Revealing Extinction Risk through Comparative Genomics and Artificial Intelligence

Objective

Understanding species extinction risk is a crucial goal in Evolutionary Biology and a contemporary societal challenge. The standard method for assessing extinction risk, the IUCN Red List, focuses on external threats and overlooks genetic factors. Despite some weak and variable correlations, genetic diversity offers insights beyond the Red List, potentially resulting in an underestimation of extinction risk. Genomic data, generated rapidly through initiatives like the Bird 10,000 Genomes Project, has the potential to enhance assessments by revealing historical population demography, genetic diversity, and the genetic load of deleterious mutations. Recently, there has been excitement about the potential of reference genomes in conservation genomics. However, the actual potential and limitations of single reference genomes in informing conservation strategies remain unexplored. To address this gap, I propose a comprehensive approach, REVEAL, which integrates comparative genomics and individual-based simulations within a robust Artificial Intelligence (AI) framework. The proposal comprises three steps: (i) simulating extinction risk using a broad parameter space with a dataset of at least 3,825 bird genomes generated by the B10K consortium, (ii) training AI models to recognise genomic signatures associated with extinction risk, based on the previous simulations, and (iii) evaluating the potential and limitations of single reference genomes for extinction risk assessment, using the trained AI models. In particular, I will focus on avian species with diverse geographic ranges and ancestral population sizes in order to compare extinction risk predictions, considering both spatial dynamics and temporal dynamics. REVEAL seeks to move beyond conventional assessments and improve our understanding of extinction risk evaluation using genomic data, ultimately enhancing our ability to formulate effective species recovery strategies.