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Record W2006395170 · doi:10.1111/1365-2435.12065

Density‐ and resource‐dependent movement characteristics in a rotifer

2013· article· en· W2006395170 on OpenAlexafffund
Daniel Kuefler, Tal Avgar, John M. Fryxell

Bibliographic record

VenueFunctional Ecology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyRotiferBrachionus calyciflorusCompetitor analysisResource (disambiguation)Movement (music)EcologyDensity dependencePopulation densityForagingPopulationEconomicsDemography

Abstract

fetched live from OpenAlex

Summary Theory predicts that consumers can increase their energy intake by spending more time within resource‐rich areas and/or by avoiding areas where local competitor densities are high. A consumer whose movements are described by a simple random walk can achieve these objectives by adjusting its turn frequency and/or speed. We recorded movements, as series of steps, by 180 individual rotifers ( B rachionus calyciflorus ) in glass capillary tubes to test the influence of resource density, competitor density and their statistical interaction on movement parameters. Four treatments contrasted opposing levels of algae (resource) and conspecific density in a 2 × 2 factorial design. Our results indicate that density‐ and resource‐dependent behaviours act through different mechanisms to shape patterns of rotifer movement. Turn frequency increased up to twofold in resource‐rich treatments, depending on the presence or absence of competitors. In contrast, swimming speed was 50% greater in the presence of competitors under all treatments, but was only slightly depressed by the presence of resources alone. We show how these two different movement mechanisms may be integrated into predictions of consumer population spread as resource and competitor densities vary. We discuss implications of the contrasting and complementary nature of these different movement mechanisms and their possible adaptations to different environmental stimuli.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.999

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)0.0020.000

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 head0.015
GPT teacher head0.186
Teacher spread0.171 · 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.

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

Citations24
Published2013
Admission routes2
Has abstractyes

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