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Record W2061352726 · doi:10.1139/b10-048

Density dependence in an experimental boreal forest understory community

2010· article· en· W2061352726 on OpenAlexafffundvenue
Michael A. Treberg, Roy Turkington

Bibliographic record

VenueBotany · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaArctic Institute of North America
KeywordsUnderstoryBiologyAbiotic componentTaigaEcologyCompetition (biology)Growing seasonDensity dependenceHabitatSeedlingBotanyAgronomyCanopy

Abstract

fetched live from OpenAlex

Density-dependent regulation in plants may occur at the level of the entire community and may differ in magnitude and direction at different life history stages such as germination, survival and growth, and under different abiotic conditions. We constructed semi-natural communities of boreal forest understory species planting 10 of the most abundant species. The experimental communities were established from seed or from cuttings and grown in sandboxes at six densities that extended far above and below average natural field density, while maintaining constant relative species proportions (a community density series (CDS)). We used two watering and fertilization levels in a factorial design. At the community level, both emergence and final per-plant shoot mass were negatively density dependent, while survival to the end of the season was facilitative. The effect of water was positive at seed emergence, whereas fertilizer negatively affected survival. Species-specific responses were also dependent on life stage. We demonstrated that density dependence is important in structuring this unproductive boreal understory habitat. The CDS approach allows us to quantify the effects of plant competition at the community and species levels, and to determine whether the importance of these biotic interactions depend on abiotic factors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.595

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)0.0000.000

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 head0.019
GPT teacher head0.263
Teacher spread0.244 · 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

Citations8
Published2010
Admission routes3
Has abstractyes

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