McLaren, M. R. | Search bioRxiv

bioRxiv2019

Reconsidering the management paradigm of fragmented populations

Habitat fragmentation and population declines call for informed management of many endangered species. The dominant paradigm for such management focuses on avoiding deleterious inbreeding effects in separated populations, by facilitating migration to maintain connectivity between them, an approach epitomized by the \"one migrant per generation\" rule. We show that this paradigm fails to take into account two important factors. First, it ignores an inherent trade-off: maintaining within-population genetic diversity is at the expense of maintaining global diversity. Migration increases local within-population genetic diversity, but also homogenizes the meta-population, which may lead to erosion of global genetic diversity. Second, this paradigm does not consider that because many fragmented species have declined in numbers only within the last century, they still carry much of the high genetic diversity characteristic of the historically large population. The conservation of a species global diversity, crucial for evolutionarily adaptation to ecological challenges such as epidemics, industrial pollutants, or climate change, is paramount to a species long-term survival. Here we discuss how consideration of these factors can inform management of fragmented populations and provide a framework to assess the impact on genetic diversity of different management strategies. We also propose two alternative management strategies to replace the \"one migrant per generation\" dogma.

ecology↗

bioRxiv2019

Consistent and correctable bias in metagenomic sequencing measurements

Measurements of biological communities by marker-gene and metagenomic sequencing are biased: The measured relative abundances of taxa or their genes are systematically distorted from their true values because each step in the experimental workflow preferentially detects some taxa over others. Bias can lead to qualitatively incorrect conclusions and makes measurements from different protocols quantitatively incomparable. A rigorous understanding of bias is therefore essential. Here we propose, test, and apply a simple mathematical model of how bias distorts marker-gene and metagenomics measurements: Bias multiplies the true relative abundances within each sample by taxon-and protocol-specific factors that describe the different efficiencies with which taxa are detected by the workflow. Critically, these factors are consistent across samples with different compositions, allowing bias to be estimated and corrected. We validate this model in 16S rRNA gene and shotgun metagenomics data from bacterial communities with defined compositions. We use it to reason about the effects of bias on downstream statistical analyses, finding that analyses based on taxon ratios are less sensitive to bias than analyses based on taxon proportions. Finally, we demonstrate how this model can be used to quantify bias from samples of defined composition, partition bias into steps such as DNA extraction and PCR amplification, and to correct biased measurements. Our model improves on previous models by providing a better fit to experimental data and by providing a composition-independent approach to analyzing, measuring, and correcting bias.

bioinformatics↗