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Record W4281961974 · doi:10.3389/fmicb.2022.846890

Community Interaction Co-limitation: Nutrient Limitation in a Marine Microbial Community Context

2022· article· en· W4281961974 on OpenAlexafffund
Catherine C. Bannon, Insa Rapp, Erin M. Bertrand

Bibliographic record

VenueFrontiers in Microbiology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsDalhousie University
FundersOcean Frontier InstituteNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSimons Foundation
KeywordsBiogeochemical cycleNutrientLimitingEcologyContext (archaeology)Microbial population biologyNutrient cycleBiochemical engineeringBiology

Abstract

fetched live from OpenAlex

The simultaneous limitation of productivity by two or more nutrients, commonly referred to as nutrient co-limitation, affects microbial communities throughout the marine environment and is of profound importance because of its impacts on various biogeochemical cycles. Multiple types of co-limitation have been described, enabling distinctions based on the hypothesized mechanisms of co-limitation at a biochemical level. These definitions usually pertain to individuals and do not explicitly, or even implicitly, consider complex ecological dynamics found within a microbial community. However, limiting and co-limiting nutrients can be produced in situ by a subset of microbial community members, suggesting that interactions within communities can underpin co-limitation. To address this, we propose a new category of nutrient co-limitation, community interaction co-limitation (CIC). During CIC, one part of the community is limited by one nutrient, which results in the insufficient production or transformation of a biologically produced nutrient that is required by another part of the community, often primary producers. Using cobalamin (vitamin B12) and nitrogen fixation as our models, we outline three different ways CIC can arise based on current literature and discuss CIC’s role in biogeochemical cycles. Accounting for the inherent and complex roles microbial community interactions play in generating this type of co-limitation requires an expanded toolset – beyond the traditional approaches used to identify and study other types of co-limitation. We propose incorporating processes and theories well-known in microbial ecology and evolution to provide meaningful insight into the controls of community-based feedback loops and mechanisms that give rise to CIC in the environment. Finally, we highlight the data gaps that limit our understanding of CIC mechanisms and suggest methods to overcome these and further identify causes and consequences of CIC. By providing this framework for understanding and identifying CIC, we enable systematic examination of the impacts this co-limitation can have on current and future marine biogeochemical processes.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.246
Teacher spread0.225 · 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

Citations32
Published2022
Admission routes2
Has abstractyes

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