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Effects of fungal pathogens on seeds of native and exotic plants: a test using congeneric pairs

2001· article· en· W1527994546 on OpenAlexafffund
C. Sean Blaney, Peter M. Kotanen

Bibliographic record

VenueJournal of Applied Ecology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsAtlantic Industries (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaConnaught FundUniversity of Toronto
KeywordsFungicideBiologyWetlandIntroduced speciesFungal pathogenInvasive speciesAgronomyEcologyBotanyPathogen

Abstract

fetched live from OpenAlex

Summary It has previously been hypothesized that low rates of attack by natural enemies may contribute to the invasiveness of exotic plants. We tested this hypothesis by investigating the influence of pathogens on survival during a critical life‐history stage: the seed bank. We used fungicide treatments to estimate the impacts of soil fungi on buried seeds of a taxonomically broad suite of congeneric natives and exotics, in both upland and wetland meadows. Seeds of both natives and exotics were recovered at lower rates in wetlands than in uplands. Fungicide addition reduced this difference by improving recovery in wetlands, indicating that the lower recovery was largely attributable to a higher level of fungal mortality. This suggests that fungal pathogens may contribute to the exclusion of upland species from wetlands. The effects of fungicide on the recovery of buried seeds did not differ between natives and exotics. Seeds of exotics were recovered at a higher rate than seeds of natives in uplands, but this effect was not attributable to fungal pathogens. Fungal seed pathogens may offer poor prospects for the management of most exotic species. The lack of consistent differences in the responses of natives vs. exotics to fungicide suggests few aliens owe their success to low seed pathogen loads, while impacts of seed‐pathogenic biocontrol agents on non‐target species would be frequent.

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 score0.144
Threshold uncertainty score0.280

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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

Citations163
Published2001
Admission routes2
Has abstractyes

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