Search

One of the most devastating emerging pathogens of wildlife is the chytrid fungus, Batrachochytrium dendrobatidis (Bd), which affects hundreds of amphibian species around the world. Genomic data from pure Bd cultures have advanced our understanding of Bd phylogenetics, genomic architecture and mechanisms of virulence. However, pure cultures are laborious to obtain and whole-genome sequencing is comparatively expensive, so relatively few isolates have been genetically characterized. Thus, we still know little about the genetic diversity of Bd in natural systems. The most common noninvasive method of sampling Bd from natural populations is to swab amphibian skin. Hundreds of thousands of swabs have been collected from amphibians around the world, but Bd DNA collected via swabs is often low in quality and/or quantity. In this study, we developed a custom Bd genotyping assay using the Fluidigm Access Array platform to amplify 192 carefully selected regions of the Bd genome. We obtained robust sequence data for pure Bd cultures and field-collected skin swabs. This new assay has the power to accurately discriminate among the major Bd clades, recovering the basic tree topology previously revealed using whole-genome data. Additionally, we established a critical value for initial Bd load for swab samples (150 Bd genomic equivalents) above which our assay performs well. By leveraging advances in microfluidic multiplex PCR technology and the globally distributed resource of amphibian swab samples, noninvasive skin swabs can now be used to address critical spatial and temporal questions about Bd and its effects on declining amphibian populations. [ABSTRACT FROM AUTHOR]

Individual-based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A 'permanent' genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python-based computer-modelling program, wisepair. We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs ( Allobates femoralis) and without-reference (faecal) genotype sample data sets of harbour seals ( Phoca vitulina) and Eurasian otters ( Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies. [ABSTRACT FROM AUTHOR]