The Nutritional Geometry of Resource Scarcity: Effects of Lean Seasons and Habitat Disturbance on Nutrient Intakes and Balancing in Wild Sifakas
Bibliographic record
Abstract
Animals experience spatial and temporal variation in food and nutrient supply, which may cause deviations from optimal nutrient intakes in both absolute amounts (meeting nutrient requirements) and proportions (nutrient balancing). Recent research has used the geometric framework for nutrition to obtain an improved understanding of how animals respond to these nutritional constraints, among them free-ranging primates including spider monkeys and gorillas. We used this framework to examine macronutrient intakes and nutrient balancing in sifakas (Propithecus diadema) at Tsinjoarivo, Madagascar, in order to quantify how these vary across seasons and across habitats with varying degrees of anthropogenic disturbance. Groups in intact habitat experience lean season decreases in frugivory, amounts of food ingested, and nutrient intakes, yet preserve remarkably constant proportions of dietary macronutrients, with the proportional contribution of protein to the diet being highly consistent. Sifakas in disturbed habitat resemble intact forest groups in the relative contribution of dietary macronutrients, but experience less seasonality: all groups' diets converge in the lean season, but disturbed forest groups largely fail to experience abundant season improvements in food intake or nutritional outcomes. These results suggest that: (1) lemurs experience seasonality by maintaining nutrient balance at the expense of calories ingested, which contrasts with earlier studies of spider monkeys and gorillas, (2) abundant season foods should be the target of habitat management, even though mortality might be concentrated in the lean season, and (3) primates' within-group competitive landscapes, which contribute to variation in social organization, may vary in complex ways across habitats and seasons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".