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Record W3172520901 · doi:10.1111/oik.08253

Changing elemental cycles, stoichiometric mismatches, and consequences for pathogens of primary producers

2021· article· en· W3172520901 on OpenAlexaff
Thijs Frenken, Rachel E. Paseka, Angélica L. González, Lale Asik, Eric W. Seabloom, Lauren A. White, Elizabeth T. Borer, Alexander T. Strauss, Angela Peace, Dedmer B. Van de Waal

Bibliographic record

VenueOikos · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of Windsor
FundersNational Socio-Environmental Synthesis CenterNational Science Foundation
KeywordsBiogeochemical cycleAutotrophStoichiometryEcological stoichiometryBiologyHost (biology)NutrientEcologyBacteriaChemistry

Abstract

fetched live from OpenAlex

Human‐induced changes in biogeochemical cycles alter the availability of carbon (C), nitrogen (N) and phosphorus (P) in the environment, leading to changes in the elemental stoichiometry of primary producers. These changes in elemental ratios may, in turn, alter the degree of stoichiometric mismatch between primary producer hosts and their pathogens. Here, we outline how ecological stoichiometry could be used as a framework to predict the effects of changing nutrient supply on stoichiometric mismatches in autotroph–pathogen interactions. We discuss empirical evidence linking pathogen performance to stoichiometric mismatches arising from shifts in elemental availability. Our synthesis indicates that fungi may be particularly sensitive to changes in N supply and viruses generally respond strongly to changes in the supply of either of these elements, but it also highlighted the need for additional data, especially for bacteria. Consequently, fungal pathogens may respond more strongly to changes in host C:N stoichiometry, whereas viruses may be highly sensitive to both changes in C:N and C:P of hosts. Additionally, our synthesis suggests that viruses may be more homeostatic than fungi, and therefore respond more strongly to changing elemental supplies. Revealing stoichiometric mismatches may greatly support our understanding of how host–pathogen interactions in primary producers will respond to changes in global biogeochemical cycles, controlling disease incidence in primary producers under scenarios of global change.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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 teacher head, 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

Citations17
Published2021
Admission routes1
Has abstractyes

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