Termite environmental tolerances are more linked to desiccation than temperature in modified tropical forests
Bibliographic record
Abstract
Termites are vital members of old-growth tropical forests, being perhaps the main decomposers of dead plant material at all stages of humification (decay). Termite abundance and diversity drop in selectively logged forest, and it has been hypothesised that this drop is due to a low tolerance to changing micro-climatic conditions. Specifically, the thermal adaptation hypothesis suggests that tropical species are operating at, or close to, their thermal optimum, and therefore, small temperature increases can have drastic effects on abundance, however, other climatic variables such as humidity might also cause termite abundance to drop. We tested termite tolerance to these two climatic variables (temperature and humidity). We found that termites had a higher CT max than expected, and that three traits, feeding group, body sclerotisation, and nesting type, were significantly correlated with CT max . We found that termite desiccation tolerance was low, however, and that all termite genera lost significantly more water in a desiccated environment than in a control. Body sclerotisation, the only trait that was tested, was surprisingly not significantly correlated with desiccation tolerance. Our results suggest that desiccation, rather than ambient temperature, may be the determining factor in dictating termite distributions in modified forests. Should climate change lead to reduced humidity within tropical rainforests, termite abundances and the rates of the functions they perform could be severely reduced.
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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 | 0.001 |
| 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) | 0.001 | 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".