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Record W4200574458 · doi:10.1101/2021.12.14.472730

Animal soil food web complexity triggers shifts in microbial communities, including PAH degraders, but without clear effects on phenanthrene phytoremediation

2021· preprint· en· W4200574458 on OpenAlexaff
Sara Correa‐García, Vincenzo Corelli, Julien Tremblay, Jessica Dozois, Eugenie Mukula, Armand Séguin, Étienne Yergeau

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsNational Research Council CanadaUniversité du QuébecInstitut National de la Recherche ScientifiqueNatural Resources Canada
Fundersnot available
KeywordsPhytoremediationPhenanthreneMicrobial population biologyWillowEcosystemSoil food webBiomass (ecology)Soil microbiologyFood webEnvironmental chemistryBiologyEnvironmental scienceEcologyContaminationChemistrySoil waterBacteria

Abstract

fetched live from OpenAlex

Abstract The aim of this study was to determine whether the complexity of the animal soil food web (SFWC) is a significant factor influencing the soil microbial communities, the productivity of the willow, and the degradation rates of 100 mg kg -1 phenanthrene contamination. The SFWC treatment had eight levels: just the microbial community (BF), or the BF with nematodes (N), springtails (C), earthworms (E), CE, CN, EN, CEN. After eight weeks of growth, the height and biomass of willows were significantly affected by the SFWC, whereas the amount of phenanthrene degraded was not affected, reaching over 95% in all pots. SFWC affected the structure and the composition of the bacterial, archaeal and fungal communities, with significant effects of SFWC on the relative abundance of fungal genera such as Sphaerosporella , a known willow symbiont during phytoremediation, and bacterial phyla such as Actinobacteriota , containing many PAH degraders. These SFWC effects on microbial communities were not clearly reflected in the community structure and abundance of PAH degraders, even though some degraders related to the Actinobacteriota and the diversity of Gram-negative degraders were affected by the SFWC treatments. Overall, our results suggest that, under our experimental conditions, SFWC does not affect significantly willow phytoremediation outcomes. Importance Polycyclic aromatic hydrocarbons (PAH) pose a threat to soil ecosystems. Phytoremediation is a green technology that can help restore ecosystems’ health affected by PAH contamination. Past research on phytoremediation of PAH has focused on the roles of plant and microbes in contaminant fates. However, soil environments usually harbor large faunal communities that interact with both the plant and the microbial communities, potentially altering the phytoremediation process. We hypothesized that soil food web complexity (SFWC), represented by increasing levels of soil fauna, influences PAH degradation through interactions with the plant and the microbes. In this study, we demonstrated that SFWC increases the plant biomass and changes the composition of the microbial community, especially the fungal structure. In our pot experiment, more complex levels of SFWC did not contribute to higher degradation rates, but the increased plant biomass and the higher relative abundance of fungal genera associated with lower plant stress could indirectly contribute to better phytoremediation outcomes. Our results highlight the importance of considering the contributions of soil fauna to the success of phytoremediation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 communication0.0010.000
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 head0.038
GPT teacher head0.223
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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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