Genomics and machine learning to disentangle the evolutionary and ecological processes of the endangered Spanish imperial eagle and related species

We are seeking a PhD candidate with a strong interest in conservation, genomics, big data analyses and machine learning.

About the Project

The impact of global climate changes on patterns of biodiversity and species distributions is a major current topic in Biology. However, we are only beginning to develop tools and model systems to address how species have responded and adapted to large-scale perturbations in the past. From these studies we can start to better understand how to monitor and preserve species diversity by preserving natural processes, monitoring patterns of genetic change in key species, and developing predictive models to help mitigate the effects of environmental perturbations. Past demographic patterns and adaptations leave a footprint in the genomes of individuals, providing insights on past population sizes and periods of hybridization or genetic introgression from other populations or species.  

In this project we will carry out a whole genome-scale study of the demographic and evolutionary history of four long-lived top predator eagle species that diverged from a common ancestor in the late Pleistocene resulting in present-day allopatric daughter species: one living exclusively in Iberia (the Spanish imperial eagle: A. adalberti, which is endangered and was in the brink of extinction in the 60’s-80’s of the last century), one in continental Eurasia (Aquila heliacal, not globally endangered), the steppe eagle (A. nipalensis), that inhabits Asia, and breeds in Central Europe and the Tawny eagle (A. rapax), that inhabits Africa. The common evolutionary and demographic history of all the four closely related species is not currently well known. The Spanish imperial eagle is highly protected in Spain due to its critical conservation status. In addition, southern Iberian Peninsula is one of the European regions most affected by climate change that is potentially threatening this (and more) species with extinction.  

We will use genomic and ML analytic tools that are already developed and have been already used to answer these questions in the past for other species. Additionally, we will develop a new, more accurate ML methodology to analyse the evolutionary and demographic history of wild species in an attempt to overcome the limitation of exploring a limited number of models and to allow the exploration of all possible (or of a substantial increased number of) demographic models.  

Primary Supervisor

Dr Begoña Martínez Cruz (Liverpool John Moores University, LJMU)

Co-supervisors

Dr Sandra Ortega Martorell (LJMU)

Dr Iván Olier Caparroso (LJMU)

Further reading

1.           F. Abascal et al., Extreme genomic erosion after recurrent demographic bottlenecks in the highly endangered Iberian lynx. Genome Biology 17 (2016).

2.           M. Casas-Marce et al., Spatiotemporal Dynamics of Genetic Variation in the Iberian Lynx along Its Path to Extinction Reconstructed with Ancient DNA. Molecular Biology and Evolution 34, 2893-2907 (2017).

3.           F. D. Collin et al., Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest. Molecular Ecology Resources 21, 2598-2613 (2021).

4.           M. Lucena-Perez et al., Genomic patterns in the widespread Eurasian lynx shaped by Late Quaternary climatic fluctuations and anthropogenic impacts. Molecular Ecology 29, 812-828 (2020).

5.           B. Martinez-Cruz, J. A. Godoy, Genetic evidence for a recent divergence and subsequent gene flow between Spanish and Eastern imperial eagles. Bmc Evolutionary Biology 7 (2007).

6.           B. Martinez-Cruz, J. A. Godoy, J. J. Negro, Population genetics after fragmentation: the case of the endangered Spanish imperial eagle (Aquila adalberti). Molecular Ecology 13, 2243-2255 (2004).

7.           B. Martinez-Cruz, J. A. Godoy, J. J. Negro, Population fragmentation leads to spatial and temporal genetic structure in the endangered Spanish imperial eagle. Molecular Ecology 16, 477-486 (2007).

8.           M. Mondal, J. Bertranpetit, O. Lao, Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania. Nature Communications 10 (2019).

9.           D. R. Schrider, A. D. Kern, Supervised Machine Learning for Population Genetics: A New Paradigm. Trends in Genetics 34, 301-312 (2018).