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290 results for “variance”
A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance
<p>Simulated data associated with the paper "A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance".</p> <p>The main dataset is contained within the mermaids.csv, with columns explained within the associated README file. Epigenetic and social network information are contained within the other two datasets</p>
Fig. 1 in Stable isotope analysis spills the beans about spatial variance in trophic structure in a fish host - parasite system from the Vaal River System, South Africa
Fig. 1. Map of the Vaal River showing the position of sampling sites (I: below Grootdraai Dam; II: Vaal Dam; III: below Vaal River Barrage; IV: Bloemhof Dam; V: below Vaalharts Weir; VI: Douglas Weir) along the Vaal River. The block (B) indicates the position of the Vaal River within South Africa and insert A indicates the position of South Africa shaded on the African continent.
-0.2 0.0 0.2 0.4 0.6 PC1 (29.8% of total variance) Fig. 8. Morphospace plot of the first two PCO axes generated in the R statistical environment (Claddis package). Branches are superimposed from a single representative topology selected from amongst the 48 MPTs. in The sauropodomorph biostratigraphy of the Elliot Formation of southern Africa: Tracking the evolution of Sauropodomorpha across the Triassic-Jurassic boundary
-0.2 0.0 0.2 0.4 0.6 PC1 (29.8% of total variance) Fig. 8. Morphospace plot of the first two PCO axes generated in the R statistical environment (Claddis package). Branches are superimposed from a single representative topology selected from amongst the 48 MPTs.
Variance components of sex determination in the copepod Tigriopus californicus estimated from a pedigree analysis
<p>Extensive theory exists regarding population sex ratio evolution that predicts equal sex ratio (when parental investment is equal). In most animals, sex chromosomes determine the sex of offspring, and this fixed genotype for sex has made theory difficult to test since genotypic variance for the trait (sex) is lacking. It has long been argued that the genotype has become fixed in most animals due to the strong selection for equal sex ratios. The marine copepod <em>Tigriopus californicus</em> has no sex chromosomes, multiple genes affecting female brood sex ratio and a brood sex ratio that responds to selection. The species thus provides an opportune system in which to test established sex ratio theory. In this paper, we further our exploration of polygenic sex determination in <em>T. californicus</em> using an incomplete diallel crossing design for analysis of the variance components of sex determination in the species. Our data confirm the presence of extra-binomial variance for sex, further confirming that sex is not determined through simple Mendelian trait inheritance. In addition, our crosses and backcrosses of isofemale lines selected for biased brood sex ratios show intermediate phenotypic means, as expected if sex is a threshold trait determined by an underlying "liability" trait controlled by many genes of small effects. Furthermore, crosses between families from the same selection line had similar increases in phenotypic variance as crosses between families from different selection lines, suggesting families from artificial selection lines responded to selection pressure through different underlying genetic bases. Finally, we estimate heritability of an individual to be male or female on the observed binary scale as 0.09 (95% CI: 0.034-0.14). This work furthers our accumulating evidence for polygenic sex determination in <em>T. californicus</em> laying the foundation for this as a model species in future studies of sex ratio evolution theory.</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>
Genetic variance in contrasting environments
<p>The evolutionary response of a trait to directional selection depends upon the level of additive genetic variance. It has been long argued that sustained selection will tend to deplete additive genetic variance as favoured alleles approach fixation. Non-additive genetic variance, due to interactions among alleles within and between loci, does not immediately contribute to an evolutionary response, although shifts in the allele frequencies within and between interacting loci may convert interaction variance into additive variance. Here we consider the possibility that an environmental shift may alter allelic interactions in ways that convert nonadditive into additive genetic variance. Specifically, we performed experiments that used a Bayesian implementation of the animal model to estimate the additive, dominance, and maternal components of variance for a pedigreed population of <em>Brassica rapa</em>. One experiment was performed in a field that mimicked agricultural conditions from which the base population was drawn, while the other was performed in the benign conditions of a greenhouse. Although the additive genetic variance was elevated in the greenhouse condition, no consistent pattens emerged that would indicate a conversion of dominance variance. The unusually low genetic variance and broad confidence intervals for the variance estimates obtained through this analysis preclude definitive interpretations. Thus, we promote further investigation to determine if between-environment changes in additive genetic variance can be traced to conversion of nonadditive variance.</p>
Data used in: Heritability and variance components of seed size in wild species: influences of breeding design and the number of genotypes tested
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Data from: Peregrine Falcons shift mean and variance in provisioning in response to increasing brood demand
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Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
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Genetic variance in contrasting environments
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Data from: Variance sum rule for entropy production
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Environmental effects on genetic variance are likely to constrain adaptation in novel environments
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Variance components of sex determination in the copepod Tigriopus californicus estimated from a pedigree analysis
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Data from: Aridity drives coordinated trait shifts but not decreased trait variance across the geographic range of eight Australian trees
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Data from: Can dominance genetic variance be ignored in evolutionary quantitative genetic analyses of wild populations?
<p>Accurately estimating genetic variance components is important for studying evolution in the wild. Empirical work on domesticated and wild outbred populations suggests that dominance genetic variance represents a substantial part of genetic variance, and theoretical work predicts that ignoring dominance can inflate estimates of additive genetic variance. Whether this issue is pervasive in natural systems is unknown, because we lack estimates of dominance variance in wild populations obtained <i>in situ</i>. Here, we estimate dominance and additive genetic variance, maternal variance, and other sources of non-genetic variance in 8 traits measured in over 9000 wild nestlings linked through a genetically resolved pedigree. We find that dominance variance, when estimable, does not statistically differ from zero and represents a modest amount (2-36%) of genetic variance. Simulations show that 1) inferences of all variance components for an average trait are unbiased; 2) the power to detect dominance variance is low; 3) ignoring dominance can mildly inflate additive genetic variance and heritability estimates but such inflation becomes substantial when maternal effects are also ignored. These findings hence suggest that dominance is a small source of phenotypic variance in the wild and highlight the importance of proper model construction for accurately estimating evolutionary potential.</p>
Short-term heritable variation overwhelms two hundred generations of mutational variance for metabolic traits in Caenorhabditis elegans
<p>Metabolic disorders have a large heritable component, and have increased markedly in human populations over the past few generations. Genome-wide association studies of metabolic traits typically find a substantial unexplained fraction of total heritability, suggesting an important role of spontaneous mutation. An alternative explanation is that epigenetic effects contribute significantly to the heritable variation. Here we report a study designed to quantify the cumulative effects of spontaneous mutation on adenosine metabolism in the nematode <i>Caenorhabditis elegans</i>, including both the activity and concentration of two metabolic enzymes and the standing pools of their associated metabolites. The only prior studies on the effects of mutation on metabolic enzyme activity, in <i>Drosophila melanogaster</i>, found that total enzyme activity presents a mutational target similar to that of morphological and life-history traits. However, those studies were not designed to account for short-term heritable effects. We find that the short-term heritable variance for most traits is of similar magnitude as the variance among MA lines. This result suggests that the potential heritable effects of epigenetic variation in metabolic disease warrant additional scrutiny.</p>
Genetic variance for behavioural 'predictability' of stress response
<p>Genetic factors underpinning phenotypic variation are required if natural selection is to result in adaptive evolution. However, evolutionary and behavioural ecologists typically focus on variation among individuals in their average trait values, and seek to characterise genetic contributions to this. As a result, less attention has been paid to if and how genes could contribute towards within-individual variance, or trait "predictability". In fact, phenotypic 'predictability' can vary among individuals, and emerging evidence from livestock genetics suggests this can be due to genetic factors. Here we test this empirically using repeated measures of a behavioural stress response trait in a pedigreed population of wild-type guppies. We ask (1) whether individuals differ in behavioural predictability, and (2) whether this variation is heritable and so evolvable under selection. Using statistical methodology from the field of quantitative genetics, we find support for both hypotheses and also show evidence of a genetic correlation structure between the behavioural trait mean and individual predictability. We show that investigating sources of variability in trait predictability is statistically tractable, and can yield useful biological interpretation. We conclude that, if widespread, genetic variance for 'predictability' will have major implications for the evolutionary causes and consequences of phenotypic variation. </p>
Data from: Expression of additive genetic variance for fitness in a population of partridge pea in two field sites
Despite the importance of adaptation in shaping biological diversity over many generations, little is known about populations' capacities to adapt at any particular time. Theory predicts that a population's rate of ongoing adaptation is the ratio of its additive genetic variance for fitness, VA (W), to its mean absolute fitness, W̅. We conducted a transplant study to quantify W̅ and standing VA (W) for a population of the annual legume Chamaecrista fasciculata in one field site from which we initially sampled it and another site where it does not currently occur naturally. We also examined genotype‐by‐environment interactions, G x E, as well as its components, differences between sites in VA (W) and in rank of breeding values for fitness. The mean fitness indicated population persistence in both sites, and there was substantial VA (W) for ongoing adaptation at both sites. Statistically significant G x E indicated that the adaptive process would differ between sites. We found a positive correlation between fitness of genotypes in the "home" and "away" environments, and G x E was more pronounced as the life cycle proceeds. This study exemplifies an approach to assessing whether there is sufficient VA (W) to support evolutionary rescue in populations that are declining.
Differences in phenotypic variance between old and young congeneric species on a small island
<p><em>Aim</em>: Insular environments theoretically promote within-species phenotypic variance, in part but not only because insular species enjoy a lower number of trophic interactions than their mainland counterparts. However, environmental factors apparently confound empirical comparisons between insular and mainland systems. To address this issue, we studied sympatric, congeneric species from the same small island, but that stemmed from separate colonization events. We predicted that the time since the species had colonized the island would correlate positively with the within-species variance in morphometrics.</p> <p><em>Location</em>: Lifu Island, New Caledonia, South Pacific.</p> <p><em>Taxa</em>: <em>Zosterops inornatus</em>, <em> Z. minutus</em>, <em> Z. lateralis</em> (Passeriformes). </p> <p><em>Methods</em>: We measured 7 morphological traits and used principal component analysis to compute axes of variation.</p> <p><em>Results</em>: The within-species variance in bill size and in feet/tail ratio corrected for body size increased with the estimated time since the species had been on the island. A similar but nonsignificant trend existed for body size and bill width.</p> <p><em>Main conclusions</em>: The morphometrics of the three species of <em>Zosterops</em> birds from Lifu supported the hypothesis that insular environments promote within-species phenotypic variance, and cast a new light on the role of competition in the erosion of phenotypic variance.</p>
Challenges and limitations of applying the flux variance similarity (FVS) method to partition evapotranspiration in a montane cloud forest
<p>Dataset</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>FVS_ori.zip</td> <td>the output from FVS method</td> </tr> <tr> <td>ModFVS.zip</td> <td>the output from ModFVS method</td> </tr> <tr> <td>CLM.zip</td> <td>the output from CLM </td> </tr> <tr> <td>Chilan_30min_sap_velocity_20200601_20211120_QC.csv</td> <td>the sap flow data in Chi-Lan</td> </tr> <tr> <td>*_clim.csv</td> <td>the observation data in Chi-Lan and Lien-Hua-Chih</td> </tr> </tbody> </table> <p> </p> <p>Codes for Analysis</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>*.ipynb</td> <td>the python code used for analyzing output</td> </tr> <tr> <td>*_FVS_process.py</td> <td>the python code used for process ModFVS method</td> </tr> </tbody> </table> <p> </p> <p>ModFVS method (fluxpart-0.2.10+rhtest-py3-none-any.whl)</p> <ul> <li>use "pip install fluxpart-0.2.10+rhtest-py3-none-any.whl" to install the package</li> <li> <p>To specify a maximum allowable relative humidity when calculating WUE, set a value for "max_rh" in "wue_options". For example, to set the max RH to 95%, you would change your example code to this:</p> <p>wue_options = {"meas_ht": 23.7,"canopy_ht":10, "ppath": "C3","ci_mod":ci_mod, "max_rh":95}</p> </li> <li> <p>Note that this code is a fork of (https://github.com/usda-ars-ussl/fluxpart)</p> </li> </ul> <p> </p> <p> </p>
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Allen Brain Atlas
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