Partitioning the effects of biodiversity and environmental heterogeneity for productivity and mortality in a tropical tree plantation
Bibliographic record
Abstract
Summary Over 5000 trees were grown in plots of differing diversity levels (1, 3 and 6 species) in a plantation established in Panama. Four and five years after establishment, we analysed parameters related to the productivity of this tropical plantation (tree survival, height and biomass as well as plot basal area) to test for the presence of biodiversity effects. The relative importance of environmental heterogeneity (such as soil, topography, and drainage) and biodiversity on tree growth and mortality was determined using partial redundancy analysis. Hierarchical clustering revealed nine different soil clusters based on soil quality and drainage. By chance, the six‐species plots were apparently established on more variable soils then on the other diversity levels. We found little evidence for spatial autocorrelation between subplots, with the exception of four subplots located on a ridge that extends on the North–South axis of the plantation and corresponds to a zone of higher productivity. The redundancy analysis indicated that environmental heterogeneity and biodiversity together explained around 50% of the variation in subplot productivity and tree mortality. Environment explained 35–57% of the variation in productivity and mortality, respectively, whereas diversity explained an additional 23–30%. Our simulation model revealed a significant positive effect of biodiversity on growth but no effect of biodiversity on mortality. The standardized effect sizes that we used to detect over‐ or under‐yielding or no effect in comparison with monoculture were highly variable and the variability was largely explained by traits related to site topography. Synthesis. In our tropical tree plantation, we detected biodiversity effects at a scale relevant to conservation and quantified the relative importance of environmental heterogeneity and diversity on tree growth and mortality. Our results support the idea that environmental factors could act as hidden sources of variability in biodiversity experiments. Environmental and spatial heterogeneity induced variable responses to biodiversity and amplified the differences between three‐ and six‐species plots. Species identity explained more variation in productivity than did the species diversity. One species,Cedrela odorata, was associated with increased productivity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".