Global responses of fine root biomass and traits to plant species mixtures in terrestrial ecosystems
Bibliographic record
Abstract
Abstract Aim Fine root traits underpin terrestrial ecosystem functioning. Despite ongoing plant diversity loss due to anthropogenic activities, our understanding of the effects of plant diversity on fine root traits remains elusive. We addressed: (a) Do fine roots modify their traits in response to species mixtures? (b) Do these responses change with the species richness in mixtures, stand age, and soil depth? (c) Do plant‐mixture induced responses of root traits differ across terrestrial ecosystems? Location Global. Time period Publication years: 1985–2019. Major taxa studied Plants. Methods We conducted a global meta‐analysis of 852 paired observations from 103 published studies to assess the effects of species mixtures on fine root biomass and traits (including root/shoot ratio, community‐weighted mean rooting depth, root length density, specific root length, mean root diameter and root nitrogen content). Results We found that the effects of species mixtures were highly dependent on species richness in mixtures, stand age, and soil depth. The positive effects of species mixtures on root biomass increased with species richness, soil depth, and mean annual temperature. Species mixture effects on root length density shifted from negative to positive with increasing stand age and soil depth and with decreased temperatures. The effects of species mixtures on specific root length shifted from positive to negative with increasing species richness and soil depth, and from negative to positive with increasing stand age. Main conclusions Our meta‐analysis highlights that the community‐level consequences of changes in plant diversity on fine root traits are not consistent, and that predicting these consequences requires taking into account the extent of changes in plant species richness, stand age, soil depth investigated, background climates, and importantly, particular fine root traits.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".