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Record W4220963507 · doi:10.5194/egusphere-egu22-5889

New insights into moss nitrogen fixation and associated N2 fixer communities from a 1000 Km latitudinal transect in Eastern Canada. 

2022· preprint· en· W4220963507 on OpenAlexaffabout
Jean‐Philippe Bellenger, Marie Renaudin, Robert L. Bradley, Isabelle Laforest‐Lapointe

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMossTransectNitrogen fixationCyanobacteriaBiologyEcologyFixation (population genetics)BotanyAnimal scienceBiochemistryGeneGeneticsBacteria

Abstract

fetched live from OpenAlex

Moss-associated cyanobacteria nitrogen (N2-) fixation can support moss growth and constitutes a major source of new N in boreal forest ecosystems. Moss-colonizing cyanobacterial biomass and their N2-fixation are usually considered linearly correlated. However, recent evidence showed that cyanobacterial biomass and N2-fixation can be disconnected, hinting that they might be affected by the different environmental and ecological drivers. These drivers are often studied using manipulative experiments (e.g. fertilization, incubation) and remain to be validated with complementary work in observational studies. Cyanobacteria are considered the major actors of N fixation. However, the nature and diversity of active microbial communities associated with feather mosses are still unclear and the effects of the environment on these community are vague. Using random forest, spearman correlations and linear mixed-effects models, we studied the main drivers of cyanobacterial biomass and N2-fixation of two dominant feather moss species collected over three years on a 1000-km latitudinal transect in the eastern Canadian boreal forest. Using RNA-based amplicon sequencing of the 16S rRNA and nifH genes we explored the active bacterial communities along this transect and along the moss shoot. We report that temperature, precipitation, and phosphorus were the main drivers of moss cyanobacterial biomass and that temperature, molybdenum and vanadium were the main drivers of N2-fixation. Cyanobacteria accounted for 33% of global bacterial communities and 65% of diazotrophic communities, respectively. Several cyanobacterial and proteobacterial methanotrophic genera, including poorly known taxa found for the first time on boreal feather mosses, were actively contributing to N2-fixation. We showed that bacteria were heterogeneously distributed along the moss shoot, with phototrophs being dominant in the apical part and methanotrophs being dominant in the basal part. Finally data showed that climate (temperature, precipitation), environmental variables (moss species, month, tree density) and nutrients (nitrogen, phosphorus, molybdenum, vanadium, iron) strongly shape the global and diazotrophic bacterial communities and create ecological niches on the moss shoot. This work provides new insights into the feather moss microbiome and its ecological and biogeochemical functions. Our data provides evidence that the feather moss microbiome plays crucial roles in supporting moss growth, health, and decomposition, as well as in the boreal forest carbon and nitrogen cycles. Finally, this study highlights the strong effects of climate and nutrients in shaping feather moss microbiome and its activity (i.e., N2 fixation). This work will help better predict the impacts of global change on this symbiosis and on nitrogen input in boreal forest ecosystems.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.228
Teacher spread0.208 · 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
Published2022
Admission routes2
Has abstractyes

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