Heritability of anti‐predatory traits: vigilance and locomotor performance in marmots
Bibliographic record
Abstract
Animals must allocate some proportion of their time to detecting predators. In birds and mammals, such anti-predator vigilance has been well studied, and we know that it may be influenced by a variety of intrinsic and extrinsic factors. Despite hundreds of studies focusing on vigilance and suggestions that there are individual differences in vigilance, there have been no prior studies examining its heritability in the field. Here, we present one of the first reports of (additive) genetic variation in vigilance. Using a restricted maximum likelihood procedure, we found that, in yellow-bellied marmots (Marmota flaviventris), the heritability of locomotor ability (h(2)=0.21), and especially vigilance (h(2) = 0.08), is low. These modest heritability estimates suggest great environmental variation or a history of directional selection eliminating genetic variation in these traits. We also found a significant phenotypic (r(P) = -0.09 +/- 0.04, P = 0.024) and a substantial, but not significant, genetic correlation (r(A) = -0.57 +/- 0.28, P = 0.082) between the two traits (slower animals are less vigilant while foraging). We found no evidence of differential survival or longevity associated with particular phenotypes of either trait. The genetic correlation may persist because of environmental heterogeneity and genotype-by-environment interactions maintaining the correlation, or because there are two ways to solve the problem of foraging in exposed areas: be very vigilant and rely on early detection coupled with speed to escape, or reduce vigilance to minimize time spent in an exposed location. Both strategies seem to be equally successful, and this 'locomotor ability-wariness' syndrome may therefore allow slow animals to compensate behaviourally for their impaired locomotor ability.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".