Comparing Physical Collection and Environmental DNA Methods for Determining Abundance Patterns of Gammarus Species along an Estuarine Gradient
Bibliographic record
Abstract
Estuaries are productive systems with rapid changes in natural stressors, such as salinity, that make perturbation detection challenging. Amphipods are vital to estuarine assemblages and may serve as anthropogenic stress indicators. However, practical methods of monitoring species composition and abundance are lacking. This study aims to describe the spatial patterns of four Gammarus spp. in northwest Atlantic estuaries and to compare their compositions and abundances between collection methods using artificial substrates, macrophyte raking, light-baited traps, and species-specific quantitative PCR analyses from sediment environmental DNA (eDNA). Sampling occurred in upper, mid, and lower estuary zones within three estuaries of Prince Edward Island (Canada). G. tigrinus was rarely found in the upper zones with any method. G. mucronatus was predominant in the upper–mid zones across the physical methods, and its abundance declined with increasing salinity. G. lawrencianus was a dominant species across zones, but its abundance did not change with salinity. G. oceanicus was predominant in lower-zone artificial substrates. Species abundances generally correlated with physical collection methods. Sediment eDNA did not detect the spatial effects observed via the physical methods but was correlated with the mean counts of G. mucronatus. The Gammarus spp. assemblages are spatially partitioned in short estuaries, though the sampling method is critical when interpreting estuary patterns. Though multiple methods are ideal for compositional comparisons, abundance monitoring should employ light traps.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".