Life‐history strategies in salmonids: the role of physiology and its consequences
Bibliographic record
Abstract
Salmonids are some of the most widely studied species of fish worldwide. They span freshwater rivers and lakes to fjords and oceans; they include short- and long-distance anadromous migrants, as well as partially migratory and non-migratory populations; and exhibit both semelparous and iteroparous reproduction. Salmonid life-history strategies represent some of the most diverse on the planet. For this reason, salmonids provide an especially interesting model to study the drivers of these different life-history pathways. Over the past few decades, numerous studies and reviews have been published, although most have focused on ultimate considerations where expected reproductive success of different developmental or life-history strategies are compared. Those that considered proximate causes generally focused on genetics or the environment, with less consideration of physiology. Our objective was therefore to review the existing literature on the role of physiology as a proximate driver for life-history strategies in salmonids. This link is necessary to explore since physiology is at the core of biological processes influencing energy acquisition and allocation. Energy acquisition and allocation processes, in turn, can affect life histories. We find that life-history strategies are driven by a range of physiological processes, ranging from metabolism and nutritional status to endocrinology. Our review revealed that the role of these physiological processes can vary across species and individuals depending on the life-history decision(s) to be made. In addition, while findings sometimes vary by species, results appear to be consistent in species with similar life cycles. We conclude that despite much work having been conducted on the topic, the study of physiology and its role in determining life-history strategies in salmonids remains somewhat unexplored, particularly for char and trout (excluding brown trout) species. Understanding these mechanistic links is necessary if we are to understand adequately how changing environments will impact salmonid populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".