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Record W2166468614 · doi:10.2980/19-2-3521

Seed size and recruitment patterns in a gradient from grassland to forest

2012· article· en· W2166468614 on OpenAlexvenueno aff
Karin Lönnberg, Ove Eriksson

Bibliographic record

VenueEcoscience · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSeedlingGerminationBiologyCanopyGrasslandCompetition (biology)LitterShade toleranceVegetation (pathology)ShadingHabitatAgronomyEcologyBotany

Abstract

fetched live from OpenAlex

Seedlings germinating from large seeds are known to endure hazards such as shading, competition, and litter coverage better than seedlings germinating from small seeds. However, few studies have assessed the relationships between seed size and recruitment comparing plant communities with different structures in order to establish the conditions under which a seed-size advantage prevails. Here, seeds from 20 species varying in seed size from 0.05 to 17.8 mg were sown in 6 different vegetation types, representing a gradient from open grassland to closed canopy coniferous forest. We hypothesized that the effect of seed size on recruitment is generally positive, but that there is a stronger positive effect of seed size in closed than in open communities. Our results provided only limited support for this hypothesis. Firstly, the results varied between years, suggesting that any seed size advantage may depend on factors varying on an annual basis. Secondly, although there were trends of significantly positive relationships between seed size and seedling emergence, seedling survival, and recruitment success, particularly in relatively more closed vegetation types, the strongest positive effects of seed size were found in intermediate (semi-open) habitats along the gradient. We conclude that the filtering of species into the investigated communities is only weakly related to seed size, and that several factors other than canopy probably influence the link between seed size and 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.878

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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