Recruitment patterns following a severe drought: long-term compositional shifts in Patagonian forests
Bibliographic record
Abstract
Severe droughts have the potential of inducing transient shifts in forest canopy composition by altering species-specific adult tree mortality patterns. However, permanent vegetation change will occur only if tree recruitment patterns are also affected. Here, we analyze how a massive mortality event triggered by the 1998–1999 drought affected adult and sapling mortality and recruitment in a mixed Nothofagus dombeyi (Mirb.) Blume – Austrocedrus chilensis (D. Don) Flor. et Boult. forests of northern Patagonia. Comparing drought-induced and tree-fall gaps, we assessed changes in forest composition, microenvironments, and seedling density and survival of both species. Drought-kill disturbance shifted species composition of both canopy and sapling cohorts in favour of A. chilensis. Drought gaps were characterized by a shadier and more xeric environment, affecting the recruitment pattern of N. dombeyi seedlings. The seedling cohort was composed mostly of A. chilensis, and its survival was always higher than that of N. dombeyi. Additionally, A. chilensis seedlings showed higher plasticity than N. dombeyi seedlings, increasing its root to shoot ratios in drought gaps. The results suggest that extreme drought itself is a strong driving force in forest dynamics, with important imprints on forest landscapes. Future climate-change scenarios, projecting an increased in frequency and severity of droughts, alert us about expected long-term compositional shifts in many forest ecosystems.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".