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Record W4408309955 · doi:10.1002/ecs2.70201

A dominant plant species and insects interactively shape plant community structure and an ecosystem function

2025· article· en· W4408309955 on OpenAlexaboutno aff
Julia N. Eckberg, Akane Hubbard, Nathan J. Sanders

Bibliographic record

VenueEcosphere · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemEcologyFunction (biology)Plant communityBiologyCommunity structureSpecies richnessEvolutionary biology

Abstract

fetched live from OpenAlex

Abstract Dominant plants and insects both structure plant communities and determine key ecosystem functions. However, dominant plants and insects can have opposing effects on plant community structure and ecosystem function. Critically, few studies have assessed the combined effects of these two drivers of plant community structure and ecosystem function. In this study, we factorially manipulated the presence of the dominant plant species Solidago canadensis (Canada goldenrod) and insects in an old field to quantify their independent and interactive effects on the plant community. Overall, insect presence mediated the effects of S. canadensis removal on plant biomass and richness. Total plant biomass was ~32% lower following S. canadensis removal only when insects were present. In contrast, subdominant plant biomass was ~75% higher following S. canadensis removal, but only when insects were reduced. Subdominant species richness was ~37% higher following S. canadensis removal when insects were present, although the abundance of most subdominant species did not vary systematically with S. canadensis removal or insect reduction. Light availability was ~49% higher following S. canadensis removal, with no effect of insect presence on light availability. Our results emphasize the interactive role of dominant plants and insects in determining the diversity and biomass of plant communities.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.960

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 head0.030
GPT teacher head0.205
Teacher spread0.174 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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