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The global drivers of mutualism breakdown

   School of Biosciences

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  Dr Guillaume Chomiki  No more applications being accepted  Self-Funded PhD Students Only

About the Project

I am seeking a highly motivated PhD student to join this exciting project on the macroevolution of mutualisms. Students with background including computer science, applied mathematics and environmental science are encouraged to apply in addition to those from pure biological sciences. The scope of the project can be modified in function of the students interests and skills. The student will work with a team of internationally renewed scientists.

Background Mutualisms –cooperation between species– are ubiquitous and linked to major transitions in the history of life, such as the evolution of eukaryotes or the conquest of the land by plants. They have allowed the diversification of new lineages, permitted species to access otherwise inaccessible resources and radically modified Earth’s geochemical cycles. Yet, understanding the origins and evolutionary trajectories of mutualistic dependences remains a major challenge. What macroecological drivers explain the macroevolutionary patterns of mutualism? Where and in what conditions do mutualism breakdown? Are the drivers convergent or divergent in major plant/insect mutualisms, namely defence against herbivores, seed dispersal, and pollination? The student will test whether climatic factors, plant habit and density-dependence predict the gain, maintenance or loss of mutualistic dependence, using large-scale phylogenetic comparative approaches and the largest plant phylogenies available.

Science Graduate School

As a PhD student in one of the science departments at the University of Sheffield, you’ll be part of the Science Graduate School. You’ll get access to training opportunities designed to support your career development by helping you gain professional skills that are essential in all areas of science. You’ll be able to learn how to recognise good research and research behaviour, improve your communication abilities and experience the breadth of technologies that are used in academia, industry and many related careers. Visit to learn more.

Aims The student will conduct comparative conduct comparative phylogenetic analyses using the largest plant phylogenies available (e.g. Zanne et al. 2014 Nature [2]) and large plant mutualism databases assembled by the PI for pollination, seed dispersal, plant defense as well as already available mycorrhizal fungus. The student will perform various phylogenetic and spatial analyses to test potential drivers of the global macroecological patterns such as climatic variables, plant traits. The student will then design comparative tools adapt trait evolution (e.g. Brownian Motion, Orstein-Ulhenbeck) and biogeographic models (e.g. DEC) that explicitly model mutualistic interactions.

Methodology This project will use (i) a wide range of large-scale phylogenetic comparative methods, (ii) spatial linear analyses to tests for spatial correlates of mutualism and climate; (iii) simulation-based inference techniques, such as Approximate Bayesian Computation (ABC), to fit the model of trait and range evolution.

Timetable of Activities Year 1: Clean databases, perform large scale comparative analyses. Year 2: Finish comparative analyses of the large datasets, perform mapping and spatial linear analyses. Develop new phylogenetic comparative tools and select groups to test them. Year 3: Test the new tools developed, finish all analyses and write up thesis.

Novelty The proposed PhD project is novel and timely for several reasons: the large-scale comparative approach is only possible now that both analytic tools, global phylogenies and datasets are available. This project will provide a novel leap forward by revealing the drivers of mutualism breakdown in some of the world’s most important mutualisms. As a result, I anticipate that this will result in several high-profile publications.

Student Training The student will receive training in (1) phylogenetic comparative methods; (2) simulation-based inference techniques, such as Approximate Bayesian Computation (ABC); (3) spatial mapping analyses.

Funding Notes

Entry Requirements
First class or upper second 2(i) in a relevant subject. To formally apply for a PhD, you must complete the University’s application form using the following link:
View Website
*All applicants should ensure that both references are uploaded onto their application as a decision will be unable to be made without this information.*
*If you are interested, please contact Dr. Guillaume Chomicki ([Email Address Removed]) with your CV attached*


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