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A spatially explicit risk assessment of salamander populations to Batrachochytrium salamandrivorans in the United StatesXuan Liu (Ed.)Aim: Amphibian populations are threatened globally by anthropogenic change and Batrachochytrium dendrobatidis (Bd), a fungal pathogen causing chytridiomycosis disease to varying degrees of severity. A closely related new fungal pathogen, Batrachochytrium salamandrivorans (Bsal), has recently left its supposed native range in Asia and decimated some salamander populations in Europe. Despite being noticed initially for causing chytridiomycosis-related population declines in salamanders, Bsal can also infect anurans and cause non-lethal chytridiomycosis or asymptomatic infections in salamanders. Bsal has not yet been detected in the United States, but given the United States has the highest salamander biodiversity on Earth, predictive assessments of salamander risk to Bsal infection will enable proactive allocation of research and conservation efforts into disease prevention and mitigation. Location: The United States, Europe and Asia. Methods: We first predicted the environmental suitability for the Bsal pathogen in the United States through an ecological niche model based on the pathogen's known native range in Asia, validated on the observed invasive range in Europe using bioclimatic, land cover, elevation, soil characteristics and human modification variables. Second, we predicted the susceptibility of salamander species to Bsal infection using a machine-learning model that correlated life history traits with published data on confirmed species infections.more »Free, publicly-accessible full text available October 1, 2023
Ecological Predictors of Zoonotic Vector Status Among Dermacentor Ticks (Acari: Ixodidae): A Trait-Based Approach
Increasing incidence of tick-borne human diseases and geographic range expansion of tick vectors elevates the importance of research on characteristics of tick species that transmit pathogens. Despite their global distribution and role as vectors of pathogens such as Rickettsia spp., ticks in the genus Dermacentor Koch, 1844 (Acari: Ixodidae) have recently received less attention than ticks in the genus Ixodes Latreille, 1795 (Acari: Ixodidae). To address this knowledge gap, we compiled an extensive database of Dermacentor tick traits, including morphological characteristics, host range, and geographic distribution. Zoonotic vector status was determined by compiling information about zoonotic pathogens found in Dermacentor species derived from primary literature and data repositories. We trained a machine learning algorithm on this data set to assess which traits were the most important predictors of zoonotic vector status. Our model successfully classified vector species with ~84% accuracy (mean AUC) and identified two additional Dermacentor species as potential zoonotic vectors. Our results suggest that Dermacentor species that are most likely to be zoonotic vectors are broad ranging, both in terms of the range of hosts they infest and the range of ecoregions across which they are found, and also tend to have large hypostomes and be small-bodiedmore »
Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein–protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals—an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.