Evidence map›Paper›PMID 39283940›Full record

ArticlePLoS neglected tropical diseases2024

Temperature dependence of mosquitoes: Comparing mechanistic and machine learning approaches.

Tejas S Athni, Marissa L Childs, Caroline K Glidden, Erin A Mordecai

Abstract readComparative Study
In one paragraph

Article in PLoS neglected tropical diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Climate sensitivity is widely but unevenly spread across zoonotic diseases.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Tejas S AthniHarvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0002-6336-0826
Marissa L ChildsEmmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, California, United States of America.ORCID 0000-0002-8597-2161
Caroline K GliddenDepartment of Biology, Stanford University, Stanford, California, United States of America.ORCID 0000-0001-9839-5781
Erin A MordecaiDepartment of Biology, Stanford University, Stanford, California, United States of America.ORCID 0000-0002-4402-5547

Funding

Medical Scientist Training ProgramT32GM144273 · NIGMS · HARVARD MEDICAL SCHOOL · PI David Shumway Jones, Jacqueline A. Lees · 2022 to 2026
$14.7M
The child health impacts of overlapping vector-borne diseases in KenyaR01AI102918 · NIAID · STANFORD UNIVERSITY · PI Angelle Desiree LaBeaud · 2013 to 2026
$7.7M
Leveraging environmental drivers to predict vector-borne disease transmissionR35GM133439 · NIGMS · STANFORD UNIVERSITY · PI Erin Mordecai · 2019 to 2026
$3.2M
Modeling the influence of temperature on the evolution of vector-virus interactionsR01AI168097 · NIAID · WADSWORTH CENTER · PI Alexander Timothy Ciota · 2022 to 2026
$2.4M
NIAID NIH HHS R01 AI102918NIAID NIH HHS R01 AI168097NIGMS NIH HHS R35 GM133439NIGMS NIH HHS T32 GM144273
6 · The paper itself

Abstract

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change.

Indexed as

Machine LearningMosquito VectorsTemperatureAedesAnimalsAnophelesCulexCulicidae

Identifiers

PMID39283940
PMCPMC11460681

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.