Developing a Mechanistic Understanding of Fish Migrations by Linking Telemetry with Physiology, Behavior, Genomics and Experimental Biology: An Interdisciplinary Case Study on Adult Fraser River Sockeye Salmon
Bibliographic record
Abstract
Abstract Fish migration represents one of the most complex and intriguing biological phenomena in the animal kingdom. How do fish migrate such vast distances? What are the costs and benefits of migration? Some of these fundamental questions have been addressed through the use of telemetry. However, telemetry alone has not and will not yield a complete understanding of the migration biology of fish a or provide solutions to problems such as identifying physical barriers to migration or understanding potential impacts of climate change. Telemetry can be coupled with other tools and techniques to yield new insights into animal biology. Using Fraser River sockeye salmon (Oncorhynchus nerka) as a model, we summarize the advances that we have made in understanding salmonid migration biology through the integration of disciplines (i.e., interdisciplinary research) including physiology, behavior, functional genomics, and experimental biology. We also discuss opportunities for using large-scale telemetry arrays and taking a more experimental approach to studies of fish migration that use telemetry (i.e., intervention studies involving endocrine implants, simulated migration studies) rather than simply focusing on descriptive or correlational techniques. Only through integrative and interdisciplinary research will it be possible to understand the mechanistic basis of fish migrations and to predict and possibly mitigate the consequences of anthropogenic impacts. Telemetry is a tool that has the potential to integrate research across disciplines and between the lab and the field to advance the science of fish migration biology. The techniques that we have applied to the study of Pacific salmon are equally relevant to other fish taxa in both marine and freshwater systems as well as migratory animals beyond ichthyofauna. The interdisciplinary approach used here was essential to address a pressing and complex conservation problem association with sockeye salmon migration.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".