Data for the article entitled: Linking sexual size dimorphism to trophic niche partitioning in a generalist predator
Bibliographic record
Abstract
Sexual size dimorphism is a common phenomenon in mammals, and researchers have been trying to demonstrate the evolutionary causes leading to sexual dimorphism. Two main hypotheses emerged: (i) the sexual selection hypothesis and (ii) the sexual competition hypothesis (also called resource partitioning hypothesis). Here, we attempted to link sexual dimorphism in fishers (Pekania pennanti (Erxleben, 1777)) with their fall diet using stable isotope profiling and body and skull measurements. We used the carcasses of 39 fishers which were caught in eastern Québec during fall 2014 by volunteer trappers as well as several potential prey items ranging from small rodents to cervids. We expected minimal niche overlap between sexes, as males should be able to exploit different prey species than females. We also expected to observe an effect of age class (adults vs. juveniles) on trophic niche. As expected, we found great evidence of sexual dimorphism in both body mass and skull measurements: males were heavier and longer than females and had a larger zygomatic and intracanine width and a longer skull. While proportions of prey in diet according to sex and age did not vary greatly, we found some evidence of niche partitioning using Layman's metrics. Indeed, females tended to have a less diversified and more similar diet compared to one another, whereas males showed more diversified and contrasted diets. Despite our limited sample size, our findings provide partial support to the sexual competition hypothesis, as the difference in body and skull size based on sex could have evolved to lessen intraspecific competition in fishers.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.593 | 0.250 |
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".