Bibliographic record
Abstract
On average, the world is getting warmer, and heatwaves are becoming more common and unpredictable – especially in areas closer to the Earth's poles. Warming near the poles may be bad news for animals, such as brook trout, living in these areas. Luckily, many animals have evolved ways to deal with heat, which could give them a buffer against severe weather. Some animals can adjust how their bodies work when the environment gets warmer, which can help them better handle brief periods of extreme heat. But these internal adjustments take time and may depend on what temperatures the animal has experienced recently. Predicting how animals will handle temperature changes is hard, because the ability to modify body function varies across the animal kingdom, and often we do not know how long it takes to make these adjustments. Erin Stewart and Graham Raby at Trent University, Canada, and Chris Wilson and Vince Frasca at the Ontario Ministry of Natural Resources and Forestry, Canada, wanted to know whether living in warmer conditions helps brook trout to better tolerate extreme temperatures and, if so, how long it takes for their tolerance to develop.To answer these questions, Stewart and colleagues used aquarium heaters and a slow, steady flow of water from a nearby creek to create three naturally fluctuating temperatures: unheated, warmed (+3°C) and warmer still (+6°C). All the water temperatures used in the experiment were within the 10–20°C range that brook trout typically survive best in. To find the hottest temperature that brook trout from each water temperature could stand before losing control of their body movements, Stewart slowly heated up an insulated tank of water containing a small number of trout until the fish reached the point where they began floating on their sides. After this, the trout were placed in a cool tank of water and monitored until they recovered. Almost all the trout survived this brush with extreme heat. To figure out whether the length of time a trout spent living in each creek water temperature (unheated or heated) influenced its heat tolerance, Stewart measured the heat tolerance of different fish from each group after 1, 4, 8, 16 and 30 days.The team found that living in warmer water improved the ability of the brook trout to handle hotter temperatures, but that it took time for the fish to adjust. Trout from the warmest treatment (+6°C) showed a marked increase in heat tolerance after only a single day of living in these warmer conditions. In contrast, trout from the warm group (+3°C) took about 8 days before they showed an improvement in survival over fish living in unheated creek water. Stewart and colleagues also found that the heat tolerance of both warmed groups continued to improve throughout the 30 days of living in the different creek water temperatures.These results suggest that brook trout can adjust to warming environments, within reasonable limits. More importantly, this study highlights an often-overlooked factor that applies to the study of heat tolerance for all animals, not just fish: how long these adjustments take. With climates changing faster than ever in history, many scientists are turning their attention to studying the heat sensitivity of animals. But, if we, as researchers and conservationists, want to use our finite time and resources wisely, we need to account for the fact that biological adjustments to warming temperatures take time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".