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Abstract Horseshoe crabs, considered living fossils with a stable morphotype spanning ∼445 million years, are evolutionarily, ecologically, and biomedically important species experiencing rapid population decline. Of the four extant species of horseshoe crabs, the Atlantic horseshoe crab, Limulus polyphemus, has become an essential component of the modern medicine toolkit. Here, we present the first chromosome-level genome assembly, and the most contiguous and complete assembly to date, for L. polyphemus using nanopore long-read sequencing and chromatin conformation analysis. We find support for three horseshoe crab-specific whole-genome duplications, but none shared with Arachnopulmonata (spiders and scorpions). Moreover, we discovered tandem duplicates of endotoxin detection pathway components Factors C and G, identify candidate centromeres consisting of Gypsy retroelements, and classify the ZW sex chromosome system for this species and a sister taxon, Carcinoscorpius rotundicauda. Finally, we revealed this species has been experiencing a steep population decline over the last 5 million years, highlighting the need for international conservation interventions and fisheries-based management for this critical species. 

Abstract Understanding the drivers of spatial patterns of genomic diversity has emerged as a major goal of evolutionary genetics. The flexibility of forward-time simulation makes it especially valuable for these efforts, allowing for the simulation of arbitrarily complex scenarios in a way that mimics how real populations evolve. Here, we present Geonomics, a Python package for performing complex, spatially explicit, landscape genomic simulations with full spatial pedigrees that dramatically reduces user workload yet remains customizable and extensible because it is embedded within a popular, general-purpose language. We show that Geonomics results are consistent with expectations for a variety of validation tests based on classic models in population genetics and then demonstrate its utility and flexibility with a trio of more complex simulation scenarios that feature polygenic selection, selection on multiple traits, simulation on complex landscapes, and nonstationary environmental change. We then discuss runtime, which is primarily sensitive to landscape raster size, memory usage, which is primarily sensitive to maximum population size and recombination rate, and other caveats related to the model’s methods for approximating recombination and movement. Taken together, our tests and demonstrations show that Geonomics provides an efficient and robust platform for population genomic simulations that capture complex spatial and evolutionary dynamics.