When, where, and why salmon become vulnerable to predation
Bibliographic record
Abstract
Abstract Diverse natural and anthropogenic factors threaten the viability of Atlantic and Pacific salmon populations during their anadromous life cycle, but other than fisheries, the proximate cause of mortality for free-swimming salmon is most likely predation. Salmon predation is frequently mediated by environmental conditions. Large-scale atmospheric forces affect salmon predation indirectly by altering streamflow, thermal regimes, and oceanographic features that then effect salmon food-webs, physiology, and interactions with other taxa. Direct effects of predation are difficult to track confidently over time due to variability in predator and salmon cooccurrence in time and space, and complicating dynamics, such as competition among predators, alternative prey, and undiagnosed compensatory and additive mortality. This synthesis of predation on salmon emphasizes the importance of considering interactive effects of predation, environmental factors, and predator abundance and distribution through the salmon life-cycle to support effective salmon management and conservation efforts. We identify actions that may promote salmon recovery and sustainability, including (i) increasing the diversity of juvenile salmon size and timing at ocean entry, (ii) quantifying the role of contact points and alternate prey availability, and (iii) upgrading ecosystem models to evaluate alternative ecosystem management strategies. Importantly, considering additive predation impacts due to predator behaviors (e.g. predators moving inshore, upstream) and foraging responses (i.e. Holling’s functional and numerical responses) should be part of management evaluations as these processes control the potential impacts of interactions with salmon at contact points modulated by salmon growth and alternate prey availability. Key objectives for future research include identifying connections with predator populations and their community spatiotemporal patterns of abundance and distribution, and understanding environmental influences on predator–salmon interactions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".