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Record W4380684872 · doi:10.1186/s13071-023-05799-w

Abundance and diversity of Culicoides Latreille (Diptera: Ceratopogonidae) in southern Ontario, Canada

2023· article· en· W4380684872 on OpenAlexafffundabout
Samantha E. Allen, Stacey L. Vigil, Tara Furukawa-Stoffer, Nicole Colucci, Aruna Ambagala, David L. Pearl, Mark G. Ruder, Claire M. Jardine, Nicole M. Nemeth

Bibliographic record

VenueParasites & Vectors · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicVector-Borne Animal Diseases
Canadian institutionsCanadian Food Inspection AgencyUniversity of Guelph
FundersOntario Veterinary College, University of GuelphMinistry of Agriculture, Food and Rural AffairsNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Guelph
KeywordsCeratopogonidaeCulicoidesEntomologyBiologyAbundance (ecology)ParasitologyDiversity (politics)EcologyZoologyAnthropology

Abstract

fetched live from OpenAlex

BACKGROUND: Culicoides Latreille (Diptera: Ceratopogonidae) is a genus of hematophagous midges feeding on various vertebrate hosts and serving as a vector for numerous pathogens important to livestock and wildlife health. North American pathogens include bluetongue (BT) and epizootic hemorrhagic disease (EHD) viruses. Little is known about Culicoides spp. distribution and abundance and species composition in Ontario, Canada, despite bordering numerous U.S. states with documented Culicoides spp. and BT and EHD virus activity. We sought to characterize Culicoides spp. distribution and abundance and to investigate whether select meteorological and ecological risk factors influenced the abundance of Culicoides biguttatus, C. stellifer, and the subgenus Avaritia trapped throughout southern Ontario. METHODS: From June to October of 2017 to 2018, CDC-type LED light suction traps were placed on twelve livestock-associated sites across southern Ontario. Culicoides spp. collected were morphologically identified to the species level when possible. Associations were examined using negative binomial regression among C. biguttatus, C. stellifer, and subgenus Avaritia abundance, and select factors: ambient temperature, rainfall, primary livestock species, latitude, and habitat type. RESULTS: In total, 33,905 Culicoides spp. midges were collected, encompassing 14 species from seven subgenera and one species group. Culicoides sonorensis was collected from three sites during both years. Within Ontario, the northern trapping locations had a pattern of seasonal peak abundance in August (2017) and July (2018), and the southern locations had abundance peaks in June for both years. Culicoides biguttatus, C. stellifer, and subgenus Avaritia were significantly more abundant if ovine was the primary livestock species at trapping sites (compared to bovine). Culicoides stellifer and subgenus Avaritia were significantly more abundant at mid- to high-temperature ranges on trap days (i.e., 17.3-20.2 and 20.3-31.0 °C compared to 9.5-17.2 °C). Additionally, subgenus Avaritia were significantly more abundant if rainfall 4 weeks prior was between 2.7 and 20.1 mm compared to 0.0 mm and if rainfall 8 weeks prior was between 0.1 and 2.1 mm compared to 0.0 mm. CONCLUSIONS: Results from our study describe Culicoides spp. distribution in southern Ontario, the potential for spread and maintenance of EHD and BT viruses, and concurrent health risks to livestock and wildlife in southern Ontario in reference to certain meteorological and ecological risk factors. We identified that Culicoides spp. are diverse in this province, and appear to be distinctly distributed spatially and temporally. The livestock species present, temperature, and rainfall appear to have an impact on the abundance of C. biguttatus, C. stellifer, and subgenus Avaritia trapped. These findings could help inform targeted surveillance, control measures, and the development of management guides for Culicoides spp. and EHD and BT viruses in southern Ontario, Canada.

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 score0.018
Threshold uncertainty score0.469

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 head0.014
GPT teacher head0.196
Teacher spread0.182 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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