Conservation et écologie du Grand rhinolophe: Que nous apprennent les analyses moléculaires?

National audienc

Population genetics as a tool for conservation biology: defining management units for the greater horseshoe bat

International audienc

Genetic resistance against parasitism in female Mediterranean mouflon: Involvement of both neutral and adaptive genetic diversity

National audienc

Adaptive duplication and genetic diversification of protein kinase R contribute to the specificity of bat-virus interactions

Several bat species act as asymptomatic reservoirs for many viruses that are highly pathogenic in other mammals. Here, we have characterized the functional diversification of the protein kinase R (PKR), a major antiviral innate defense system. Our data indicate that PKR has evolved under positive selection and has undergone repeated genomic duplications in bats in contrast to all studied mammals that have a single copy of the gene. Functional testing of the relationship between PKR and poxvirus antagonists revealed how an evolutionary conflict with ancient pathogenic poxviruses has shaped a specific bat host-virus interface. We determined that duplicated PKRs of the Myotis species have undergone genetic diversification, allowing them to collectively escape from and enhance the control of DNA and RNA viruses. These findings suggest that viral-driven adaptations in PKR contribute to modern virus-bat interactions and may account for bat-specific immunity.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera trap images. The DeepFaune initiative is an ongoing project, with new partners joining regularly, which allows us to continuously add new species to the classification model