Diet composition of reintroduced Red-and-Green Macaws reflects gradual adaptation to life in the wild
Bibliographic record
Abstract
Abstract Over the last two centuries, the Red-and-Green Macaw (Ara chloropterus) has become locally extinct in Argentina. In an attempt to restore its key ecosystem functions as both disperser and regulator of large-seeded plants, a reintroduction project was initiated at the Iberá National Park in northeastern Argentina. The ability of released individuals to find food is crucial, especially when working with captive-bred animals, as long-term establishment of a self-sustaining population depends on their short-term ability to exploit wild food sources. Monitoring of feeding habits is usually conducted through behavioral observation, but in recent years DNA metabarcoding has emerged as an alternative for obtaining highly resolved data on diet composition. In this study, we use a combination of both techniques to characterize the breadth and composition of the reintroduced macaws' diet. In addition, we compare the efficiency of both observational and molecular techniques to assess diet composition in a frugivorous bird. Individuals fed on a variety of plant species (n = 49) belonging to a broad phylogenetic spectrum (28 families). Dietary richness estimated by direct observation and DNA metabarcoding was similar, though smaller than the combination of the two datasets as both techniques detected at least 15 species not recorded by the other method. While the total number of detected species was higher for observational data, the rate of species-detection per sampling day was higher for DNA metabarcoding. These results suggest that a combination of both methods is required in order to obtain the most accurate account of the total diversity of the diet of a bird species. The ability of reintroduced macaws to successfully exploit local food resources throughout the year indicates a good level of adjustment to the release site, an important step towards the creation of a stable, self-sustaining population of Red-and-Green Macaws in Northern Argentina.
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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".