POSSIBLE HONOURS THESIS PROJECTS (2020-2021)
(1) Species interactions and the evolution of song in birds —
When closely related species live together, they face conflicting challenges. Risk of hybridization and misplaced aggression favour the divergence of signals like colour patches and song to avoid confusion, but signal efficacy in shared environments (local adaptation) favours the convergence of signals across species. Do closely related species that live together diverge or converge in their signals? This project would take advantage of an existing comparative dataset on closely related birds to address this question, working closely with PhD student Haley Kenyon.
(2) Behavioural dominance and hybrid pairing in birds —
Closely related species sometimes hybridize with each other, but we may expect females of subordinate species to be more likely to mate with males of dominant species, than vice versa. A bias in the direction of hybridization matters because it can influence the evolutionary consequences of hybridization for traits that enhance premating isolation (reinforcement). The goal of this project is to use existing data on behavioural dominance of closely related species and patterns of hybrid pairing to test for a bias in patterns of hybridization. Should we find evidence for bias, we will further explore the potential consequences for the evolution of male signals versus female preference.
(3) The effects of urbanization in the face of compounding challenges —
Most species decline in the face of urbanization, but even these species can persist in some cities. One hypothesis to explain this variation in the impact of urbanization is the degree to which species face other challenges. Compounding challenges like climate and competition may ultimately lead to the disappearance of birds from cities; the absence of these challenges may allow species to cope with urbanization. We have collected a global dataset on the breeding occurrence of urban birds that would allow testing this hypothesis. This work would require modelling non-urban challenges for diverse bird species using range data, global climate data, and GIS.