Wood decomposition is increased by insect diversity, selection effects, and interactions between insects and microbes
Bibliographic record
Abstract
Biodiversity drives ecosystem processes, but its influence on deadwood decomposition is poorly understood. To test the effects of insect diversity on wood decomposition, we conducted a mesocosm experiment manipulating the species richness and functional diversity of beetles. We applied a novel approach using computed tomography scanning to quantify decomposition by insects and recorded fungal and bacterial communities. Decomposition rates increased with both species richness and functional diversity of beetles, but the effects of functional diversity were linked to beetle biomass, and to the presence of one large-bodied species in particular. This suggests that mechanisms behind observed biodiversity effects are the selection effect, which is linked to the occurrence probability of large species, and the complementarity effect, which is driven by functional differentiation among species. Additionally, beetles had significant indirect effects on wood decomposition via bacterial diversity, fungal community composition, and fungal biomass. Our experiment shows that wood decomposition is driven by beetle diversity and its interactions with bacteria and fungi. This highlights that both insect and microbial biodiversity are critical to maintaining ecosystem functioning.
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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 | 0.001 | 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".