MétaCan
Menu
Back to cohort
Record W2908355668 · doi:10.1371/journal.pone.0210122

Mapping the global potential distributions of two arboviral vectors Aedes aegypti and Ae. albopictus under changing climate

2018· article· en· W2908355668 on OpenAlexaboutno aff
Mahmoud Kamal, Mohamed Kenawy, Magda H. Rady, Abdallah M Samy

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsAedes albopictusAedes aegyptiChikungunyaDengue feverAedesOutbreakClimate changeEcologyArbovirusEcological nicheBiologyVector (molecular biology)Representative Concentration PathwaysEnvironmental scienceGeographyClimate modelVirologyHabitat

Abstract

fetched live from OpenAlex

BACKGROUND: Aedes aegypti and Ae. albopictus are the primary vectors that transmit several arboviral diseases, including dengue, chikungunya, and Zika. The world is presently experiencing a series of outbreaks of these diseases, so, we still require to better understand the current distributions and possible future shifts of their vectors for successful surveillance and control programs. Few studies assessed the influences of climate change on the spatial distributional patterns and abundance of these important vectors, particularly using the most recent climatic scenarios. Here, we updated the current potential distributions of both vectors and assessed their distributional changes under future climate conditions. METHODS: We used ecological niche modeling approach to estimate the potential distributions of Ae. aegypti and Ae. albopictus under present-day and future climate conditions. This approach fits ecological niche model from occurrence records of each species and environmental variables. For each species, future projections were based on climatic data from 9 general circulation models (GCMs) for each representative concentration pathway (RCP) in each time period, with a total of 72 combinations in four RCPs in 2050 and 2070. All ENMs were tested using the partial receiver operating characteristic (pROC) and a set of 2,048 and 2,003 additional independent records for Ae. aegypti and Ae. albopictus, respectively. Finally, we used background similarity test to assess the similarity between the ENMs of Ae. aegypti and Ae. albopictus. RESULTS: The predicted potential distribution of Ae. aegypti and Ae. albopictus coincided with the current and historical known distributions of both species. Aedes aegypti showed a markedly broader distributional potential across tropical and subtropical regions than Ae. albopictus. Interestingly, Ae. albopictus was markedly broader in distributional potential across temperate Europe and the United States. All ecological niche models (ENMs) were statistically robust (P < 0.001). ENMs successfully anticipated 98% (1,999/2,048) and 99% (1,985/2,003) of additional independent records for both Ae. aegypti and Ae. albopictus, respectively (P < 0.001). ENMs based on future conditions showed similarity between the overall distributional patterns of future-day and present-day conditions; however, there was a northern range expansion in the continental USA to include parts of Southern Canada in case of Ae. albopictus in both 2050 and 2070. Future models also anticipated further expansion of Ae. albopictus to the East to include most of Europe in both time periods. Aedes aegypti was anticipated to expand to the South in East Australia in 2050 and 2070. The predictions showed differences in distributional potential of both species between diverse RCPs in 2050 and 2070. Finally, the background similarity test comparing the ENMs of Ae. aegypti and Ae. albopictus was unable to reject the null hypothesis of niche similarity between both species (P > 0.05). CONCLUSION: These updated maps provided details to better guide surveillance and control programs of Ae. aegypti and Ae. albopictus. They have also significant public health importance as a baseline for predicting the emergence of arboviral diseases transmitted by both vectors in new areas across the world.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.995

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.051
GPT teacher head0.244
Teacher spread0.194 · 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

Citations305
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuePLoS ONESame topicSpecies Distribution and Climate ChangeFrench-language works237,207