Temporal changes in arthropod activity in tropical anthropogenic forests
Bibliographic record
Abstract
Arthropod communities in the tropics are increasingly impacted by rapid changes in land use. Because species showing distinct seasonal patterns of activity are thought to be at higher risk of climate-related extirpation, global warming is generally considered a lower threat to arthropod biodiversity in the tropics than in temperate regions. To examine changes associated with land use and weather variables in tropical arthropod communities, we deployed Malaise traps at three major anthropogenic forests (secondary reserve forest, oil palm forest, and urban ornamental forest (UOF)) in Peninsular Malaysia and collected arthropods continuously for 12 months. We used metabarcoding protocols to characterize the diversity within weekly samples. We found that changes in the composition of arthropod communities were significantly associated with maximum temperature in all the three forests, but shifts were reversed in the UOF compared with the other forests. This suggests arthropods in forests in Peninsular Malaysia face a double threat: community shifts and biodiversity loss due to exploitation and disturbance of forests which consequently put species at further risk related to global warming. We highlight the positive feedback mechanism of land use and temperature, which pose threats to the arthropod communities and further implicates ecosystem functioning and human well-being. Consequently, conservation and mitigation plans are urgently needed.
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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 | 0.001 |
| 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) | 0.001 | 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".