A 40-year Evaluation of Drivers of African Rainforest Change
Bibliographic record
Abstract
Abstract Background: Tropical forests are repositories of much of the world’s biodiversity and are critical for mitigation of climate change. Yet, the drivers of forest dynamics are poorly understood. This is in large part due to the lack of longitudinal data on forest change and changes in drivers.Methodology: We quantify changes in tree abundance, diversity, and stand structure along transects first enumerated in 1978 and resampled 2019 in Kibale National Park, Uganda. We tested five predictions. First, based on the purported role of seed dispersal and herbivory and our quantification of changes in the abundance of frugivores and herbivores, we tested two predictions of how faunal change could have influenced forest composition. Second, based on an evaluation of life history strategies, we tested two predictions concerning how the forest could have changed following disturbance that happened prior to written history. Finally, based on a 50-year climate record, we test the possible influence of climate change on forest dynamics. Results: More trees were present on the assessed transects in 2019 (508) than in 1978 (436), species richness remained similar, but diversity declined as the number of dominant species increased. Rainfall increased by only 3 mm over the 50 years but this effect was not significant. Annual average monthly maximum temperature increased significantly by 2.2°C over 50 years. The abundance of frugivorous and folivorous primates and elephants increased over the 50 years of monitoring. The predictions that as the abundance of seed dispersing frugivores increases the abundance of their preferred fruiting tree species would increases and that as the abundance of folivorous would cause a decline in their preferred species were both not supported. Since Kibale was disturbed prior to historical records, we predicted that light-demanding species would decrease in abundance, while shade-tolerant species would increase - this was supported. Finally, while temperature increased over the 50 years, we found no means to predict a priori how individual species would respond.Conclusions: Our study revealed subtle changes in the tree community over 40 years, sizable increases in primate numbers, a substantial increase in the elephant population and an increase in local temperature. Yet, a clear picture of what set of interactions impact the change in the tree community remains elusive. Our data on tree life-history strategies and frugivore/herbivore foraging preferences suggest that trees species are under opposing pressures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".