Temperature sensitivity of termites determines global wood decay rates
Bibliographic record
Abstract
Abstract Animals, such as termites, have largely been overlooked as global-scale drivers of biogeochemical cycles 1,2 , despite site-specific findings 3,4 . Deadwood turnover, an important component of the carbon cycle, is driven by multiple decay agents. Studies have focused on temperate systems 5,6 , where microbes dominate decay 7 . Microbial decay is sensitive to temperature, typically doubling per 10°C increase (decay effective Q 10 = ~2) 8–10 . Termites are important decayers in tropical systems 3,11–13 and differ from microbes in their population dynamics, dispersal, and substrate discovery 14–16 , meaning their climate sensitivities also differ. Using a network of 133 sites spanning 6 continents, we report the first global field-based quantification of temperature and precipitation sensitivities for termites and microbes, providing novel understandings of their response to changing climates. Temperature sensitivity of microbial decay was within previous estimates. Termite discovery and consumption were both much more sensitive to temperature (decay effective Q 10 = 6.53), leading to striking differences in deadwood turnover in areas with and without termites. Termite impacts were greatest in tropical seasonal forests and savannas and subtropical deserts. With tropicalization 17 (i.e., warming shifts to a tropical climate), the termite contribution to global wood decay will increase as more of the earth's surface becomes accessible to termites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".