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Record W2473266722 · doi:10.1093/femsec/fiw156

Strong linkage between plant and soil fungal communities along a successional coastal dune system

2016· article· en· W2473266722 on OpenAlexaffabout
Alice Roy‐Bolduc, Étienne Laliberté, Stéphane Boudreau, Mohamed Hijri

Bibliographic record

VenueFEMS Microbiology Ecology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversité LavalCenter for Northern StudiesUniversité de Montréal
Fundersnot available
KeywordsEdaphicBiologyPlant communityEcologyEcological successionSpecies richnessEcosystemVegetation (pathology)Soil water

Abstract

fetched live from OpenAlex

Complex interactions between plants and soil microorganisms drive key ecosystem and community properties such as productivity and diversity. In nutrient-poor systems such as sand dunes, plant traits and fungal symbioses related to nutrient acquisition can strongly influence vegetation dynamics. We investigated plant and fungal communities in a relic foredune plain located on an archipelago in Québec, Canada. We detected distinct communities across the edaphic and successional gradient. Our results showed a clear increase in plant species richness, as well as in the diversity of nutrient-acquisition strategies. We also found a strong correlation between aboveground vegetation and soil fungal communities, and both responded similarly to soil physicochemical properties. Soil pH influenced the composition of plant and fungal communities, and could act as an important environmental filter along this relic foredune plain. The increasing functional diversity in plant nutrient-acquisition strategies across the gradient might favor resource partitioning and facilitation among co-occurring plant species. The coordinated changes in soil microbial and plant communities highlight the importance of aboveground-belowground linkages and positive biotic interactions during ecological succession in nutrient-poor environments.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.199
Teacher spread0.185 · 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.

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

Citations50
Published2016
Admission routes2
Has abstractyes

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