Including environmental covariates clarifies the relationship between endangered Atlantic salmon (<i>Salmo salar</i>) abundance and environmental <scp>DNA</scp>
Bibliographic record
Abstract
Abstract Collecting environmental DNA (eDNA) as a nonlethal sampling approach has been valuable in detecting the presence/absence of many imperiled taxa; however, its application to indicate species abundance poses many challenges. A deeper understanding of eDNA dynamics in aquatic systems is required to better interpret the substantial variability often associated with eDNA samples. Our sampling design took advantage of natural variation in juvenile Atlantic salmon ( Salmo salar ) distribution and abundance along 9 km of a single river in the Province of New Brunswick (Canada), covering different spatial and temporal scales to address the unknown seasonal impacts of environmental variables on the quantitative relationship between eDNA concentration and species abundance. First, we asked whether accounting for environmental variables strengthened the relationship between eDNA and salmon abundance by sampling eDNA during their spring seaward migration. Second, we asked how environmental variables affected eDNA dynamics during the summer as the parr abundance remained relatively constant. Spring eDNA samples were collected over a 6‐week period (12 times) near a rotary screw trap that captured approximately 18.6% of migrating smolts, whereas summer sampling occurred (i) at three distinct salmon habitats (9 times) and (ii) along the full 9 km (3 times). We modeled eDNA concentration as a product of fish abundance and environmental variables, demonstrating that (1) with inclusion of abundance and environmental covariates, eDNA was highly correlated with spring smolt abundance and (2) the relationships among environmental covariates and eDNA were affected by seasonal variation with relatively constant parr abundance in summer. Our findings underscore that with appropriate study design that accounts for seasonal environmental variation and life history phenology, eDNA salmon population assessments may have the potential to evaluate abundance fluctuations in spring and summer.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".