Data from: Interactions among trees: a key element in the stabilising effect of species diversity on forest growth
Bibliographic record
Abstract
1.There is mounting evidence that species diversity increases the temporal stability of forest growth. This stabilising effect of diversity has mainly been attributed to species differences in their response to fluctuating environmental conditions. Interactions among individuals could also contribute to the stabilising effect of diversity by increasing the mean and reducing the variance of tree growth, however, this has never been directly demonstrated. 2.We used tree‐ring width chronologies from temperate and boreal mixed stands of Eastern Canada to identify the role of interactions among individuals in the stabilising effect of diversity on forest growth. Using neighbourhood competition index and a mixed model we compared the effect of interspecific and intraspecific interactions on the mean and the variance of tree growth. 3.We found that interspecific interactions are less detrimental to tree growth than intraspecific interactions. We also found that interspecific interactions buffer tree response to drought and thereby reduce the variance of tree growth. 4.Our results indicate diversity may increase the mean and reduce the variance of tree growth through interactions among individuals. Thus, we demonstrate interactions among individuals play a role in the stabilising effect of diversity on forest growth, and in doing so, we bring to light other mechanisms of the insurance hypothesis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.045 |
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".