Long‐term declines in nutritional quality of tropical leaves
Bibliographic record
Abstract
Global change is affecting plant and animal populations and many of the changes are likely subtle and difficult to detect. Based on greenhouse experiments, changes in temperature and rainfall, along with elevated CO2, are expected to impact the nutritional quality of leaves. Here, we show a decline in the quality of tree leaves 15 and 30 years after two previous studies in an undisturbed area of tropical forest in Kibale National Park, Uganda. After 30 years in a sample of multiple individuals of ten tree species, the mature leaves of all but one species increased in fiber concentrations, with a mean increase of 10%; tagged individuals of one species increased 13% in fiber. After 15 years, in eight tree species the fiber of young leaves increased 15%, and protein decreased 6%. Like many folivores, Kibale colobus monkeys select leaves with a high protein-to-fiber ratio, so for these folivores declining leaf quality could have a major impact. Comparisons among African and Asian forests show a strong correlation between colobine biomass and the protein-to-fiber ratio of the mature leaves from common tree species. Although this model, predicts a 31% decline in monkey abundance for Kibale, we have not yet seen these declines.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".