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Record W4406037988 · doi:10.7717/peerj.18674

The non-indigenous dung beetle (<i>Onthophagus nuchicornis</i>) can effectively reproduce using the dung of indigenous eastern North American mammals

2025· article· en· W4406037988 on OpenAlexafffund
Alexe Indigo, Kate Stokes, Olivia Burchell, Paul Manning

Bibliographic record

VenuePeerJ · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsDung beetleBiologyIndigenousVulpesScarabaeidaeEcologyLivestockScarabaeinaeScarabaeoideaPredation

Abstract

fetched live from OpenAlex

Non-indigenous dung beetle (Coleoptera: Scarabaeoidea) species in North America are important contributors to ecosystem functions, particularly in pasture-based livestock systems. Despite the significant body of research surrounding non-indigenous (and often invasive) dung beetles in agricultural contexts, there has been minimal study concerning the impact that these species may have on indigenous dung beetle populations in natural environments. Here we examine the possible impact of the introduced dung beetle Onthophagus nuchicornis on indigenous dung beetle populations via use of indigenous mammal dung. Using a controlled laboratory experiment, we quantified how readily beetles could use dung from bobcat ( Lynx rufus ), red fox ( Vulpes vulpes ), moose ( Alces alces ), raccoon ( Procyon lotor ), and domestic sheep ( Ovis aries ). To determine the suitability of each dung type for reproduction, we measured offspring abundance and fitness. While the number of developed offspring was significantly different among treatments, offspring fitness did not vary significantly across dung types. Our results demonstrate the generalist feeding habits of the non-indigenous dung beetle O. nuchicornis can allow this species to use the dung of various wild animals indigenous to eastern North America.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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