Most adaption processes have a polygenic genetic basis, but even with the recent explosive growth of genomic data we are still lacking a unified framework describing the dynamics of selected alleles. ...Building on recent theoretical and empirical work we introduce the concept of adaptive architecture, which extends the genetic architecture of an adaptive trait by factors influencing its adaptive potential and population genetic principles. Because adaptation can be typically achieved by many different combinations of adaptive alleles (redundancy), we describe how two characteristics - heterogeneity among loci and non-parallelism between replicated populations - are hallmarks for the characterization of polygenic adaptation in evolving populations. We discuss how this unified framework can be applied to natural and experimental populations.
The next justice Eisgruber, Christopher L; Eisgruber, Christopher L
2007., 20090518, 2009, 2007, 2008-01-01
eBook
The Supreme Court appointments process is broken, and the timing couldn’t be worse--for liberals or conservatives. The Court is just one more solid conservative justice away from an ideological sea ...change--a hard-right turn on an array of issues that affect every American, from abortion to environmental protection. But neither those who look at this prospect with pleasure nor those who view it with horror will be able to make informed judgments about the next nominee to the Court--unless the appointments process is fixed now. In The Next Justice, Christopher Eisgruber boldly proposes a way to do just that. He describes a new and better manner of deliberating about who should serve on the Court--an approach that puts the burden on nominees to show that their judicial philosophies and politics are acceptable to senators and citizens alike. And he makes a new case for the virtue of judicial moderates.
Divergent selection applied to one or more traits drives local adaptation and may lead to ecological speciation. Divergent selection on many traits might be termed “multidimensional” divergent ...selection. There is a commonly held view that multidimensional divergent selection is likely to promote local adaptation and speciation to a greater extent than unidimensional divergent selection. We disentangle the core concepts underlying dimensionality as a property of the environment, phenotypes, and genome. In particular, we identify a need to separate the overall strength of selection and the number of loci affected from dimensionality per se, and to distinguish divergence dimensionality from dimensionality of stabilizing selection. We then critically scrutinize this commonly held view that multidimensional selection promotes speciation, re-examining the evidence base from theory, experiments, and nature. We conclude that the evidence base is currently weak and generally suffers from confounding of possible causal effects. Finally, we propose several mechanisms by which multidimensional divergent selection and related processes might influence divergence, both as a driver and as a barrier.
ABSTRACT
We present a novel perspective on life‐history evolution that combines recent theoretical advances in fluctuating density‐dependent selection with the notion of pace‐of‐life syndromes ...(POLSs) in behavioural ecology. These ideas posit phenotypic co‐variation in life‐history, physiological, morphological and behavioural traits as a continuum from the highly fecund, short‐lived, bold, aggressive and highly dispersive ‘fast’ types at one end of the POLS to the less fecund, long‐lived, cautious, shy, plastic and socially responsive ‘slow’ types at the other. We propose that such variation in life histories and the associated individual differences in behaviour can be explained through their eco‐evolutionary dynamics with population density – a single and ubiquitous selective factor that is present in all biological systems. Contrasting regimes of environmental stochasticity are expected to affect population density in time and space and create differing patterns of fluctuating density‐dependent selection, which generates variation in fast versus slow life histories within and among populations. We therefore predict that a major axis of phenotypic co‐variation in life‐history, physiological, morphological and behavioural traits (i.e. the POLS) should align with these stochastic fluctuations in the multivariate fitness landscape created by variation in density‐dependent selection. Phenotypic plasticity and/or genetic (co‐)variation oriented along this major POLS axis are thus expected to facilitate rapid and adaptively integrated changes in various aspects of life histories within and among populations and/or species. The fluctuating density‐dependent selection POLS framework presented here therefore provides a series of clear testable predictions, the investigation of which should further our fundamental understanding of life‐history evolution and thus our ability to predict natural population dynamics.
Understanding how environmental variation affects phenotypic evolution requires models based on ecologically realistic assumptions that include variation in population size and specific mechanisms by ...which environmental fluctuations affect selection. Here we generalize quantitative genetic theory for environmentally induced stochastic selection to include general forms of frequency-and density-dependent selection. We show how the relevant fitness measure under stochastic selection relates to Fisher’s fundamental theorem of natural selection, and present a general class of models in which density regulation acts through total use of resources rather than just population size. In this model, there is a constant adaptive topography for expected evolution, and the function maximized in the long run is the expected factor restricting population growth. This allows us to generalize several previous results and to explain why apparently “K-selected” species with slow life histories often have low carrying capacities. Our joint analysis of density-and frequency-dependent selection reveals more clearly the relationship between population dynamics and phenotypic evolution, enabling a broader range of eco-evolutionary analyses of some of the most interesting problems in evolution in the face of environmental variation.
The link between biotic interaction intensity and strength of selection is of fundamental interest for understanding biotically driven diversification and predicting the consequences of environmental ...change. The strength of selection resulting from biotic interactions is determined by the strength of the interaction and by the variance between fitness and the trait under selection. When the relationship between trait and absolute fitness is constant, selection strength should be a direct function of mean population interaction intensity. To test this prediction, we excluded pollinators for intervals of different length to induce five levels of pollination intensity within a single plant population. Pollen limitation (PL) increased from 0 to 0.77 across treatments, accompanied by a fivefold increase in the opportunity for selection. Trait-fitness covariance declined with PL for number of flowers, but varied little for other traits. Pollinatormediated selection on plant height, corolla size, and spur length increased by 91%, 34%, and 330%, respectively, in the most severely pollen-limited treatment compared to open-pollinated plants. The results indicate that realized biotic selection can be predicted from mean population interaction intensity when variation in trait-fitness covariance is limited, and that declines in pollination intensity will strongly increase' selection on traits involved in the interaction.
Various evolutionary computation algorithms have shown their excellent performance for high-dimensional feature selection (FS). However, most of current FS methods choose a global feature subset and ...ignore the correlations between feature subsets and sample subspaces, which limits the performance of methods in the sample space with different probability distributions. To solve the issue, we propose a two-stage clonal selection algorithm for filter-based local feature selection (TSCSA-LFS) that integrates symmetric uncertainty and a discrete clonal selection algorithm with three contributions. First, unlike conventional feature selection methods, TSCSA-LFS introduces local sample behaviors and assigns subsets of features for different sample regions. Second, an improved discrete clonal selection algorithm is developed for searching relevant features, which contains mutual information-based individual initialization, a differential evolution-based mutation strategy pool and a local search technique. Third, a two-part antibody representation is employed for automatical adjustment of the weight-related parameter. Our method shows the promising experimental results compared with well-known global FS and clonal selection-based local FS methods on fourteen high-dimensional datasets. For instance, our method can obviously outperform local FS, filter-based and hybrid methods on at least nine datasets
•A two-stage clonal selection algorithm for filter-based local feature selection is proposed.•A feature subset for each sample region is selected based on a local clustering idea.•A discrete clonal selection algorithm is developed to improve the search ability.•A two-part antibody representation is designed to obtain optimal feature subsets.•Experiments verify the effectiveness of the proposed method on high-dimensional data.
This article reviews 100 years of research on recruitment and selection published in the Journal of Applied Psychology. Recruitment and selection research has been present in the Journal from the ...very first issue, where Hall (1917) suggested that the challenge of recruitment and selection was the Supreme Problem facing the field of applied psychology. As this article shows, the various topics related to recruitment and selection have ebbed and flowed over the years in response to business, legal, and societal changes, but this Supreme Problem has captivated the attention of scientist-practitioners for a century. Our review starts by identifying the practical challenges and macro forces that shaped the sciences of recruitment and selection and helped to define the research questions the field has addressed. We then describe the evolution of recruitment and selection research and the ways the resulting scientific advancements have contributed to staffing practices. We conclude with speculations on how recruitment and selection research may proceed in the future. Supplemental material posted online provides additional depth by including a summary of practice challenges and scientific advancements that affected the direction of selection and recruitment research and an outline of seminal articles published in the Journal and corresponding time line. The 100-year anniversary of the Journal of Applied Psychology is very much the celebration of recruitment and selection research, although predictions about the future suggest there is still much exciting work to be done.
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