Project Rationale
Despite being among the largest remaining ‘marine wildernesses’, the Southern Ocean is impacted by climate change and human activities. Conservation and management of Southern Ocean ecosystems relies on understanding patterns of and processes causing the distribution of marine life, but this is logistically difficult. Recently, the Retrospective Analysis of Antarctic Tracking Data (RAATD) [1] compiled animal tracking data for 17 seabird and marine mammal species to model Areas of Ecological Significance (AES) with the rationale that these areas represent places of high prey abundance, diversity and accessibility [1]. However, the mechanisms and dynamics of these AES have only received superficial attention – what causes these to be important areas for seabirds and marine mammals? This PhD project will thus use the RAATD dataset [2], augmented with additional tracking data, in conjunction with various physical and biological oceanographic datasets to investigate: 1) what oceanographic processes and patterns underly AES in the Southern Ocean; and 2) the extent to which intra- and inter-annual environmental and behavioral variation affects the presence and persistence of AES in the Southern Ocean. The results will be used to assess the possibility of dynamic and/or process-based conservation and management approaches, and to what extent AES can be forecast.
Methodology
Modelling Areas of Ecological Significance (AES) is done using a habitat selection modelling framework, wherein animal tracking data are modelled, using classification and regression machine learning algorithms, as a response to spatiotemporally-matched environmental data. Areas of high habitat selection among several species are considered AES. Remote-sensed, in-situ and modelled biological and physical oceanographic data will be used to examine the processes driving the formation of AES: these data will be statistically analyzed in their spatiotemporal dimensions (time, longitude, latitude and depth) to understand the dynamics of AES within and between years. For inter-annual variation, this will include drivers such as El Niño-Southern Oscillation (ENSO) and Southern Annular Mode (SAM), while examination of intra-annual variation will focus on seasonal changes. Analysis of animal tracking data using statistical movement models will provide additional behavioral information to further elucidate the potential processes, in space and time, driving formation and dynamics of AES [3]. Analysis will be conducted in programme R, and basic experience with one of the programming environments R, Python or Matlab is essential.
Training
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the School of Ocean and Earth Science. Specific training will include:
- General scientific computing and data analysis
- Manipulation and analysis of electronic animal tracking data
- Manipulation and analysis of oceanographic datasets
- Statistical habitat modelling using state-of-the-art machine learning algorithms
There is a possibility for some general oceanographic fieldwork in the Southern Ocean and the student will spend time at the University of Southampton as well as the British Antarctic Survey, providing opportunities to expand their network.
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