Tree diversity effects on forest productivity increase through time because of spatial partitioning
Bibliographic record
Abstract
Abstract Background Experimental manipulations of tree diversity have often found overyielding in mixed-species plantations. While most experiments are still in the early stages of stand development, the impacts of tree diversity are expected to accumulate over time. Here, I present findings from a 31-year-old tree diversity experiment (as of 2018) in Japan. Results I find that the net diversity effect on stand biomass increased linearly through time. The species mixture achieved 64% greater biomass than the average monoculture biomass 31 years after planting. The complementarity effect was positive and increased exponentially with time. The selection effect was negative and decreased exponentially with time. In the early stages (≤ 3 years), the positive complementarity effect was explained by enhanced growths of early- and mid-successional species in the mixture. Later on (≥ 15 years), it was explained by their increased survival rates owing to vertical spatial partitioning — i.e. alleviation of self-thinning via canopy stratification. The negative selection effect resulted from suppressed growths of late-successional species in the bottom layer. Conclusions The experiment provides pioneering evidence that the positive impacts of diversity-driven spatial partitioning on forest biomass can accumulate over multiple decades. The results indicate that forest biomass production and carbon sequestration can be enhanced by multispecies afforestation strategies.
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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 | 0.000 | 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 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".