Ichthyoplankton metabarcoding: An efficient tool for early detection of invasive species establishment
Bibliographic record
Abstract
Detection of invasive species is critical for management but is often limited by challenges associated with capture, processing and identification of early life stages. DNA metabarcoding facilitates large-scale monitoring projects to detect establishment early. Here, we test the use of DNA metabarcoding to monitor invasive species by sequencing over 5000 fishes in bulk ichthyoplankton samples (larvae and eggs) from four rivers of ecological and cultural importance in southern Canada. We were successful in detecting species known from each river and three invasive species in two of the four rivers. This includes the first detection of early life-stage rudd in the Credit River. We evaluated whether sampling gear affected the detection of invasive species and estimates of species richness, and found that light traps outperform bongo nets in both cases. We also found that the primers used for the amplification of target sequences and the number of sequencing reads generated per sample affect the consistency of species detections. However, these factors have less impact on detections and species richness estimates than the number of samples collected and analysed. Our analyses also show that incomplete reference databases can result in incorrectly attributing DNA sequences to invasive species. Overall, we conclude that DNA metabarcoding is an efficient tool for monitoring the early establishment of invasive species by detecting evidence of reproduction but requires careful consideration of sampling design and the primers used to amplify, sequence and classify the diversity of native and potentially invasive species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".