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Temperature and the effects of elemental food quality on Daphnia

2011· article· en· W1795582272 on OpenAlexafffund
Bryan J. McFeeters, Paul C. Frost

Bibliographic record

VenueFreshwater Biology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDaphnia pulexDaphnia magnaDaphniaEcological stoichiometryFood qualityBiologyRespirationBranchiopodaCladoceraRespiration rateEcologyWater qualityZooplanktonPulexAnimal scienceEnvironmental chemistryFood scienceChemistryBotanyEcosystemToxicity

Abstract

fetched live from OpenAlex

1. We examined the responses of two species of Daphnia to changes in food phosphorus (P) content, with animals reared at three different water temperatures. Specifically, we measured mass-specific growth rate (MSGR), body P content and respiration rate of Daphnia magna and Daphnia pulex acclimatised to 10, 17.5 and 25 °C and fed food carbon : phosphorus (C : P) ratios of either 150 or 500. 2. The responses of these three physiological variables to temperature–food quality interactions were species-specific. There was a significant interactive effect of temperature and food quality on D. magna, as the greatest proportional effect of food quality on growth was observed at 10 °C and reductions in body P because of low food P content were relatively greater at 25 °C. These effects may reflect the temperature dependence of mechanisms that reduce elemental constraints associated with food quality in D. magna. By contrast, there were no interactive effects between food quality and temperature on MSGR, body P or mass-specific respiration of D. pulex. 3. It thus appears that temperature can alter food quality effects on Daphnia but the nature of these alterations depends upon the daphniid species and its thermal adaptability. Significant temperature–food quality interactions will complicate efforts to understand zooplankton nutrition in nature and warrant future consideration.

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 score0.119
Threshold uncertainty score0.377

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations53
Published2011
Admission routes2
Has abstractyes

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