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20 results for “Environmental variance”
Environmental effects on genetic variance are likely to constrain adaptation in novel environments
<p>Adaptive plasticity allows populations to cope with environmental variation but is expected to fail as conditions become unfamiliar. In novel conditions, populations may instead rely on rapid adaptation to increase fitness and avoid extinction. Adaptation should be fastest when both plasticity and selection occur in directions of the multivariate phenotype that contain abundant genetic variation. However, tests of this prediction from field experiments are rare. Here, we quantify how additive genetic variance in a multivariate phenotype changes across an elevational gradient, and test whether plasticity and selection align with genetic variation. We do so using two closely related, but ecologically distinct, sister species of Sicilian daisy (Senecio, Asteraceae) adapted to high and low elevations on Mount Etna. Using a paternal half-sibling breeding design, we generated and then reciprocally planted c.19,000 seeds of both species, across an elevational gradient spanning each species' native elevation, and then quantified mortality and five leaf traits of emergent seedlings. We found that genetic variance in leaf traits changed more across elevations than between species. The high-elevation species at novel lower elevations showed changes in the distribution of genetic variance among the leaf traits, which reduced the amount of genetic variance in the directions of selection and the native phenotype. By contrast, the low-elevation species mainly showed changes in the amount of genetic variance at the novel high elevation, and genetic variance was concentrated in the direction of the native phenotype. For both species, leaf trait plasticity across elevations was in a direction of the multivariate phenotype that contained a moderate amount of genetic variance. Together, these data suggest that where plasticity is adaptive, selection on genetic variance for an initially plastic response could promote adaptation. However, large environmental effects on genetic variance are likely to reduce adaptive potential in novel environments.</p>
Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
<ol> <li>How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual 'realized' growth rates have gained in popularity.</li> <li>Realized LTREs have been used particularly to understand how variation in vital rates translates into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. </li> <li>We extend realized LTREs in two ways. First, we further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows us to compare the resultant effect on the growth rate of different environmental factors that may each act via multiple vital rates. Second, we show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses.</li> <li>We illustrate how to use the approach in an IPM for data from a 26-year study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50–120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density, and unexplained random variation via multiple vital rates, and from demographic stochasticity.</li> <li>The case study suggests that variation in first-year survival via the random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.</li> </ol>
Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
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Environmental effects on genetic variance are likely to constrain adaptation in novel environments
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Environmentally triggered variability in the genetic variance-covariance of herbivory resistance of an exotic plant Solidago altissima
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Data from: Cooperative breeding in birds increases the within-year fecundity mean without increasing the variance: A potential mechanism to buffer environmental uncertainty
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Mean survival rate and their temporal environmental (process) variance for 93 species of vertebrates
<p><span>Current environmental changes may increase temporal variability of life-history traits of species, which can significantly affect their long-term population growth rate and their extinction risk. There is a need to estimate environmental variance of life-history traits (EV) and to examine whether there is a general relationship between EV and average survival rate that can be used as a guideline for analyses of population growth and extinction risk for populations where only information about mean survival is available. For this purpose we present a comprehensive compilation of 285 EV estimates from 93 species belonging to five vertebrate taxa (mammals, birds, reptiles, amphibians and fish) covering mean survival rates from 0.01 to 0.98. Since variances are dependent on the mean and tightly constrained for lower and upper mean survival rates, we assessed whether any observed relationship persisted after applying two types of variance stabilizing transformations: relativized EVs (observed / mathematical maximum) and logit scaled EVs. With raw EVs at the arithmetic scale, mean-variance relationships of survival rates and their EVs were hump-shaped with small EVs at low and high survival rates, and higher (and widely variable) EVs at intermediate survival rates. When mean survival rates were related to relativized EVs the hump-shaped pattern remained albeit less distinct than for raw EVs, but when transforming EVs to logit scale the pattern of the relationship between mean survival rates and their EVs largely disappeared. </span></p>
Genetic and environmental variance of semen quality in Nordic Holstein bulls
<p>Figure S1. Plots of genetic Variance at different age (month) for the semen quality traits. Va: genetic variance, Conc= concentration; Pre= pre-cryopreservation; post= post-cryopreservation; Mot= sperm motility; Via= sperm viability; NDOS= number of doses per ejaculate.</p> <p>Figure S2. Permanent environment Variance at different age (month). Vpe: permanent environment variance, Conc= concentration; Pre= pre-cryopreservation; post= post-cryopreservation; Mot= sperm motility; Via= sperm viability; NDOS= number of doses per ejaculate.</p> <p>Figure S3. Residual Variance at different age (month). Ve= residual variance, Conc= concentration; Pre= pre-cryopreservation; post= post-cryopreservation; Mot= sperm motility; Via= sperm.</p>
Data for: Estimating density dependence, environmental variance and long-term selection on a stage-structured life history
<p>We model growth of a density-dependent stage-structured population undergoing small or moderate fluctuations around a deterministically stable equilibrium in a stochastic environment, assuming that a weighted sum of stage abundances, N, exerts density dependence on the stage-specific vital rates of survival and reproduction. We approximate the dynamics of N as a onedimensional stochastic process with three key parameters: the density-independent growth rate and the net density dependence and environmental variance in the life history. Comparisons of populations and species with different life histories are facilitated using the key parameters, which we show how to estimate from long-term demographic data on fluctuations in the vital rates. We also show that life history evolution is a stochastic maximization of a simple function of the key parameters. Elements in the long-term selection gradient acting on the life history can be expressed as sensitivities of this function with respect to density-independent, density-dependent, and stochastic components of the vital rates. Using years of demographic data on a great tit population, we estimate the key demographic parameters, which accurately predict the observed mean, coefficient of variation, and fluctuation rate of N, and also evaluate the long-term selection gradient on the population.</p>
Genetic & Environmental Determinants Of Immune Phenotype Variance: A Longitudinal Assessment
ClinicalTrials.gov study NCT05381857. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Genetic & Environmental Determinants Of Immune Phenotype Variance: Establishing A Path Towards Personalized Medicine
ClinicalTrials.gov study NCT01699893. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Variance in reproductive success is driven by environmental factors not mating system in Bonytail
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Data for: Estimating density dependence, environmental variance and long-term selection on a stage-structured life history
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Mean survival rate and their temporal environmental (process) variance for 89 species of vertebrates
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Data from: Quantitative genetic variance in experimental fly populations evolving with or without environmental heterogeneity
Heterogeneous environments are typically expected to maintain more genetic variation in fitness within populations than homogeneous environments. However, the accuracy of this claim depends on the form of heterogeneity as well as the genetic basis of fitness traits and how similar the assay environment is to the environment of past selection. Here we measure quantitative genetic variance for three traits important for fitness using replicated experimental populations of Drosophila melanogaster evolving under four selective regimes: constant salt-enriched medium (Salt), constant cadmium-enriched medium (Cad), and two heterogeneous regimes that vary either temporally (Temp) or spatially (Spatial). As theory predicts, we found that Spatial populations tend to harbor more genetic variation than Temp populations or those maintained in a constant environment that is the same as the assay environment. Contrary to expectation, Salt populations tend to have more genetic variation than Cad populations in both assay environments. We discuss the patterns for quantitative genetic (QG) variances across regimes in relation to previously reported data on genome-wide sequence diversity. For some traits, the QG patterns are similar to the diversity patterns of ecological selected SNPs whereas the QG patterns for some other traits resembled that of neutral SNPs.
Data from: Environmental stress correlates with increases in both genetic and residual variances: a meta-analysis of animal studies
Adaptive evolutionary responses are determined by the strength of selection and the amount of genetic variation within traits, however, both are known to vary across environmental conditions. As selection is generally expected to be strongest under stressful conditions, understanding how the expression of genetic variation changes across stressful and benign environmental conditions is crucial for predicting the rate of adaptive change. While theory generally predicts increased genetic variation under stress, previous syntheses of the field has found limited support for this notion. These studies have focused on heritability, which is dependent on other environmentally sensitive, but non-genetic, sources of variation. Here, we aim to complement these studies with a meta-analysis where we examine changes in coefficient of variation (CV) in maternal, genetic, and residual variances across stressful and benign conditions. Confirming previous analyses, we did not find any clear direction in how heritability changes across stressful and benign conditions. However, when analyzing CV, we found higher genetic and residual variance under highly stressful conditions in life-history traits but not in morphological traits. Our findings are of broad significance to contemporary evolution suggesting that rapid evolutionary adaptive response may be mediated by increased evolutionary potential in stressed populations.
Milieu Intérieur Collection - Genetic & Environmental Determinants Of Immune Phenotype Variance
ClinicalTrials.gov study NCT03905993. IPD Sharing: YES. Countries: 0. Publications: 4.
Data from: The influence of environmental variance on the evolution of signalling behavior
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Data from: Environmental stress correlates with increases in both genetic and residual variances: a meta-analysis of animal studies
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Data from: Quantitative genetic variance in experimental fly populations evolving with or without environmental heterogeneity
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