MétaCan
Menu
Back to cohort
Record W2147109212 · doi:10.1371/journal.pone.0130294

Modelling the Northward Expansion of Culicoides sonorensis (Diptera: Ceratopogonidae) under Future Climate Scenarios

2015· article· en· W2147109212 on OpenAlexafffundabout
Anna Zuliani, Alessandro Massolo, Tim Lysyk, Gregory D. Johnson, Shawn J. Marshall, Kathryn Berger, Susan C. Cork

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicVector-Borne Animal Diseases
Canadian institutionsAgriculture and Agri-Food CanadaLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersUniversity of Calgary
KeywordsCulicoidesCeratopogonidaeClimate changeOrbivirusClimate change scenarioRepresentative Concentration PathwaysLand coverEcologyClimatologyAfrican horse sicknessEnvironmental scienceSpecies distributionGeographyClimate modelPhysical geographyBiologyLand useHabitat

Abstract

fetched live from OpenAlex

Climate change is affecting the distribution of pathogens and their arthropod vectors worldwide, particularly at northern latitudes. The distribution of Culicoides sonorensis (Diptera: Ceratopogonidae) plays a key role in affecting the emergence and spread of significant vector borne diseases such as Bluetongue (BT) and Epizootic Hemorrhagic Disease (EHD) at the border between USA and Canada. We used 50 presence points for C. sonorensis collected in Montana (USA) and south-central Alberta (Canada) between 2002 and 2012, together with monthly climatic and environmental predictors to develop a series of alternative maximum entropy distribution models. The best distribution model under current climatic conditions was selected through the Akaike Information Criterion, and included four predictors: Vapour Pressure Deficit of July, standard deviation of Elevation, Land Cover and mean Precipitation of May. This model was then projected into three climate change scenarios adopted by the IPCC in its 5th assessment report and defined as Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. Climate change data for each predictor and each RCP were calculated for two time points pooling decadal data around each one of them: 2030 (2021-2040) and 2050 (2041-2060). Our projections showed that the areas predicted to be at moderate-high probability of C. sonorensis occurrence would increase from the baseline scenario to 2030 and from 2030 to 2050 for each RCP. The projection also indicated that the current northern limit of C. sonorensis distribution is expected to move northwards to above 53°N. This may indicate an increased risk of Culicoides-borne diseases occurrence over the next decades, particularly at the USA-Canada border, as a result of changes which favor C. sonorensis presence when associated to other factors (i.e. host and pathogen factors). Recent observations of EHD outbreaks in northern Montana and southern Alberta supported our projections and considerations. The results of this study can inform the development of cost effective surveillance programs, targeting areas within the predicted limits of C. sonorensis geographical occurrence under current and future climatic conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.216
Teacher spread0.137 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations48
Published2015
Admission routes3
Has abstractyes

Explore more

Same venuePLoS ONESame topicVector-Borne Animal DiseasesFrench-language works237,207