Understanding Disease Transmission in Bottlenose Dolphins – Fellowship Training Grant

SUMMARY: Researchers will evaluate the relationships between season changes, population dynamics, and climate change and their impact on disease transmission in bottlenose dolphins to help predict and monitor future disease outbreaks.  

THE PROBLEM: In 2013, almost half the Atlantic bottlenose dolphin population died from infection with a morbillivirus, a disease that causes pneumonia, brain inflammation and damage to the immune system. Researchers are concerned that the disease burden in this dolphin population may shift with season, immunity, and climate change. First, dolphins move seasonally due to migration, which can affect the disease burden along the coastline.  

Next, protecting dolphins from past infections may influence the timing of the next massive outbreak. Climate change could likely alter dolphins' seasonal movements and immunity and increase deaths in future outbreaks, but it’s unclear where or how.    

Our limited understanding of the impact of these factors on infectious disease risk in dolphins and other marine mammal species hampers the research community's ability to respond effectively and efficiently to existing threats, like morbilliviruses, and forecast future outbreaks. There is an urgent need for systematic data-driven work that evaluates these changing dynamics to help improve conservation and response measures.    

THE PROJECT: To help fill this knowledge gap, researchers will study how the spread of disease via seasonal contact affects bottlenose dolphins, a very social animal. The team will also determine how population immunity drives outbreak timing and how climate change may worsen future epidemics.  

POTENTIAL IMPACT: This work is essential for understanding the health and welfare of coastal bottlenose dolphins and other marine mammals. Future disease outbreaks could be catastrophic for these charismatic animals. Findings from this study will help inform the development of much-needed disease models that could predict and forecast future threats under different environmental changes for many years to come, helping with disease response and monitoring efforts.