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Abstract The spongy moth, Lymantria dispar, is an irruptive forest pest native to Eurasia where its range extends from coast to coast and overspills into northern Africa. Accidentally introduced from Europe in Massachusetts in 1868–1869, it is now established in North America where it is considered a highly destructive invasive pest. A fine‐scale characterization of its population genetic structure would facilitate identification of source populations for specimens intercepted during ship inspections in North America and would enable mapping of introduction pathways to help prevent future incursions into novel environments. In addition, detailed knowledge of L. dispar's global population structure would provide new insight into the adequacy of its current subspecies classification system and its phylogeographic history. To address these issues, we generated >2000 genotyping‐by‐sequencing‐derived SNPs from 1445 contemporary specimens sampled at 65 locations in 25 countries/3 continents. Using multiple analytical approaches, we identified eight subpopulations that could be further partitioned into 28 groups, achieving unprecedented resolution for this species' population structure. Although reconciliation between these groupings and the three currently recognized subspecies proved to be challenging, our genetic data confirmed circumscription of the japonica subspecies to Japan. However, the genetic cline observed across continental Eurasia, from L. dispar asiatica in East Asia to L. d. dispar in Western Europe, points to the absence of a sharp geographical boundary (e.g., the Ural Mountains) between these two subspecies, as suggested earlier. Importantly, moths from North America and the Caucasus/Middle East displayed high enough genetic distances from other populations to warrant their consideration as separate subspecies of L. dispar. Finally, in contrast with earlier mtDNA‐based investigations that identified the Caucasus as L. dispar's place of origin, our analyses suggest continental East Asia as its evolutionary cradle, from where it spread to Central Asia and Europe, and to Japan through Korea.

Abstract Spatial synchrony is a common characteristic of spatio‐temporal population dynamics across many taxa. While it is known that both dispersal and spatially autocorrelated environmental variation (i.e., the Moran effect) can synchronize populations, the relative contributions of each, and how they interact, are generally unknown. Distinguishing these mechanisms and their effects on synchrony can help us to better understand spatial population dynamics, design conservation and management strategies, and predict climate change impacts. Population genetic data can be used to tease apart these two processes as the spatio‐temporal genetic patterns they create are expected to be different. A challenge, however, is that genetic data are often collected at a single point in time, which may introduce context‐specific bias. Spatio‐temporal sampling strategies can be used to reduce bias and to improve our characterization of the drivers of spatial synchrony. Using spatio‐temporal analyses of genotypic data, our objective was to identify the relative support for these two mechanisms to the spatial synchrony in population dynamics of the irruptive forest insect pest, the spruce budworm (Choristoneura fumiferana), in Quebec (Canada). AMOVA, cluster analysis, isolation by distance, and sPCA were used to characterize spatio‐temporal genomic variation using 1,370 SBW larvae sampled over four years (2012–2015) and genotyped at 3,562 SNP loci. We found evidence of overall weak spatial genetic structure that decreased from 2012 to 2015 and a genetic diversity homogenization among the sites. We also found genetic evidence of a long‐distance dispersal event over >140 km. These results indicate that dispersal is the key mechanism involved in driving population synchrony of the outbreak. Early intervention management strategies that aim to control source populations have the potential to be effective through limiting dispersal. However, the timing of such interventions relative to outbreak progression is likely to influence their probability of success.