Use of the MiFish Marker (12S) for the Development of an Approach for Characterizing Coastal Species Using Environmental DNA (eDNA)
Bibliographic record
Abstract
Species characterization by environmental DNA (eDNA) is a method that allows the use of DNA released into the environment by organisms from various sources (secretions, faeces, gametes, tissues, etc.). It is a complementary tool to standard sampling methods for the identification of biodiversity. This project provides a list of fish and marine mammal species whose DNA has been detected in water samples collected between 2019 and 2021 using the mitochondrial marker MiFish (12S). The surveys were carried out in the summer of 2019 (July 14-18) and (July 30 - August 5), in the fall of 2020 (October 27-28) and in the summer-fall of 2021 (May 31 - June 3) and (August 24-25) between Forestville and Godbout (Haute-Côte-Nord). Sampling was carried out between 1-50 meters depth in 91 stations, with 1 to 3 replicates per station. The surveys took place from the CCGS Leim and Clovert, as well as from the shore. Two liters of water were filtered through a 1.2 µm fiberglass filter. DNA extractions were performed with the DNeasy Blood and Tissues or PowerWater extraction kit (Qiagen). Negative field, extraction and PCR controls were added at the different stages of the protocol. The libraries were prepared either by Génome Québec (2019, 2020) or by the Genomics Laboratory of the Maurice-Lamontagne Institute (2021), then sequenced on a NovaSeq 4000 PE250 system by Génome Québec. The bioinformatics analysis of the sequences obtained was carried out using an analysis pipeline developed in the genomics laboratory. A first step made it possible to obtain a table of molecular operational taxonomic units (MOTU) using the cutadapt software for the removal of the adapters and the R package DADA2 for the filtration, the fusion, removal of chimeras and compilation of data. The MOTUs table was then corrected using the R package metabaR to eliminate the tag-jumping and take contaminants into consideration. Samples showing a strong presence of contaminating MOTUs were removed from the dataset. The MOTUs were also filtered to remove all remaining adapter sequences and also retain only those of the expected size (around 170 bp). Finally, taxonomic assignments were made on the MOTUs using the BLAST+ program and the NCBI-nt database. Taxonomic levels (species, genus or family) were assigned using a best match method (Top hit), with a threshold of 95%. Only assignments at the level of fish and marine mammals were considered, and the taxa detected were compared to a list of regional species, and corrected if necessary. The species detections of the different replicas have been combined. The file provided includes generic activity information, including site, station name, date, marker type, assignment types used for taxa identification, and a list of taxa or species. The list of taxa has been verified by a biodiversity expert from the Maurice-Lamontagne Institute. This project was funded by Fisheries and Oceans Canada's Coastal Environmental Baseline Program under the Oceans Protection Plan. This initiative aims to acquire baseline environmental data that contributes to the characterization of significant coastal areas and supports evidence-based assessments and management decisions to preserve marine ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".