Self-replacement after small-scale partial crown dieback in austral <i>Nothofagus dombeyi</i> forests affected by an extreme drought
Bibliographic record
Abstract
The spectrum of structural response in drought-induced mortality (degree of partial crown mortality) could play a key role in pervasive changes in plant composition, as individual openings could reset self-replacement dynamics or trigger shifts in vegetation. Here we capture the community pattern 17 years after a drought episode over a range of canopy responses in Nothofagus dombeyi forests. We applied a widespread demographic approach to address evidence of species shifts vs. self-replacement and to relate partial dieback to understory structure and composition. Assuming that the outcome of growth release of the understory components can be observed 17 years after canopy loss, this study reveals evidence of self-replacement in N. dombeyi forests heavily affected by drought. Alternatively, when the co-dominant species A. chilensis is widely present in the understory and large gaps are opened, a compositional shift may be possible, with a potential change in forest functionality. Individual partial openings do not favor more shrubby communities or a shift toward a new community. Thus, partial crown dieback contributes to self-replacement mechanisms by hampering strong growth release in understory shrubs, as evident in plots with high mortality, and by facilitating the growth of dominant tree species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".