Research themes

Much of our work involves trying to better understand some aspect of species, such as traits or geographical distributions, by incorporating their evolutionary history (phylogeny). Below are some of the ways we commonly do this, along with some examples of the kinds of projects you could pursue in the lab if you were to join!

Community phylogenetics

My R package 'pez' contains a number of new eco-phylogenetic methods and tools
Our R package pez contains a number of new eco-phylogenetic methods and tools

Community phylogenetics (sometimes called eco-phylogenetics) attempts to link the macro-evolutionary processes that act on clades to their present-day ecological structure. Most community phylogenetics studies use phylogeny as a proxy for missing functional trait data; we move past this and use community phylogenetic structure to detect shifts in the rate of past evolution (e.g., Pearse et al. 2013). We maintain the R package pez (Pearse et al. 2015), which implements a large number of community phylogenetic metrics and models (see Pearse et al. 2014 for a review). As well as exploring the community phylogenetic structure of transects that we are establishing in Utah, we are currently developing a new generation of models that explicitly model environmental filtering, species’ diversification, dispersal, and co-existence. Ask us for more details!

Potential student projects: Making use of existing global databases of species’ distributions, do closely-related species tend to co-occur and, if so, why? Using a dataset of Madagascan lemur traits and phylogeny, are rapidly-evolving traits associated with competition among species?

Conservation planning

Map of Britain highlighting areas with potentially dangerous species. Part of my EDAM conservation prioritisation scheme
Conservation prioritisation map of the United Kingdom’s plants

It is time that makes historical sites like castles so intuitively priceless to humans, and phylogeny can reveal the millions of years of time that make species intuitively valuable. Many of the empirical and methodological advances I have described above have implications for predictions of future change, but I strongly believe phylogeny itself can directly inform conservation decision-making. We have already prioritised the conservation of British flora using our novel ‘EDAM’ approach, which incorporates phylogeny, threat, and uncertainty in both (Pearse et al. 2015).  We are currently collaborating with the Zoological Society of London to make practical use of our prioritisation research.

Potential student projects: Pick a country and/or group of species. Using our prioritisation approach, find out which species and/or parts of the world are most at threat, and help us make useful products that will affect conservation interventions in the real world, not just the lab!

Trait evolution

Analysing leaf shape using stalkless: detecting shape in a noisy background
Analysing leaf shape using stalkless: detecting shape in a noisy background

Species’ traits define everything from what they look like to how they interact with their environment. Understanding how traits evolve can help us understand how we might expect species’ to evolve in response to climate change and other stressors. We’re particularly interested in how multiple trait co-evolve, constraining and trading-off against each other, and understanding how multiple shapes can evolve with the same functional consequences for species. Much of our work to-date has focused on plant leaves for this, making use of our stalkless image analysis pipeline.

Potential student projects: Compare the rates of evolution of plant function traits (e.g., photosynthetic rates) with morphological traits (e.g., leaf symmetry) – which are fastest, and why? Are there groups of traits that have evolved in concert, and if so why?

Software development

Overview of the workflow in phyloGenerator version 1. Version 2 is somewhat different.
Overview of the workflow in phyloGenerator version 1- help us update version 2!

Asking new kinds of questions means writing the software to answer those questions, and so we spend a lot of time writing code. Much of the code we have written has been focused on phylogenetic methods (phyloGenerator; Pearse et al. 2013). We also spend a lot of time working with existing data, and working on new ways to share the data we have collected ourselves.

Potential student projects: We are writing an automatically-updated database of species’ traits – help us! Help write the update to phyloGenerator (currently at version 2) that allows it to build even larger phylogenies more rapidly.