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Record W2126544822 · doi:10.1139/x06-025

On the formation of dense understory layers in forests worldwide: consequences and implications for forest dynamics, biodiversity, and succession

2006· article· en· W2126544822 on OpenAlexvenueno aff
Alejandro A. Royo, Walter P. Carson

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersU.S. Forest ServiceDirectorate for Biological Sciences
KeywordsUnderstoryEcological successionRegeneration (biology)EcologyDisturbance (geology)Forest dynamicsBiodiversityCanopyCompetition (biology)Secondary successionBiologyEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

The mechanistic basis underpinning forest succession is the gap-phase paradigm in which overstory disturbance interacts with seedling and sapling shade tolerance to determine successional trajectories. The theory, and ensuing simulation models, typically assume that understory plants have little impact on the advance regeneration layer's composition. We challenge that assumption by reviewing over 125 papers on 38 species worldwide that form dense and persistent understory canopies. Once established, this layer strongly diminishes tree regeneration, thus altering the rate and direction of forest succession. We term these dense strata recalcitrant understory layers. Over half of the cases reviewed were linked to increases in canopy disturbance and either altered herbivory or fire regimes. Nearly 75% of the studies declared that competition and allelopathy were the likely interference mechanisms decreasing tree regeneration, yet only 25% of the studies used manipulative field experiments to test these putative mechanisms. We present a conceptual model that links the factors predisposing the formation of recalcitrant understory layers with their interference mechanisms and subsequent impacts on succession. We propose that their presence constricts floristic diversity and argue for their explicit inclusion in forest dynamics theory and models. Finally, we offer management suggestions to limit their establishment and mitigate their impacts.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.074
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.292
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations607
Published2006
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Forest ResearchSame topicEcology and Vegetation Dynamics StudiesFrench-language works237,207