Revealing Extinction Risk through Comparative Genomics and Artificial Intelligence

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