PHD Project

December 14, 2016

Learning from the landscape: can the patch dynamics of reef communities guide their conservation and spatial management?

Learning from the landscape Can the patch dynamics of reef communities guide their conservation and spatial management

The very presence of engineering species within a landscape promotes high levels of biodiversity and ecosystem goods/services. However, their loss and degradation is one of the most significant drivers of global biodiversity loss. Most studies overlook the spatial dynamics that drive the persistence of the engineering species themselves.

In this project, we will address key questions in the spatial ecology of engineering species: 1) how much of a species do we need to conserve in order to ensure that it persists in a landscape, and 2) how much do we need to remove in order to eradicate a species (e.g. an invasive)?

Engineering communities are usually persistent, as the processes that act to remove it (i.e. disturbance) are balanced by those that allow it to expand (i.e. recruitment/growth). We can hypothesise that larger patches will experience relatively less removal risk and have greater expansion potential than those that are smaller, thereby growing to dominate the landscape as time progresses. But do larger patches covering a constrained spatial area confer extended benefits to the wider persistence of these species over those smaller and more distributed, and how does the spatial arrangement of patches affect this?

Using high-spatial resolution (sub-cm) multi-annual landscape-scale photo-mosaics, image classification and cellular automata modelling, you will blend ecology and computer science to determine how two model engineering species influence spatio-temporal patterns of their persistence within their landscapes. You will work with data from one of the world’s most remote tropical coral reefs (the Palmyra Atoll) and from spatially dynamic intertidal worm reefs formed by Sabellaria alveolata. This novel project combines computer science, ecology and fieldwork to train you in next-generation data analysis and visualisation. In an increasingly data-driven world, such skills are in high demand across a range of sectors and employers.

Applicants should hold a minimum of a UK Honours Degree at 2:1 level or above in subjects such as Computer Science, Geography, Landscape Ecology, Mathematics, Statistics or related fields. Some experience in computational approaches and programming is desirable, as is an interest in photogrammetry and remote sensing.

For further information, please contact Andrew Davies ( or Gareth Williams (