Genetic connectivity constrained by natural barriers in a key agricultural pest: insights from mitochondrial DNA analysis

Abstract

In the context of anthropogenic global change, the study of landscape effects on species movement has garnered increasing attention. Landscape genetics offer indirect yet attractive means to capture species dispersal events across generations and their interaction with landscapes. However, landscape genetic patterns tend to exhibit significant variations across taxa and rely on the molecular makers adopted. Here, we investigated how landscapes influence population connectivity of an important tea pest, Empoasca onukii, using mitochondrial DNA sequences of 1,518 individuals from 57 locations in mainland China and offshore islands. We analyzed the inter-population genetic divergence and integrated multiple models to explicitly quantify their association with geographic distance, environmental heterogeneity, and landscape barriers. Analyses revealed a reduction in gene flow on islands, along the Yangtze River, and across mountainous regions of Western China. Models explicitly detected the predominant contributions of topographic complexity to population divergence and evidenced that mountains may serve as effective dispersal barriers for E. onukii. These results suggest that the limited gene exchange resulting from low population connectivity among mountains might generate the observed patterns of mitochondrial genetic variations, which contrasts the climate-related pattern previously observed on microsatellites. The findings enhance our comprehension of the evolutionary and epidemic dynamics of E. onukii, and highlight the demand of considering species-specific traits when studying population landscape genetic patterns. Moreover, the study emphasizes the necessity of employing multiple molecular markers to comprehensively elucidate landscape effects on population connectivity across diverse species for valuable insights into biodiversity conservation, pest control, and other management decisions.