty, such as sub-sampling frequency, have a greater influence on analyses than mixing or proxy counting resolution; however, the greatest influence is from the interaction effect of the combined uncertainties of sub-sampling with proxy counting. Third, I apply change point analyses to the …
abstract: Contemporary ecosystems are experiencing increasing anthropogenic pressure and there is growing concern over how they will respond to such novel pressures. Palaeoecology offers crucial understanding of how ecosystems have responded to environmental change in the past, and provides valuable insight into how ecosystems will change in the future. However, palaeo-proxy records are associated with many uncertainties from environmental processes and process and observer error that may limit the inferences drawn from them. There is a call for palaeoecology to move from a descriptive to a quantitative discipline by quantifying uncertainties and employing rigorous statistical data analysis methods. Thus, the sources of uncertainty in the data must be assessed for their influence on statistical analyses, in terms of false positive and false negative pattern identification (e.g. of regime shifts or indicators of resilience). Virtual assessments of how uncertainty in palaeoecological data affect statistical analyses are few and do not adequately represent the underlying ecological dynamics or the multivariate data typical of proxy records. This lack of understanding of uncertainties is a key knowledge gap in the scientific literature that I seek to address in this thesis. First, I develop a model for generating multivariate proxy data based on underlying ecological dynamics. Second, I apply multivariate statistics to the simulated data under increasing levels of uncertainty introduced from core mixing, sub-sampling and proxy counting to determine which individual and combined sources of uncertainty have the greatest influence on the analyses. Results suggest that individual sources of uncertainty, such as sub-sampling frequency, have a greater influence on analyses than mixing or proxy counting resolution; however, the greatest influence is from the interaction effect of the combined uncertainties of sub-sampling with proxy counting. Third, I apply change point analyses to the …
author: Asena, Quinn
license: https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
date published: 2021
date modified: 2026-07-24T01:03:37Z
provider: University of Auckland
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: ResearchSpace@Auckland

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2292/58282