Modelling cetacean morbillivirus outbreaks in an endangered killer whale population

• Published in: Biological Conservation
• Main Authors: Weiss, Michael N., Franks, Daniel W., Balcomb, Kenneth C., Ellifrit, David K., Silk, Matthew J., Cant, Michael A., Croft, Darren P.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2020
• Subjects: Killer Whale; Killer whale
• Online Access: https://eprints.whiterose.ac.uk/175836/; https://eprints.whiterose.ac.uk/175836/1/SRKW_disease_transmission_Author_Accepted.docx; https://doi.org/10.1016/j.biocon.2019.108398

The emergence of novel diseases represents a major hurdle for the recovery of endangered populations, and in some cases may even present the threat of extinction. In recent years, epizootics of infectious diseases have emerged as a major threat to marine mammal populations, particularly group-living odontocetes. However, little research has explored the potential consequences of novel pathogens in endangered cetacean populations. Here, we present the first study predicting the spread of infectious disease over the social network of an entire free-ranging cetacean population, the southern resident killer whale community (SRKW). Utilizing 5 years of detailed data on close contacts between individuals, we build a fine-scale social network describing potential transmission pathways in this population. We then simulate the spread of cetacean morbillivirus (CeMV) over this network. Our analysis suggests that the SRKW population is highly vulnerable to CeMV. The majority of simulations resulted in unusual mortality events (UMEs), with mortality rates predicted to be at least twice the recorded maximum annual mortality. We find only limited evidence that this population's social structure inhibits disease spread. Vaccination is not likely to be an efficient strategy for reducing the likelihood of UMEs, with over 40 vaccinated individuals (>50% of the population) required to reduce the likelihood of UMEs below 5%. This analysis highlights the importance of modelling efforts in designing strategies to mitigate disease, and suggests that populations with strong social preferences and distinct social units may still be highly vulnerable to disease outbreaks.