Assessing the predictability of extinction risk using machine learning and pattern recognition
Lead Research Organisation:
Imperial College London
Department Name: Life Sciences
Abstract
As global biodiversity continues to decline it is critical that we have robust tools to rapidly assess the extinction risk of species, particularly in the context of future environmental change. The International Union for Conservation of Nature's (IUCN) Red List of Threatened Species defines a series of objective criteria used to assess and monitor the extinction risk of, at present, over 90,000 species. However, for many taxonomic groups, assessments are lacking.
Approaches such as the Sampled approach to the Red List Index (SRLI) use smaller number of representative species in order to capture the overall extinction risk of lesser known taxonomic groups. However, even this approach can be logistically complicated and slow to implement.
In this project we aim to understand how new machine learning approaches can be used to predictively assess the extinction risk of species and how these predictions might vary (e.g. across species traits, taxonomic groups and biogeographical realms). Importantly, understanding the 'predictability' of different species can inform which species require full assessments and which species may usefully be assessed via machine learning tools. We will then use this approach to improve the assessment process and to investigate the how predicted assessments may change under future land use and climate change scenarios.
Approaches such as the Sampled approach to the Red List Index (SRLI) use smaller number of representative species in order to capture the overall extinction risk of lesser known taxonomic groups. However, even this approach can be logistically complicated and slow to implement.
In this project we aim to understand how new machine learning approaches can be used to predictively assess the extinction risk of species and how these predictions might vary (e.g. across species traits, taxonomic groups and biogeographical realms). Importantly, understanding the 'predictability' of different species can inform which species require full assessments and which species may usefully be assessed via machine learning tools. We will then use this approach to improve the assessment process and to investigate the how predicted assessments may change under future land use and climate change scenarios.
People |
ORCID iD |
James Rosindell (Primary Supervisor) | |
Valentina Marconi (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
NE/P012345/1 | 30/09/2017 | 29/09/2027 | |||
2234257 | Studentship | NE/P012345/1 | 28/01/2019 | 31/01/2025 | Valentina Marconi |
Description | Daisy Balogh travel fund for travel and subsistence expenses related to PhD research |
Amount | £905 (GBP) |
Organisation | Zoological Society of London |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start |
Description | International Institute for Applied Systems Analysis Young Scientist Summer Program |
Organisation | International Institute for Applied Systems Analysis |
Country | Austria |
Sector | Academic/University |
PI Contribution | I was selected to take part in the Young Scientists Summer Program at IIASA, where I worked within the newly created Biodiversity group on a study on assessing predictability of vertebrate population trends. |
Collaborator Contribution | I carried out the work under the supervision of Dr. Martin Jung at IIASA. |
Impact | I am in the process of writing up the results of our analysis, which I aim to submit to a journal in the next 6 months |
Start Year | 2021 |