Evidence map›Paper›PMID 41237194›Full record

ArticlePLoS computational biology2025

Spatial close-kin mark-recapture methods to estimate dispersal parameters and barrier strength for mosquitoes.

John M Marshall, Shuyi Yang, Jared B Bennett, Igor Filipović, Gordana Rašić

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. EliminatingInsects · 2026
    Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

John M MarshallDivisions of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America.ORCID 0000-0003-0603-7341
Shuyi YangDivisions of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America.
Jared B BennettDivisions of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America.ORCID 0000-0003-4718-257X
Igor FilipovićMosquito Genomics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
Gordana RašićMosquito Genomics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.ORCID 0000-0002-3057-0093

Funding

Understanding mosquito movement and its relevance to control through genetic analysisR01AI143698 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI MARSHALL, JOHN MACKY · 2020 to 2024
$1.7M
Conditional and female lethal approaches for mosquito controlR01AI190001 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Omar Sultan Akbari · 2025 to 2026
$1.2M
NIAID NIH HHS R01 AI143698NIAID NIH HHS R01 AI190001
6 · The paper itself

Abstract

Close-kin mark-recapture (CKMR) methods have recently been used to infer demographic parameters for several aquatic and terrestrial species. For mosquitoes, the spatial distribution of close-kin pairs has been used to estimate mean dispersal distance, of relevance to vector-borne disease transmission and genetic biocontrol strategies. Close-kin methods have advantages over traditional mark-release-recapture (MRR) methods as the mark is genetic, removing the need for physical marking and recapturing that may interfere with movement behavior. Here, we extend CKMR methods to accommodate spatial structure alongside life history for mosquitoes and comparable insects. We derive kinship probabilities for parent-offspring and full-sibling pairs in a spatial context, where an individual in each pair may be a larva or adult. Using the dengue vector Aedes aegypti as a case study, we use an individual-based model of mosquito life history to test the effectiveness of this approach at estimating parameters such as mean dispersal distance, daily staying probability, and the strength of a barrier to movement. Considering a simulated population of 9,025 adult mosquitoes arranged on a 19-by-19 grid, we find the CKMR approach provides unbiased and precise estimates of mean dispersal distance given a total of 2,500 adult females sampled over a three-month period using 25 traps evenly spread throughout the landscape. The CKMR approach is also able to estimate parameters of more complex dispersal kernels, such as the daily staying probability of a zero-inflated exponential kernel, or the strength of a barrier to movement, provided the magnitude of these parameters is greater than 0.5. These results suggest that CKMR provides an insightful characterization of mosquito dispersal that is complementary to conventional MRR methods.

Indexed as

AedesAnimal DistributionCulicidaeModels, BiologicalAnimalsComputational BiologyComputer SimulationFemaleMosquito Vectors

Identifiers

PMID41237194
PMCPMC12638032

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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.