Data and code from: Fine-scale landscape genomics show asymmetric patterns of gene flow for the invasive mosquito Aedes albopictus
Data files
Jul 22, 2026 version files 8.05 MB
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albopictus-2018-adults.gtx
7.84 MB
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Ding_Reed2026_network_analysis.Rmd
3.35 KB
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Network_node_metrics_bySite_withPopgen.xlsx
14.80 KB
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README.md
2.63 KB
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W18-multivariate.rda
192.80 KB
Abstract
Mosquito-borne viruses like dengue, Zika, and chikungunya pose increasing health risks in the United States due to the expanding range of Aedes albopictus, a highly invasive mosquito species that now has a global distribution. Aedes albopictus thrive in artificial containers associated with anthropogenic land use, allowing populations to reach high numbers in urban and peri-urban environments. While the global spread of Ae. albopictus has been well-characterized, the effects of heterogeneous urban landscapes on dispersal and gene flow at fine spatial scales remain unclear. This study analyzed the genetic connectivity of Aedes albopictus populations collected in Wake County, North Carolina in 2018. We used single nucleotide polymorphisms SNP data from double-digest restriction-enzyme associated DNA sequencing (ddRADseq), and examined genetic connectivity through PCA and genetic network analysis. We then evaluated migration and source sink dynamics using a Bayesian approach for SNP data (BA3-SNP). We found little evidence of genetic clustering or isolated populations of Ae. albopictus in Wake County, suggesting high gene flow between sites. Migration analysis demonstrated asymmetric gene flow from rural to urban regions within Wake County, with greater gene flow occurring between and within urban regions. These findings suggest that Ae. albopictus populations demonstrate substantial gene flow within local metropolitan areas, with urban city centers serving as genetic sinks and surrounding suburban and rural regions serving as sources. This study highlights how heterogeneous landscapes shape mosquito population connectivity and migration at fine spatial scales, which is critical for informing vector control and public health intervention strategies.
Dataset DOI: 10.5061/dryad.2bvq83c2x
Description of the data and file structure
This is for the landscape level genomic data including the raw SNP data file that can be imported to R and the node data for the network analysis.
Files and variables
File: albopictus-2018-adults.gtx
Description: This is the SNP data generated by ddRADseq following the protocol in the manuscript. This is prior to the filtering steps described in the methods.
File: Network_node_metrics_bySite_withPopgen.xlsx
Description: This is the node metrics generated by popgen and for the network analysis.
Variables:
- Wake_region: geographic region of Wake County that each site was assigned to
- RAL = central Raleigh
- NWR = northwest Raleigh
- NER = northeast Raleigh
- E = east
- MS = mid-south
- S = south
- W = west
- MW = mid-west
- NW = northwest
- NE = northeast.
- ID: unique identifier for each sample site, with each site representing a node in the network.
- n: number of individuals per site that were retained in the final genetic dataset after filtering
- Deg, close, btw, str, siw, and miw are node centrality measures calculated using graph4lg in R.
- Deg: degree centrality
- close: closeness centrality
- btw: betweenness centrality
- Str: "strength”, which is the sum of the weights of all edges connected to a given node, where edge weight represents genetic distance
- Siw: sum of the inverse edge weights of all edges connected to a given node
- Miw: average of the inverse edge weights of all edges connected to a given node.
- Hobs.gst, Hexp.gst, and Fis.gst are genetic diversity measures calculated using gstudio in R
- Hobs.gst: observed heterozygosity
- Hexp.gst: expected heterozygosity
- Fis.gst: inbreeding coefficient
- City: city the site is located in
- Urban_dev: urban development index used to characterize sites based on mean % impervious surface and % forest cover within a 100m radius of each site
- High: regions classified as Urban
- Low: regions classified as Rural
File: W18-multivariate.rda
Description: R rda file for the PCA anlysis
File: Ding_Reed2026_network_analysis.Rmd
Description: R markdown file that includes code for how we analyzed the network analysis
