A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning

Introduction - Control of vector populations is an effective strategy for addressing vector-borne disease transmission. Effective vector control requires knowledge of habitat use and connectivity. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of animal African trypanosomiasis, which is a wasting disease in livestock and represents a serious socioeconomic burden across sub-Saharan Africa. Methods and Results - We used random forest regression to: (i) Build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania, and (ii) provide novel vector control recommendations. Inputs for the models included field-survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic dat...

引言——调控病媒种群是应对虫媒传染病（vector-borne disease）传播的有效策略。高效的病媒防控需明晰其栖息地利用与种群连通性特征。本研究旨在加深对淡足舌蝇（Glossina pallidipes）相关生物学认知，该物种是动物非洲锥虫病（animal African trypanosomiasis）的传播媒介，而动物非洲锥虫病可引发家畜消耗性病症，在撒哈拉以南非洲地区造成了严重的社会经济负担。

材料与方法及结果——本研究采用随机森林回归（random forest regression）开展如下工作：（1）构建并整合肯尼亚与坦桑尼亚北部区域的淡足舌蝇栖息地适宜性模型与遗传连通性模型；（2）提出创新性的病媒防控建议。模型输入数据包括：349个诱捕位点的实地调查记录、采自29个采样点的659头采采蝇的11个微卫星位点（microsatellite loci）遗传数据，以及遥感环境数据。本研究构建的适宜性与连通性模型分别可解释发生数据与遗传数据中约80%与67%的方差……