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Record W4414284821 · doi:10.1128/aem.01204-25

Testing the priming effect in the deep ocean: are microorganisms too starved to consume recalcitrant organic carbon?

2025· article· en· W4414284821 on OpenAlexafffund
Richard A. LaBrie, Corday R. Selden, Nagissa Mahmoudi

Bibliographic record

VenueApplied and Environmental Microbiology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDissolved organic carbonMicroorganismMicrobial population biologyMicrobial biodegradationHeterotrophDeep seaCarbon cycleSeawaterPriming (agriculture)

Abstract

fetched live from OpenAlex

ABSTRACT Deep ocean dissolved organic carbon (DOC) is one of the largest pools of reduced carbon on Earth. Many DOC compounds escape microbial degradation and persist for thousands of years in the ocean. Although many hypotheses have been proposed, the mechanisms responsible for this long-term stability remain unresolved. Heterotrophic microorganisms in the deep ocean are energetically starved and exhibit low metabolic activity. Here, we investigated whether the severe energy limitation in deep-sea environments acts as a barrier to microbial degradation of DOC. We hypothesized that alleviating this energetic barrier through the addition of labile compounds (i.e., the priming effect) could stimulate microbial consumption of DOC. We conducted 62-day bottle incubations with deep seawater from the Southern Ocean that were amended with simple organic carbon, nitrogen, and/or phosphorus-containing compounds. We tracked DOC concentration, cell abundance, and microbial community structure over the course of the experiment. Our results show no evidence of a priming effect regardless of the priming compound. Interestingly, priming compounds were selected for distinct microbial populations even when the compounds were chemically similar. Pseudoalteromonas and Pseudomonas were enriched across all amended bottles, and their competition for labile substrates likely contributed to observed variations in DOC consumption. Our results suggest that the persistence of DOC is not driven by the energetic state of deep-sea microorganisms. These findings indicate that inputs of fresh carbon to the deep ocean are unlikely to trigger extensive degradation of the existing DOC pool, reinforcing its role as a stable long-term reservoir of carbon. IMPORTANCE The oceans store vast amounts of dissolved organic carbon (DOC) that can resist microbial degradation for thousands of years. The mechanisms that underlie the long-term stability of DOC in the ocean are still debated. Microorganisms in this environment exhibit low metabolic activity and are energetically starved. We tested whether the microbial degradation of DOC could be stimulated through the addition of labile compounds. Surprisingly, alleviating energetic constraints did not stimulate the consumption of deep ocean DOC. Additionally, our results suggest that competition among taxa is an important constraint on dissolved organic carbon consumption, with implications for ecosystem processing. Our study suggests that the long-term stability of deep ocean DOC reflects inherent chemical or structural resistance to microbial degradation, rather than ecological or energetic constraints. This finding is also of consequence for ongoing geoengineering efforts that aim to remove atmospheric carbon by increasing carbon export to the deep sea.

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)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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