Hundreds of millions of people get a mosquito-borne disease every year and nearly one million die. Transmission of these infections is primarily tackled through the control of mosquito vectors. The accurate quantification of mosquito dispersal is critical for the design and optimization of vector control programs, yet the measurement of dispersal using traditional mark-release-recapture (MRR) methods is logistically challenging and often unrepresentative of an insect’s true behavior. Using Aedes aegypti (a major arboviral vector) as a model and two study sites in Singapore, we show how mosquito dispersal can be characterized by the spatial analyses of genetic relatedness among individuals sampled over a short time span without interruption of their natural behaviors. Using simple oviposition traps, we captured adult female Ae. aegypti across high-rise apartment blocks and genotyped them using genome-wide SNP markers. We developed a methodology that produces a dispersal kernel for distance which results from one generation of successful breeding (effective dispersal), using the distance separating full siblings and 2nd- and 3rd-degree relatives (close kin). The estimated dispersal distance kernel was exponential (Laplacian), with a mean dispersal distance (and dispersal kernel spread σ) of 45.2 m (95% CI 39.7–51.3 m), and 10% probability of a dispersal > 100 m (95% CI 92–117 m). Our genetically derived estimates matched the parametrized dispersal kernels from previous MRR experiments. If few close kin are captured, a conventional genetic isolation-by-distance analysis can be used, as it can produce σ estimates congruent with the close-kin method if effective population density is accurately estimated. Genetic patch size, estimated by spatial autocorrelation analysis, reflects the spatial extent of the dispersal kernel “tail” that influences, for example, the critical radii of release zones and the speed of Wolbachia spread in mosquito replacement programs. We demonstrate that spatial genetics can provide a robust characterization of mosquito dispersal. With the decreasing cost of next-generation sequencing, the production of spatial genetic data is increasingly accessible. Given the challenges of conventional MRR methods, and the importance of quantified dispersal in operational vector control decisions, we recommend genetic-based dispersal characterization as the more desirable means of parameterization.

中文翻译：

---

使用空间遗传学来量化蚊子的扩散，以用于控制程序。

每年有数亿人得蚊子传播的疾病，有近一百万人死亡。这些感染的传播主要通过控制蚊媒来解决。蚊虫传播的准确定量对于矢量控制程序的设计和优化至关重要，但是使用传统的标记释放捕获（MRR）方法进行的传播测量在逻辑上具有挑战性，并且通常不能代表昆虫的真实行为。使用埃及伊蚊（一个主要的虫媒病毒载体）作为模型，并在新加坡有两个研究点，我们展示了如何通过在短时间内采样的个体之间的遗传相关性进行空间分析来表征蚊子的扩散，而不会干扰其自然行为。使用简单的产卵陷阱，我们捕获了成年雌性Ae。埃及人跨越高层公寓楼，并使用全基因组SNP标记对它们进行基因分型。我们开发了一种方法，该方法可使用完全兄弟姐妹与二度和三度亲戚（近亲）之间的距离来产生一代成功育种（有效扩散）所产生的距离。估计的散布距离核是指数的（拉普拉斯算子），平均散布距离（和散布核扩散σ）为45.2 m（95％CI 39.7-51.3 m），散布概率大于100 m（95％CI）的概率为10％ 92–117 m）。我们的遗传推论估计与先前MRR实验中的参数化分散核相匹配。如果几乎没有近亲被捕获，则可以使用常规的按距离遗传隔离分析，如果可以准确估计有效的人口密度，它可以产生与近亲方法一致的σ估计。通过空间自相关分析估计的遗传斑块大小反映了扩散核“尾巴”的空间范围，该区域影响例如释放区的临界半径和蚊子替代程序中Wolbachia传播的速度。我们证明，空间遗传学可以提供蚊子传播的强大特征。随着下一代测序成本的降低，空间遗传数据的产生越来越容易获得。鉴于常规MRR方法所面临的挑战以及量化分散在操作矢量控制决策中的重要性，我们建议基于遗传的分散特性作为更理想的参数化手段。