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Allelochemical and soil fungi co-determine conspecific density dependence in a temperate forest

2024· preprint· en· W4403764530 on OpenAlexaff
Zhichao Xu, Jonathan Bennett, Meihui Zhu, Fei Lin, Ji Ye, Pengcheng Jiang, Zikun Mao, Xugao Wang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAllelopathyBiologySeedlingTemperate rainforestTemperate forestBotanyTemperate climateMycorrhizaGerminationEcologySymbiosisEcosystem

Abstract

fetched live from OpenAlex

Ecological theory predicts that high local diversity observed in plant communities could be maintained by soilborne pathogens and allelopathic autotoxicity that trigger negative conspecific density dependence (CDD), but mutualistic fungi and allelopathic promotion could simultaneously counteract these biotic processes. Here, we combined a phenolic-acid addition experiment of tree seedlings associated with different mycorrhizal fungi with an extensive field survey to test the allelopathy-fungi mechanisms relate to CDD in a natural temperate forest. Overall, allelopathic effects on seedling growth were generally stronger than fungal effects, and allelochemicals altered how plants interacted with soil fungi in a dose-dependent manner. Contrary to expectation, ectomycorrhizal trees suffered stronger negative CDD than arbuscular mycorrhizal trees driven largely by allelopathy, although ectomycorrhizal fungi could offset some of this allelopathic autotoxicity. Together, allelopathy may thus be an important driver of CDD via affecting both plants and plant-microbe interactions, although the precise effects should be species specific.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.249
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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