Forest productivity increases with evenness, species richness and trait variation: a global meta‐analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary 1. Although there is ample support for positive species richness–productivity relationships in planted grassland experiments, a recent 48‐site study found no diversity–productivity relationship (DPR) in herbaceous communities. Thus, debate persists about diversity effects in natural versus planted systems. Additionally, current knowledge is weak regarding the influence of evenness on the DPRs, how DPRs are affected by the variation in life‐history traits among constituent species in polycultures and how DPRs differ among biomes. The impacts of these factors on DPRs in forest ecosystems are even more poorly understood. 2. We performed a meta‐analysis of 54 studies to reconcile DPRs in forest ecosystems. We quantified the net diversity effect as log effect size [ln(ES)], the log ratio of the productivity in polycultures to the average of those in monocultures within the same type of mixture, site condition and stand age of each study. The first use of a boosted regression tree model in meta‐analysis, a useful method to partition the effects of multiple predictors rather than relying on vote‐counting of individual studies, unveiled the relative influences of individual predictors. 3. Global average ln(ES) was 0.2128, indicating 23.7% higher productivity in polycultures than monocultures. The final model explained 21% of the variation in ln(ES). The predictors that substantially accounted for the explained variation included evenness (34%), heterogeneity of shade tolerance (29%), richness (13%) and stand age (15%). In contrast, heterogeneity of nitrogen fixation and growth habits, biome and stand origin (naturally established versus planted) contributed negligibly (each ≤ 4%). Log effect size strongly increased with evenness from 0.6 to 1 and with richness from 2 to 6. Furthermore, it was higher with heterogeneity of shade tolerance and generally increased with stand age. 4. Synthesis. Our analysis is, to our knowledge, the first to demonstrate the critical role of species evenness, richness and the importance of contrasting traits in defining net diversity effects in forest polycultures. While testing the specific mechanisms is beyond the scope of our analysis, our results should motivate future studies to link richness, evenness, contrasting traits and life‐history stage to the mechanisms that are expected to produce positive net biodiversity effects such as niche differentiation, facilitation and reduced Janzen–Connell effects.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 it