Elucidating the spatio-temporal dynamics of an emerging wildlife pathogen using approximate Bayesian computation.

Area of Science:

• Ecology
• Evolutionary Biology
• Parasitology

Background:

• Emerging pathogens pose significant threats to biodiversity and human health.
• Understanding pathogen origins and spread is vital for control and mitigation.
• Tracing wildlife pathogen dynamics is challenging due to limited data and unknown ranges.

Purpose of the Study:

• To retrace the spatio-temporal dynamics of the emerging pathogen Tracheliastes polycolpus.
• To determine the origin and introduction history of T. polycolpus in Western Europe.
• To demonstrate the utility of approximate Bayesian computation (ABC) for studying emerging wildlife pathogens.

Main Methods:

• Utilized classical population genetics tools.
• Applied approximate Bayesian computation (ABC) to reconstruct pathogen history.
• Analyzed genetic data to infer introduction time and colonization patterns.

Main Results:

• T. polycolpus populations in France likely originated from a single introduction event in the 1920s.
• Three distinct colonization waves into peripheral watersheds occurred within two decades post-introduction.
• Pathogen populations remained undetected at low densities for approximately 10 years before significant expansion.

Conclusions:

• ABC successfully reconstructed the colonization history of T. polycolpus, even with limited data.
• The study provides insights into the eco-evolutionary factors driving pathogen emergence.
• Findings support the use of genetic tools for managing emerging wildlife diseases.