Food selection in the black howler monkey following habitat disturbance: implications for the importance of mature leaves
Bibliographic record
Abstract
Abstract: Primates commonly consume leaves that are high in protein but low in digestion-inhibiting fibre. Due to the fact that mature leaves do not meet these criteria, they are typically avoided and many leaf-eating primates select for leaves high in protein and low in fibre leading to the theory that food selection is based on protein maximization. However, feeding records for a population of black howler monkey (Alouatta pigra) in Monkey River, Belize, collected over a 5-y period, together with synchronous phenological data, indicate that this population does not meet the expectation and actually prefer mature leaves. This study aims to describe the nutritional composition of the food supply and investigate the possibility that, rather than to maximize protein ingestion, mature leaves are eaten to balance nutrient intake. Macronutrient analyses (moisture, lipids, protein, NDF, ADF and simple sugars) were conducted on a sample of 96 plant samples from 18 food species of this population of black howler. Results reported here show that mature leaves eaten by howlers in this forest contain sufficient protein to meet minimum metabolic requirements (range: 11.6–24%; mean: 16.4% ± 3.8%) and have significantly higher concentrations of simple sugars than young leaves (means of 7.2% ± 2.7% vs. 4.4% ± 2.3% respectively). Thus, it appears that mature leaf ingestion is likely serving to balance energy and protein intake. This result may be due to the disruptive effects of a hurricane in 2001 that resulted in a loss of 80% of the howler population, changed forest composition and may have affected plant chemistry. Despite this, the data reported here suggest that the accepted view that mature leaves are simply fallback foods for primates, eaten only in times of preferred food scarcity, may have to be revised.
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 | 0.001 |
| 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".