Balancing ecological integrity with new infrastructure in sub-Saharan Africa: Using GPS collars to achieve sustainable development

MVLS COLLEGE STUDENTSHIPS 2017

Up to 10 fully funded studentship positions are available in the College of Medical Veterinary, Medical and Life Sciences. Projects are available from each of the research institutes within the College. Our next intake will be for PhD projects commencing October 2017.

The positions are fully funded for 3.5 years, and includes an annual consumable allowance.
All projects can be viewed here: http://www.gla.ac.uk/colleges/mvls/graduateschool/mvlscollegestudentships/projects/ - NOTE: DETAIL WITHIN STEP 6 OF APPLICATION PROCESS THE SUPERVISOR AND PROJECT TITLE.

Supervisors:
Dr Grant Hopcraft - [Email Address Removed]
Dr Tom Morrison - [Email Address Removed]
Prof Jason Matthiopoulos - [Email Address Removed]
Dr Neil Burgess (United Nations Environment Programme’s World Conservation Monitoring Centre)

Abstract:
How do we achieve infrastructure development for poverty alleviation while maintaining ecological connectivity for wildlife? International assistance brings much needed infrastructure to developing countries in the form of roads, power, water, energy extraction and agriculture, however there is an urgent need to evaluate whether infrastructure projects are exacerbating the fragmentation of landscapes on which wild animal populations and ecotourism activities rely (witness the recent debate about the Serengeti road). The primary objective of this PhD project is to develop a framework for optimizing international strategies surrounding efforts to service poor communities while maintaining ecological connectivity in sub-Saharan Africa, which is a global hotspot of large mammal diversity. Using a suite of new analytical tools, and existing datasets on animal movement for a variety of species (herbivores, carnivores, grazers, browsers, ruminants, equids, large and small) combined with high resolution satellite imagery and GIS layers, the project will generate predictions of animal movement and connectivity in different landscape contexts based on how GPS-tracked animals use various features (e.g. rainfall and vegetation gradients, river networks, topographic relief). These predictions will be integrated with demographic data and will form the basis for species-specific, and landscape-specific, models of connectivity that can be used to predict the spatial cost-consequence-outcomes of different infrastructure development strategies.