Fitness cost of infection and long-term demographic consequences of an epidemic in a social carnivore: a case study of canine distemper virus transmission in spotted hyenas

Lucile MArescot & Sarah Benhaiem

CIRAD, Centre de Biologie pour la Gestion des Populations, Montpellier, France

Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany

lucile.marescot@cirad.fr; benhaiem@izw-berlin.de

Link to seminar: https://umontpellier-fr.zoom.us/webinar/register/WN_rAUgPUGjRRK5rfU5SrWTVA

Summary

Population structure and social organization of wildlife play a key role in the transmission and dynamics of infectious diseases. In social species such as the spotted hyena, Crocuta crocuta, fitness costs of infection can vary not only between age and sex but also between the dominance rank of individuals. The way and extent to which individual heterogeneity in the level of exposure to pathogens and in the immune responses to infection can influence the impacts of epidemics and pathogen persistence are still poorly understood. In this talk, we aim to bring some answers to this question by giving an overview of the studies undertaken at the Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW), in Berlin. This work is part of a DFG-funded project on the impact of a severe epidemic of canine distemper virus in the spotted-hyena population in Serengeti National Park in Tanzania. We first developed a multi-event SIR capture-recapture (SIR-CR) model accounting for imperfect detection and uncertainty in the infection status of individuals and examined the mediated effects of life-history traits (age and social status) on the infection and survival probabilities of individuals.  Based on the outcomes in parameter inference from the SIR-CR model we developed a matrix population model and an individual-based model to further study the demographic consequences of infectious diseases and the impact of the social structure on pathogen transmission. Finally, we conducted a simulation study to assess the sensitivity of demographic and eco-epidemiological indicators to variations in the level of uncertainty in the disease diagnosis.

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