Similar plastic responses to elevated temperature among different‐sized brook trout populations
Bibliographic record
Abstract
The potential influence of population size on the magnitude of phenotypic plasticity, a key factor in adaptation to environmental change, has rarely been studied. Conventionally, small populations might exhibit consistently lower plasticity than large populations if small population habitats are generally poor in quality and if genetic diversity underpinning plasticity is lost as population size is reduced. Alternatively, small populations might exhibit (1) consistently higher plasticity as a response to the increased environmental variation that can accompany habitat fragment size reduction or (2) greater variability in plasticity, as fragmentation can increase variability in habitat types. We explored these alternatives by investigating plasticity to increasing temperature in a common garden experiment using eight fragmented populations of brook trout varying nearly 50-fold in census size (179-8416) and 10-fold in effective number of breeders (18-135). Across six early-life-history traits and three temperatures, we found almost no evidence for differences in either the magnitude or variability of plasticity in relation to population size, despite that one temperature represented an extreme climate warming scenario. The documentation of similar plastic responses of small and large populations suggests that phenotypic plasticity is not reduced as population size decreases, and that even very small populations of some species might have the ability to respond to 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".