Antipredator strategies of Alaskan moose: are maternal trade-offs influenced by offspring activity?
Bibliographic record
Abstract
To maximize fitness, mothers must both provision and protect neonates, demands that may be in conflict, particularly in systems that still experience high levels of natural predation. Whether variation in offspring behaviour alters this putative conflict is not known. The objective of this study was to test hypotheses about the extent to which neonatal activity and ecological variables mediate trade-offs between maternal vigilance and foraging. To address these questions we contrasted data from behavioural observations on female moose (Alces alces) that differed in parity, calf activity, and habitat use at a site in south-central Alaska where they are subject to high levels of grizzly bear (Ursus arctos) and wolf (Canis lupus) predation. Our analyses revealed that females with active juveniles were more vigilant (and as a consequence spent less time feeding) than those with inactive young; vigilance of females without attendant young was intermediate. Distance to apparent protective refugia (e.g., vegetative cover) was positively related to vigilance for all calf-status categories, but lactating females spent more time closer to thick vegetation than did nonlactating females. These results suggest that (i) mothers adjust vigilance when young are inactive to compensate for the loss of foraging opportunities during periods of neonate activity, thereby reducing juvenile vulnerability and increasing the overall feeding rate, and (ii) females with young reduce foraging compromises and, presumably, predation risk by spending more time close to protective cover than do nonlactating females. We conclude that maternal trade-offs can be highly labile and that mothers are able to adjust rapidly to environment-specific situations.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".