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Abstract Understanding the processes that shape genetic diversity by either promoting or preventing population divergence can help identify geographic areas that either facilitate or limit gene flow. Furthermore, broadly distributed species allow us to understand how biogeographic and ecogeographic transitions affect gene flow. We investigated these processes using genomic data in the Northern Alligator Lizard (Elgaria coerulea), which is widely distributed in Western North America across diverse ecoregions (California Floristic Province and Pacific Northwest) and mountain ranges (Sierra Nevada, Coastal Ranges, and Cascades). We collected single-nucleotide polymorphism data from 120 samples of E. coerulea. Biogeographic analyses of squamate reptiles with similar distributions have identified several shared diversification patterns that provide testable predictions for E. coerulea, including deep genetic divisions in the Sierra Nevada, demographic stability of southern populations, and recent post-Pleistocene expansion into the Pacific Northwest. We use genomic data to test these predictions by estimating the structure, connectivity, and phylogenetic history of populations. At least 10 distinct populations are supported, with mixed-ancestry individuals situated at most population boundaries. A species tree analysis provides strong support for the early divergence of populations in the Sierra Nevada Mountains and recent diversification into the Pacific Northwest. Admixture and migration analyses detect gene flow among populations in the Lower Cascades and Northern California, and a spatial analysis of gene flow identified significant barriers to gene flow across both the Sierra Nevada and Coast Ranges. The distribution of genetic diversity in E. coerulea is uneven, patchy, and interconnected at population boundaries. The biogeographic patterns seen in E. coerulea are consistent with predictions from co-distributed species. 

Research on plant-pollinator interactions requires a diversity of perspectives and approaches, and documenting changing pollinator-plant interactions due to declining insect diversity and climate change is especially challenging. Natural history collections are increasingly important for such research and can provide ecological information across broad spatial and temporal scales. Here, we describe novel approaches that integrate museum specimens from insect and plant collections with field observations to quantify pollen networks over large spatial and temporal gradients. We present methodological strategies for evaluating insect-pollen network parameters based on pollen collected from museum insect specimens. These methods provide insight into spatial and temporal variation in pollen-insect interactions and complement other approaches to studying pollination, such as pollinator observation networks and flower enclosure experiments. We present example data from butterfly pollen networks over the past century in the Great Basin Desert and Sierra Nevada Mountains, United States. Complementary to these approaches, we describe rapid pollen identification methods that can increase speed and accuracy of taxonomic determinations, using pollen grains collected from herbarium specimens. As an example, we describe a convolutional neural network (CNN) to automate identification of pollen. We extracted images of pollen grains from 21 common species from herbarium specimens at the University of Nevada Reno (RENO). The CNN model achieved exceptional accuracy of identification, with a correct classification rate of 98.8%. These and similar approaches can transform the way we estimate pollination network parameters and greatly change inferences from existing networks, which have exploded over the past few decades. These techniques also allow us to address critical ecological questions related to mutualistic networks, community ecology, and conservation biology. Museum collections remain a bountiful source of data for biodiversity science and understanding global change.