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The expansion of agriculture is responsible for the mass conversion of biologically diverse natural environments into managed agroecosystems dominated by a handful of genetically homogeneous crop species. Agricultural ecosystems typically have very different abiotic and ecological conditions from those they replaced and create potential niches for those species that are able to exploit the abundant resources offered by crop plants. While there are well-studied examples of crop pests that have adapted into novel agricultural niches, the impact of agricultural intensification on the evolution of crop mutualists such as pollinators is poorly understood. We combined genealogical inference from genomic data with archaeological records to demonstrate that the Holocene demographic history of a wild specialist pollinator of Cucurbita (pumpkins, squashes, and gourds) has been profoundly impacted by the history of agricultural expansion in North America. Populations of the squash bee Eucera pruinosa experienced rapid growth in areas where agriculture intensified within the past 1,000 y, suggesting that the cultivation of Cucurbita in North America has increased the amount of floral resources available to these bees. In addition, we found that roughly 20% of this bee species’ genome shows signatures of recent selective sweeps. These signatures are overwhelmingly concentrated in populations from eastern North America where squash bees were historically able to colonize novel environments due to human cultivation of Cucurbita pepo and now exclusively inhabit agricultural niches. These results suggest that the widespread cultivation of crops can prompt adaptation in wild pollinators through the distinct ecological conditions imposed by agricultural environments. 

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone. 

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone. 

Context Bees are the most important pollinators of crops worldwide. For most bees, patches of semi-natural habitat within or adjacent to crops can provide important nesting and food resources. Despite this, land cover change is rapidly reducing the abundance of semi-natural habitat within agroecological landscapes, with potentially negative consequences for bee communities and the services they provide. Objectives Identify how the availability of semi-natural habitat impacts bee communities across biogeographic regions, which may reveal commonalities and key governing principles that transcend a single region or taxa. Methods We analyze and compare the drivers of bee community composition in cotton fields within Brazil and the U.S. to reveal how land cover and land cover change impact bee community composition across these two regions. Results We show that the most critical factors impacting bee communities in cotton agroecosystems are the same in Brazil and the U.S.: bee abundance increases with cotton bloom density and the abundance of semi-natural habitat. Further, the loss of semi-natural habitat over a 5-year period negatively impacts bee abundance in both agroecosystems. Conclusions Given the importance of bee abundance for the provision of pollination service in cotton plants, our findings highlight the significance of small semi-natural habitat fragments in supporting key ecosystem service providers for both tropical and temperate cotton agroecological systems. We underscore the important role that local land managers play in biodiversity conservation, and the potential contribution they can make to pollination provision by supporting agricultural landscapes that conserve fragments of semi-natural habitat.