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Positive Interactions of Native Species Melt Invasional Meltdown over Long-Term Plant Succession

2022· preprint· en· W4281803351 on OpenAlexaff
Deyi Yin, Scott J. Meiners, Ming Ni, Qing Ye, Fangliang He, Marc W. Cadotte

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsThe Scarborough HospitalUniversity of TorontoUniversity of AlbertaUniversité de Sherbrooke
Fundersnot available
KeywordsEcological successionEcologyInvasive speciesBiologyIntroduced speciesPsychological resilienceRelative species abundanceAbundance (ecology)PsychologySocial psychology

Abstract

fetched live from OpenAlex

Positive interactions have been hypothesized to influence plant community dynamics and species invasions. However, their prevalence and importance relative to negative interactions remain unclear, but are fundamentally important for both theoretical and applied ecology. We examined pairwise biotic interactions using over 50 years of successional data to assess the prevalence of positive interactions and their effects on each focal species (either native or exotic). We found that positive interactions were widespread and the relative frequency of positive and negative interactions varied with establishment stage and between native and exotic species. Specifically, positive interactions were more frequent during early establishment and less frequent at later stages. Positive interactions involving native species were more frequent and stronger than those between exotic species, reducing the impact of invasional meltdown on succession. Our study highlights the role of positive native interactions in shielding communities from biological invasion and enhancing the potential for long-term resilience.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.076
GPT teacher head0.269
Teacher spread0.193 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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