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Record W1998918998 · doi:10.1139/x08-149

Recruitment patterns following a severe drought: long-term compositional shifts in Patagonian forests

2008· article· en· W1998918998 on OpenAlexvenueno aff
María Luisa Suárez Alonso, Thomas Kitzberger

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDeserts and xeric shrublandsCanopySeedlingBiologyEcologyMicrositeBotanyHabitat

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.305
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations148
Published2008
Admission routes1
Has abstractyes

Explore more

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