Data from: Bony traits and genetics drive intraspecific variation in vertebrate elemental composition
Bibliographic record
Abstract
Interspecific variation in elemental composition is well known and often leads to predictable differences in ecosystem interactions, but little is known about the extent, causes and importance of intraspecific variation in elemental composition. If intraspecific variation is substantial and has a genetic basis, it may underlie an important mechanism of evolutionary interplay with ecology as individuals compensate for evolutionary changes in elemental demand. To investigate the extent and causes of intraspecific elemental variation in vertebrates, we sampled evolutionary model species Gasterosteus aculeatus (Threespine Stickleback) from 12 locations in British Columbia, Canada. Fish were phenotyped, genotyped for Eda alleles underlying lateral plate variation and assayed for elemental content (C, N, P). We found stickleback vary widely in elemental composition (2.2 – 6.5%P; 3.0 – 9.4:1 N:P). Phenotypic models explained the majority of this variation using bony armour traits (pelvis length, lateral plate count), bone mineralization, body size and condition. Subsequent genetic models found allelic variation at Eda generates a 7-14% change in whole organism N:P. As Eda allele frequencies are commonly changed through strong natural selection in freshwater habitats, we infer that stickleback elemental composition can evolve rapidly. Further, as genetics are known to drive variation in many of the other influential traits, we conclude that genetic variation constitutes a major source of variation in the elemental composition of Gasterosteus aculeatus. As such, we find that elemental composition has a large evolutionary potential which may underlie important evo-eco interactions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.136 | 0.032 |
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".