Linking feeding ecology and population abundance: a review of food resource limitation on primates
Bibliographic record
Abstract
Abstract We review studies that consider how food affects primate population abundance. In order to explain spatial variation in primate abundance, various correlates that parameterize quality and quantity of food in the habitat have been examined. We propose two hypotheses concerning how resource availability and its seasonality determine animal abundance. When the quality of fallback foods (foods eaten during the scarcity of preferred foods) is too low to satisfy nutritional requirement, total annual food quantity should determine population size, but this relationship can be modified by the quality or the quantity of fallback foods. This mechanism has been established for Japanese macaques and sportive lemurs that survive lean seasons by fat storage or extremely low metabolism. Second, when fallback food quality is high enough to satisfy nutritional requirement but quantity is limited, quantity of fallback food should be a limiting factor of animal abundance. This is supported by the correlation between fig density, which is a high‐quality fallback food, and gibbon and orangutan abundance. For a direct test of these hypotheses, we need more research that determines both the quality of food that animals require to satisfy their nutritional requirement and the quantity of food production. Leaves are often regarded as superabundant, but this assumption needs careful examination.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".