Parallel evolution across replicate populations has provided evolutionary biologists with iconic examples of adaptation. When multiple populations colonize seemingly similar habitats, they may evolve similar genes, traits, or functions. Yet, replicated evolution in nature or in the laboratory often yields inconsistent outcomes: Some replicate populations evolve along highly similar trajectories, whereas other replicate populations evolve to different extents or in distinct directions. To understand these heterogeneous outcomes, biologists are increasingly treating parallel evolution not as a binary phenomenon but rather as a quantitative continuum ranging from parallel to nonparallel. By measuring replicate populations’ positions along this (non)parallel continuum, we can test hypotheses about evolutionary and ecological factors that influence the extent of repeatable evolution. We review evidence regarding the manifestation of (non)parallel evolution in the laboratory, in natural populations, and in applied contexts such as cancer. We enumerate the many genetic, ecological, and evolutionary processes that contribute to variation in the extent of parallel evolution.
Policies ensuring that research data are available on public archives are increasingly being implemented at the government [1], funding agency [2-4], and journal [5, 6] level. These policies are predicated on the idea that authors are poor stewards of their data, particularly over the long term [7], and indeed many studies have found that authors are often unable or unwilling to share their data [8-11]. However, there are no systematic estimates of how the availability of research data changes with time since publication. We therefore requested data sets 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 data set being extant fell by 17% per year. In addition, the odds that we could find a working e-mail 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.
Reproducibility is the benchmark for results and conclusions drawn from scientific studies, but systematic studies on the reproducibility of scientific results are surprisingly rare. Moreover, many modern statistical methods make use of 'random walk' model fitting procedures, and these are inherently stochastic in their output. Does the combination of these statistical procedures and current standards of data archiving and method reporting permit the reproduction of the authors' results? To test this, we reanalysed data sets gathered from papers using the software package STRUCTURE to identify genetically similar clusters of individuals. We find that reproducing structure results can be difficult despite the straightforward requirements of the program. Our results indicate that 30% of analyses were unable to reproduce the same number of population clusters. To improve this, we make recommendations for future use of the software and for reporting STRUCTURE analyses and results in published works.
Adaptation to new environments often occurs in the face of gene flow. Under these conditions, gene flow and recombination can impede adaptation by breaking down linkage disequilibrium between locally adapted alleles. Theory predicts that this decay can be halted or slowed if adaptive alleles are tightly linked in regions of low recombination, potentially favouring divergence and adaptive evolution in these regions over others. Here, we compiled a global genomic data set of over 1,300 individual threespine stickleback from 52 populations and compared the tendency for adaptive alleles to occur in regions of low recombination between populations that diverged with or without gene flow. In support of theory, we found that putatively adaptive alleles (F and d outliers) tend to occur more often in regions of low recombination in populations where divergent selection and gene flow have jointly occurred. This result remained significant when we employed different genomic window sizes, controlled for the effects of mutation rate and gene density, controlled for overall genetic differentiation, varied the genetic map used to estimate recombination and used a continuous (rather than discrete) measure of geographic distance as proxy for gene flow/shared ancestry. We argue that our study provides the first statistical evidence that the interaction of gene flow and selection biases divergence toward regions of low recombination.
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