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Almost all wildlife populations are exposed to multiple anthropogenic stressors, including chemical contaminants, noise and habitat disturbance. These can interact with natural causes of morbidity and mortality to result in adverse effects, such as decline in health, resilience, fecundity and population size. Understanding the impact and interactions of multiple stress factors at an individual level is a necessary precursor to attempting quantification of impact at population levels. In turn, this information can be used to develop more effective and proportional management and mitigation strategies.
Data from examination of marine strandings can provide a wealth of information about the individual at post mortem, including cause of death, nutritional status, feeding and reproductive history, pathogen presence and contaminant burden. This interdisciplinary studentship will utilise a 30-year dataset collected by the Scottish Marine Animal Stranding Scheme (SMASS) based on the necropsy examination of 3000+ marine animals stranded around the Scottish coast from 1992-current. The studentship aims to improve assessments of human impacts on key marine mammal populations by employing novel statistical and modelling tools to integrate veterinary pathological and diagnostic data derived from post mortem examinations and multiple ancillary diagnostic tests.
This project will be primarily computational but will work closely with veterinary diagnosticians, and experience of, and interest in, marine wildlife health monitoring and surveillance would be an advantage. The ideal candidate will have had received masters-level training in biology with a strong component of statistics, mathematical modelling, machine learning, or equivalent; they will have experience using one or more programming languages among R, Python, or Julia for data analysis. Laboratory or veterinary science experience will be a significant advantage. The overall aim of this interdisciplinary studentship will be to:
1. Build a predictive model of the likelihood of stranding based on the extrinsic factors alone
2. Develop a methodological framework for understanding key drivers of marine mammal morbidity.
3. Assess which frameworks and statistical methodologies can best be applied to the type of ’real world’ data available from opportunistic surveillance networks such as SMASS,
4. Evaluate how known stressors can be robustly quantified using the current and emerging veterinary diagnostic toolbox. Develop and apply scoring systems to historic necropsy data to inform model parametrisation,
5. Assess associations among biological, ecological and pathophysiological profiles at an individual level,
6. Explore changes over space and time to identify trends with potential to adversely impact populations,
7. Generate and test hypotheses about how qualitative and quantitative changes in different stressors might impact strandings and pathology read-outs,
8. Develop policy-focused recommendations to optimise the collection of key parameters for monitoring future trends in marine mammal health
The student will work closely with the Scottish Marine Animal Stranding Scheme team and will have opportunity to be closely involved with the programme and assist in the investigation of marine strandings. Complementary training will be provided where needed.