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Record W4229834711 · doi:10.22215/etd/2015-10741

The Effects of Fisheries Capture on the Physiology and Post-Release Fate of Adult Pacific Salmon

2015· dissertation· en· W4229834711 on OpenAlexafffund
Graham D. Raby

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaFisheries and Oceans CanadaCanadian Wildlife FederationPacific Salmon FoundationUniversity of British ColumbiaFisheries Society of the British Isles
KeywordsStressorMortality rateRealmMedicineFisheryDemographyBiologyGeographySurgeryNeuroscience

Abstract

fetched live from OpenAlex

All animals encounter acute stressors during their lifetimes, and while the immediate response to those stressors is well understood and presumed to be adaptive, relatively few studies have linked those responses with subsequent fitness outcomes.This problem finds particular relevance in the fisheries realm, where there is interest in developing a) an understanding of what leads to mortality for caught-and-released animals, b) methods for reducing post-release mortality, and c) predictors of delayed mortality.Pacific salmon are a tractable model for studying post-release mortality because the migration success of individuals after release can be easily and effectively tracked, and migration failure means zero lifetime fitness.In this thesis I report on research in which I used physiological assessments and tracked Pacific salmon fitness outcomes in the wild to examine the response to and recovery from capture, and whether individual differences in responses could be linked to migration or spawning failure.A key finding that arose throughout was that reflex impairment is an effective indicator of the whole-animal response to capture stressors, is correlated with dermal injury, reflects underlying physiological processes, and can predict delayed mortality.I demonstrate that mortality rates currently used in management models are likely inaccurate, but use several lines of evidence to show that mortality could be reduced using different capture and handling techniques.Specifically, more proactive efforts to reduce handling time reduced physiological disturbance and reflex impairment.Revival using industry-standard revival totes and novel in-river recovery bags did not reduce delayed mortality, although the latter and forced-flow revival boxes appeared effective at expediting short-term revival.I found some evidence that sensitivity to capture stressors may change dynamically iii throughout the spawning migration, with fish becoming particularly resilient once reaching spawning areas.Well-controlled experiments are required if the knowledge gaps arising from this thesis are to be addressed: namely, how does resilience to capture stressors change over the course of the spawning migration, and when does facilitated revival benefit fish survival?Collectively, the work presented in this thesis provides a useful addition to our understanding of the effects of fisheries capture on the physiology and survival of fish.my undergraduate degree persuaded me to agree to join his lab for graduate school -a decision that has benefited me enormously.Working with Scott's dynamic research team in British Columbia was a remarkable experience that I'll never forget, and I realized early on that I wanted to continue working with him and Steve for an extended period, which is why this document is a doctoral dissertation rather than an M.Sc.thesis.Steve's energy, drive, and accomplishments have inspired and motivated me, and he was always positive and encouraging.I am grateful to both supervisors for the opportunities they afforded me throughout my graduate career, and for shaping my personal and professional development for the better.

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)0.001
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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