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Record W2919777525 · doi:10.1002/ecy.2674

Microbial and animal nutrient limitation change the distribution of nitrogen within coupled green and brown food chains

2019· article· en· W2919777525 on OpenAlexafffund
Robert W. Buchkowski, Shawn Leroux, Oswald J. Schmitz

Bibliographic record

VenueEcology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDetritivoreNutrientEcosystemNutrient cycleAbiotic componentEcologyDetritusEnvironmental scienceNitrogen cycleFood chainHerbivoreBiotic componentOrganismBiologyFood webNitrogenChemistry

Abstract

fetched live from OpenAlex

Numerous biotic mechanisms can control ecosystem nutrient cycling, but their full incorporation into ecological models or experimental designs can result in inordinate complexity. Including organismal nutrient limitation in models of highly dimensional systems (i.e., those with many nutrient pools/species) presents a critical challenge. We evaluate the importance of explicitly considering microbial and animal nutrient limitation to predict ecosystem nitrogen cycling across plant-based and detritus-based food chains. We investigate how eight factorial scenarios of microbial, herbivore, and microbi-detritivore (i.e., omnivores consuming microbes and detritus) nitrogen or carbon limitation alter the stocks and flows of nitrogen in an ecosystem model. We used a combination of partial derivatives of model equilibrium solutions and numerical simulations using randomly drawn parameter sets to explore the impact of each nutrient limitation scenario on nutrient stocks and flows. We show that switching microbes, herbivores, or microbi-detritivores from nitrogen to carbon limitation consistently altered the ecosystem response to changes in inorganic nitrogen supply, plant C:N ratio, and microbial C:N ratio. Organism nutrient limitation changed ecosystem nitrogen flows by altering the feedbacks between the abiotic and biotic pools. For example, microbi-detritivore nutrient limitation determined whether the microbial response to changes in inorganic nitrogen supply and C:N ratios was dependent on the size of detrital carbon or detrital nitrogen pool. Such correlated responses among biotic and abiotic pools set up a network of predictable changes in ecosystem properties sensitive to organism nutrient limitation. Scenarios with microbial limitation were generally sufficient to capture the suite of ecosystem responses to increasing inorganic nitrogen supply, while scenarios with animal limitation added new behavior whenever C:N ratios changed. We make the case for explicitly considering both microbial and animal nutrient limitation when predicting the flow and distribution of nitrogen across green and brown food chains.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.336

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 head0.015
GPT teacher head0.206
Teacher spread0.191 · 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 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

Citations22
Published2019
Admission routes2
Has abstractyes

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