Growth rate and size variability among juvenile lake sturgeon, Acipenser fulvescens: implications for recruitment
Bibliographic record
Abstract
There is a growing recognition that conservation programs using hatchery-reared fish should strive to produce individuals that represent phenotypes present in natural environments. Size variability within cohorts, mediated through inter-individual differences in growth rates, provides one avenue by which phenotype can be studied. High growth rates are generally equated with greater fitness. However, there is evidence that fish with slower relative growth and smaller sizes continue to persist within populations. This thesis aimed to better understand the causes and potential implications of variable sizes and growth rates on the potential recruitment of hatchery-reared Lake Sturgeon, Acipenser fulvescens. Studies were developed to 1) determine the mechanisms behind these observed variations and 2) assess the behavioural and physiological consequences of being either a fast- or slow-grower (i.e., large or small). Laboratory studies concluded that external factors, most notably the presence of conspecifics during feeding events, influenced size variability more than inherent predispositions towards faster or slower growth. Examination of size-dependent versus size-independent feeding interactions further confirmed that variability does not appear to be the result of underlying fixed behaviours. The consequence of slower growth rates and smaller sizes did not lead to higher mortality or reduced body condition during a low temperature challenge. Recapture rates and downstream movements following stocking events of both young-of-the-year (YOY) and yearling Lake Sturgeon in the Winnipeg River, Manitoba could not be correlated to inter-individual differences in size. Although initial study on the cause of growth and size divergence may lead one to conclude that slow-growing (i.e., small) Lake Sturgeon represent a substandard phenotype, subsequent studies could not point to inferior performance of these individuals when compared to faster-growing (i.e., large) individuals of the same age. As such, the practice of size-selection for relatively faster-growing and larger individuals in future Lake Sturgeon enhancement programs is discouraged, at least until there are more conclusive findings to suggest otherwise. Future studies should continue to look at recruitment in relation to growth rate and size among naturally produced Lake Sturgeon juveniles in order to put the results of this research into context.
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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".