Ms Daniella Rabaiotti1,2, Professor Rosie Woodroffe1, Professor Tim Coulson3, Professor Richard Pearson2
1Zoological Society of London, London, United Kingdom, 2University College London, London, United Kingdom, 3University of Oxford, Oxford, UK
Predicting areas that will be climatically suitable for species in the future is key when predicting the impact of climate change on wildlife. Currently, most habitat suitability models use correlative methods, using the current climate a species inhabits to predict where it will live in the future. There have been growing calls, however, for more mechanistic methodologies, which take into account species’ ecology and demographics when identifying areas a species’ will be able to move into under different climate change scenarios. Here we present the results of a spatially explicit mechanistic population model, built using real-world field data from an endangered social carnivore, the African wild dog (Lycaon pictus). Through using detailed long term datasets on species’ demographics we can determine the effect of temperature of survival and reproduction and use this to predict where species will persist under future climatic regimes. The results of this model identify where the African wild dog is most likely to persist under rising temperatures, both inside its current range and more widely across Africa.
Dani Rabaiotti is a PhD student at the Zoological Society of London and UCL investigating the impact of climate change on African wild dogs, supervised by Professor Rosie Woodroffe and Professor Richard Pearson. Her main research interests are movement ecology, quantitative ecology and science communication.