Philip Lavretsky

Anthropogenic hybridization, or higher and non‐natural rates of gene flow directly and indirectly induced by human activities, is considered a significant threat to biodiversity. The primary concern for conservation is the potential for genomic extinction and loss of adaptiveness for native species due to the extensive introgression of non‐native genes. To alleviate or reverse trends for such scenarios requires the direct integration of genomic data within a model framework for effective management. Towards this end, we developed the simRestore R program as a decision‐making tool that integrates ecological and genomic information to simulate ancestry outcomes from optimized conservation strategies. In short, the program optimizes supplementation and removal strategies across generations until a set native genetic threshold is reached within the studied population. Importantly, in addition to helping with initial decision‐making, simulations can be updated with the outcomes of ongoing efforts, allowing for the adaptive management of populations. After demonstrating functionality, we apply and optimize among actionable management strategies for the endangered Hawaiian duck for which the current primary threat is genetic extinction through ongoing anthropogenic hybridization with feral mallards. Simulations demonstrate that supplemental and removal efforts can be strategically tailored to move the genetic ancestry of Hawaii's hybrid populations towards Hawaiian duck without the need to completely start over. Further, we discuss ecological parameter sensitivity, including which factors are most important to ensure genetic outcomes (i.e. number of offspring). Finally, to facilitate use, the program is also available online as a Shiny Web application.

Anopheles stephensi invasion in the Horn of Africa (HoA) poses a substantial risk of increased malaria disease burden in the region. An understanding of the history of introduction(s), establishment(s) and potential A. stephensi sources in the HoA is needed to predict future expansions and establish where they may be effectively controlled. To this end, we take a landscape genomic approach to assess A. stephensi origins and spread throughout the HoA, information essential for vector control. Specifically, we assayed 2070 genome‐wide single nucleotide polymorphisms across 214 samples spanning 13 populations of A. stephensi from Ethiopia and Somaliland collected in 2018 and 2020, respectively. Principal component and genetic ancestry analyses revealed clustering that followed an isolation‐by‐distance pattern, with genetic divergence among the Ethiopian samples significantly correlating with geographical distance. Additionally, genetic relatedness was observed between the northeastern and east central Ethiopian A. stephensi populations and the Somaliland A. stephensi populations. These results reveal population differentiation and genetic connectivity within HoA A. stephensi populations. Furthermore, based on genetic network analysis, we uncovered that Dire Dawa, the site of a spring 2022 malaria outbreak, was one of the major hubs from which sequential founder events occurred in the rest of the eastern Ethiopian region. These findings can be useful for the selection of sites for heightened control to prevent future malaria outbreaks. Finally, we did not detect significant genotype–environmental associations, potentially due to the recency of their colonization and/or other anthropogenic factors leading to the initial spread and establishment of A. stephensi. Our study highlights how coupling genomic data at landscape levels can shed light into even ongoing invasions.