Interacting and dynamic effects of species and structural diversity promote annual woody biomass production in a tropical tree diversity experiment
Bibliographic record
Abstract
Different aspects of diversity, such as species richness and structural diversity, have been shown to enhance forest ecosystem functions, including biomass production. However, whether diversity-productivity relationships change over time or with climatic conditions remains uncertain. We analyzed above-ground woody biomass increment (AWBI) derived from annual inventory data and micro-densitometry on stem disks from ‘Sardinilla’ in Panama, one of the oldest tropical tree diversity experiments. We investigated AWBI in five tree species growing in monospecific and species-rich neighborhoods. We hypothesized that a) species and structural diversity would increase AWBI, with these effects strengthening over time, b) species diversity effects on AWBI would be mediated by structural diversity, and c) overyielding in diverse neighborhoods would persist under drought. We observed higher AWBI in species-rich compared to monospecific neighborhoods despite slightly decreasing wood density. The strong complementarity effects in mixtures increased over time, indicating progressive strengthening of diversity effects. Species diversity strongly effected AWBI by directly enhancing productivity and indirectly, via increasing structural diversity. Structural diversity had a direct positive effect on AWBI, but this effect weakened with tree age. Overyielding in species-rich neighborhoods persisted or even increased under extremely dry conditions likely due to complementary water use among species. Our results corroborate that mixed planted forests are more productive and have a greater ability to maintain their performance under stressful conditions compared to monocultures. Forest management aiming at maximizing carbon sequestration in plantations should include fast-growing species with high wood density and promote not only tree species richness but also structural diversity.
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 | 0.000 | 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".