MSc (Res) in Biology: Behaviour, Ecology and Evolution

• How to apply

  You may apply for placement in advertised projects (see the list of current projects further on this page) or contact potential supervisors directly.

  Potential candidates are recommended to make contact with a potential supervisor before applying. If you are self-funded and interested in working with a supervisor who does not currently have a project listed, please contact that person directly.

  Biology has two dates for admission to this degree: September and January each year.

  If you would like to make a formal application to study for an MSc (Res) at St Andrews, please complete an application using the online system. 

• Entry requirements

  You should have an undergraduate Honours degree at 2.1 level or better in biological or environmental sciences. Students from backgrounds such as mathematics may be accepted under exceptional circumstances.

  If you studied for your first degree outside of the UK, please see the international entry requirements.

  For non-native English speakers, please see the English language requirements.

  Applicants will be short-listed by the project supervisor. Short-listed applicants will be interviewed by members of the School of Biology Postgraduate Recruitment Committee and/or other academic staff, with successful performance at interview being a requirement for entry onto the degree.

• Fees and funding

  For postgraduate tuition fees for Biology MSc (Res) programmes, please see the University's research tuition fees page. 

  Scholarships, research council funding or other arrangements may be available for this programme. See the research scholarships page for more information.

• Agent-based modelling for animal behaviour

  Supervisor: Dr V Anne Smith

  Co-supervisor: Dr Lauren Guillette

  Masters projects are available in applying agent-based modelling to elucidate rules underlying animal cognition and behaviour.

  You will be jointly supervised by a computational biologist (Smith) and an experimental biologist and psychologist (Guillette).

  Depending on student interest, projects may include working with extant data to build and evolve models, or incorporate significant amounts of hands-on behaviour experiments.

  Please contact Dr V Anne Smith (anne.smith@st-andrews.ac.uk) to discuss projects which may suit.

  Relevant references

  • DJ White and VA Smith. 2007. Testing measures of animal social association by computer simulation. Behaviour 144:1447-1468.
  • VA Smith. 2008. Evolving an agent-based model to probe behavioural rules in flocks of cowbirds. Proceedings of the Eleventh International Conference on Artificial Life MIT Press, Cambridge, MA, pp 561-568.
  • Guillette, L.M., Scott, A.C.Y. and Healy, S.D. 2016 Social learning in nest-building birds: the role of familiarity. Proceedings of the Royal Society B: Biological Sciences, 283, 20152685.
  • Guillette, L.M. and Healy, S.D. 2017. The roles of vocal and visual interactions in social learning zebra finches: A video playback experiment. Behavioural Processes, 139, 43-49.

• Animal cognition

  Supervisor: Dr Sue Healy

  Cognition plays a central role in the lives of many animals, whether with regard to learning and remembering where to find food or home, making decisions over choices of food or mates, or in interacting with others. Current research projects are focused on two areas:

  • determining how birds know what nest to build (behavioural and neurobiological laboratory work on zebra finches; behavioural fieldwork on UK blue tits and African weavers)
  • using free-living hummingbirds (Canadian Rocky Mountains, collaborator Professor Andy Hurly, University of Lethbridge) as a model system to investigate cognition in the wild.

  Relevant references

  • Healy Lab - Cognition in the Wild

• Behavioural adaptation to climate change

  Supervisor: Natalie Pilakouta

  The main aim of our work is to improve our ability to predict population responses to human-induced environmental changes and, in particular, climate change. We are particularly interested in:

  1. how environmental change may alter animal behaviour.
  2. whether such changes in animal behaviour influence the capacity of populations to adapt to environmental change.

  Our research integrates behavioural ecology, evolutionary biology, and ecophysiology, using a wide range of methods, such as experimental evolution, field-based studies, and molecular techniques. We work mainly on fish and insect study systems, but we also use meta-analyses to answer questions with a broad taxonomic scope.

  Relevant references

  • Pilakouta Lab

• Behavioural and evolutionary ecology of insects

  Supervisor: Dr David Shuker

  Dr Shuker's group studies the behavioural ecology of insect reproduction. Their current research focuses on sexual behaviour in five species of seed bug (Family Lygaeidae). In particular, they are interested in instances of when 'good mating systems go bad', including heterospecific mating encounters (or 'reproductive interference') and failed copulations ('mating failure'). Both heterospecific matings and mating failure should be strongly disfavoured by natural and sexual selection, and yet both are more common than previously realised.

  Your project will explore one or both of these phenomena in the group's bugs, with a mix of behavioural and ecological experiments, grounded in mating systems theory.

  Relevant references

  • Burdfield-Steel, E.R. and Shuker, D.M. (2011) Reproductive interference. Current Biology, 21: R450-451.
  • Burdfield-Steel, E.R. and Shuker, D.M. (2014) The evolutionary ecology of the Lygaeidae. Ecology & Evolution, 4: 2278-2301.
  • Greenway, E.V., Dougherty, L.R. and Shuker, D.M. (2015) Mating failure. Current Biology, 25: R534-R536.
  • Greenway, E.V. and Shuker, D.M. (2015) The repeatability of mating failure in a polyandrous insect. Journal of Evolutionary Biology, 28: 1578-1582.
  • Shuker, D.M., Currie, N., Hoole, T. and Burdfield-Steel, E.R. (2015) The extent and costs of reproductive interference among four species of true bug. Population Ecology, 57: 321-331.
  • Shuker, D.M. and Simmons, L.W. (eds) (2014) The Evolution of Insect Mating Systems, Oxford University Press.

• Computational biology

  Supervisor: Dr V Anne Smith

  Masters projects are available in areas of machine learning applied to molecular, neural, and ecological systems. Dr Smith's group concentrates on inference of network structure from observational data, but also explores optimisation, agent-based modelling, and evolutionary algorithms, in the context of analysing biological questions. An emphasis is placed on evolutionary perspectives.

  Projects could be ideal bridges for students with degrees either in mathematics, computer science, or biology subjects to move into the interdisciplinary area of computational biology.

  Please contact Dr V Anne Smith (anne.smith@st-andrews.ac.uk) to discuss your interests and particular projects which may suit. 

  Relevant references

  • W Verleyen, SP Langdon, D Faratian, DJ Harrison, VA Smith. 2015. Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis. Scientific Reports 5:15563
  • I Milns, CM Beale and VA Smith. 2010. Revealing ecological networks using Bayesian network inference algorithms. Ecology 91:1892-1899
  • C Echtermeyer, TV Smulders and VA Smith. 2010. Causal pattern recovery from neural spike train data using the Snap Shot Score. Journal of Computational Neuroscience 29:231-252
  • DJ White and VA Smith. 2007. Testing measures of animal social association by computer simulation. Behaviour 144:1447-1468

• Computational neuroscience: live imaging data

  Supervisor: Dr V Anne Smith

  Co Supervisor: Dr Stefan Pulver, School of Psychology and Neuroscience, University of St Andrews

  Live imaging of neural activity provides a wealth of data on neural activity in living animals; however, current computational analyses lag behind technological development.

  Masters projects are available in collaboration between a computational biologist (Smith) and an experimental neuroscientist (Pulver), developing and applying computational methods for ‘mining’ of live imaging datasets.

  Projects can address various aspects of analysis, from automatic image processing to answering biological questions by inferring neural information flow. Students can work entirely computationally, or have the opportunity to gain skills in experimental neuroscience.

  Please contact Dr V Anne Smith (anne.smith@st-andrews.ac.uk) to discuss your interests and potential projects.

  Relevant references

  • Pulver SR, Bayley TG, Taylor AL, Berni J, Bate M, Hedwig BJ. 2015. Imaging fictive locomotor patterns in larval Drosophila J. Neurophysiol. 114:2564-77
  • Lemon WC, Pulver SR, Höckendorf B, McDole K, Branson K, Freeman J, Keller PJ. 2015. Whole-central nervous system functional imaging in larval Drosophila. Nat. Commun. 11:7924
  • Echtermeyer C, Smulders TV, Smith VA. 2010. Causal pattern recovery from neural spike train data using the Snap Shot Score. Journal of Computational Neuroscience 29:231-252
  • Smith VA, Yu J, Smulders TV, Hartemink AJ, Jarvis ED. 2006. Computational inference of neural information flow networks. PLoS Computational Biology 2:e161.

• DNA repair in archaea and humans

  Supervisors: Professor Malcolm White and Dr Carlos Penedo

  DNA repair is essential for all forms of life. There are many overlapping DNA repair pathways that contribute to the maintenance of genetic integrity.

  This project will be focused on improving our understanding of the Nucleotide Excision Repair (NER) pathway in humans. It will involve training in biochemistry and molecular biology, with an emphasis on the use of cutting-edge techniques to study DNA:protein interactions.

• Evolutionary developmental biology

  Supervisor: Dr David Ferrier

  Dr Ferrier's project seeks to understand how the diversity of animal forms have evolved via changes to their development, usually taking the homeobox genes of the Hox/ParaHox and related clusters as a starting point.

  The project studies a variety of invertebrate species (including amphioxus, Ciona, annelids, arthropods, cnidarians and sponges), with the aim of focusing on major transitions in animal evolution, including the origins of the animal kingdom, the origin of the bilaterally symmetrical animals (bilaterians) and the origin of chordates and vertebrates.

  Relevant references

  • Evolutionary Developmental Genomics Group

• Evolutionary, developmental, cell, and regeneration biology and genomics

  Supervisor: Dr Ildiko Somorjai

  Have you ever wondered why some animals regenerate well, and humans do not? Are you interested in how new genes are born, and what generates diversity in animal body forms? The Somorjai Lab addresses these problems from evolutionary, developmental and cell biological perspectives.

  The lab predominantly uses the marine invertebrate chordate “amphioxus” due to its genetic and anatomical similarly to simple vertebrates. They also work on flatworms, which have amazing regenerative powers and multipotent stem cells.

  The project will depend on the student’s interests and background, but could include:

  • gene expression analyses
  • embryology
  • immunohistochemistry
  • confocal microscopy
  • genomics
  • phylogenetic analyses. 

  Find out more about the Somorjai Lab. 

  Relevant references

  • Bertrand S, Escriva H. Evolutionary crossroads in developmental biology: amphioxus. Development. 2011 Nov;138(22):4819-30.
  • Somorjai IM, Somorjai RL, Garcia-Fernàndez J, Escrivà H. Vertebrate-like regeneration in the invertebrate chordate amphioxus. Proc Natl Acad Sci U S A. 2012 109(2):517-22.
  • Dailey, SC, Planas, RF, Espier, AR, Garcia-Fernandez, J and Somorjai, IML Asymmetric distribution of pl10 and bruno2, new members of a conserved core of early germline determinants in cephalochordates. Frontiers in Ecology and Evolution. 2016. 3, 156.

• Evolutionary quantitative genetics

  Supervisor: Dr Michael Morrissey

  Evolutionary quantitative genetics provides a general framework for modelling how natural selection and genetic variation interact to generate adaptive evolutionary change.

  Conducting a research project in this field will provide a student with a solid foundation in a key area of evolutionary biology as well as broadly useful analytical skills.

  Several projects are available that are suitable to an MSc by research, including:

  • Selection of morphology and phenology in heterogeneous environments: conduct a field study to collect trait, fitness, and microenvironmental data in a local wild annual plant population, and conduct analyses of selection of those traits that account simultaneously for the effects of both traits and microenvironmental variation on fitness.
    
    
  • The genetic basis of plasticity and evolutionary consequences of non-linear reaction norms: collect trait data from fruit flies raised across a range of diet treatments.  This will allow inference of the genetic basis of plasticity, and assessment of likely changes in the mean and variance of phenotype under responses to different selective regimes.

  Interested candidates should contact Dr Michael Morrissey to discuss these or other potential projects before preparing an application.

• Evolution of same-sex sexual behaviour

  Supervisor: Dr Nathan Bailey

  Co-supervisor: Dr Kevin Judge, MacEwan University

  Same-sex sexual behaviour (SSB) is frequently portrayed as an evolutionary paradox. But is it? The Bailey lab has explored causes of its expression at low but persistent levels in insects.

  This project will apply a comparative approach to test hypotheses about the evolution of SSB, capitalising on an extensive resource of videotaped behavioural interactions across multiple field cricket species.

  Students will have flexibility to tailor the project to their specific interests. The project is based at St Andrews.

  Relevant references

  • Bailey NW, Zuk M (2009) Same-sex sexual behavior and evolution. Trends in Ecology and Evolution 24:439-446.
  • Hoskins JL, Ritchie MG, Bailey NW (2015) A test of genetic models for the evolutionary maintenance of same-sex sexual behaviour. Proceedings of the Royal Society of London, B 282:20150429.
  • Judge KA, Ting JJ, Schneider J, Fitzpatrick MJ (2010) A lover, not a fighter: mating causes male crickets to lose fights. Behavioral Ecology and Sociobiology 64:1971-1979.
  • Bailey NW, Hoskins JL, Green J, Ritchie MG (2013) Measuring same-sex sexual behaviour: the influence of the male social environment. Animal Behaviour 86:91-100.
  • Bailey NW, French N (2012) Same-sex sexual behaviour and mistaken identity in male field crickets (Teleogryllus oceanicus). Animal Behaviour 84:1031-1038.

• Experimental evolution in microbial systems

  Supervisor: Dr V Anne Smith

  How repeatable is evolution? What happens to synthetically engineered gene circuits under adaptive pressures? How do microbial communities persist? Masters projects are available in experimental evolution in microbial systems, particularly yeast.

  The laboratory has access to a state-of-the-art Bioscreen C machine which propagates 200 microbial cultures simultaneously.

  Topics addressed can range from basic features of evolution and mechanisms underlying adaptation, to the exploration of robustness and persistence of biodiversity in artificial microbiomes, to impact of evolutionary considerations on design of systems for synthetic biology.

  Please contact Dr V Anne Smith (anne.smith@st-andrews.ac.uk) to discuss your interests and particular projects which may suit.

• Genetics and sexual isolation

  Supervisor: Professor Michael Ritchie

  Finding genes that influence speciation is a major aim of evolutionary biology. Professor Ritchie's group has identified genes with the potential to influence sexual isolation, the coordinated behaviours involved in species-specific mating choice. Species of Drosophila allow examination of mutants (some home-made) in these genes, and precise manipulation of gene expression.

  Professor Ritchie offers a range of projects examining the behavioural consequences of altering these genes. Do mutants have abnormal courtship behaviour such as male songs or female preferences, court the wrong species, or suffer in sperm competition? Such projects allow behavioural, physiological and genetic studies of gene action and speciation.

  Relevant references

  • Wilkinson, G. S., et al. 2015. The locus of sexual selection: moving sexual selection studies into the post-genomics era. Journal of Evolutionary Biology 28: 739-755.
  • Ritchie, M. G. and Butlin, R. K. 2014. The genetics of insect mating systems. Pages 59-77 in Shuker, D. M. and Simmons, L. W. (eds.) The Evolution of Insect Mating Systems. OUP, Oxford.
  • Neville, M. C., et al. 2014. Male-specific fruitless isoforms target neurodevelopmental genes to specify a sexually dimorphic nervous system. Current Biology 24: 229-241.

• Natural and sexual selection on insect signals in Oceania

  Supervisor: Dr Nathan Bailey

  Co-supervisor: Professor Leigh Simmons, University of Western Australia

  How do animal signals evolve under conflicting evolutionary pressures? Sexual selection favouring conspicuous signals to attract mates can be counteracted by natural selection acting against energetic or predation costs.

  This project will use field crickets to examine how variable climatic factors constrain, or alternatively facilitate, the evolution of chemical signals used during mate choice in populations across Australia and Oceania.

  Students will have flexibility to shape the project to suit their own interests. The project is based in St Andrews.

  Relevant references

  • Pascoal S, Risse JE, Zhang X, Blaxter M, Cezard T, Challis RJ, Gharbi K, Hunt J, Kumar S, Langan E, Liu X, Rayner JG, Ritchie MG, Snoek BL, Trivedi U, Bailey NW (2020) Field cricket genome reveals the footprint of recent, abrupt adaptation in the wild. Evolution Letters.
  • Berson JD, Zuk M, Simmons LW (2019) Natural and sexual selection on cuticular hydrocarbons: a quantitative genetic analysis. Proceedings of the Royal Society of London, B. 286:20190677.
  • Moran PA, Hunt J, Mitchell C, Ritchie MG, Bailey NW (2019) Behavioural mechanisms of sexual isolation involving multiple modalities and their inheritance. Journal of Evolutionary Biology. 32:243-258.
  • Berson JD, Simmons LW (2019) Female cuticular hydrocarbons can signal indirect fecundity benefits in an insect. Evolution. 73:982-989.
  • Pascoal S, Mendrok M, Wilson AJ, Hunt J, Bailey NW (2017) Sexual selection and population divergence II. Divergence in different sexual traits and signal modalities in field crickets (Teleogryllus oceanicus). Evolution. 71:1614-1626.

• Pesticides and the costs to beneficial insects

  Supervisor: Dr David Shuker

  Pesticides have an important role to play in helping us feed human populations. However, pesticides also bring negative effects, in terms of both human health and the health of the ecosystems around us. It is becoming increasingly clear that pesticides disrupt non-target species, often in ways that are more subtle than just killing them, but that still bring negative ecological consequences.

  Dr Shuker's group studies the sub-lethal effects of controversial neonicotinoid pesticides on an important class of beneficial insects, the parasitic wasps. Your project will explore how neonicotinoids disrupt important life-history and behavioural decisions, such as sex allocation and mating.

  Relevant references

  • Cook, N., Green, J., Shuker, D.M. and Whitehorn, P.R. (2016) Exposure to the neonicotinoid imidacloprid disrupts sex allocation cue use during superparasitism in the parasitoid wasp Nasonia vitripennis. Ecological Entomology, 41: 693-697.
  • Ellis, C., Park, K., Whitehorn, P.R., David, A. and Goulson, D. (2017) The neonicotinoid insecticide thiacloprid impacts upon bumblebee colony development under field conditions. Environmental Science and Technology, 51: 1727-1732.
  • Whitehorn, P.R., Cook, N., Blackburn, C.V., Gill, S.M., Green, J. and Shuker, D.M. (2015) Sex allocation theory reveals a hidden cost of neonicotinoid exposure in a parasitoid wasp. Proceedings of the Royal Society, Series B, 282: 20150389.
  • Whitehorn, P.R., O'Connor, S., Wackers, F.L. and Goulson, D. (2012) Neonicotinoid pesticide reduces bumble bee colony growth and queen production. Science, 336: 351-352.

• Predation, behaviour, animal communication

  Supervisor: Professor Graeme Ruxton

  Professor Ruxton is interested in how prey protect themselves from predators, particularly through their appearance (for example, camouflage and mimicry) and through grouping together.

  There are a number of projects in this area for students wanting to stretch their understanding in behavioural ecology, experimental design, animal behaviour and zoology generally.

  Professor Ruxton also has a strong interest in enhancing the practice of experimental design and statistical analysis across biology, and would welcome enquiries from students with a maths or statistics background interested in applying their skills within whole organism biology.

• Social influences on the shooting behaviour of archerfish

  Supervisors: Mike Webster and Luke Rendell

  Archerfish (Toxotes sp.) have a unique hunting style, spitting jets of water at invertebrates above the surface, capturing them when they fall into the water. This behaviour is remarkable from a neurobiological perspective, as the fish must rapidly process and update visual information in order to aim accurately, and account for refraction, the trajectory of the water jet and then that falling prey in order to intercept it. This behaviour is also shaped by the social environment. Archerfish can learn which targets to shoot for a reward and how to aim accurately by copying others. On the other hand, competition for prey is intense, with shooters often losing downed prey to rivals. For this reason archerfish are also very sensitive to the presence and behaviour of others. We have run an archerfish lab for several years, investigating how learning, competition and individual personalities shape archerfish shooting decisions. We offer strong quantitative and methodological training. We have openings for MRes projects to address questions relating to:

  • How archerfish perceive and respond to competitors.
  • How archerfish use social information to learn to shoot novel targets.
  • Whether learning performance in a shooting context is related to learning ability more generally.
  • The development and validation of novel stimuli such as virtual and robot demonstrators and automated shot-logging and reward apparatus.

  Please contact the supervisors to discuss this project.

  Further reading

  • Jones NAR, Webster MM, Templeton CN, Schuster S & Rendell L (2018). Presence of an audience and consistent inter-individual differences affect archerfish shooting behaviour. Animal Behaviour 131 95-103.
  • Jones NAR, Webster MM, Newport C, Templeton CN, Schuster S & Rendell L (2020). Cognitive styles: Speed accuracy trade-offs underlie individual differences in archerfish. Animal Behaviour 160, 1-14.
  • Jones NAR, Klump BC, Abaurrea TM, Harrower S, Marr C, Scott L, Rendell L & Webster MM (2021). Short range hunters: exploring the function and constraints of water shooting in dwarf gouramis. Journal of Experimental Biology 224: jeb243477.
  • Jones NAR, Spence-Jones HC, Webster MM & Rendell LE (2021). Individual behavioural traits not social context affects learning about novel objects in archerfish. Behavioral Ecology and Sociobiology 75, 58.
  • Schuster, S., 2007. Archerfish. Current Biology, 17(13), pp.R494-R495.

• Collective behaviour of dead fish?

  Supervisor: Dr Mike Webster (mmw1@st-andrews.ac.uk)

  Many of us are familiar with spectacular examples of animal collective movement, such as starling murmurations and shimmering schools of fish. The collective motion of such groups can be understood using interaction rules. These rules capture how individuals interact with their close neighbours, maintaining maximum and minimum distances from them and the extent to which they match their heading and speed. Scaled across the whole group, these local-level interactions shape the properties and behaviour of the entire flock, swarm or school. A recent study attempted to infer similar interaction rules from a school of several hundred fossilised fish preserved in a slab of limestone [1]. The fish were polarised and showed evidence of interaction rules; nearest neighbours that were close together had trajectories that moved them apart and those neighbours that were further away from each other were following paths that brought them close together. The authors suggest that this provides evidence that the interaction rules behind collective behaviour appeared tens of millions of years ago. This seems reasonable enough, but is that what is really going on here? One interpretation of this finding is that the school was frozen in time, perhaps trapped by a sudden fall of sediment. Critics suggest that this is implausible as the sediment would surely displace the fish and any obscure any trace of the fine scale interactions between them. Another explanation is that the final assembly of the group happened after death. Perhaps the fish died in a drought and were reordered by flowing water shortly afterwards, with their streamlined bodies aligning with the current and the vortices generated as the water moved around them affecting the positioning of their near neighbours. The first suggestion is difficult (and unethical) to test directly, but the second is amenable to experimental investigation. Using dead fish and a flow tank, this project will investigate the feasibility of the second explanation, that flowing water can organise dead fish into assemblages that have the hallmarks of collective behaviour.   

   [1] Mizumoto et al. (2019). Inferring collective behaviour from a fossilized fish shoal. Proc Roy Soc B, 286, 20190891

   Relevant references

  • Couzin et al. (2003). Self-organization and collective behavior in vertebrates. Advances in the Study of Behavior, 32, 10-1016.
  • Katz et al. (2011). Inferring the structure and dynamics of interactions in schooling fish. Proceedings of the National Academy of Sciences, 108, 18720-18725.
  • Ward et al. (2017). Local interactions and global properties of wild, free-ranging stickleback shoals. Royal Society Open Science, 4, p.170043.

• Using Information Theory to describe genomic differentiation and relatedness in spatially structured populations

  Supervisor: Oscar E. Gaggiotti

  Describing spatial patterns of genetic diversity is a fundamental first step in the study of biotic and abiotic drivers of biodiversity as well as ecological and evolutionary process underlying its temporal changes. Since its inception, the field of population genetics developed a rich body of theory focused on summary statistics describing genetic variation, which has led to fundamental insights into evolutionary processes. However, being specific to the field, population genetics summary statistics are not readily applicable to studies focused on bringing together the fields of ecology and evolution. A first step in overcoming this roadblock was the introduction of summary statistics based on Information Theory, that have been favoured by ecologists (Sherwin et al., 2017) and the development of frameworks that take into account the hierarchical structure of biodiversity from populations to whole ecosystems (Gaggiotti et al., 2018). One limitation of these advances is that they are poorly adapted to the study of species with continuous spatial distributions. The typical approach adopted by population geneticists in this case is to use statistics that measure relatedness between individuals and model how they change across spatially continuous habitats (Rousset, 2000). Thus, the purpose of this MSc project is to extend the framework of Gaggiotti et al. (2018), which describes differentiation between demographic units at different levels of the hierarchy, to the level of pairs of individuals. More precisely, the objective is to develop genetic relatedness statistics based on Information Theory and evaluate their performance using sophisticated simulation methods as well as population genomic databases available for dolphins and whales.

  The student will be based in the group of Prof. Oscar Gaggiotti in St Andrews. Candidates postulating for this project should have a strong computational biology background and good knowledge of population genetics as taught at the undergraduate level. Students without this background should not apply.

  Relevant references

  • O. E. Gaggiotti, A. Chao, P. Peres-Neto, C.-H. Chiu, C. Edwards, M.-J. Fortin, L. Jost, C. M. Richards, and K. A. Selkoe. Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales. EVOLUTIONARY APPLICATIONS, 11(7, SI):1176–1193, AUG 2018. ISSN 1752-4571. doi: 10.1111/eva.12593.
  • F. Rousset. Genetic di↵erentiation between individuals. JOURNAL OF EVOLUTIONARY BIOLOGY, 13(1):58–62, JAN 2000. ISSN 1010-061X.
  • W. B. Sherwin, A. Chao, L. Jost, and P. E. Smouse. Information theory broadens the spectrum of molecular ecology and evolution. TRENDS IN ECOLOGY & EVOLUTION, 32(12):948–963, DEC 2017. ISSN 0169-5347. doi: 10.1016/j.tree.2017.09.012.

• Deep Learning: Balancing, Linkage and Effects of Selection (DEEPBLUES)

  Supervisor: Dr Carolin Kosiol and Dr Svitlana Braichenko

  Our research team focuses on the development of computational methods to investigate adaptations occurring at both inter- and intraspecies timescales. Presently, we incorporate Deep Learning (DL) methodologies to examine the phenomenon of balancing selection across diverse species.

  The detection of balancing selection is an exciting problem challenging evolutionary geneticists for decades. It is often intertwined with linkage disequilibrium (LD), which connects genetic sites. This master’s project focuses on using Ancestral Recombination Graphs (ARGs) to generate testing and training data for DL with balancing selection while accounting for LD. The master’s student will work closely with Research Fellow Dr Svitlana Braichenko on the implementation of ARGs. Subsequently, we will apply our newly developed DL method, which draws upon Voznica et al. (2022) and has been trained on the data generated, to detect balancing selection in the populations of great apes and fruit flies.

  Please reach out to Dr Carolin Kosiol (ck202@st-andrews.ac.uk) or Dr Svitlana Braichenko (sb442@st-andrews.ac.uk) for discussions.

  Relevant references

  • N. De Maio, C. Schlötterer, and C. Kosiol, ‘Linking Great Apes Genome Evolution across Time Scales Using Polymorphism-Aware Phylogenetic Models’, Molecular Biology and Evolution, vol. 30, no. 10, pp. 2249–2262, Oct. 2013, doi: 10.1093/molbev/mst131.
  • R. Borges, B. Boussau, S. Höhna, R. J. Pereira, and C. Kosiol, ‘Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes’, Methods in Ecology and Evolution, vol. 13, no. 11, pp. 2339–2346, 2022, doi: 10.1111/2041-210X.13980.
  • H. A. Hejase, Z. Mo, L. Campagna, and A. Siepel, ‘A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph’, Molecular Biology and Evolution, vol. 39, no. 1, p. msab332, Jan. 2022, doi: 10.1093/molbev/msab332.
  • J. Voznica et al., ‘Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks’, Nature Communications, vol. 13, no. 1, Art. no. 1, Jul. 2022, doi: 10.1038/s41467-022-31511-0.
  • B. D. Bitarello, D. Y. C. Brandt, D. Meyer, and A. M. Andrés, ‘Inferring Balancing Selection From Genome-Scale Data’, Genome Biology and Evolution, vol. 15, no. 3, p. evad032, Mar. 2023, doi: 10.1093/gbe/evad032.