PLANT-WILDLIFE DYNAMICS IN THE TROPICAL RAINFOREST: PHENOLOGY, TROPHIC INTERACTIONS, AND HABITAT CONNECTIVITY
Bibliographic record
Abstract
The tropical rainforests of Borneo are particularly distinctive for their inter-annual phenology, with supra-annual mast fruiting events that cause dramatic fluctuations in fruit availability. Tracking its phenology is therefore vital for forest wildlife, especially frugivores and granivores whose populations depend on these resources. This dissertation investigates animal–habitat dynamics in Borneo, examining it from the perspective of plant phenology, animal responses, and restoration practices. In the first chapter, I examined phenological change in rainforests of Borneo, focusing on tracking dipterocarp tree flowering using unoccupied aerial vehicles (UAVs). Dipterocarps exhibit the supra-annual cycle, during which individual trees bloom for four to five weeks. This short duration, coupled with persistent cloud cover, makes it difficult to monitor flowering using space-borne remote sensing. I demonstrated that UAV imagery, with spatial resolutions ranging from 20 centimeters to 5 meters, can reliably distinguish flowering in individual trees, highlighting the potential of UAVs to provide large-scale, high-resolution phenological monitoring in tropical forests. In the second chapter, I investigated how the fruiting of dipterocarps influences habitat use across trophic levels, drawing parallels to bottom-up cascades observed in temperate systems. I tested whether pulses in dipterocarp fruit availability propagate through animal communities using Structural Equation Modelling (SEM). My results revealed that resource pulses significantly influenced certain primary consumers but had little detectable effect on secondary consumers. The lack of response at higher trophic levels suggests that tropical animal communities may be buffered against cascading effects of pulsed resources, perhaps due to the presence of alternative food sources or the dominance of large-bodied granivores that mostly lack predators. Because many tropical rainforest animals must remain mobile to track resources, maintaining and restoring landscape connectivity is essential to support their movements. In my final chapter, I conducted a systematic review of the literature on forest restoration in Malaysian Borneo to assess whether indicators reported included measures of wildlife connectivity. My analysis revealed that none of the reviewed studies incorporated landscape scale connectivity indicators, highlighting a major gap in ensuring restoration outcomes for wildlife. I outlined recommendations to improve restoration practices, especially towards achieving the Kunming–Montreal Global Biodiversity Framework Target 2.
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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 | 0.000 | 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".