Fine-scale landscape genomics helps explain the slow spatial spread of Wolbachia through the Aedes aegypti population in Cairns, Australia

Abstract

The endosymbiotic bacterium Wolbachia suppresses the capacity for arbovirus transmission in the mosquito Aedes aegypti, and can spread spatially through wild mosquito populations following local introductions. Recent introductions in Cairns, Australia have demonstrated slower than expected spatial spread. Potential reasons for this include: (i) barriers to Ae. aegypti dispersal; (ii) higher incidence of long-range dispersal; and (iii) intergenerational loss of Wolbachia. We investigated these three potential factors using genome-wide single-nucleotide polymorphisms (SNPs) and an assay for the Wolbachia infection wMel in 161 Ae. aegypti collected from Cairns in 2015. We detected a small but significant barrier effect of Cairns highways on Ae. aegypti dispersal using distance-based redundancy analysis and patch-based simulation analysis. We detected a pair of putative full-siblings in ovitraps 1312 m apart, indicating long-distance female movement likely mediated by human transport. We also found a pair of full-siblings of different infection status, indicating intergenerational loss of Wolbachia in the field. These three factors are all expected to contribute to the slow spread of Wolbachia through Ae. aegypti populations, though from our results it is unclear whether Wolbachia loss and long-distance movement are sufficiently common to reduce the speed of spatial spread appreciably. Our findings inform the strategic deployment of Wolbachia-infected mosquitoes during releases, and show how parameter estimates from laboratory studies may differ from those estimated using field data. Our landscape genomics approach can be extended to other host/symbiont systems that are being considered for biocontrol.

A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito

“…aegypti to stay within the building of its release, and when dispersing farther rarely travel more than 150 m (Harrington et al, ; Maciel‐de‐Freitas, Codeco, & Lourenco‐de‐Oliveira, ; Ordonez‐Gonzalez, Mercado‐Hernandez, Flores‐Suarez, & Fernandez‐Salas, ; Russell, Webb, Williams, & Ritchie, ). Occasional long‐distance dispersal >500 m has also been recorded through MRR (Honorio et al, ; Reiter et al, ); while these dispersal distances are larger than the sampling scale of this study, they have been observed in genomic studies (Schmidt et al, ). Likewise, our finding that buildings or the space between them acts as a dispersal barrier is consistent with observations of fine‐scale dispersal barriers in Ae.…”

“…However, these methods are often laborious and do not capture past dispersal patterns that produce gene flow. New methods and tools in landscape genetics can help understand past dispersal and gene flow patterns, including potential environmental parameters that limit dispersal (Schmidt, Filipović, Hoffmann, & Rašić, ; Watts et al, ). The advent of high‐density sequencing technologies has also provided increased power for landscape genomic studies conducted at spatial scales fine enough to investigate dispersal discretely over generations, and to measure individual acts of movement (Schmidt, Rašić, et al, ).…”

Quantifying Aedes aegypti dispersal in space and time: a modeling approach

“…This information is fundamental to implement and monitor control strategies based on insemination success of released male mosquitoes (Evans et al 2019) and is derived through an array of novel genetics and molecular biology tools applied to genetic mapping, DNA sequencing, or detection of Wolbachia spp. infection (Hemme et al 2010, Schmidt et al 2018. MRR should still be considered complementary and not alternative to molecular and genetic tools, as they provide us with direct estimates on mosquito dispersal capacity as well as on the effect of environmental factors on dispersal that more novel methods are not yet able to provide.…”

MGDrivE: A modular simulation framework for the spread of gene drives through spatially-explicit mosquito populations

“…Ae. aegypti mosquitoes, for instance, are thought to be relatively local dispersers, often remaining in the same household for the duration of their lifespan [47]. For modeling the fine-scale spread of gene drive systems in this species, metapopulations the size of households may be appropriate.…”

“…By default, movement rates between metapopulations are derived from a zero-inflated exponential dispersal kernel, the degree of zero-inflation and mean dispersal distance of which may be defined by the user. That said; the movement kernel may be expanded arbitrarily to account for barriers to movement such as roads [47] and other factors without altering the overarching model structure. Movement rates between nodes are then used to calculate a matrix of node transition probabilities, which is incorporated in the tensor algebraic model formulation described in the S1 User Manual.…”