Asian elephants are associated with a more robust mammalian community in tropical forests
Bibliographic record
Abstract
Megaherbivores are experiencing a global extinction crisis before we fully understand their ecological functions. While the role of megaherbivores as ecosystem engineers-enhancing environmental structure complexity and facilitating seed dispersal-is well-documented, their influence on animal community assemblies remains less explored, especially in tropical forests. This knowledge gap is crucial for effective, functional-oriented conservation planning. Therefore, we investigated the association between Asian elephants (Elephas maximus) and mammalian community assemblages-from community to species level-in tropical forests of Southwest China, using long-term monitoring data from camera traps. Our results revealed that the presence of Asian elephants was associated with a more robust co-occurrence network within mammalian communities. Additionally, elephants were positively correlated with the abundance of mammal species, especially ungulates and primates. At the species level, while some mammals temporarily avoided Asian elephants, most retained their diel activity patterns, presumably because they were neither in a predator-prey relationship nor intense competitors. Our results show that Asian elephants not only affect vegetation but also are associated with a more robust mammalian community. Consequently, protecting elephants is a pivotal conservation action towards maintaining robust animal communities in Asian tropical forests.
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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 | 0.001 | 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) | 0.002 | 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".