City puzzles: Does urban land scape affect genetic population structure in Aedes aegypti?

Abstract

Cities usually offer a suitable environment for the dengue vector Aedes aegypti, providing oviposition sites, accessibility to human hosts and nectar meals. However, large urban centres are highly heterogeneous environments, forming a patched landscape that could affect Ae. aegypti population dynamics and dispersal. Here, we performed a genome-wide analysis using Rad-seq data from 99 Ae. aegypti specimens collected in three areas within Buenos Aires city with varying levels of urbanization/land use: highly urbanized Area 1, intermediate Area 2 and poorly urbanized Area 3. We found an inverse association between urbanization levels and spatial genetic structure. Populations from highly urbanized Area 1 did not present genetic structure whereas two and three clusters were detected in Areas 2 and 3, respectively. In the case of Area 3, initial analyses showed separation in clusters was mostly due to elevated consanguinity within sites although three clusters were still detected after closely related individuals were discarded. Mosquitoes around each site displayed a high degree of isolation, evidencing a close dependence between the vector and human dwellings. Interestingly, specimens from distant boroughs (within the limits of the city) and the city's outskirts formed a single cluster with inner city sites (Area 1), highlighting the role of passive transport in shaping population structure. Genetic distances were poorly correlated with geographic distances in Buenos Aires, suggesting a stronger influence of passive than active dispersal on population structure. Only Area 2 displayed a significant isolation-by-distance pattern (p = 0.046), with males dispersing more than females (p = 0.004 and p = 0.016, respectively). Kinship analyses allowed us to detect full-siblings located 1.5 km apart in Area 1, which could be due to an extreme event of active female dispersal. Effective population size was higher in Area 2 confirming that cemeteries represent highly favourable environments for Ae. aegypti and need to be specifically targeted. Our results suggest that control programs should take into account urban landscape heterogeneity in order to improve vector control.

Fig 1. Collection sites for Ae. aegypti in different areas within Buenos Aires city.

Sampling sites in each area are depicted with red dots. In Area 1, collection sites located in Monte Grande (MG), and Lugano (LUG) are not directly depicted in the map for scale purposes. Maps were created using Qgis and layer files were all retrieved from public domain source available at: https://data.buenosaires.gob.ar/dataset/.

Fig 2

Loiselle’s kinship coefficients across geographic distances (m) for individual pairs in Area 1 (A), Area 2 (B) and Area 3 (C). For Area 1, individuals were previously separated according to the collection season.

Fig 3

Comparative DAPC analysis for Ae. aegypti samples from Area 2 (A) and Area 3 (B) in Buenos Aires city. On the left, scatter plot with individuals (curve area/dots) assigned to the detected clusters using the k means function find.clusters. On the right, individual membership probability to each cluster (vertical lines) using compoplot in Adegenet. Clustering analyses for samples from Area 1 yielded only one cluster and therefore were not included in DAPC analyses.

Fig 4

STRUCTURE analysis for Ae. aegypti samples from Area 1 (A), Area 2 (B) and Area 3 (C) in Buenos Aires City. Individual membership probability to each cluster is depicted with vertical lines with different colours representing each cluster.

Fig 5. DAPC analysis for Ae. aegypti samples from different locations in Northwestern, Northeastern and Central Argentina.

On the left, scatter plot with individuals (dots) assigned to the three detected clusters using the k means function find.clusters. On the right, individual membership probability to each cluster (vertical lines).

Fig 6. Unrooted Neighbor-joining tree for Ae. aegypti specimens collected in different regions of Argentina.

The tree was constructed using a genetic distances matrix (Nei’s F_ST as implemented in R package ape).