Self-pruning in tree crowns is influenced by functional strategies and neighborhood interactions
Bibliographic record
Abstract
Abstract As canopy closure causes forest stands to face increasing light limitation, trees’ lower branches begin to die back. This process, called self-pruning, defines a crown’s base and depth and shapes the structure of entire stands. Self-pruning is often thought to occur after shading causes individual branches to transition from net carbon sources to sinks. Under this explanation, we would expect resource-conservative and shade-tolerant species to initiate self-pruning under deeper shade because their branches need less light to maintain a positive carbon balance. However, the notion that branches are fully autonomous may be complicated by ‘correlative inhibition,’ in which plants preferentially allocate resources towards sunlit branches. Consistent with this idea, we predicted that within species, trees with sunlit tops would initiate self-pruning at a higher light threshold. Lastly, we predicted that plot-level diversity in self-pruning strategies would correlate with productivity and total crown volume. We tested these predictions in an experiment where 12 temperate tree species were planted in plots of varying diversity and composition. We measured characteristics of crown size and position as well as the light level at the crown base (denoted L base ), which we took as an estimate of the light threshold of self-pruning. As we predicted, more shade-tolerant and resource-conservative species self-pruned at a deeper level of shade (lower L base ). In addition, most species had higher L base when they had more light at the crown top, suggestive of correlative inhibition. With respect to their neighbors’ traits, though, conservative and acquisitive species showed contrary patterns of plasticity: conservative species had lower L base around conservative neighbors, while acquisitive species had lower L base around acquisitive neighbors. However, all species declined in crown depth when they grew alongside larger, more acquisitive neighbors. As predicted, plots with a greater interspecific diversity of L base had greater basal area and crown volume. Using simulations, we showed that plasticity in crown depth between monocultures and mixtures strengthened the relationship with crown volume, primarily due to competitive release experienced by acquisitive species. By placing shade-induced self-pruning in a comparative context, we clarify how forest function emerges from competition for light between individual trees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".