<b>Abstract</b><br/>Few studies have investigated the causes of evolutionary rate variation among plant nuclear genes, especially in recently diverged species still capable of hybridizing in the wild. The recent advent of Next Generation Sequencing (NGS) permits investigation of genome wide rates of protein evolution and the role of selection in generating and maintaining divergence. Here, we use individual whole-transcriptome sequencing (RNAseq) to refine our understanding of the population genomics of a wild species of sunflowers (Helianthus spp.) and the factors that affect rates of protein evolution. We aligned 35 GB of transcriptome sequencing data and identified 433,257 polymorphic sites (SNPs) in a reference transcriptome comprising 16,312 genes. Using SNP markers, we identified strong population clustering largely corresponding to the three species analyzed here (Helianthus annuus, H. petiolaris, H. debilis), with one distinct early generation hybrid. Then, we calculated the proportions of adaptive substitution fixed by selection (alpha) and identified gene ontology categories with elevated values of alpha. The “response to biotic stimulus” category had the highest mean alpha across the three interspecific comparisons, implying that natural selection imposed by other organisms plays an important role in driving protein evolution in wild sunflowers. Finally, we examined the relationship between protein evolution (dN/dS ratio) and several genomic factors predicted to co-vary with protein evolution (gene expression level, divergence and specificity, genetic divergence [FST], and nucleotide diversity [pi]). We find that variation in rates of protein divergence was correlated with gene expression level and specificity, consistent with results from a broad range of taxa and timescales. This would in turn imply that these factors govern protein evolution both at a microevolutionary and macroevolutionary timescale. Our results contribute to a general understanding of the determinants of rates of protein evolution and the impact of selection on patterns of polymorphism and divergence.

<b>Abstract</b><br/>Policies ensuring that research data are available on public archives are increasingly being implemented at the government, funding agency, and journal level. These policies are predicated on the idea that authors are poor stewards of their data, particularly over the long term, and indeed many studies have found that authors are often unable or unwilling to share their data. However, there are no systematic estimates of how the availability of research data changes with time since publication. We therefore requested datasets from a relatively homogenous set of 516 articles published between 2 and 22 years ago, and found that availability of the data was strongly affected by article age. For papers where the authors gave the status of their data, the odds of a dataset being extant fell by 17% per year. In addition, the odds that we could find a working email address for the first, last or corresponding author fell by 7% per year. Our results reinforce the notion that, in the long term, research data cannot be reliably preserved by individual researchers, and further demonstrate the urgent need for policies mandating data sharing via public archives.

<b>Abstract</b><br/>Latest technological developments in evolutionary biology bring new challenges in documenting the intricate genetic architecture of species in the process of divergence. Sympatric populations of lake whitefish represent one of the key systems to investigate this issue. Despite the value of random genotype-by-sequencing methods and decreasing cost of sequencing technologies, it remains challenging to investigate variation in coding regions, especially in the case of recently duplicated genomes as in salmonids, as this greatly complicates whole genome resequencing. We thus designed a sequence capture array targeting 2773 annotated genes to document the nature and the extent of genomic divergence between sympatric dwarf and normal whitefish. Among the 2728 genes successfully captured, a total of 2182 coding and 10 415 noncoding putative single-nucleotide polymorphisms (SNPs) were identified after applying a first set of basic filters. A genome scan with a quality-refined selection of 2203 SNPs identified 267 outlier SNPs in 210 candidate genes located in genomic regions potentially involved in whitefish divergence and reproductive isolation. We found highly heterogeneous FST estimates among SNP loci. There was an overall low level of coding polymorphism, with a predominance of noncoding mutations among outliers. The heterogeneous patterns of divergence among loci confirm the porous nature of genomes during speciation with gene flow. Considering that few protein-coding mutations were identified as highly divergent, our results, along with previous transcriptomic studies, imply that changes in regulatory regions most likely had a greater role in the process of whitefish population divergence than protein-coding mutations. This study is the first to demonstrate the efficiency of large-scale targeted resequencing for a nonmodel species with such a large and unsequenced genome.

<b>Abstract</b><br/>This article documents the public availability of three transcriptome sequences. These genomic resources were developed for the following species: Helianthus anomalus, Helianthus deserticola and Helianthus paradoxus.

<b>Abstract</b><br/>The data underlying scientific papers should be accessible to researchers both now and in the future, but how best can we ensure that these data are available? Here we examine the effectiveness of four approaches to data archiving: no stated archiving policy, recommending (but not requiring) archiving, and two versions of mandating data deposition at acceptance. We control for differences between data types by trying to obtain data from papers that use a single, widespread population genetic analysis, STRUCTURE. At one extreme, we found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost 1000-fold compared to having no policy. However, archiving rates at journals with less stringent policies were only very slightly higher than those with no policy at all. We also assessed the effectiveness of asking for data directly from authors and obtained over half of the requested datasets, albeit with ∼8 d delay and some disagreement with authors. Given the long-term benefits of data accessibility to the academic community, we believe that journal-based mandatory data archiving policies and mandatory data availability statements should be more widely adopted.

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