The world’s oceans are currently facing major stressors in the form of overexploitation and anthropogenic climate change. The Baltic Sea was home to the first “industrial” fishery ∼800 y ago targeting the Baltic herring, a species that is still economically and culturally important today. Yet, the early origins of marine industries and the long-term ecological consequences of historical and contemporary fisheries remain debated. Here, we study long-term population dynamics of Baltic herring to evaluate the past impacts of humans on the marine environment. We combine modern whole-genome data with ancient DNA (aDNA) to identify the earliest-known long-distance herring trade in the region, illustrating that extensive fish trade began during the Viking Age. We further resolve population structure within the Baltic and observe demographic independence for four local herring stocks over at least 200 generations. It has been suggested that overfishing at Øresund in the 16th century resulted in a demographic shift from autumn-spawning to spring-spawning herring dominance in the Baltic. We show that while the Øresund fishery had a negative impact on the western Baltic herring stock, the demographic shift to spring-spawning dominance did not occur until the 20th century. Instead, demographic reconstructions reveal population trajectories consistent with expected impacts of environmental change and historical reports on shifting fishing targets over time. This study illustrates the joint impact of climate change and human exploitation on marine species as well as the role historical ecology can play in conservation and management policies.

Genomic assignment tests can provide important diagnostic biological characteristics, such as population of origin or ecotype. Yet, assignment tests often rely on moderate‐ to high‐coverage sequence data that can be difficult to obtain for fields such as molecular ecology and ancient DNA. We have developed a novel approach that efficiently assigns biologically relevant information (i.e., population identity or structural variants such as inversions) in extremely low‐coverage sequence data. First, we generate databases from existing reference data using a subset of diagnostic single nucleotide polymorphisms (SNPs) associated with a biological characteristic. Low‐coverage alignment files are subsequently compared to these databases to ascertain allelic state, yielding a joint probability for each association. To assess the efficacy of this approach, we assigned haplotypes and population identity in Heliconius butterflies, Atlantic herring, and Atlantic cod using chromosomal inversion sites and whole‐genome data. We scored both modern and ancient specimens, including the first whole‐genome sequence data recovered from ancient Atlantic herring bones. The method accurately assigns biological characteristics, including population membership, using extremely low‐coverage data (as low as 0.0001x) based on genome‐wide SNPs. This approach will therefore increase the number of samples in evolutionary, ecological and archaeological research for which relevant biological information can be obtained.