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Record W2920764637 · doi:10.7717/peerj.6596

DNA metabarcoding allows non-invasive identification of arthropod prey provisioned to nestling Rufous hummingbirds (<i>Selasphorus rufus</i>)

2019· article· en· W2920764637 on OpenAlexafffundabout
Alison J. Moran, Sean W. J. Prosser, Jonathan A. Moran

Bibliographic record

VenuePeerJ · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsRoyal Roads UniversityUniversity of GuelphBirds Canada
FundersRoyal Roads University
KeywordsBiologyHummingbirdPredationZoologyArthropodHoneydewForagingEcologyDNA barcoding

Abstract

fetched live from OpenAlex

Hummingbirds consume sugars from nectar, sap and honeydew, and obtain protein, fat and minerals from arthropods. To date, the identity of arthropod taxa in hummingbird diets has been investigated by observation of foraging or examination of alimentary tract contents. Direct examination of nestling provisioning adds the extra complication of disturbance to the young and mother. Here, we show that arthropod food items provisioned to Rufous hummingbird ( Selasphorus rufus ) nestlings can be identified by a safe and non-invasive protocol using next-generation sequencing (NGS) of DNA from nestling fecal pellets collected post-fledging. We found that females on southern Vancouver Island (British Columbia, Canada) provisioned nestlings with a wide range of arthropod taxa. The samples examined contained three Classes, eight Orders, 48 Families, and 87 Genera, with from one to 15 Families being identified in a single pellet. Soft-bodied Dipterans were found most frequently and had the highest relative abundance; hard-bodied prey items were absent from almost all samples. Substantial differences in taxa were found within season and between years, indicating the importance of multi-year sampling when defining a prey spectrum.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.996

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations43
Published2019
Admission routes3
Has abstractyes

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Same venuePeerJSame topicEnvironmental DNA in Biodiversity StudiesFrench-language works237,207