The Role of Minerals in Food Selection in a Black Howler Monkey (<i>Alouatta Pigra</i>) Population in Belize Following a Major Hurricane
Bibliographic record
Abstract
As plants may contain low levels of some minerals including sodium, copper, and phosphorous, herbivores may become deficient in these nutrients. In 2001, Hurricane Iris hit the Monkey River Forest in Belize causing substantial damage to the food supply of the black howler monkey population (Alouatta pigra) living there. This included an 18-month absence in fruit production and a complete loss of figs that are high in calcium. In this article, we describe the post-hurricane diet of this monkey population and compare the mineral content of food items to each other and to recommendations for non-human primates [NRC 2003]. We also investigate food selection in relation to potentially limiting minerals. Behavioral data from four groups of howlers (2002-2006) and samples of all ingested food items were collected and a sample of 99 plant from 18 food species was analyzed for mineral content. Unexpectedly, the post-hurricane diet contained more mature leaves than new leaves despite the availability of new leaves. Leaves contained higher amounts of minerals than reproductive parts and with the exception of Cecropia peltata stems, plant parts were low in sodium. Cecropia peltata is a pioneer species that grows following habitat disturbance thus the ingestion of these stems may be a potential mechanism to avoid sodium deficiency in this damaged forest. Calcium and zinc were found above recommended values in most food items; however, both positively predicted food selection, which may reflect a difference between their abundance and their bioavailability. However, as mature leaves contained more calcium than other plant parts, their high post-hurricane consumption may also be a response to the absence of figs and the need to find an alternate calcium source. This study highlights how habitat disturbance may affect mineral abundance and the dietary choices of primates.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".