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Record W3162752490 · doi:10.1186/s12862-021-01826-5

Potential use of Helianthus tuberosus to suppress the invasive alien plant Ageratina adenophora under different shade levels

2021· article· en· W3162752490 on OpenAlexaff
Shicai Shen, Gaofeng Xu, Diyu Li, Shaosong Yang, Guimei Jin, Shufang Liu, David R. Cléments, Aidong Chen, Jia Rao, Lila Wen, Qiong Tao, Shuiying Zhang, Jiazhen Yang, Fudou Zhang

Bibliographic record

VenueBMC Ecology and Evolution · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth Enhancement Techniques
Canadian institutionsTrinity Western UniversityWestern University
FundersApplied Basic Research Foundation of Yunnan ProvinceNational Natural Science Foundation of China
KeywordsHelianthusBiologyMonocultureCropShootBiomass (ecology)BotanyHorticultureCompetition (biology)Invasive speciesSpecific leaf areaPhotosynthesisAgronomySunflowerEcology

Abstract

fetched live from OpenAlex

BACKGROUND: An ecological approach for managing biological invasions in agroecosystems is the selection of alternative crop species to manage the infestation of invasive alien plants through competition. In the current study, plant growth, photosynthesis, and competitive ability of the crop Helianthus tuberosus L. (Jerusalem artichoke) and the invasive alien plant Ageratina adenophora (Spreng.) R. M. King and H. Rob were compared under varying shade levels by utilizing a de Wit replacement series method. We hypothesized that H. tuberosus had higher competitive ability than A. adenophora even under shaded conditions. RESULTS: The results showed the main stem, leafstalk length, leaf area, underground biomass, and aboveground biomass of A. adenophora were significantly lower compared to H. tuberosus in monoculture although A. adenophora had a greater number of branches that were longer on average. Under full sunlight, the total shoot length (stem + branch length), main stem length and branch length of A. adenophora were significantly suppressed (P < 0.05) by increasing proportions of H. tuberosus, and the same morphological variables of H. tuberosus were significantly higher with decreasing proportions of H. tuberosus. With increasing shade rates and plant ratios, the plant height, branch, leaf, and biomass of both plants were significantly suppressed, but to a greater degree in the case of A. adenophora. The net photosynthetic rate (Pn) of H. tuberosus and A. adenophora increased gradually from July to September, then decreased in October. The Pn of H. tuberosus was consistently higher than that of A. adenophora. Although the Pn for both species was significantly reduced with increasing shade rates and plant ratios, A. adenophora experienced greater inhibition than H. tuberosus. The relative yield (RY) of A. adenophora was significantly less than 1.0 (P < 0.05) in mixed culture under all shade levels, indicating that the intraspecific competition was less than interspecific competition. The RY of H. tuberosus was significantly less than 1.0 under 40-60% shade and greater than 1.0 (P < 0.05) under 0-20% shade in mixed culture, respectively, showing that intraspecific competition was higher than interspecific competition under low shade, but the converse was true under high shade. The relative yield total (RYT) of A. adenophora and H. tuberosus was less than 1.0 in mixed culture, indicating that there was competition between the two plants. The fact that the competitive balance (CB) index of H. tuberosus was greater than zero demonstrated a higher competitive ability than A. adenophora even at the highest shade level (60%). CONCLUSIONS: Our results suggest that H. tuberosus is a promising replacement control candidate for managing infestations of A. adenophora, and could be widely used in various habitats where A. adenophora invades.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 head0.037
GPT teacher head0.223
Teacher spread0.186 · 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 designBench or experimental
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

Citations9
Published2021
Admission routes1
Has abstractyes

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