Understanding how phenotypes evolve requires disentangling the effects of mutation generating new variation from the effects of selection filtering it. Tests for selection frequently assume that mutation introduces phenotypic variation symmetrically around the population mean, yet few studies have tested this assumption by deeply sampling the distributions of mutational effects for particular traits. Here, we examine distributions of mutational effects for gene expression in the budding yeast Saccharomyces cerevisiae by measuring the effects of thousands of point mutations introduced randomly throughout the genome. We find that the distributions of mutational effects differ for the ten genes surveyed and are inconsistent with normality. For example, all ten distributions of mutational effects included more mutations with large effects than expected for normally distributed phenotypes. In addition, some genes also showed asymmetries in their distribution of mutational effects, with new mutations more likely to increase than decrease the gene’s expression or vice versa. Neutral models of regulatory evolution that take these empirically determined distributions into account suggest that neutral processes may explain more expression variation within natural populations than currently appreciated.
Hodgins-Davis, A., Duveau, F., Walker, E. A., & Wittkopp, P. J. (2019). Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae. BioRxiv, 551804. https://doi.org/10.1101/551804
Inland lakes play a critical role in ecosystem stability, and robust validation of lake models is essential for understanding their dynamics. While remote sensing data can assist with lake surface temperature validation, in situ data typically provides more accurate, reliable data not limited to only the lake surface. However, in situ temperature data for many individual lakes, particularly in North America, is difficult for researchers to quickly access in a standardized format. This database offers a well-organized collection of in situ near-surface and subsurface temperatures from 134 sites divided among 29 large North American inland lakes collected from a variety of sources. The database includes multiple subsurface temperatures throughout the depth profile of 84 of these sites, providing comprehensive data for lake model evaluation. All lakes selected for this database are large enough (over approximately 30 km^2 to be represented by large-scale operational weather models, supporting robust lake model validation efforts on the lakes that have the greatest impact on climatology.
Sorensen, T., Espey, E., Kelley, J.G.W. et al. A database of in situ water temperatures for large inland lakes across the coterminous United States. Sci Data 11, 282 (2024). https://doi.org/10.1038/s41597-024-03103-8