Acoustic Imaging Observes Predator–Prey Interactions between Bull Trout and Migrating Sockeye Salmon Smolts
Bibliographic record
Abstract
Abstract Annual migrations by juvenile Pacific salmon Oncorhynchus spp. smolts are predictable, presenting opportunities for predators to exploit these seasonal prey pulses. Directly observing predator–prey interactions to understand factors affecting predation may be possible via dual-frequency identification sonar (DIDSON) acoustic imaging. Within Chilko Lake, British Columbia, prior telemetry and stomach content analyses suggested that the out-migration of Sockeye Salmon Oncorhynchus nerka smolts influences the movements and aggregations of Bull Trout Salvelinus confluentus that feed extensively on smolts during their out-migration. Bull Trout captured at a government-installed counting fence exhibited high consumption of smolts, but it is only assumed that feeding occurred directly at the fence. We used DIDSON to assess fine-scale predator–prey interactions between Sockeye Salmon smolts and Bull Trout over 10 d during the 2016 smolt out-migration. We found that smolt–Bull Trout interactions were correlated with smolt densities at the counting fence, consistent with the prior diet studies in the system. Predator–prey interactions were also coupled with nocturnal migratory behaviors of Sockeye Salmon smolts, presumably to minimize predation risk. These results demonstrate that DIDSON technology can record interactions between predators and migrating prey at a resolution that can identify variability in space and time and provide insight on the role of anthropogenic structures (e.g., counting fences) in mediating such interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".