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Accelerating robust population monitoring and evaluation with drones


Project Description

A core goal of ecology is to understand the causes and consequences of variation in organisms’ abundance at both local and landscape scales. Indeed, the solutions to many applied problems in conservation ecology and pest management hinge upon this multi-scale perspective. At the local scale, ecologists have traditionally used detailed schedules of individual reproduction and mortality to infer key population properties such as expected extinction time. This approach is powerful but resource- and time-intensive, limiting its application to a handful of populations at a time. In contrast, large-scale spatial processes are typically studied using coarse-grained data such as presence-absence records. These data are easy to acquire but contain limited information about the dynamics of populations across a landscape.

There is an urgent need for new methods to monitor and evaluate populations at large spatial scales. To be effective, such tools must enable ecologists to overcome the trade-off between the quality and quantity of data collected, thereby unlocking greater predictive abilities. Unmanned Arial Systems (’drones’) facilitate rapid, cost-effective collection of ecological data. However, their use in ecology is still in its infancy because we still lack robust methods to link UAS-derived data to local and regional population projections. The primary aim of this project is to develop such an approach, focussing on sessile (’static’) populations. The specific objectives are:

1) Develop methods to automate the enumeration of population states from UAS-derived image data.
2) Construct methods to forecast population dynamics from multi-scale data.
3) Apply these methods to upscale from local to landscape-scale population processes.

This project will leverage already existing datasets collected from distinct habitats, including carnivorous plants from fire-prone ecosystems in Spain/Morocco, succulents from the Karoo desert in South Africa, small shrubs from the great basin desert in the USA, and shallow coral systems in Australia. For some of these species and ecosystems, long-term high-resolution demographic data already exist that will allow the PhD student to test the proposed methods and tools. The possibility also exists to supplement these with further data collection from other systems the UK and overseas, as per the candidate’s interests.

The PhD will suit a motivated student excited by the opportunity to work across traditional discipline boundaries, using state-of-the-art population monitoring and modelling techniques. The project is appropriate for a biology graduate with interests in developing their quantitative skills, as well as candidates with an engineering, mathematics or statistics background who wish to make the transition to mathematical biology. Potential candidates are encouraged to email Dr Dylan Childs () with informal enquiries before submitting a formal application.

Funding Notes

Fully funded studentships cover: (i) a stipend at the UKRI rate (at least £14,777 per annum for 2019-2020), (ii) research costs, and (iii) tuition fees. Studentship(s) are available to UK and EU students who meet the UK residency requirements.
This PhD project is part of the NERC funded Doctoral Training Partnership “ACCE” (Adapting to the Challenges of a Changing Environment View Website. ACCE is a partnership between the Universities of Sheffield, Liverpool, York, CEH, and NHM.
Shortlisted applicants will be invited for an interview to take place at the University of Sheffield the w/c 11th February 2019.

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