Underground storage organs (USO) allow plants to retreat underground during periods of resource scarcity and/or abiotic stress. These adaptations help plants survive seasonal climates and have evolved repeatedly across the vascular plant tree of life. USOs develop as modifications of various plant tissues, including root, leaf, and stem tissues. The non-model plant Bomarea multiflora (Alstroemeriaceae) offers a unique opportunity to study the development and evolution of USOs as it has two types of USOs (rhizomes and root tubers), allowing for direct comparison. B. multiflora’s dual USOs and its phylogenetic position within the monocots makes it a good candidate for future development as a model species for USO development. Here we analyze a transcriptomic dataset of four distinct tissue types in B. multiflora: aerial shoot, rhizome, fibrous root, and root tuber tissues. We use ZigZag, a recently developed hierarchical Baysian model that determines the probability of active expression for each gene in each tissue type. We found 56 genes differentially expressed between individual tissues and 29 between root vs. shoot tissue. We review these genes and describe avenues for future investigation of USO developmental pathways within monocots.

Cryptic diversity, the existence of genetically distinct but morphologically similar taxa that thus were previously classified as a single entity, is a fascinating subject in evolutionary biology and alpha taxonomy, but can be challenging to assess in practice. Genetic analyses have proven successful in identifying cryptic taxa, but are often impractical to employ as a starting point. Morphology thus can play an important role in cases of possible cryptic diversity, especially in determining if further study is warranted. Here, we use statistical analyses on morphological data to assess a possible case of cryptic diversity within Allium acuminatum, a species of wild onion native to western North America. Specimens collected primarily from several counties in Washington State (Kittitas, Yakima, and Klickitat) have been noted to differ morphologically from formal descriptions of the species. Morphological data was recorded for 165 specimens from the University of Washington Herbarium, Burke Museum collection. The data was then analyzed using a Factor Analysis with Mixed Data (FAMD) algorithm, and the results of the FAMD were then analyzed with a k-means clustering algorithm. The k-means clustering results were then plotted on a geospatial map using the original locality data from the herbarium specimens, and geospatial patterns for the clusters were assessed visually. Finally, t-tests and chi-squared tests were performed for the continuous and categorical traits, respectively, between the k-means cluster groups. The k-means clustering algorithm generated 3 clusters from the FAMD data, one of which was strongly centered around the area of interest (Kittitas, Yakima, and Klickitat counties), according to the geospatial map. Further, the statistical tests showed that, for 10 of the 14 traits analyzed, there were notable differences between the k-means cluster groups with a high level of statistical significance (p ≤ 0.0001).  Most of these differences were reflected in the cluster centered around the area of interest. These results indicate there is detectable morphological variation within A. acuminatum, and this variation is centered around the geographical area of interest. Additionally, we believe these results indicate further study is warranted to determine if the morphologically different populations are worthy of taxonomic recognition using more sophisticated methods, such as molecular techniques.