Effects Of Lake Warming On Behavioural Thermoregulatory Tactics In A Cold-Water Stenothermic Fish
Bibliographic record
Abstract
Despite some evidence of within-population phenotypic variation in fish thermal behaviour, the occurrence of alternative tactics of this behaviour is rarely explicitly considered when studying natural populations. Brook charr provide an example of within-population variability in behavioural thermoregulation as revealed by a recent study on a lacustrine population of this species. The objectives of the present study were (i) to determine the influence of natural variability in the lake's thermal profiles on the expression of thermoregulatory tactics, and (ii) to determine the vertical and horizontal movements of individuals at different periods of the day to better understand the spatio-temporal behaviour associated with each thermoregulatory tactic. During summer 2010, 30 adult brook charr were equipped with thermo-sensitive radio transmitters to monitor their selected temperatures and daily movements. These individuals exhibited the same four behavioural thermoregulatory tactics observed in 2003 and 2005, but the expression of two of these was weaker in 2010. This result was associated with lake warming, which constrained the expression of two thermoregulatory tactics: brook charr significantly decreased their selected temperatures and daily movements when the mean daily epilimnion temperature was above 22.4°C. This study shows for the first time that the expression of behavioural thermoregulatory tactics is related to the lake's thermal regime and that the tactics are plastic through time.
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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 | 0.000 | 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".