Biology subjects

Dejean, T.

Publications and source records attributed to Dejean, T..

3 recordsLinked to original sources

bioRxiv2021

How many replicates to accurately estimate fish biodiversity using environmental DNA on coral reefs?

Quantifying the diversity of species in rich tropical marine environments remains challenging. Environmental DNA (eDNA) metabarcoding is a promising tool to face this challenge through the filtering, amplification, and sequencing of DNA traces from water samples. However, the reliability of biodiversity detection from eDNA samples can be low in marine environments because eDNA density is low and certainly patchy in this vast, heterogenous and dynamic environment. So, the number of sampling replicates and filtered volume necessary to obtain accurate estimates of biodiversity in rich tropical marine environments using eDNA metabarcoding is still unknown. Here, we used a paired sampling design of 30L per replicate on 68 reef transects from 8 sites in three tropical regions and identified fish Molecular Taxonomic Units (MOTUs) using a 12S marker. We quantified local biodiversity variation as MOTU richness, compositional turnover and compositional nestedness between replicated pairs of seawater samples. We report strong turnover of MOTUs between replicated pairs of samples undertaken in the same location, time, and conditions. Paired samples contained non-overlapping assemblages rather than subsets of one-another. As a result, localised diversity accumulation curves showed that even 6 replicates (180L) in the same location underestimated local diversity (for an area <1km). However, sampling of regional diversity using [~]25 replicates in variable locations (often covering 10s of km) achieved saturation of biodiversity accumulation curves. Our results demonstrate high variability of diversity estimates perhaps arising from heterogeneous and local distribution of eDNA distribution in seawater or highly skewed frequencies of eDNA traces. This high compositional variability has consequences for using eDNA to monitor temporal and spatial biodiversity changes of local assemblages. Future biomonitoring efforts could be strongly undermined by a high level of false-negative detections under low replication protocols. We reveal the need to increase replicates or increase sampled water volume to better inform management of marine biodiversity using eDNA.

ecology↗

bioRxiv2021

Fast processing of environmental DNA metabarcoding sequence data using convolutional neural networks

1The intensification of anthropogenic pressures have increased consequences on biodiversity and ultimately on the functioning of ecosystems. To monitor and better understand biodiversity responses to environmental changes using standardized and reproducible methods, novel high-throughput DNA sequencing is becoming a major tool. Indeed, organisms shed DNA traces in their environment and this "environmental DNA" (eDNA) can be collected and sequenced using eDNA metabarcoding. The processing of large volumes of eDNA metabarcoding data remains challenging, especially its transformation to relevant taxonomic lists that can be interpreted by experts. Speed and accuracy are two major bottlenecks in this critical step. Here, we investigate whether convolutional neural networks (CNN) can optimize the processing of short eDNA sequences. We tested whether the speed and accuracy of a CNN are comparable to that of the frequently used OBITools bioinformatic pipeline. We applied the methodology on a massive eDNA dataset collected in Tropical South America (French Guiana), where freshwater fishes were targeted using a small region (60pb) of the 12S ribosomal RNA mitochondrial gene. We found that the taxonomic assignments from the CNN were comparable to those of OBITools, with high correlation levels and a similar match to the regional fish fauna. The CNN allowed the processing of raw fastq files at a rate of approximately 1 million sequences per minute which was 150 times faster than with OBITools. Once trained, the application of CNN to new eDNA metabarcoding data can be automated, which promises fast and easy deployment on the cloud for future eDNA analyses.

bioinformatics↗

bioRxiv2020

Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach

Environmental DNA (eDNA) is gaining a growing popularity among scientists but its applicability to biodiversity research and management remains limited in river systems by the lack of knowledge about the spatial extent of the downstream transport of eDNA. Here, we assessed the ability of eDNA inventories to retrieve spatial patterns of fish assemblages along two large and species rich Neotropical rivers. We first examined overall community variation with distance through the distance decay of similarity and compared this pattern to capture-based samples. We then considered previous knowledge on individual species distributions, and compared it to the eDNA inventories for a set of 53 species. eDNA collected from 28 sites in the Maroni and 25 sites in the Oyapock rivers permitted to retrieve a decline of species similarity with distance between sites. The distance decay of similarity derived from eDNA was similar, and even more pronounced, than that obtained with capture-based methods (gil-nets). In addition, the species upstream-downstream distribution range derived from eDNA matched to the known distribution of most species. Our results demonstrate that environmental DNA does not represent an integrative measure of biodiversity across the whole upstream river basin but provide a relevant picture of local fish assemblages. Importantly, the spatial signal gathered from eDNA was therefore comparable to that gathered with local capture based methods, which describes fish fauna over a few hundred metres.

ecology↗