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NERC E4 Thermal sensitivity of UK butterflies in space versus time

  • Full or part time
    Dr A Phillimore
    Dr NJB Isaac
    Dr S Collins
  • Application Deadline
    Thursday, January 09, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

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.

Funding Notes

This project is eligible for the E4 Doctoral Training Partnership. The E4 projects are currently available for full NERC studentship funding which is competitive by interview AND AVAILABLE ONLY TO UK citizens and to EU citizens who have worked or studied in the UK for the previous 3 years.
For application details see View Website
Further details here - View Website

References

Devictor, Vincent, Chris Van Swaay, Tom Brereton, Lluís Brotons, Dan Chamberlain, Janne Heliölä, Sergi Herrando et al. "Differences in the climatic debts of birds and butterflies at a continental scale." Nature climate change 2, no. 2 (2012): 121.

Isaac, N. J., Girardello, M., Brereton, T. M., & Roy, D. B. (2011). Butterfly abundance in a warming climate: patterns in space and time are not congruent. Journal of Insect Conservation, 15(1-2), 233-240.

Roy, D. B., Oliver, T. H., Botham, M. S., Beckmann, B., Brereton, T., Dennis, R. L., ... & Thomas, J. A. (2015). Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Global change biology, 21(9), 3313-3322.

Related Subjects

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