Predictive island macroecology and biogeography: integrating taxonomic, functional and phylogenetic diversity

Over the last decade there has been a substantial focus shift in island macroecology and biogeography from standard taxonomic/species diversity towards a more comprehensive understanding of all types of diversity (i.e. taxonomic, functional and phylogenetic diversity) (e.g. see Mazel et al 2014; Monnet et al. 2014), emphasising a more predictive role for the disciplines (Mouquet et al. 2015). How these different types of diversity differ in regards to common macroecological patterns, such as nestedness (see Matthews et al. 2015), beta-diversity, diversity-area relationships, and species abundance distributions, remains poorly researched. Large numbers of island datasets exist and should enable examination of these differences, and many methods for decoupling and applying these different types of diversity have already been published.

This project aims to complete a synthesis of island macroecological and biogeographical patterns across the three types of diversity, with a focus on making predictions regarding how these patterns may change given future global environmental change. Depending on the successful candidate’s interests, there is also the potential to apply the above framework to a large number of urban ecological gradient datasets that have already been collected.

The successful candidate will be joining a growing Biogeography group at the University to work on secondary (global) datasets but there is the potential to conduct field work to complement the existing data, depending on the candidate’s interests. The project will involve data manipulation and capture, leading to a quantitative statistical analysis of patterns and processes. Some familiarity with R (or another programming language) is preferable but not necessarily essential. Training in coding is available within the Birmingham lab. The project will also involve collaboration with other researchers working on related ideas (e.g. Kostas Triantis – University of Athens, Jon Sadler – University of Birmingham, and Rob Whittaker – University of Oxford).