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Record W4232124707 · doi:10.1890/09-0204

CONSUMER-BASED LIMITATIONS DRIVE OAK RECRUITMENT FAILURE

2009· article· en· W4232124707 on OpenAlexaff
Andrew MacDougall, Alisha Duwyn, Natalie T. Jones

Bibliographic record

VenueEcology · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEcologyGeographyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

A number of North American oaks are experiencing recruitment failure, with explanatory hypotheses including a range of consumer- or resource-based limitations. Using a factorial experiment with transplanted seedlings, we demonstrate how direct and indirect consumer effects prevent recruitment by Quercus garryana, a keystone savanna species of northwestern North America. Foremost, intense herbivory by mostly exotic small mammals severely damaged or killed 100% of unprotected seedlings during winter. Many damaged seedlings survived but were 64% smaller in size and produced 75% fewer leaves. Herbivory by deer had no detectable impact despite being long hypothesized as a major contributor to oak decline. Indirectly, herbivory altered the outcome of moisture limitation and competition. Without small mammals, summer drought and a dense exotic grass cover associated with fire suppression significantly reduced growth but caused little mortality. With small mammals, both significantly increased mortality of herbivore-damaged seedlings. Herbivore damage also increased the likelihood of severe insect attack, possibly due to reduced investment in foliar defense by recovering seedlings. These results show that no single factor necessarily prevents seedling establishment by oaks, but that the combination of herbivory, undisturbed exotic grass swards, and summer drought creates an almost insurmountable barrier for recruitment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.083
GPT teacher head0.251
Teacher spread0.168 · 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.

Study designTheoretical or conceptual
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

Citations2
Published2009
Admission routes1
Has abstractyes

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