Fancourt, Max Sebastian ORCID: https://orcid.org/0000-0002-8980-2354 (2023) Identifying Biodiversity Controls on Stability of Forest Ecosystems and Their Services. PhD thesis, University of Leeds.
Abstract
The high and multifaceted value of forests globally provides a strong motivation to better understand how they respond to perturbation, and the key variables that moderate this response. However, forest-stability research lacks a unified framework for defining and quantifying stability, and has historically focused on smaller spatial scales, resulting in considerable uncertainty about the variables that moderate climate-forest stability at landscape scales. Our results highlight the importance of understanding forest stability when seeking to explain landscape scale variation in forest response to climate perturbation. In all case studies when investigating climate perturbation, the magnitude of the perturbation alone was insufficient to explain productivity patterns. Therefore, any examination of productivity response to perturbation without considering variance in stability will be missing a crucial component. The methods presented in this thesis demonstrate that it is possible to quantify and describe spatial patterns in stability of forests to climate perturbations at landscape scales, and to understand the mechanisms behind the variation in stability that we observe. Investigation of which variables were important revealed that for both tropical and temperate forests, the background climate that a forest has experienced was the single most important group of explanatory variables, except when functional traits were directly included in models (which were then most important). Background climate, we argue, ultimately acts as a measure of the selective pressure acting on the community, and thus is informative of the community composition in terms of species and functional traits present. The finding that functional traits are important in understanding the response of forest ecosystems joins a growing body of literature highlighting the power of a functional trait approach in understanding variation in productivity responses, and offers a mechanistic understanding of the processes underlying stability, and giving us valuable insights into how these forests may respond to ongoing climate change.
Metadata
Supervisors: | Ziv, Guy and Galbraith, David |
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Related URLs: | |
Keywords: | Forest stability, stability, machine learning, remote sensing, resistance, resilience, aboveground biomass, ENSO, El Nino Southern Osciliation, drought, Amazon rainforest, UK forests, |
Awarding institution: | University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) |
Depositing User: | Max Sebastian Fancourt |
Date Deposited: | 23 Oct 2023 13:16 |
Last Modified: | 23 Oct 2023 13:16 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:33707 |
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