Bibliographic record
Abstract
Individual- and population-level responses to thermal change will be pivotal for species’ resilience and adaptive responses to climate change. Thermal tolerance of ectotherms has been extensively studied under laboratory conditions, but comparatively few studies have assessed intra- and interpopulation variation under natural conditions or in situ. We measured field critical thermal maximum (CTmax) of brook trout (Salvelinus fontinalis) populations in the field at twenty sites across Ontario, Canada, to assess their thermal tolerance in situ and examine potential factors underlying intraspecific variation in thermal performance. We modelled CTmax data as a function of short-term stream temperature data to assess plasticity and local adaptation, and used full-season ambient temperature data to calculate thermal safety margins (TSM) for each population. CTmax ranged between 27.41 °C and 30.46 °C and acclimation periods between 4 and 40 days were strong predictors of site CTmax, aligning closely with lab-based studies. Seasonal temperature profiles varied substantially among sites, with mean 30-day stream temperature accounting for 65% of the among-site variation in CTmax. TSMs ranged between 4.15 °C and 15.51 °C and reflected differences among site thermal regimes. Streams in watersheds with more urban or agricultural development had the lowest TSMs in addition to those that were fed by lake surface water. This work emphasizes the importance of locally-based conservation and management practices that act at or below the population-level, as both watershed characteristics and local adaptation drive intraspecific variation in thermal tolerance and thus dictate the resiliency of brook trout under climate change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.820 | 0.531 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".