Hybridization and polyploidization are common in vascular plants and important drivers of biodiversity by facilitating speciation and ecological diversification. A primary limitation to making broad synthetic discoveries in hybrid and allopolyploid biodiversity research is the absence of a standardized framework to compare data across studies and biological scales.
Here, I present a new quantitative framework to investigate and interpret patterns in hybrid and allopolyploid biology called the divergence index (DI). The DI framework produces standardized data that are comparable across studies and variables. To show how the DI framework can be used to synthesize data, I analyzed published biochemical, physiological, and ecological trait data of hybrids and allopolyploids. I also apply key ecological and evolutionary concepts in hybrid and polyploid biology to translate nominal outcomes, including transgression, intermediacy, expansion, and contraction, in continuous DI space.
Biochemical, physiological, ecological, and evolutionary data can all be analyzed, visualized, and interpreted in the DI framework. The DI framework is particularly suited to standardize and compare variables with very different scales. When using the DI framework to understand niche divergence, a metric of niche overlap can be used to complement insights to centroid and breadth changes.
The DI framework is an accessible framework for hybrid and allopolyploid biology and represents a flexible and intuitive tool that can be used to reconcile outstanding problems in plant biodiversity research.
Methods for generating and analyzing data from polyploid species are not new to Applications in Plant Sciences, yet a special issue on the topic still presents an exciting opportunity to explore newly emerging research techniques. The complexity associated with the existence of multiple genomes in a single nucleus has meant that despite decades of research, there are still unexplored frontiers at the molecular, phylogenetic, ecological, and evolutionary levels. Some uncharted areas persist despite the forays of excellent research by dedicated scientists, and some remain unmapped because the community avoids polyploid species due to a lack of tools or data. The eight articles in this special issue provide new waypoints and allow us to push the boundaries of our knowledge of polyploid lineages. The tools and applications offered here range from critical techniques for determining the ploidy level of an organism, through synthetic reviews of the optimal treatment of polyploid data for phylogenomics and population genomics, to leveraging and developing new tools to further our understanding of genome dynamics and whole-plant responses to polyploidy. We look forward to the impact that these tools and innovative approaches will have in accelerating the expansion of research into the nature and impact of polyploidy across plant taxa in the coming years.
Despite the generally acknowledged prevalence of polyploidy across plants, determining the ploidy of any given species or specimen is far from trivial. Techniques for answering this question include direct chromosome counts, but also indirect measures through flow cytometry (Smith et al., 2018), measurements of spore sizes (Kuo et al., 2021), and even spectroscopy (Buono and Albach, 2023). This issue features two new tools to facilitate the accurate assessment of ploidy—one method with a long tradition in plant science, and another that takes advantage of modern sequencing data. Ramirez-Castillo et al. (2024) developed a method using croziers, or fiddleheads, to count chromosomes in different fern species. Although roots are typically used for mitotic chromosome counts, the ability to incorporate croziers as potential sources of material allows for a wider array of availability for samples. Ramirez-Castillo et al. use an enzyme pretreatment with a cellulose–pectinase solution to improve permeability of the tissue for the uptake of colchicine to arrest chromosomes at metaphase. Chromosome counting is the original method by which polyploidy was first described in plants (reviewed in Soltis et al., 2014), and the method of Ramirez-Castillo et al. continues this legacy. Moving from chromosomes to sequence data, Gaynor et al. (2024) present nQuack, an R package that allows for ploidy estimation from sequence data ranging from whole-genome resequencing to target enrichment. Building on the methodology of nQuire (Weiß
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