NERC E4 Thermal sensitivity of UK butterflies in space versus time

Interested individuals must follow the "how to apply" link on the Geosciences E4 Doctoral Training Partnership web page: http://www.ed.ac.uk/e4-dtp/how-to-apply

Summary
This project will test the reliability of space-for-time substitutions as a means of projecting the responses of UK butterfly populations to rising temperatures.
Project background

Predicting how species will respond to climate change is one of the major challenges that today’s ecologists are faced with. A very common approach for projecting a species distribution into the future rests on the assumption that the relationships between climatic variables and abundance estimated across space are transferable over time as climate changes. This assumption has rarely been tested.

The exceptional long-term population data arising from the UK butterfly monitoring (UKBMS) scheme provides a rare opportunity to test the pivotal assumption of space for time transferability of the temperature-abundance relationship. You will also have an opportunity to develop comparative analyses aimed at identifying traits that predispose species to be climate winners or losers.

Research questions

1. How well does space-for-time substitution work for predicting the responses of butterfly populations to temperature?
2. Do we see a difference in the lag of the temporal thermal response between the warm and cold edge of a species distribution?
3. Do some species have attributes that make them more sensitive to temperature?
Methodology

Interested individuals must follow the "how to apply" link on the Geosciences E4 Doctoral Training Partnership web page: http://www.ed.ac.uk/e4-dtp/how-to-apply

The first year will be spent learning, developing and applying statistical methods (based on a generalised linear mixed models) to temperature and peak abundance data for individual butterfly species. In years 2 and 3 comparative analyses will be conducted across a broad sample of butterfly species, with the aim of identifying general trends and traits that correlate with species responses.
Training

A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. In addition you will receive substantial support and training in the application of Bayesian mixed models in R and will have the opportunity to attend courses on spatial mixed modelling (e.g., R-INLA).
Requirements

An undergraduate degree in evolutionary biology, ecology, zoology or conservation biology is required. Other degrees will be considered if the candidate can demonstrate that they have an appropriate background and skill set. Experience of running statistical analysis in R would be benefical.