Fine-scale residency and temperature-driven habitat selection in a migratory shark species
Bibliographic record
Abstract
Abstract Many marine species exhibit complex and diverse movements that vary across spatial and temporal scales. These movements must be accounted for when designing effective management and conservation efforts. While environmental cues such as temperature and salinity have been shown to influence the movements of mobile species, it is increasingly documented that social factors can also influence space use and population behaviour. Understanding how various factors influence movement enhances our ability to predict the space use of highly dynamic ocean species. Spurdog (Squalus acanthias), known for their tendency to aggregate and use of both coastal and oceanic environments, are an ideal model species for studying drivers of movement. To investigate movement and habitat selection in spurdog, we conducted an acoustic telemetry study from June 2016 to July 2017, tagging 51 spurdog in a partially enclosed fjordic sea loch on the west coast of Scotland. The thermal profile of Loch Etive was recorded to complement the movement data. Our study revealed temperature as a pivotal driver of movement and habitat selection in spurdog, with the unique thermal environment of the fjord enabling year-round residency at a previously undocumented spatial scale, suggesting that such habitats may be especially important for mobile marine species. This study demonstrates the importance of understanding environmental influences on space use and movement to develop effective, climate-resilient management strategies for spurdog and other mobile marine species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".