Tree Diversity Explains Variation in Ecosystem Function in a Neotropical Forest in Panama
Bibliographic record
Abstract
ABSTRACT Many experimental studies show that a decline in species number has a negative effect on ecosystem function, however less is known about this pattern in natural communities. We examined the relative importance of environment, space, and diversity on ecosystem function, specifically tree carbon storage in four plant types (understory/canopy; trees/palms), in a tropical forest in central Panama. The objectives of this study were to detect the relationship between tree diversity and carbon storage given the environmental and spatial variation that occur in natural forests and to determine which species diversity measure is more important to tree carbon storage: richness or dominance. We used redundancy analyses to partition the effect of these sources of variation on tree carbon storage. We showed that together, environment, space, and diversity accounted for 43 percent of tree carbon storage, where diversity (19%) alone is the most important source of variation and explained more variation than space (13%) and environment (1%) together. Therefore, even in natural forests where substantial environment and spatial variation can be found, it is still possible to detect the effect of diversity on ecosystem function at scales relevant to conservation. Moreover, both richness and dominance are important to explain the variation on tree carbon storage in natural forests suggesting that these two diversity measures are complementary. Thus, tree diversity is important to predict tree carbon storage in hyperdiverse forests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".