<scp>eDNA</scp> metabarcoding as a means to track distributions of different fish species in a protected area
Bibliographic record
Abstract
Abstract In Lar National Park (Caspian Sea basin, Iran), the Caspian trout ( Salmo caspius ) population faces different threats, including introduced fish species. Due to the harsh environmental conditions and limited accessibility, monitoring of fish species via conventional approaches proves difficult. Hence, environmental DNA metabarcoding may prove an appropriate tool for monitoring fishes within the park. Environmental DNA samples from eight stream sites in the National Park were sequenced via metabarcoding of the 12S rRNA gene, and the species identified via eDNA metabarcoding were compared to the results of electrofishing performed at the same localities on the same day. No significant difference in the number of Caspian Sea trout DNA sequence reads was detected among the collection sites ( p > 0.05). The highest number of reads was detected in Dalichay Stream, but the highest population density determined via electrofishing was in Siahpalas Stream. The discrepancy between the eDNA read count and trout population density, as well as the limited sampling scheme within this study, limit our ability to provide a robust conclusion about the application of environmental DNA metabarcoding for assessment of fish density in Lar National Park. Environmental DNA metabarcoding detected more species than electrofishing, but no significant differences in the composition of the local fish community were observed. Introduced fish species were all observed or detected in Siahpalas Stream, which is characterized by high water temperature, muddy substrate, and lower flow rate. A significant effect of flow rate and total dissolved solids on the presence of introduced fish species ( p = 0.02) and of flow rate alone on relative abundance of introduced fish species ( p = 0.03) was detected. To standardize the application of eDNA metabarcoding as a biodiversity assessment tool in Lar National Park, future studies should characterize the parameters that affect eDNA persistence and detectability in the system.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".