Biotic and abiotic drivers of soil microbial functions across tree diversity experiments
Bibliographic record
Abstract
Abstract Aim Soil microorganisms are essential for the functioning of terrestrial ecosystems. Although soil microbial communities and functions may be linked to the tree species composition and diversity of forests, there has been no comprehensive study of how general potential relationships are and if these are context-dependent. A global network of tree diversity experiments (TreeDivNet) allows for a first examination of tree diversity-soil microbial function relationships across environmental gradients. Location Global Major Taxa Studied Soil microorganisms Methods Soil samples collected from eleven tree diversity experiments in four biomes across four continents were used to measure soil basal respiration, microbial biomass, and carbon use efficiency using the substrate-induced respiration method. All samples were measured using the same analytical device in the same laboratory to prevent measurement bias. We used linear mixed-effects models to examine the effects of tree species diversity, environmental conditions, and their interactions on soil microbial functions. Results Across biodiversity experiments, abiotic drivers, mainly soil water content, significantly increased soil microbial functions. Potential evapotranspiration (PET) increased, whereas soil C-to-N ratio (CN) decreased soil microbial functions under dry soil conditions, but high soil water content reduced the importance of other abiotic drivers. Tree species richness and phylogenetic diversity had overall similar, but weak and context-dependent (climate, soil abiotic variables) effects on soil microbial respiration. Positive tree diversity effects on soil microbial respiration were most pronounced at low PET, low soil CN, and high tree density. Soil microbial functions increased with the age of the experiment. Main conclusions Our results point at the importance of soil water content for maintaining high levels of soil microbial functions and modulating effects of other environmental drivers. Moreover, overall tree diversity effects on soil microbial functions seem to be negligible in the short term (experiments were 1-18 years old). However, context-dependent tree diversity effects (climate, soil abiotic variables) have greater importance at high tree density, and significant effects of experimental age call for longer-term studies. Such systematic insights are key to better integrate soil carbon dynamics into the management of afforestation projects across environmental contexts, as today’s reforestation efforts remain focused largely on aboveground carbon storage and are still dominated by less diverse forests stands of commercial species.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".