Moinard, S. | Search bioRxiv

bioRxiv2025

Arctic plant species display contrasting levels of chloroplast DNA copy numbers

DNA metabarcoding has revolutionised our ability to characterise biodiversity at unprecedented spatial and temporal scales, outperforming most traditional methods for biodiversity monitoring. However, DNA metabarcoding is not without limitations, in particular regarding the quantitative relationship between sequence read abundances and species numerical abundances or biomass (i.e., quantitative performance). Variation in DNA copy numbers has been often pinpointed as a potentially important factor biasing DNA metabarcoding quantitative performance but empirical comparisons of DNA copy number variation across species remain rare. Here, we identify chloroplast DNA copy number variation (cpDNA CNV) in plants as a potentially important factor that could impact quantitative performance in DNA metabarcoding studies. Using digital droplet PCR, we quantified chloroplast copy number variation in four common high Arctic plant species and between two plant tissue types (green tissues and roots). The amount of cpDNA per unit of dry mass varied by a factor of 3 (for roots) to 7.6 (for green tissues) among species, and up to 67 when comparing cpDNA copy numbers between green and root tissues from the same species. Despite significant differences in cpDNA copy numbers among species for both green tissues and roots, the most pronounced differences in cpDNA copy numbers were clearly between the two tissue types tested. These findings suggest that variation in cpDNA copy numbers among plant species and particularly plant tissue types can be an important but underestimated factor impacting plant sequence reads abundance in DNA metabarcoding datasets. We call for more extensive cpDNA CNV referencing efforts from wild-ranging plants to improve the use of DNA metabarcoding for research and biodiversity monitoring.

ecology↗

bioRxiv2023

Towards quantitative DNA Metabarcoding: A method to overcome PCR amplification bias

Metabarcoding analyses have recently undergone significant development due to the power of this technique in biodiversity monitoring. However, it is still difficult to draw accurate quantitative conclusions about the ecosystems studied, mainly because of biases inherent in the environmental DNA or introduced during the experimental process. These biases alter the relationship between the amount of DNA observed and the biomass or number of individuals of the species detected. Two of the biases inherent in metabarcoding have been measured: the ratio between total DNA and target DNA concentrations, and the PCR amplification bias. A method for their correction is proposed. All experimental tests were performed on mock alpine plant communities using the marker Sper01, which is expected to have low amplification bias due to its highly conserved priming sites. Our approach combines standard quantitative PCR techniques (qPCR and digital droplet PCR) with a realistic stochastic model of PCR dynamics that accounts for PCR saturation. The model was used to estimate PCR efficiencies for each species and to infer the true species proportions of the mock communities from the read relative frequencies. The corrections are easy to implement and can be applied to previously generated DNA metabarcoding data. This work demonstrates the relative importance of the two biases considered and is an open door to quantitative metabarcoding data, although many other biases remain to be considered.

ecology↗