Maternal glucocorticoids have minimal effects on HPA axis activity and behavior of juvenile wild North American red squirrels
Bibliographic record
Abstract
As a response to environmental cues, maternal glucocorticoids (GCs) may trigger adaptive developmental plasticity in the physiology and behavior of offspring. In North American red squirrels (Tamiasciurus hudsonicus), mothers exhibit increased GCs when conspecific density is elevated, and selection favors more aggressive and perhaps more active mothers under these conditions. We tested the hypothesis that elevated maternal GCs cause shifts in offspring behavior that may prepare them for high-density conditions. We experimentally elevated maternal GCs during gestation or early lactation. We measured two behavioral traits (activity and aggression) in weaned offspring using standardized behavioral assays. Because maternal GCs may influence offspring hypothalamic-pituitary-adrenal (HPA) axis dynamics, which may in turn affect behavior, we also measured the impact of our treatments on offspring HPA axis dynamics (adrenal reactivity and negative feedback), and the association between offspring HPA axis dynamics and behavior. Increased maternal GCs during lactation, but not gestation, slightly elevated activity levels in offspring. Offspring aggression and adrenal reactivity did not differ between treatment groups. Male, but not female, offspring from mothers treated with GCs during pregnancy exhibited stronger negative feedback compared with those from control mothers, but there were no differences in negative feedback between lactation treatment groups. Offspring with higher adrenal reactivity from mothers treated during pregnancy (both controls and GC-treated) exhibited lower aggression and activity. These results suggest that maternal GCs during gestation or early lactation alone may not be a sufficient cue to produce substantial changes in behavioral and physiological stress responses in offspring in natural populations.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".