Harrison, P. A. | bioRxiv Science

bioRxiv2021

Is the genomics cart before the restoration ecology horse? Insights from qualitative interviews and trends from the literature

Harnessing new technologies is vital to achieve global imperatives to restore degraded ecosystems. We explored the potential of genomics as one such tool. We aimed to understand barriers hindering the uptake of genomics, and how to overcome them, via exploratory interviews with leading scholars in both restoration and its sister discipline of conservation - a discipline that has successfully leveraged genomics. We also conducted an examination of research trends to explore some insights that emerged from the interviews, including publication trends that have used genomics to address restoration and conservation questions. Our qualitative findings revealed varied perspectives in harnessing genomics. For example, scholars in restoration without genomics experience felt genomics was over-hyped. Scholars with genomics experience emphatically emphasised the need to proceed cautiously in using genomics in restoration. Both genomics-experienced and less-experienced scholars called for case studies to demonstrate the benefits of genomics in restoration. These qualitative data contrasted with our examination of research trends, which revealed 70 restoration genomics studies, particularly studies using environmental DNA as a monitoring tool. We provide a roadmap to facilitate the uptake of genomics into restoration, to help the restoration sector meet the monumental task of restoring huge areas to biodiverse and functional ecosystems.

scientific communication and education↗

bioRxiv2020

Regarding the F-word: the effects of data Filtering on inferred genotype-environment associations

Genotype-environment association (GEA) methods have become part of the standard landscape genomics toolkit, yet, we know little about how to filter genotype-by-sequencing data to provide robust inferences for environmental adaptation. In many cases, default filtering thresholds for minor allele frequency and missing data are applied regardless of sample size, having unknown impacts on the results. These effects could be amplified in downstream predictions, including management strategies. Here, we investigate the effects of filtering on GEA results and the potential implications for adaptation to environment. Using empirical and simulated datasets derived from two widespread tree species to assess the effects of filtering on GEA outputs. Critically, we find that the level of filtering of missing data and minor allele frequency affect the identification of true positives. Even slight adjustments to these thresholds can change the rate of true positive detection. Using conservative thresholds for missing data and minor allele frequency substantially reduces the size of the dataset, lessening the power to detect adaptive variants (i.e. simulated true positives) with strong and weak strength of selections. Regardless, strength of selection was a good predictor for GEA detection, but even SNPs under strong selection went undetected. We further show that filtering can significantly impact the predictions of adaptive capacity of species in downstream analyses. We make several recommendations regarding filtering for GEA methods. Ultimately, there is no filtering panacea, but some choices are better than others, depending largely on the study system, availability of genomic resources, and desired objectives of the study.

genomics↗