Interview with the authors: Applying genomic data in wildlife monitoring

Massive parallel sequencing has led to an explosion of sequence data in recent years. However, the methods used to obtain such data are usually high-cost and time-intensive, and often require high-quality samples. This creates limits as to whether and how well such data can be used by researchers working in applied conservation science. Here, we speak to Alina von Thaden about her recent study in Molecular Ecology Resources. Using European wildcats as a case study, Alina and co-authors present a relatively low-cost and time-efficient workflow for the development and optimisation of microfluidic SNP panels, which can be used to obtain SNP data from minimally invasive samples. Beyond outlining the workflow and its applications, they go so far as to estimate the costs of their pipeline, providing valuable practical information for conservation scientists. Read on for an in-depth view of this study.

Monitoring elusive European wildcats (Felis silvestris) is heavily reliant on noninvasively collected DNA samples. Photo credit: Annsophie Schmidt.

What led to your interest in this topic / what was the motivation for this study? 

We are mainly working on genetic monitoring of large carnivores and most of our research is based on noninvasively collected wildlife samples such as hairs, faeces and saliva traces. The field demands for very fast and reliable genetic analyses of samples with degraded DNA. And since funding is generally sparse in applied conservation, our methods need to be cost-effective and suitable for high-throughput approaches.

Genomic tools, on the other hand, usually involve large amounts of data, complex bioinformatic pipelines and typically rely on samples with high-quality DNA. We have been looking into ways to combine the advantages of genomics with the challenges of conservation monitoring. For some years now, we have been working with microfluidic arrays combined with reduced SNP panels and wanted to share our experiences with other labs interested in applying them.

What difficulties did you run into along the way? 

Setting-up and optimizing methodological resources comes along with several challenges – but there is a lot to learn! Most important to me was to remain skeptical about the results and to constantly validate them through analyzing the data from several perspectives and with different software. The validation of the technology also took a lot of extra lab hours, but we are confident that the workflow and guidelines that we present now will save others a lot of hands-on time and costs when optimizing SNP panels for degraded samples.

What is the biggest or most surprising finding from this study? 

First of all, after years of developing the framework, we applied it to a new SNP panel designed for dog-wolf hybridization assessment (to be published) and found that the lab work for generating a new ready-to-use marker panel took us only a few weeks. To see the approach being proved effective was great and encouraged us to share it with the community.

Secondly, a large proportion of noninvasively collected samples could be run without or with only very few genotyping errors as compared to more traditional microsatellite-based genotyping (see also von Thaden et al. 2017). This has direct implications for genotyping costs and thus promotes the broader establishment of a genomic technology in applied conservation.

Alina von Thaden collecting reference samples of European wildcat (Felis silvestris) for testing a newly developed SNP panel. Photo credit: Annsophie Schmidt.

Moving forward, what are the next steps for this research? 

One of our next steps is to apply the technology to historical samples from museum collections. Additionally, we are going to implement the SNP panel from our current paper in routine genetic monitoring of European wildcats in Germany.

We currently develop other reduced SNP panels for a variety of endangered species in our lab, such as dormice and European bison. Besides neutral variation, we also aim to integrate functional markers, such as SNPs associated with disease susceptibility.

Further, we will test alternative platforms that will allow generating larger SNP sets for degraded samples. Ultimately, our long-term goal is the effective implementation of an “applied genomic wildlife monitoring” approach.

What would your message be for students about to start their first research projects in this topic? 

Get in contact with other groups working in this area! Sharing ideas and experience really helps to shape your project and refine the aims of your research. Most people are very cooperative and happy to contribute or answer questions.

What have you learned about science over the course of this project? 

Perseverance and tenacity. When exploring new directions the research journey may well become bumpy and lead you somewhere else than you initially expected. But it’s worth it – keep your goal in mind and be ready to rethink your strategy.

Describe the significance of this research for the general scientific community in one sentence.

Bridging the gap between genomics and applied conservation is a key prerequisite for effective wildlife management, especially in the light of rapid biodiversity declines.

Describe the significance of this research for your scientific community in one sentence.

We demonstrate how reduced SNP panels can be efficiently developed and optimized for genotyping based on degraded wildlife samples.

References

von Thaden, A., Cocchiararo, B., Jarausch, A., Jüngling, H., Karamanlidis, A. A., Tiesmeyer, A. … Muñoz-Fuentes, V. (2017). Assessing SNP genotyping of noninvasively collected wildlife samples using microfluidic arrays. Scientific Reports, 7, 83. https://doi.org/10.1038/s41598-017-10647-w

Full paper

von Thaden, A., Nowak, C., Tiesmeyer, A., Reiners, T. E., Alves, P. C., Lyons, L. A., … & Hegyeli, Z. (2020). Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism (SNP) panels. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.13136

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