Sex and seasonal differences in diet and nutrient intake in Verreaux's sifakas (<i>Propithecus verreauxi</i>)
Bibliographic record
Abstract
Fluctuations in food availability are a major challenge faced by primates living in seasonal climates. Variation in food availability can be especially challenging for females, because of the high energetic costs of reproduction. Therefore, females must adapt the particular demands of the different reproductive stages to the seasonal availability of resources. Madagascar has a highly seasonal climate, where food availability can be extremely variable. We investigated the seasonal changes in diet composition, nutrient and energy intake of female and male sifakas (Propithecus verreauxi) in a dry deciduous forest in western Madagascar. We examined how females adjust their diet to different reproductive stages. Seasonality affected the diet of both sexes; particularly in the dry season (Apr-Oct) with low availability of food items, especially fruits, males and females had a reduced nutrient and energy intake compared to the wet season (Nov-Mar) with higher food and fruit availability. The comparison of the diet between sexes in different reproductive stages showed that during the late stage of lactation (Nov-Jan) females had higher food intake, and as a result they had a higher intake of macronutrients (crude protein, fat and non-structured carbohydrates (TNC)) and energy than males. These differences were not present during the pregnancy of females, with both sexes having similar intake of macronutrients and energy during that stage. The increase in the intake of macronutrients observed for females during late lactation could be related to the higher energetic demands of this stage of reproduction. Thus, the observed pattern in the diet indicates that sifaka females are following a capital breeding strategy, whereby females potentially store enough nutrients to cope with the reproduction costs in periods of low food availability.
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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 | 0.001 | 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".