Evolutionary change during experimental ocean acidification.

Rising atmospheric carbon dioxide (CO2) conditions are driving unprecedented changes in seawater chemistry, resulting in reduced pH and carbonate ion concentrations in the Earth's oceans. This ocean acidification has negative but variable impacts on individual performance in many marine species. However, little is known about the adaptive capacity of species to respond to an acidified ocean, and, as a result, predictions regarding future ecosystem responses remain incomplete. Here we demonstrate that ocean acidification generates striking patterns of genome-wide selection in purple sea urchins (Strongylocentrotus purpuratus) cultured under different CO2 levels. We examined genetic change at 19,493 loci in larvae from seven adult populations cultured under realistic future CO2 levels. Although larval development and morphology showed little response to elevated CO2, we found substantial allelic change in 40 functional classes of proteins involving hundreds of loci. Pronounced genetic changes, including excess amino acid replacements, were detected in all populations and occurred in genes for biomineralization, lipid metabolism, and ion homeostasis--gene classes that build skeletons and interact in pH regulation. Such genetic change represents a neglected and important impact of ocean acidification that may influence populations that show few outward signs of response to acidification. Our results demonstrate the capacity for rapid evolution in the face of ocean acidification and show that standing genetic variation could be a reservoir of resilience to climate change in this coastal upwelling ecosystem. However, effective response to strong natural selection demands large population sizes and may be limited in species impacted by other environmental stressors.

The simple fool's guide to population genomics via RNA-Seq: an introduction to high-throughput sequencing data analysis.

High-throughput sequencing technologies are currently revolutionizing the field of biology and medicine, yet bioinformatic challenges in analysing very large data sets have slowed the adoption of these technologies by the community of population biologists. We introduce the 'Simple Fool's Guide to Population Genomics via RNA-seq' (SFG), a document intended to serve as an easy-to-follow protocol, walking a user through one example of high-throughput sequencing data analysis of nonmodel organisms. It is by no means an exhaustive protocol, but rather serves as an introduction to the bioinformatic methods used in population genomics, enabling a user to gain familiarity with basic analysis steps. The SFG consists of two parts. This document summarizes the steps needed and lays out the basic themes for each and a simple approach to follow. The second document is the full SFG, publicly available at http://sfg.stanford.edu, that includes detailed protocols for data processing and analysis, along with a repository of custom-made scripts and sample files. Steps included in the SFG range from tissue collection to de novo assembly, blast annotation, alignment, gene expression, functional enrichment, SNP detection, principal components and F(ST) outlier analyses. Although the technical aspects of population genomics are changing very quickly, our hope is that this document will help population biologists with little to no background in high-throughput sequencing and bioinformatics to more quickly adopt these new techniques.