Functional diversity and identity influence the self‐thinning process in young forest communities
Bibliographic record
Abstract
Abstract There is increasing evidence that the strength of tree diversity effects on productivity varies considerably over the course of forest development. Evidence points to canopy closure and the subsequent self‐thinning as key phases of forest development during which positive diversity effects emerge. A number of studies have shown that self‐thinning can differ among species, and also in mixtures compared with monocultures. Yet, how diversity influences the process of canopy closure and self‐thinning remains poorly understood. In this study, using 11 years of growth and mortality records from a large diversity experiment, we fitted self‐thinning trajectories for 37 tree communities with equal initial densities and explored whether and how functional diversity and identity may affect these trajectories. We then examined whether the diversity effects on self‐thinning were influenced by differences in growth or in mortality. We found that tree communities' functional diversity and identity strongly influence the self‐thinning process. First, we observed that tree communities dominated by early successional species, and slow‐growing evergreens begin self‐thinning at a larger mean tree size. Second, we found that mixing species with contrasting resource‐use strategies, and the dominance of deciduous, fast‐growing species, reduce tree mortality rate in relation to mean tree size during self‐thinning (i.e. shallower self‐thinning slope). The lower rates of self‐thinning in these functionally diverse communities seem to be explained by both an increase in tree growth and a reduction in density‐related mortality simultaneously over time. Synthesis . Overall, this study highlights that increasing tree diversity has the potential to enhance forest productivity in the long term through a better performance during the self‐thinning process when competition for resources is most intense.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".