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Abstract As climate change advances, environmental gradients may decouple, generating novel multivariate environments that stress wild populations. A commonly invoked mechanism of evolutionary rescue is adaptive gene flow tracking climate shifts, but gene flow from populations inhabiting similar conditions on one environmental axis could cause maladaptive introgression when populations are adapted to different environmental variables that do not shift together. Genomic architecture can play an important role in determining the effectiveness and relative magnitudes of adaptive gene flow and in situ adaptation. This may have direct consequences for how species respond to climate change but is often overlooked. Here, we simulated microevolutionary responses to environmental change under scenarios defined by variation in the polygenicity, linkage, and genetic redundancy of two independent traits, one of which is adapted to a gradient that shifts under climate change. We used these simulations to examine how genomic architecture influences evolutionary outcomes under climate change. We found that climate‐tracking (up‐gradient) gene flow, though present in all scenarios, was strongly constrained under scenarios of lower linkage and higher polygenicity and redundancy, suggesting in situ adaptation as the predominant mechanism of evolutionary rescue under these conditions. We also found that high polygenicity caused increased maladaptation and demographic decline, a concerning result given that many climate‐adapted traits may be polygenic. Finally, in scenarios with high redundancy, we observed increased adaptive capacity. This finding adds to the growing recognition of the importance of redundancy in mediating in situ adaptive capacity and suggests opportunities for better understanding the climatic vulnerability of real populations. 

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.