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The global drivers of shifts in mutualistic dependence

  • Full or part time
  • Application Deadline
    Friday, January 10, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

We are 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.
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 [1]. 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 mutualistic dependence? Are they 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.

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 a number of 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. The student will then select groups for which well-sampled phylogenies are available (e.g. some plant/pollinator or ant/plant interactions) and test several hypotheses regarding of how mutualisms impact macroevolution namely (i) that mutualisms constrain clade biogeography in horizontally transmitted mutualisms as a function of the level of specialization and dependence of the mutualism (Chomicki et al. 2019 TREE [1]) or (ii) that specialized mutualism reduce the pace of interaction-related trait evolution via stabilizing selection (Chomicki and Renner, 2017 PNAS [3]).

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 is also novel because: (1) it will provide a new reading of the diversity of mutualisms through an essential, unifying aspect: dependence. While various hypotheses regarding the impact of mutualisms on macroevolution exists (e.g. Chomicki et al. 2019 TREE [1]), the tools to test them have been so far lacking. As a result, we 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 analyses.

Funding Notes

This project is in competition with others for funding. Success will depend on the quality of applications received, relative to those for competing projects. Potential applicants should contact the supervisor (), with a CV and covering letter, detailing your reasons for applying for the project.


1. Chomicki et al. (2019). TREE 34: 698-711. 2. Zanne et al. (2014). Nature 506 : 89-92. 3. Chomicki G. & Renner S.S. (2017). PNAS 114: 3951-3956.

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