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Nonlinear Selection (nonlinear + selection)
Selected AbstractsEXPERIMENTAL EVIDENCE FOR MULTIVARIATE STABILIZING SEXUAL SELECTIONEVOLUTION, Issue 4 2005Robert Brooks Abstract Stabilizing selection is a fundamental concept in evolutionary biology. In the presence of a single intermediate optimum phenotype (fitness peak) on the fitness surface, stabilizing selection should cause the population to evolve toward such a peak. This prediction has seldom been tested, particularly for suites of correlated traits. The lack of tests for an evolutionary match between population means and adaptive peaks may be due, at least in part, to problems associated with empirically detecting multivariate stabilizing selection and with testing whether population means are at the peak of multivariate fitness surfaces. Here we show how canonical analysis of the fitness surface, combined with the estimation of confidence regions for stationary points on quadratic response surfaces, may be used to define multivariate stabilizing selection on a suite of traits and to establish whether natural populations reside on the multivariate peak. We manufactured artificial advertisement calls of the male cricket Teleogryllus commodus and played them back to females in laboratory phonotaxis trials to estimate the linear and nonlinear sexual selection that female phonotactic choice imposes on male call structure. Significant nonlinear selection on the major axes of the fitness surface was convex in nature and displayed an intermediate optimum, indicating multivariate stabilizing selection. The mean phenotypes of four independent samples of males, from the same population as the females used in phonotaxis trials, were within the 95% confidence region for the fitness peak. These experiments indicate that stabilizing sexual selection may play an important role in the evolution of male call properties in natural populations of T. commodus. [source] Timing is everything: flexible phenology and shifting selection in a colonial seabirdJOURNAL OF ANIMAL ECOLOGY, Issue 2 2009Thomas E. Reed Summary 1In order to reproduce successfully in a temporally varying environment, iteroparous animals must exhibit considerable behavioural flexibility across their lifetimes. By adjusting timing of breeding each year, parents can ensure optimal overlap between the energy intensive period of offspring production and the seasonal peak in favourable environmental conditions, thereby increasing their chances of successfully rearing young. 2Few studies investigate variation among individuals in how they respond to fluctuating conditions, or how selection acts on these individual differences, but this information is essential for understanding how populations will cope with rapid environmental change. 3We explored inter-annual trends in breeding time and individual responses to environmental variability in common guillemots Uria aalge, an important marine top predator in the highly variable California Current System. Complex, nonlinear relationships between phenology and oceanic and climate variables were found at the population level. Using a novel application of a statistical technique called random regression, we showed that individual females responded in a nonlinear fashion to environmental variability, and that reaction norm shape differed among females. 4The pattern and strength of selection varied substantially over a 34-year period, but in general, earlier laying was favoured. Females deviating significantly from the population mean laying date each year also suffered reduced breeding success, with the strength of nonlinear selection varying in relation to environmental conditions. 5We discuss our results in the wider context of an emerging literature on the evolutionary ecology of individual-level plasticity in the wild. Better understanding of how species-specific factors and local habitat features affect the timing and success of breeding will improve our ability to predict how populations will respond to climate change. [source] A tale of two matrices: multivariate approaches in evolutionary biologyJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 1 2007M. W. BLOWS Abstract Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (,) which describes the individual fitness surface. The second is the genetic variance,covariance matrix (G) that influences the multivariate response to selection. A common approach to the empirical analysis of these matrices is the element-by-element testing of significance, and subsequent biological interpretation of pattern based on these univariate and bivariate parameters. Here, I show why this approach is likely to misrepresent the genetic basis of quantitative traits, and the selection acting on them in many cases. Diagonalization of square matrices is a fundamental aspect of many of the multivariate statistical techniques used by biologists. Applying this, and other related approaches, to the analysis of the structure of , and G matrices, gives greater insight into the form and strength of nonlinear selection, and the availability of genetic variance for multiple traits. [source] Linear vs. nonlinear selection for the propagation speed of the solutions of scalar reaction-diffusion equations invading an unstable equilibriumCOMMUNICATIONS ON PURE & APPLIED MATHEMATICS, Issue 5 2004Marcello Lucia We revisit the classical problem of speed selection for the propagation of disturbances in scalar reaction-diffusion equations with one linearly stable and one linearly unstable equilibrium. For a wide class of initial data this problem reduces to finding the minimal speed of the monotone traveling wave solutions connecting these two equilibria in one space dimension. We introduce a variational characterization of these traveling wave solutions and give a necessary and sufficient condition for linear versus nonlinear selection mechanism. We obtain sufficient conditions for the linear and nonlinear selection mechanisms that are easily verifiable. Our method also allows us to obtain efficient lower and upper bounds for the propagation speed. © 2004 Wiley Periodicals, Inc. [source] | ||||||||||