Publication

Assessing connectivity despite high diversity in island populations of a malaria mosquito

Bergey, C. M., Lukindu, M., Wiltshire, R. M., Fontaine, M. C., Kayondo, J. K. & Besansky, N. J., 9-Oct-2019, In : Evolutionary Applications.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Bergey, C. M., Lukindu, M., Wiltshire, R. M., Fontaine, M. C., Kayondo, J. K., & Besansky, N. J. (2019). Assessing connectivity despite high diversity in island populations of a malaria mosquito. Evolutionary Applications. https://doi.org/10.1111/eva.12878

Author

Bergey, Christina M. ; Lukindu, Martin ; Wiltshire, Rachel M. ; Fontaine, Michael C. ; Kayondo, Jonathan K. ; Besansky, Nora J. / Assessing connectivity despite high diversity in island populations of a malaria mosquito. In: Evolutionary Applications. 2019.

Harvard

Bergey, CM, Lukindu, M, Wiltshire, RM, Fontaine, MC, Kayondo, JK & Besansky, NJ 2019, 'Assessing connectivity despite high diversity in island populations of a malaria mosquito', Evolutionary Applications. https://doi.org/10.1111/eva.12878

Standard

Assessing connectivity despite high diversity in island populations of a malaria mosquito. / Bergey, Christina M.; Lukindu, Martin; Wiltshire, Rachel M.; Fontaine, Michael C.; Kayondo, Jonathan K.; Besansky, Nora J.

In: Evolutionary Applications, 09.10.2019.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Bergey CM, Lukindu M, Wiltshire RM, Fontaine MC, Kayondo JK, Besansky NJ. Assessing connectivity despite high diversity in island populations of a malaria mosquito. Evolutionary Applications. 2019 Oct 9. https://doi.org/10.1111/eva.12878


BibTeX

@article{b0a1e4ec835e4549997b5b707ee163de,
title = "Assessing connectivity despite high diversity in island populations of a malaria mosquito",
abstract = "Documenting isolation is notoriously difficult for species with vast polymorphic populations. High proportions of shared variation impede estimation of connectivity, even despite leveraging information from many genetic markers. We overcome these impediments by combining classical analysis of neutral variation with assays of the structure of selected variation, demonstrated using populations of the principal African malaria vector Anopheles gambiae. Accurate estimation of mosquito migration is crucial for efforts to combat malaria. Modeling and cage experiments suggest that mosquito gene drive systems will enable malaria eradication, but establishing safety and efficacy requires identification of isolated populations in which to conduct field‐testing. We assess Lake Victoria islands as candidate sites, finding one island 30 kilometers offshore is as differentiated from mainland samples as populations from across the continent. Collectively, our results suggest sufficient contemporary isolation of these islands to warrant consideration as field‐testing locations and illustrate shared adaptive variation as a useful proxy for connectivity in highly polymorphic species.",
author = "Bergey, {Christina M.} and Martin Lukindu and Wiltshire, {Rachel M.} and Fontaine, {Michael C.} and Kayondo, {Jonathan K.} and Besansky, {Nora J.}",
year = "2019",
month = "10",
day = "9",
doi = "10.1111/eva.12878",
language = "English",
journal = "Evolutionary Applications",
issn = "1752-4571",
publisher = "Wiley",

}

RIS

TY - JOUR

T1 - Assessing connectivity despite high diversity in island populations of a malaria mosquito

AU - Bergey, Christina M.

AU - Lukindu, Martin

AU - Wiltshire, Rachel M.

AU - Fontaine, Michael C.

AU - Kayondo, Jonathan K.

AU - Besansky, Nora J.

PY - 2019/10/9

Y1 - 2019/10/9

N2 - Documenting isolation is notoriously difficult for species with vast polymorphic populations. High proportions of shared variation impede estimation of connectivity, even despite leveraging information from many genetic markers. We overcome these impediments by combining classical analysis of neutral variation with assays of the structure of selected variation, demonstrated using populations of the principal African malaria vector Anopheles gambiae. Accurate estimation of mosquito migration is crucial for efforts to combat malaria. Modeling and cage experiments suggest that mosquito gene drive systems will enable malaria eradication, but establishing safety and efficacy requires identification of isolated populations in which to conduct field‐testing. We assess Lake Victoria islands as candidate sites, finding one island 30 kilometers offshore is as differentiated from mainland samples as populations from across the continent. Collectively, our results suggest sufficient contemporary isolation of these islands to warrant consideration as field‐testing locations and illustrate shared adaptive variation as a useful proxy for connectivity in highly polymorphic species.

AB - Documenting isolation is notoriously difficult for species with vast polymorphic populations. High proportions of shared variation impede estimation of connectivity, even despite leveraging information from many genetic markers. We overcome these impediments by combining classical analysis of neutral variation with assays of the structure of selected variation, demonstrated using populations of the principal African malaria vector Anopheles gambiae. Accurate estimation of mosquito migration is crucial for efforts to combat malaria. Modeling and cage experiments suggest that mosquito gene drive systems will enable malaria eradication, but establishing safety and efficacy requires identification of isolated populations in which to conduct field‐testing. We assess Lake Victoria islands as candidate sites, finding one island 30 kilometers offshore is as differentiated from mainland samples as populations from across the continent. Collectively, our results suggest sufficient contemporary isolation of these islands to warrant consideration as field‐testing locations and illustrate shared adaptive variation as a useful proxy for connectivity in highly polymorphic species.

U2 - 10.1111/eva.12878

DO - 10.1111/eva.12878

M3 - Article

JO - Evolutionary Applications

JF - Evolutionary Applications

SN - 1752-4571

ER -

ID: 99280375