Self‐pruning in tree crowns is influenced by functional strategies and neighbourhood interactions
Bibliographic record
Abstract
Abstract As canopy closure imposes light limitation in forests, the dieback of trees' lower branches, known as self‐pruning, defines their live crown base and shapes the structure and function of entire stands. Self‐pruning is often thought to occur after shading causes individual branches to transition from carbon sources to sinks. Under this assumption, we would expect resource‐conservative and shade‐tolerant species to initiate self‐pruning under deeper shade because their branches require less light to maintain a positive carbon balance. However, this pattern may be complicated by ‘correlative inhibition,’ which may cause plants to 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 community‐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 crown dimensions and position as well as the fraction of light reaching the crown base (denoted Lbase), which we took as an estimate of the light threshold of self‐pruning. As predicted, shade‐tolerant and resource‐conservative species self‐pruned at a deeper level of shade (lower Lbase). Within species, trees generally had higher Lbase when they had more light at the crown top, suggestive of correlative inhibition. With respect to their neighbours' traits, though, conservative and acquisitive species showed contrary patterns of plasticity: conservative species had lower Lbase around conservative neighbours, and acquisitive species around acquisitive neighbours. As predicted, plots with a greater diversity of Lbase had greater basal area and crown volume. Using simulations, we showed that adjustment of crown depth from monocultures to mixtures strengthened the relationship between diversity of Lbase and crown volume, primarily due to competitive release that benefited acquisitive species. We provide evidence that self‐pruning strategies are intimately connected to resource acquisition strategies and propose that Lbase may serve as a functional trait to quantify them. Our results reinforce the role of tree architectural diversity in the functioning of light‐limited forests. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 | 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".