Recent work has show that evolution can occur over short time scale (as little as one generation). Rapid adaptation is espeically important in the context of climate change and novel environments. Many organisms have recently shifted their biogeographic range as a response to the changing climate, including my study species, Kellet's whelks. Kellet's whelk expanded its range poleward past a natural biogeographic break at Point Conception ~40 years ago, allowing us to ask questions about how they've rapidly evolved to novel environments in their expanded range. 

Our work is nicely summed up by Flanagan et al (2025): 

"Lee et al. (2024) studied Kellet's whelk, whose range is expanding along the North American Pacific coast. Using transcriptomic data from offspring collected from historical and expanded populations, they discovered several putatively adaptive loci associated with cold tolerance and metabolic stress, suggesting that genetic adaptation may play a critical role in helping marine species withstand colder temperatures as the whelks shift their ranges poleward."

The analysis of large datasets has become increasingly common. Due to the limitations imposed by working with large datasets in biology and related fields, data are often filtered to identify informative subsets of data to reduce the costs of future analyses and expedite downstream analysis. 

When conducting large-scale grouping of individuals to populations or geographic locations, for example, researchers often choose (ascertain) subsets of genetic loci that are most differentiated among populations and thus the most informative for assigning individuals to a population of origin. Researchers can choose informative or candidate loci using many different criteria. Unfortunately, choosing informative loci from a distribution (e.g., of FST values) can cause high-grading bias, an overestimation of power in a subset of loci due to model overfitting. We demonstrate this problem and offer some solutions in our 2025 paper in Molecular Ecology Resources.