Does Tree Species Composition Affect Productivity in a Tropical Planted Forest?
Bibliographic record
Abstract
Abstract With growing pressure on primary forests from destructive land uses, increasing the diversity of native species plantations can increase ecosystem service provision, such as timber production or carbon sequestration, thus better supporting sustainable livelihoods. Understanding the effects of tree species composition on productivity can inform plantation design and ecological restoration strategies. However, tree species composition effects have been neglected in experimental biodiversity‐ecosystem function ( BEF ) research. This study uses a 10‐yr data set from one of the first tropical planted forest experiments established with native species and designed for BEF research at scales relevant to forest management. At our site in Sardinilla, Panama, we established plots containing 6 species from a pool of 18, in four combinations, to investigate how composition affects species and plot productivity. We used basal area as a proxy for productivity through time, measured annually, and summed this at species and plot levels for analysis. We found that plots that differed in species composition appeared to differ in temporal rate of basal area increase, but did not differ in BA after 10 yr. Species were generally consistent in size between compositions, and composition performance was correlated with the size of component species, suggesting that species identities were most important in determining plot productivity. Our results suggest that species choice can be based on preferences for individual species, as species performance was consistent across composition contexts. We make recommendations for the use of particularly productive species that also provide multiple services such as Guazuma ulmifolia, Spondias mombin , and Anacardium excelsum .
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".