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The adaptive landscape and the G-matrix are keys concepts for understanding how quantitative characters evolve during adaptive radiation. In particular, whether the adaptive landscape can drive convergence of phenotypic integration (i.e., the pattern of phenotypic variation and covariation summarized in the P-matrix) is not well studied. We estimated and compared P for 19 morphological traits in eight species of Caribbean Anolis lizards, finding that similarity in P among species was not correlated with phylogenetic distance. However, greater similarity in P among ecologically similar Anolis species (i.e., the trunk-ground ecomorph) suggests the role of convergent natural selection. Despite this convergence and relatively deep phylogenetic divergence, a large portion of eigenstructure of P is retained among our eight focal species. We also analyzed P as an approximation of G to test for correspondence with the pattern of phenotypic divergence in 21 Caribbean Anolis species. These patterns of covariation were coincident, suggesting that either genetic constraint has influenced the pattern of among-species divergence or, alternatively, that the adaptive landscape has influenced both G and the pattern of phenotypic divergence among species. We provide evidence for convergent evolution of phenotypic integration for one class of Anolis ecomorph, revealing yet another important dimension of evolutionary convergence in this group.

Adaptive responses to autocorrelated environmental fluctuations through evolution in mean reaction norm elevation and slope and an independent component of the phenotypic variance are analyzed using a quantitative genetic model. Analytic approximations expressing the mutual dependencies between all three response modes are derived and solved for the joint evolutionary outcome. Both genetic evolution in reaction norm elevation and plasticity are favored by slow temporal fluctuations, with plasticity, in the absence of microenvironmental variability, being the dominant evolutionary outcome for reasonable parameter values. For fast fluctuations, tracking of the optimal phenotype through genetic evolution and plasticity is limited. If residual fluctuations in the optimal phenotype are large and stabilizing selection is strong, selection then acts to increase the phenotypic variance (bethedging adaptive). Otherwise, canalizing selection occurs. If the phenotypic variance increases with plasticity through the effect of microenvironmental variability, this shifts the joint evolutionary balance away from plasticity in favor of genetic evolution. If microenvironmental deviations experienced by each individual at the time of development and selection are correlated, however, more plasticity evolves. The adaptive significance of evolutionary fluctuations in plasticity and the phenotypic variance, transient evolution, and the validity of the analytic approximations are investigated using simulations.

Stabilizing selection is important in evolutionary theories of the maintenance of genetic variance and has been invoked as the key process determining macroevolutionary patterns of trait evolution. However, manipulative evidence for the extent of stabilizing selection, particularly on multivariate traits, is lacking. We used artificial disruptive selection in Drosophila serrata as a tool to determine the relative strength of stabilizing selection experienced by multivariate trait combinations with contrasting levels of genetic and mutational variance. Contrary to expectation, when disruptive selection was applied to the major axis of standing genetic variance, gmax, we observed a significant and repeatable decrease in its phenotypic variance. In contrast, the multivariate trait combination predicted to be under strong stabilizing selection showed a significant and repeatable increase in its phenotypic variance. Correlated responses were observed in all selection treatments, and viability selection operating on extreme phenotypes of traits genetically correlated with those directly selected on limited our ability to increase their phenotypic range. Our manipulation revealed that multivariate trait combinations were subject to stabilizing selection; however, we did not observe a direct relationship between the strength of stabilizing selection and the levels of standing genetic variance in multivariate trait combinations. Contrasting patterns of allele frequencies underlying traits with high versus low levels of standing genetic variance may be implicated in determining the response to artificial selection in multivariate trait combinations.Stabilizing selection is important in evolutionary theories of the maintenance of genetic variance and has been invoked as the key process determining macroevolutionary patterns of trait evolution. However, manipulative evidence for the extent of stabilizing selection, particularly on multivariate traits, is lacking. We used artificial disruptive selection in Drosophila serrata as a tool to determine the relative strength of stabilizing selection experienced by multivariate trait combinations with contrasting levels of genetic and mutational variance. Contrary to expectation, when disruptive selection was applied to the major axis of standing genetic variance, gmax, we observed a significant and repeatable decrease in its phenotypic variance. In contrast, the multivariate trait combination predicted to be under strong stabilizing selection showed a significant and repeatable increase in its phenotypic variance. Correlated responses were observed in all selection treatments, and viability selection operating on extreme phenotypes of traits genetically correlated with those directly selected on limited our ability to increase their phenotypic range. Our manipulation revealed that multivariate trait combinations were subject to stabilizing selection; however, we did not observe a direct relationship between the strength of stabilizing selection and the levels of standing genetic variance in multivariate trait combinations. Contrasting patterns of allele frequencies underlying traits with high versus low levels of standing genetic variance may be implicated in determining the response to artificial selection in multivariate trait combinations.

Cowpea, in addition to being a food and feed crop, plays a key role in sustainable farming. The present study’s goal is to develop new high-yielding cowpea varieties. A Field experiment was carried out across 3 summer seasons and the breeding program included 28 distinct cowpea varieties, out of which five potential parents were selected for this investigation. Local cultivars, i.e., Cream 7 ‘Cr7’, Dokki 331 ‘D331’, Commercial 1 ‘Com1’, and introduced cultivars, i.e., Colossus ‘Col’ and Asian Introduction ‘AI’ were utilized to produce six crosses in two generations apart; F 1 and F 2 : Col x AI, Col x Com1, Cr7 x AI, Cr7 x Com1, D331 x AI, and D331 x Com1. ‘AI’ and ‘Com1’ were superior in pod length, pod diameter, number of seeds/pod and seeds weight/pod, whereas ‘Col’, ‘Cr7’ and ‘D331’ were superior in seeds yield/plant, number of pods/plant and the least number of aborted ovules/pod. The genotypes/crosses showed greater genotypic variance (GV) than phenotypic variance (PV) for number of pods/plant, pod length, number of seeds/pod, number of aborted ovules/pod, fresh pod weight, seeds weight/pod, and seeds yield/plant. All studied variables showed high heritability (H%) in genotypes/crosses, despite the exception of seeds weight/pod, which ranged from 29.14 in ‘D331’ to 83.7 in F 2 of Col x Com1. F 2 plants and their parents’ genotypes showed greater H%. Cr7 x AI developed the most H%, 99.04% for number of pods/plant. D331 x Com1 and Cr7 x AI exhibited moderate H% for fresh pod weight in F 1 , but all other crosses had high H%. F 1 and F 2 crosses yielded moderate to high GCV and PCV for number of seeds/pod. Variations in parental genotypes and crossings reflect genetic diversity and the possibility of selection. Crossing with ‘AI,’ and ‘Com1’ genotypes enhanced the performance of the other varieties, ‘Col’, ‘D331’ and ‘Cr7’. Cr7 x Com1 and D331 x AI were selected as the most promising crosses for cowpea breeding programs.

.— The ability of populations to undergo adaptive evolution depends on the presence of quantitative genetic variation for ecologically important traits. Although molecular measures are widely used as surrogates for quantitative genetic variation, there is controversy about the strength of the relationship between the two. To resolve this issue, we carried out a meta‐analysis based on 71 datasets. The mean correlation between molecular and quantitative measures of genetic variation was weak (r = 0.217). Furthermore, there was no significant relationship between the two measures for life‐history traits (r =−0.11) or for the quantitative measure generally considered as the best indicator of adaptive potential, heritability (r =−0.08). Consequently, molecular measures of genetic diversity have only a very limited ability to predict quantitative genetic variability. When information about a population's short‐term evolutionary potential or estimates of local adaptation and population divergence are required, quantitative genetic variation should be measured directly.

Information on the extent of genetic variability and association among quantitative traits are vital for any crop improvement program and the development of suitable selection strategies. Limited research has been carried out thus far on potato genetic variability and trait association. This study on genetic variability and association among quantitative traits was conducted to assess the extent of genetic variability among yield and agronomic traits to identify superior varieties for the breeding program. To this effect, 20 improved varieties and a local cultivar were planted at two locations in central Ethiopia during the main cropping season of 2017/18 in a randomized complete block design using three replications. Analysis of variance of tuber yield and yield traits at each location and over locations, revealed the existence of highly significant ( P < 0.01) differences among varieties in all agronomic and yield traits. Phenotypic coefficient of variation values ranged from 0.75% (specific gravity) to 32.22% (total starch yield) while the genotypic coefficient of variation values ranged between 0.70% (specific gravity) to 30.22% (total starch yield). Maximum difference between phenotypic and genotypic coefficient of variation values were noted for stem number, average tuber number, average tuber weight, number of leaves per plant and tuber yield. Hence, these traits are substantially influenced by the physiological status of the seed tuber at planting and by the environment, post emergence. Range of variability for most of the traits was high, indicating ample scope for selection and improvement in these traits. The estimated values for broad sense heritability and genetic advance, as percent of mean, ranged from 33.52% to 98.66% and 1.35% to 58.26%, respectively. All the traits had high heritability values, except average tuber number per hill, days to physiological maturity, average tuber weight and number of leaves per plant with moderate heritability values.

Background Host genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this important question can be addressed by estimating the heritability ( h 2 ) of the microbiome—the proportion of variance in the abundance in each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample. Results We derived an analytical approximation for the heritability that one obtains when using such relative, and not absolute, abundances, based on an underlying quantitative genetic model for absolute abundances. Based on this, we uncovered three problems that can arise when using relative abundances to estimate microbiome heritability: (1) the interdependency between taxa can lead to imprecise heritability estimates. This problem is most apparent for dominant taxa. (2) Large sample size leads to high false discovery rates. With enough statistical power, the result is a strong overestimation of the number of heritable taxa in a community. (3) Microbial co-abundances lead to biased heritability estimates. Conclusions We discuss several potential solutions for advancing the field, focusing on technical and statistical developments, and conclude that caution must be taken when interpreting heritability estimates and comparing values across studies. DUhQwzbuthLiGAmYiSRiRd Video Abstract

Phenotypic plasticity, the ability of an organism to express multiple phenotypes in response to the prevailing environmental conditions without genetic change, may result in a response to anthropogenic environmental change. Given that increasing climate variability is predicted to pose a greater risk than directional climate change, we tested the effect of a water temperature differential of 4 °C on the Arctic charr phenotypic within a single generation. We demonstrate that Arctic charr phenotype can respond rapidly and markedly to an environmental temperature cue. The plastic response to different temperature regimes comprised a shift in the mean expressed phenotype but also coupled with a reduction in the between-individual phenotypic variation in the expressed head shape. The magnitude of shape difference between temperature conditions was cumulative over time but the rate of divergence diminished as fish became larger. Overall, individuals raised in the elevated temperature treatment expressed a phenotype analogous to a benthivorous ecotype of this species, rather than that of the parental pelagic feeding form. The response of cold-water freshwater species to temperature change is likely to be an interaction between the capacity of the organism for phenotypic plasticity, the mean speed of change in the environment, and the degree of short interval variation in the environment.

Background Chickpea is prone to many abiotic stresses such as heat, drought, salinity, etc. which cause severe loss in yield. Tolerance towards these stresses is quantitative in nature and many studies have been done to map the loci influencing these traits in different populations using different markers. This study is an attempt to meta-analyse those reported loci projected over a high-density consensus map to provide a more accurate information on the regions influencing heat, drought, cold and salinity tolerance in chickpea. Results A meta-analysis of QTL reported to be responsible for tolerance to drought, heat, cold and salinity stress tolerance in chickpeas was done. A total of 1512 QTL responsible for the concerned abiotic stress tolerance were collected from literature, of which 1189 were projected on a chickpea consensus genetic map. The QTL meta-analysis predicted 59 MQTL spread over all 8 chromosomes, responsible for these 4 kinds of abiotic stress tolerance in chickpea. The physical locations of 23 MQTL were validated by various marker-trait associations and genome-wide association studies. Out of these reported MQTL, CaMQAST1.1, CaMQAST4.1, CaMQAST4.4, CaMQAST7.8, and CaMQAST8.2 were suggested to be useful for different breeding approaches as they were responsible for high per cent variance explained (PVE), had small intervals and encompassed a large number of originally reported QTL. Many putative candidate genes that might be responsible for directly or indirectly conferring abiotic stress tolerance were identified in the region covered by 4 major MQTL- CaMQAST1.1, CaMQAST4.4, CaMQAST7.7, and CaMQAST6.4, such as heat shock proteins, auxin and gibberellin response factors, etc. Conclusion The results of this study should be useful for the breeders and researchers to develop new chickpea varieties which are tolerant to drought, heat, cold, and salinity stresses.

Phenotypic traits partly determine expected survival and reproduction, and so have been used as the basis for demographic models of population dynamics. Within a population, the distribution of phenotypic traits depends upon their transmission from parents to offspring, yet we still have a limited understanding of the factors shaping phenotypic transmission in wild populations. Phenotypic transmission can be measured using the phenotypic parent–offspring correlation (C), defined as the slope of the regression of offspring phenotypic trait on parental phenotypic trait, both traits measured at the same age, often at birth. This correlation reflects phenotypic variation due to both additive genetic effects and parental effects. Researchers seldom account for the possible influence of selection on estimates of the phenotypic parent–offspring correlation. However, because individuals must grow, survive, and reproduce before giving birth to offspring, these ,aphic processes might influence the phenotypic parent–offspring correlation in addition to the inheritance process, the latter being the direct relationship between parental and offspring phenotypic traits when the parental trait is measured at age of reproduction and the offspring trait is measured at birth. Here we used a female-based population model to study the relative effects of fertility and viability selections, trait ontogeny and inheritance on C. The relative influence of each demographic process is estimated by deriving the exact formulas for the proportional changes in C to changes in the parameters of integral projection models structured by age and phenotypic traits. We illustrate our method for two long-lived species. We find that C can be strongly affected by both viability and fertility selections, mediated by growth and inheritance. Generally, demographic processes that result in mothers reproducing at similar phenotypic traits regardless of their birth traits, such as high fertility selection or converging developmental trajectories, lead to a decreased C. More generally, our models show how the age and phenotypic dependence of fertility and viability selections can influence phenotypic mother–offspring correlation to a much larger extent than ontogeny and inheritance. Our results suggest that accounting for such dependence is needed to model the distribution of offspring phenotypic traits and the ecoevolutionary dynamics of phenotypic traits reliably.