Quantifying extinction risk using historical iucn red list data
File(s)
Author(s)
Bates, Ryan
Type
Thesis
Abstract
We are experiencing species declines and extinctions so severe that they've been labelled the Earth's sixth mass extinction event, and biodiversity has been deemed below planetary safe limits. Monitoring and predicting the extinctions of species is of the utmost importance to our ability to reverse these changes. The most widely used tool for this is the IUCN Red List of Threatened Species.
The Red List has proved to be an invaluable tool for assessing the relative extinction risk of species, and has been used as a progress marker for key initiatives such as the Global Biodiversity Framework. The Red List places species into discrete categories of increasing extinction risk based on a set of criteria to enable it to be used across the breadth of the tree of life. However, the categorical nature of the Red List limits the ability to use it to conduct quantitative downstream analyses.
In this thesis, I have developed a methodology to allow the generation of extinction risk from historic IUCN data. I have also shown how this can be used to investigate individual species attributes that may contribute to a species' risk profile in ways that the Red List does not fully capture. Furthermore, I use my methods to predict species' outcomes under a variety of conservation scenarios, using the Global Biodiversity Framework's goal A and target 4 as guidelines. I hope, with this work, to facilitate the global effort to predict extinctions, by providing a way to use the largest body of existing data, the IUCN Red List, in a more quantitative manner.
The Red List has proved to be an invaluable tool for assessing the relative extinction risk of species, and has been used as a progress marker for key initiatives such as the Global Biodiversity Framework. The Red List places species into discrete categories of increasing extinction risk based on a set of criteria to enable it to be used across the breadth of the tree of life. However, the categorical nature of the Red List limits the ability to use it to conduct quantitative downstream analyses.
In this thesis, I have developed a methodology to allow the generation of extinction risk from historic IUCN data. I have also shown how this can be used to investigate individual species attributes that may contribute to a species' risk profile in ways that the Red List does not fully capture. Furthermore, I use my methods to predict species' outcomes under a variety of conservation scenarios, using the Global Biodiversity Framework's goal A and target 4 as guidelines. I hope, with this work, to facilitate the global effort to predict extinctions, by providing a way to use the largest body of existing data, the IUCN Red List, in a more quantitative manner.
Version
Open Access
Date Issued
2024-05-08
Date Awarded
01/04/2025
License URL
Advisor
Rosindell, James
Gumbs, Rikki
Böhm, Monika
Gray, Claudia
Sponsor
Natural Environment Research Council (Great Britain)
Publisher Department
Department of Life Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)