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Record W7082262963 · doi:10.1093/icesjms/fsaf162

When, where, and why salmon become vulnerable to predation

2025· article· en· W7082262963 on OpenAlexaff

Bibliographic record

VenueICES Journal of Marine Science · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsFisheries and Oceans CanadaUniversity of Victoria
Fundersnot available
KeywordsPredationForagingEcosystemCompetition (biology)PredatorHabitatAbundance (ecology)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.010
GPT teacher head0.251
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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