Seasonal variability in condition and spatial distribution of Chinook Salmon: Implications for ecosystem-based management
Bibliographic record
Abstract
Abstract Objective Although many Chinook Salmon Oncorhynchus tshawytscha populations overlap in nearshore areas prior to spawning migrations, it is unclear how life history diversity influences physical condition and habitat use. Here, we explored multiple dimensions of Chinook Salmon marine ecology. First, does condition differ between immature and mature fish, among stocks, and between wild and hatchery individuals? Second, is abundance correlated with abiotic variables? Third, does habitat use consistently covary with life history stage, stock, and wild versus hatchery rearing history? Methods We collected data on Chinook Salmon stock identity, condition, and abundance using a fisheries-independent troll survey along the west coast of Vancouver Island, British Columbia. We then fitted generalized additive models and geostatistical generalized linear models to quantify variability in condition, abundance, and spatial distribution. Result Fork length and lipid content varied seasonally, with maturation stage, and among stocks but did not differ with rearing history. Although immature fish were initially less lipid rich than mature fish, the lipid content of immature individuals ultimately exceeded that of mature individuals. Chinook Salmon abundance covaried with bottom depth, slope, and sampling date, while diel and tidal effects were weak. Abundance varied among ecological groups by up to an order of magnitude. Chinook Salmon habitat use differed among size-classes and stocks but did not differ with rearing history. The spatial distributions of each size-class changed over summer, consistent with ontogenetic dispersal and variation in the migration timing of spawners. Conclusion Seasonal changes in Chinook Salmon condition suggested that immature individuals transition from growth to lipid storage, emphasizing that prey availability may impact overwinter survival. Stock-specific patterns in size, lipid content, and abundance highlighted ecological diversity during marine residence. Although distributions varied seasonally, abundance was greatest in high-relief areas. Finally, our estimated spatial distributions suggest that responses to environmental conditions vary with ontogeny and among populations but not with rearing history.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".