Predicting Wild Hosts for Amphibian Chytridiomycosis: Integrating Host Life-History Traits with Pathogen Environmental Requirements

ABSTRACT

Confronting the risk of emerging infectious diseases of wildlife is an important objective for biosecurity and conservation. Batrachochytrium dendrobatidis (Bd), cause of the emerging disease chytridiomycosis, has been associated with amphibian declines and extinctions globally. Characterizing Bd's host range is important for understanding community disease dynamics, predicting further declines, minimizing the cost of surveillance and developing pre-emptive management actions. In this study, we integrated a metric of potential host exposure to the pathogen (derived from spatial models of environmental suitability for Bd) with host species’ life-history and ecological traits to identify risk factors associated with Bd infection in our model system (Australia). We found that the most informative predictor of a species’ infection status was their general occupancy in areas predicted to be environmentally suitable for Bd. Body size, range size, habitat association, and Family were also important but were less useful for prediction. This information allowed us to make predictions regarding infection risk in poorly or unstudied species and shortlist new potential hosts for targeted future surveillance. Our study highlights the utility of species distribution modeling for wildlife diseases and examines the role of sampling bias and host life-history traits in risk analyses for chytridiomycosis.

Key Words:

• Batrachochytrium dendrobatidis
• chytrid fungus
• wildlife disease
• risk assessment
• infection risk
• life-history traits

ACKNOWLEDGMENTS

We are indebted to the many authors and contributors named in Murray et al. (2010) and Murray et al. (2011a) for the production of the Bd occurrence database and for the Bd species distribution modeling work. In particular, we thank K. Mc- Donald, K. Aplin, H. Hines, D. Mendez, A. Felton, P. Kirkpatrick, D. Hunter, R. Campbell, M. Pauza, M. Driessen, S. Richards, M. Mahony, A. Freeman, A. Phillott, J-M. Hero, K. Kriger, D. Driscoll, R. Retallick, R. Puschendorf, D. Rosauer, H. McCallum, L. Berger, R. Speare, and J. VanDerWal. KAM thanks D. Segan for GIS dominance, M. Watts for advice on machine-learning methods, and R. Wilson for lab support. KAM was partially supported by an Australian Postgraduate Award, an Australian Biosecurity CRC professional development award, and a Wildlife Preservation Society of Australia student research award.