Drivers and consequences of life-history variation in steelhead trout
Bibliographic record
Abstract
Species with complex life histories -where individuals undergo a rapid ontogenetic shift followed by a change in habitat -may respond to environmental and ecological drivers in unanticipated ways.For example, traits shaped by conditions in one habitat could carry over to the next habitat leading to unpredicted consequences.Steelhead trout are anadromous with plastic, complex life-histories that display broad variation in both freshwater and marine phases of their life cycle.Here, I linked environmental variation (e.g., nutrient subsidies, marine climate) to life-history traits (e.g., smolt size, size at maturity) and carry-over effects to understand how these factors may affect population production in the depressed Keogh River, BC population.Beginning with a 40-year timeseries of steelhead abundance and juvenile life-history data, I examined how smolt production and traits (length and age) vary with freshwater rearing conditions.Steelhead smolt production, length-at-age, and age covary with temperature, artificial nutrient addition, and pink salmon spawning abundance (spawners generate a nutrient subsidy via eggs and carcass tissues that young steelhead eat).Next, I investigated how pink salmon egg abundance (and thus pink salmon spawning abundance) translated to egg consumption by young steelhead across varying fish communities.Using experimental egg additions, I discovered that increasing egg availability disrupted size-and speciesbased dominance hierarchies, allowing small juvenile steelhead and less competitive fishes to access eggs.In Chapter 4, I developed and validated improvements to scalebased fish length back-calculation to estimate smolt length from adult scales for use in Chapter 5. Finally, using three decades of archived adult steelhead scales, I examined how smolt traits (i.e., sex and back-calculated length) combine with marine environmental conditions to affect adult life-history traits and female steelhead egg production across three decades.I discovered carry-over effects of the freshwater environment on adult traits via relationships between smolt length and age-and lengthat-maturity.However, the opposing effects of smolt length on age-at-maturity (which affects length-at-maturity) and length-at-maturity dampened the positive effect of smolt length on egg production.Collectively, this thesis informs steelhead management and demonstrates the complicated inter-relationships between environment and traits across complex and plastic life-histories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".