Long-term stability of productivity increases with tree diversity in Canadian forests
Bibliographic record
Abstract
The temporal stability of forest productivity is a key ecosystem function and an essential service to humanity. Plot-scale tree diversity experiments with observations over 10 to 11 y indicate that tree diversity increases stability under various environmental changes. However, it remains unknown whether these small-scale experimental findings are relevant to the longer-term stability of natural forests. Using 7,500 natural forest plots across much of Canada, monitored over three to four decades on average, we provide strong evidence that higher temporal stability (defined as the mean productivity divided by its SD over time) is consistently associated with greater tree functional, phylogenetic, and taxonomic diversity across all lengths of observations. Specifically, increasing functional diversity from its minimum to maximum values increases stability, mean productivity, and the temporal SD of productivity by 14%, 36%, and 28%, respectively. Our results highlight that the promotion of functionally, phylogenetically, and/or taxonomically diverse forests could enhance the long-term productivity and stability of natural 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".