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Record W2074430205 · doi:10.1139/b03-099

Predispersal seed predation and population dynamics in the perennial understorey herb <i>Actaea spicata</i>

2003· article· en· W2074430205 on OpenAlexvenueno aff
Heléne Fröborg, Ove Eriksson

Bibliographic record

VenueCanadian Journal of Botany · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersVetenskapsrådet
KeywordsSeed predationPredationBiologySeedlingPerennial plantPopulationDeciduousUnderstoryEcologyBotanySeed dispersalDemographyCanopyBiological dispersal

Abstract

fetched live from OpenAlex

Predispersal seed predation by a moth larva, Eupithecia immundata Leinig &amp; Zeller (Geometridae), and its impact on population dynamics of the perennial herb Actaea spicata L. were studied during 7 years in deciduous and mixed coniferous forest populations in southeastern Sweden. Twelve population matrix models were constructed based on transition probabilities among six stage classes in the populations. The fraction of seeds consumed varied between 21% and 80% but was not significantly correlated with seed production. Experimental seed addition compensating for seed losses caused by the seed predator resulted in increased seedling emergence in one of the populations. Population growth rate was positive (λ &gt; 1) in 1 of 6 years in the deciduous forest population and in 2 of 6 years in the mixed coniferous forest population. Survival among reproductive individuals contributed most strongly to λ in both populations. In some years, the projected λ changed from positive to negative values because of seed loss caused by E. immundata. However, results suggest that seed predation did not affect λ to any large extent, despite a considerable seed predation. This study is one of the rare efforts to estimate the impact of seed predation on population dynamics of perennial plants.Key words: Actaea spicata, demography, population dynamics, predispersal seed predation, seedling 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 score0.460
Threshold uncertainty score0.547

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations38
Published2003
Admission routes1
Has abstractyes

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