We describe a postgenomic in silico approach for identifying genes that are likely to be essential and estimate their proportion in haploid genomes. With the knowledge of all sites eligible for mutagenesis and an experimentally determined partial list of nonessential genes from genome mutagenesis, a Bayesian statistical method provides reasonable predictions of essential genes with a subsaturation level of random mutagenesis. For mutagenesis, a transposon such as Himar1 is suitable as it inserts randomly into TA sites. All of the possible insertion sites may be determined a priori from the genome sequence and with this information, data on experimentally hit TA sites may be used to predict the proportion of genes that cannot be mutated. As a model, we used the Mycobacterium tuberculosis genome. Using the Himar1 transposon, we created a genetically defined collection of 1,425 insertion mutants. Based on our Bayesian statistical analysis using Markov chain Monte Carlo and the observed frequencies of transposon insertions in all of the genes, we estimated that the M. tuberculosis genome contains 35% (95% confidence interval, 28%-41%) essential genes. This analysis further revealed seven functional groups with high probabilities of being enriched in essential genes. The PE-PGRS (Pro-Glu polymorphic GC-rich repetitive sequence) family of genes, which are unique to mycobacteria, the polyketide͞ nonribosomal peptide synthase family, and mycolic and fatty acid biosynthesis gene families were disproportionately enriched in essential genes. At subsaturation levels of mutagenesis with a random transposon such as Himar1, this approach permits a statistical prediction of both the proportion and identities of essential genes of sequenced genomes.

Shifts in phenology are a well‐documented ecological response to changes in climate, which may or may not be adaptive for a species depending on the climate sensitivity of other ecosystem processes. Furthermore, phenology may be affected by factors in addition to climate, which may accentuate or dampen climate‐driven phenological responses. In this study, we investigate how climate and population demographic structure jointly affect spawning phenology of a fish species of major commercial importance: walleye pollock (Gadus chalcogrammus). We use 32 years of data from ichthyoplankton surveys to reconstruct timing of pollock reproduction in the Gulf of Alaska and find that the mean date of spawning has varied by over 3 weeks throughout the last >3 decades. Climate clearly drives variation in spawn timing, with warmer temperatures leading to an earlier and more protracted spawning period, consistent with expectations of advanced spring phenology under warming. However, the effects of temperature were nonlinear, such that additional warming above a threshold value had no additional effect on phenology. Population demographics were equally as important as temperature: An older and more age‐diverse spawning stock tended to spawn earlier and over a longer duration than a younger stock. Our models suggest that demographic shifts associated with sustainable harvest rates could shift the mean spawning date 7 days later and shorten the spawning season by 9 days relative to an unfished population, independent of thermal conditions. Projections under climate change suggest that spawn timing will become more stable for walleye pollock in the future, but it is unknown what the consequences of this stabilization will be for the synchrony of first‐feeding larvae with production of zooplankton prey in spring. With ongoing warming in the world’s oceans, knowledge of the mechanisms underlying reproductive phenology can improve our ability to monitor and manage species under changing climate conditions.