Effects of species diversity on fine root productivity in diverse ecosystems: a global meta‐analysis
Bibliographic record
Abstract
Abstract Aim Positive relationships between plant species diversity and above‐ground productivity have been observed across a wide range of terrestrial ecosystems. Despite a critical contribution of below‐ground productivity to overall terrestrial productivity, no consensus exists about the nature of the relationship between species diversity and below‐ground productivity. Location Global. Methods We collected data from published studies conducted in natural and planted forests and experimental grassland, crop and pot systems that were purposely implemented to isolate the effects of plant species diversity from other factors, such as soil conditions and topographic features. We conducted meta‐analyses of 170 observations for root biomass and 23 observations for root production, derived from 48 published studies, using weighted linear modelling with bootstrap procedures to reconcile the effects of diversity on fine root productivity. Results We found that species mixtures had, on average 28.4% higher fine root biomass and 44.8% higher annual production than monocultures. Higher fine root biomass in species mixtures than in monocultures was consistent across natural forests, planted grasslands, croplands and pot systems, except for young planted forests. Transgressive overyielding was only evident for planted grasslands. The log response ratio of fine root biomass in species mixtures to that in respective monocultures increased with species richness across all ecosystem types, and also increased with experiment age in grasslands. Main conclusions Our meta‐analysis reveals positive effects of species diversity on below‐ground productivity. Despite profound differences in environments among terrestrial ecosystems, our analysis demonstrated that below‐ground productivity responds similarly to variations in species richness. Furthermore, our study also reveals shifts in the effects of diversity over time in both forests and grasslands. Future efforts are needed to further understand below‐ground productivity–diversity relationships.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".