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Record W2073747028 · doi:10.1080/10256016.2014.915824

Sources of variation in small rodent trophic niche: new insights from DNA metabarcoding and stable isotope analysis

2014· article· en· W2073747028 on OpenAlexaff
Eeva M. Soininen, Dorothée Ehrich, Nicolas Lecomte, Nigel G. Yoccoz, Arnaud Tarroux, Dominique Berteaux, Gilles Gauthier, Ludovic Gielly, Christian Brochmann, Galina Gussarova, Rolf A. Ims

Bibliographic record

VenueIsotopes in Environmental and Health Studies · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversité LavalUniversité du Québec à RimouskiUniversité de Moncton
Fundersnot available
KeywordsTrophic levelNicheRodentIsotope analysisBiologyStable isotope ratioEcologyEvolutionary biology

Abstract

fetched live from OpenAlex

Intraspecific competition for food is expected to increase the trophic niche width of consumers, defined here as their diet diversity, but this process has been little studied in herbivores. Population densities of small rodents fluctuate greatly, providing a good study model to evaluate effects of competition on trophic niche. We studied resource use in five arctic small rodent populations of four species combining DNA metabarcoding of stomach contents and stable isotope analysis (SIA). Our results suggest that for small rodents, the most pronounced effect of competition on trophic niche is due to increased use of secondary habitats and to habitat-specific diets, rather than an expansion of trophic niche in primary habitat. DNA metabarcoding and SIA provided complementary information about the composition and temporal variation of herbivore diets. Combing these two approaches requires caution, as the underlying processes causing observed patterns may differ between methodologies due to different spatiotemporal scales.

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 score0.010
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.029
GPT teacher head0.243
Teacher spread0.214 · 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

Citations29
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueIsotopes in Environmental and Health StudiesSame topicEnvironmental DNA in Biodiversity StudiesFrench-language works237,207