Thermal performance curves dataset based on <em>Littorina littorea</em> from different locations along a latitudinal gradient: Physiological and life history traits
Bibliographic record
Abstract
Intraspecific variation in functional traits may indicate adaptation to environmental gradients and is crucial to understanding species distributions and range dynamics. We tested the hypotheses that: (1) thermal performance of Littorina littorea follows a countergradient cline, reflecting compensation in northern individuals and (2) local differentiation occurs at the range limits of its non-native Atlantic distribution. Snails from 10 locations, spanning 10° of latitude and approximately 1700 km, were exposed to a gradient of 12 temperatures in the laboratory, and survival, growth, feeding, metabolic rate and heat tolerance were measured. Thermal performance curves obtained were compared and used to calculate performance parameters: that is, thermal optimum, maximum performance and thermal breadth. Snails from the northern and southern range edges exhibited a gradual divergence in growth performance parameters relative to central populations. Snails from colder northern locations exhibited greater survival, feeding rate and heat tolerance when exposed to higher temperatures. The dataset can be used in meta-analyses focused on evaluating differences among taxa and populations in thermal performance of functional traits, as well as thermal tolerance and acclimation capacity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.020 |
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".