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Diseases of Aquatic Organisms

    DAO prepress abstract   -  DOI:

    Environmental determinants of Batrachochytrium dendrobatidis and the likelihood of further dispersion in the face of climate change in Texas, USA

    Andrea Villamizar-Gomez*, Hsiao-Hsuan Wang, Miranda R. Peterson, William E. Grant, Michael R. J. Forstner

    *Corresponding author:

    ABSTRACT: One of the major drivers of amphibian population declines is Batrachochytrium dendrobatidis (Bd). We sought to identify the major environmental drivers of Bd prevalence in Texas, USA, by drawing results from museum specimens. We sampled one of the largest museum collections in Texas, the Biodiversity Research and Testing Collections (BRTC) at Texas A&M University. Our sampling focused on the 9 amphibian species with the widest geographical distribution within the state, where we sub-sampled 30% of each species per decade from 1930 to present via skin swabs, totaling 1501 independent sampling events, and used quantitative real-time PCR (qPCR) to detect the pathogen presence. We analyzed several geo-referenced variables describing climatic conditions to identify potential factors influencing the likelihood of presence of Bd using boosted regression trees. Our final model suggests the most influential variables are: mean temperature of driest quarter, annual mean temperature, temperature annual range, and mean diurnal range. The most likely suitable range for Bd is currently found in the Blackland prairie and Cross timbers ecoregions. Results of our future (to the year 2040) projections suggest that Bd could expand its current distribution. Our model could play an important role when developing an integrated conservation plan through: (1) focusing future field work on locations with a high likelihood of presence, (2) assisting in choice of locations for restoration, and (3) developing future research plans including those necessary for projecting reaction to climate change. Our model also could integrate new presence data of Bd when they become available to enhance prediction precision.