Male endocrine response to seasonally varying environmental and social factors in a neotropical primate, <i>Cebus capucinus</i>
Bibliographic record
Abstract
OBJECTIVE: Circannual variation in reproduction is pervasive in birds and mammals. In primates, breeding seasonality is variable, with seasonal birth peaks occurring even in year-round breeders. Environmental seasonality is reportedly an important contributor to the observed variation in reproductive seasonality. Given that food availability is the primary factor constraining female reproduction, predictions concerning responsiveness to environmental seasonality focus on females, with studies of males focusing primarily on social factors. We examined the influence of both environmental and social factors on male fecal testosterone (fT) and glucocorticoids (fGC) in moderately seasonally breeding white-faced capuchin monkeys (Cebus capucinus) in Costa Rica. METHODS: Over 17 months, we collected 993 fecal samples from 14 males in three groups. We used LMM to simultaneously examine the relative effects of photoperiod, fruit biomass, rainfall, temperature, female reproductive status (i.e., number of periovulatory periods, POPs), and male age and dominance rank on monthly fT and fGC levels. RESULTS: Male age and rank had large effects on fT and fGC. Additionally, some hormone variation was explained by environmental factors: photoperiod in the previous month (i.e., lagged photoperiod) was the best environmental predictor of monthly fT levels, whereas fGC levels were best explained by lagged photoperiod, fruit biomass, and rainfall. POPs predicted monthly fT and fGC, but this effect was reduced when all variables were considered simultaneously, possibly because lagged photoperiod and POP were highly correlated. CONCLUSIONS: Males may use photoperiod as a cue predicting circannual trends in the temporal distribution of fertile females, while also fine-tuning short-term hormone increases to the actual presence of ovulatory females, which may occur at any time during the year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".