Residualization of Hatchery Steelhead: A Meta-Analysis of Hatchery Practices
Bibliographic record
Abstract
Abstract Freshwater residualization, whereby anadromous juvenile salmonids fail to emigrate seawards within the primary migration period, causes considerable economic and ecological management concern. Previous studies have attempted to identify possible factors contributing to residualization, including both fish-related and release methodology–related attributes, in order to develop measures to reduce it. Here, we synthesize 48 previous estimates of the residualization rates of hatchery-reared steelhead Oncorhynchus mykiss from 16 studies and evaluate the cross-study effects of several factors that can be controlled by hatchery managers. The proportion of fish in hatchery release groups that residualized ranged from 0% to 17% (average, 5.6%). Characteristics of the release process were dominant in affecting residualization rates, while characteristics of individual steelhead primarily determined which, but not how many, individuals residualized. Releases of fewer fish and those located closer to the ocean or to a confluence with a major river produced fewer residuals than larger releases located further upstream. Acclimation ponds also appeared to reduce residualization, but there was no evidence of a release date effect across locations and years. Within a release year, individuals from endemic broodstock had higher residualization rates than those from hatchery-propagated broodstock while smaller individuals and larger males were more likely to residualize than individuals of intermediate size (∼213 mm fork length). To meet management objectives of reducing steelhead residualization, we recommend releases closer to an ocean or large river, particularly for releases of relatively few fish, in conjunction with the use of acclimation ponds. Management effort should focus on selective harvesting of hatchery residuals, a process which may be supported by rearing and release strategies. These objectives may trade off with conservation objectives; straying risk and genetic effects should especially be taken into account. Received September 22, 2011; accepted June 28, 2012
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".