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Genomic basis for an informed conservation management of Pelophylax water frogs in Luxembourg.

Hannah WeigandJennifer Cross Lopez de LlergoAlain C Frantz
Published in: Ecology and evolution (2022)
Genetic identification methods have become increasingly important for species that are difficult to identify in the field. A case in point is Pelophylax water frogs. While their morphological determination is highly complex, they include species protected under EU law and some that are classified as invasive. Additionally, genetic data can provide insights into their complex breeding systems, which may or may not involve the reproductive dependency of one species on another. Here, we generate baseline data for water frog monitoring in Luxembourg. We applied a countrywide sampling approach and used SNPs generated by ddRAD sequencing to identify individuals and infer the breeding systems present in the country. We found Pelophylax lessonae and P . kl. esculentus throughout Luxembourg, mostly living in syntopy. In general, a reproductive dependency of P . kl. esculentus on P . lessonae (L-E system) was revealed. Besides this general system, we detected triploid P . kl. esculentus in six ponds. This indicates a modified L-E system with reproductive dependency of the triploids on the diploid P . kl. esculentus . The invasive P . cf. bedriagae was detected in three ponds in southern Luxembourg, with evidence for hybridization with native water frogs. In addition to the ddRAD data, we tested a simple genetic method for future monitoring based on the MND1  marker. It showed in almost all cases, an identical species identification as the ddRAD data and was successfully applied to DNA extracts from mouth swabs. Combining this method with our baseline data will enable informed choices for the protection of native water frog species in Luxembourg.
Keyphrases
  • electronic health record
  • big data
  • genome wide
  • copy number
  • single cell
  • single molecule
  • cystic fibrosis
  • genetic diversity
  • data analysis
  • machine learning
  • gene expression
  • artificial intelligence
  • mass spectrometry