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Record W2100614895 · doi:10.1890/09-1139.1

Mycorrhizal networks counteract competitive effects of canopy trees on seedling survival

2010· article· en· W2100614895 on OpenAlexfundno aff
Michael G. Booth, Jason D. Hoeksema

Bibliographic record

VenueEcology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersArctic Institute of North AmericaAmerican Philosophical Society
KeywordsSeedlingBiologyCanopyUnderstoryMycorrhizaEcosystemEctomycorrhizaMycorrhizal fungiEcologyBotanyAgroforestrySymbiosisHorticulture

Abstract

fetched live from OpenAlex

The dynamics of forest ecosystems depend largely on the survival of seedlings in their understories, but seedling survival is known to be limited by preemption of light and soil resources by overstory trees. It has been hypothesized that "common mycorrhizal networks," wherein roots of seedlings are linked to overstory trees by symbiotic mycorrhizal fungi, offset some or all of the negative effects of trees on seedlings. Here we report the results of an unambiguous experimental test of this hypothesis in a monodominant Pinus radiata forest. We also tested the hypothesis that adaptive differentiation among plant populations causes local plant genotypes to respond more positively to mycorrhizal networks than nonlocal plant genotypes. Our results demonstrate large positive effects of overstory mycorrhizal networks on seedling survival, along with simultaneous negative effects of tree roots, regardless of whether plant genotypes were locally derived. Physiological and leaf-chemistry measurements suggest that seedlings connected to common mycorrhizal networks benefited from increased access to soil water. The similar magnitude of the positive and negative overstory effects on seedlings and the ubiquity of mycorrhizal networks in forests suggest that mycorrhizal networks fundamentally influence the demographic and community dynamics of forest trees.

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 score0.028
Threshold uncertainty score0.998

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)0.0010.000

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 head0.003
GPT teacher head0.215
Teacher spread0.211 · 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

Citations124
Published2010
Admission routes1
Has abstractyes

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