Tuesday 1 December 2020

Wallace Coffee Talks - 8th December 2020

Wallace Coffee Talks - Autumn 2020
1st December - 2pm - Online (Zoom)

Fancy a cup of coffee or tea and learning more about the researchers at Swansea university? Come join us at the Wallace coffee talks: an informal seminar series where students, staff and others related to Swansea university speak about their research or personal interests.

Rowan Durrant
Modelling a transmissible cancer epidemic 
Devil facial tumour disease (DFTD) is a transmissible cancer of Tasmanian devils. Despite only being first observed in 1996, DFTD has now spread over most of the island of Tasmania and has caused devil population sizes to decline by up to 90%. Models of disease can be useful tools for predicting disease trajectory and evaluate mitigation strategies, but currently most models of DFTD are restricted to the local spatial scale. We created an individual-based metapopulation model that allowed us to investigate what drives a regional outbreak, and to test out a potential DFTD management method. Our findings show that DFTD-devil coexistence lies in a fine balance of within-population mixing, disease transmission rates and long-distance dispersal, and that DFTD management attempts can have potentially adverse outcomes for devil populations.

Charlotte Christensen   
Quantifying grooming budgets in wild chacma baboons (Papio ursinus) using tri-axial accelerometers
Non-human primates spend a considerable part of their day grooming. These sociopositive interactions have been linked to both social benefits (increased tolerance, coalition support) and physiological benefits, e.g. lower physiological stress levels through modulation of hypothalamus-pituitary-adrenal (HPA)-axis activity. Accurately quantifying the total time invested in grooming simultaneously for multiple individuals in a group, throughout day- and night-time is an impossible task for a human observer. For my PhD, I used tri-axial accelerometers (Daily Diaries) which recorded data continuously for 24 hours/day to obtain grooming budgets from chacma baboons (Papio ursinus). Using machine learning (random forest models), receiving and giving of grooming was identified with high accuracy (>79%) and recall (>78%). Whilst self-grooming has been identified from acceleration data in other species, this is the first-time social grooming (allogrooming) has been successfully identified and quantified for a primate species. Using absolute grooming budgets in combination with non-invasive hormone sampling, I aim to test hypotheses on the proximate mechanisms underpinning the link between sociality and HPA-axis activity.