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Record W4312184316 · doi:10.1002/tafs.10386

Swimming depths and water temperatures encountered by radio-archival-tagged Chinook Salmon during their spawning migration in the Yukon River basin

2022· article· en· W4312184316 on OpenAlexaboutno aff
John H. Eiler, Michele Masuda, Allison N. Evans

Bibliographic record

VenueTransactions of the American Fisheries Society · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChinook windEscapementSpawn (biology)OncorhynchusFisheryEnvironmental scienceFish migrationStructural basinFish <Actinopterygii>OceanographyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract Objective Historically, Chinook Salmon Oncorhynchus tshawytscha have supported important fisheries throughout the Yukon River basin, but dramatic declines in abundance since the late 1990s have resulted in smaller returns, severe reductions in harvests, and difficulties in meeting escapement goals. These observations coincide with major climatic changes in the northern Pacific, characterized by a general warming trend throughout the region. Our objective was to document the migratory patterns of the fish in relation to the environmental conditions encountered in order to assess the impact of climate change and help manage the returns. Methods We used radio-archival tags to track the distribution and movements of adult Chinook Salmon returning to the Yukon River to spawn. The tags were equipped with sensors that recorded the swimming depth of the fish and water temperatures encountered during the upriver migration. Spawning ground surveys and fishery returns were used to recover the tags to download the sensor data. Result Ninety-five (71.4%) of the 133 tags tracked upriver were recovered, including 35 (26.3%) returned by fishermen and 60 (45.1%) retrieved on the spawning grounds. Upriver movements were characterized by continuous and highly variable fluctuations in depth throughout the migration, ranging from <5 m to >20 m in the lower river and progressively less as fish moved upstream into shallower waters. Swimming depth was not influenced by time of day. Temperatures encountered by the fish were generally warmer in 2004, but this pattern was not consistent throughout the basin and was driven by conditions in the lower main stem, with temperatures frequently >18°C and periodically exceeding 21°C. There was no obvious behavioral response to the warm conditions, with comparable movements and survival rates when conditions were cooler. Temperatures in terminal tributaries often exceeded the upper range generally considered optimal during spawning (13°C), but signs of impaired behavior or prespawning mortality were not observed. A thermal diel pattern was evident as fish left the main stem and approached their spawning grounds, with temperatures declining from early evening to early morning and increasing during daylight hours, suggesting that assessments based on average daily temperature may not adequately reflect exposure to suboptimal conditions. Conclusion Although the fish during our study frequently encountered temperatures associated with adverse effect on salmon, impaired behavior and increased mortality were not evident. However, the current warming trend occurring throughout the northern Pacific is predicted to continue and may impact salmon populations more severely. Our findings provide a baseline for comparing past conditions and migratory patterns with those of present and future returns. Radio-archival tags not only provided site-specific information, but substantially increased the number of tags recovered, with a recovery rate considerably higher than reported for most archival tag studies. The ability to obtain larger samples and more representative results is a major advantage for addressing many resource issues currently facing fishery managers and local communities.

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)0.000
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 score0.921
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.179
Teacher spread0.175 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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