Diversity in Resource Use Strategies Promotes Productivity in Young Planted Tree Species Mixtures
Bibliographic record
Abstract
Mixed-species forestry is a promising approach to enhance productivity, increase carbon sequestration, and mitigate climate change. Diverse forests, composed of species with varying structures and functional trait profiles, may have higher functional and structural diversity, which are attributes relevant to a number of mechanisms that can influence productivity. However, it remains unclear whether the context-dependent roles of functional identity, functional diversity, and structural diversity can lead to a generalized understanding of tree diversity effects on stand productivity. To address these gaps, we analyzed growth data from 83,600 trees from 89 species across 21 young tree diversity experiments spanning five continents and three biomes. Results revealed a positive saturating relationship between tree species richness and stand productivity, with reduced variability in growth rates among more diverse stands. Structural equation modeling demonstrated that functional diversity mediated the positive effects of species richness on productivity. We additionally report a negative relationship between structural diversity and productivity, which decreased with increasing species richness. When partitioning net diversity effects, we found that selection effects played a dominant role in driving the overall increase in productivity in these predominantly young stands, contributing 77% of the net diversity effect. Selection effects increased with diversity in wood density. Furthermore, acquisitive species with lower wood density and higher leaf nitrogen content had higher productivity in more diverse stands, while conservative species showed neutral to slightly negative responses to species mixing. Together, these results suggest that combining acquisitive with conservative species allows acquisitive species to drive positive selection effects while conservative species tolerate competition. Thus, contrasting resource-use strategies can enhance productivity to optimize mixed-species forestry, with potential for both ecological and economic benefits.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".