2 Unifying Life History Analyses for Inference of Fitness and Population Growth

Department of Ecology, Evolution, and Behavior, Minnesota Center for Community Genetics, University of Minnesota, St. Paul, Minnesota 55108, USA.
The American Naturalist (Impact Factor: 3.83). 08/2008; 172(1):E35-47. DOI: 10.1086/588063
Source: PubMed


The lifetime fitnesses of individuals comprising a population determine its numerical dynamics, and genetic variation in fitness results in evolutionary change. This dual importance of individual fitness is well understood, but empirical fitness records generally violate the assumptions of standard statistical approaches. This problem has undermined comprehensive study of fitness and impeded empirical synthesis of the numerical and genetic dynamics of populations. Recently developed aster models remedy this problem by explicitly modeling the dependence of later-expressed components of fitness (e.g., fecundity) on those expressed earlier (e.g., survival to reproduce). Moreover, aster models employ different sampling distributions for different components of fitness (e.g., binomial for survival over a given interval and Poisson for fecundity). Analysis is done by maximum likelihood, and the resulting distributions for lifetime fitness closely approximate observed data. We illustrate the breadth of aster models' utility with three examples demonstrating estimation of the finite rate of increase, comparison of mean fitness among genotypic groups, and analysis of phenotypic selection. Aster models offer a unified approach to addressing the breadth of questions in evolution and ecology for which life-history data are gathered.

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    • "Aster models for life-history traits were used to characterize phenotypic selection on characteristics within each water treatment (aster package) [44]. These models utilize maximum likelihood linear methods and improve upon previous least squares methods by modeling multiple components of fitness into a single variable, as well as by specifying a particular distribution for each component of fitness [45] [46] [47]. All characteristics were regressed with the combined fitness variable based on two components: survival to reproduction (Bernoulli distribution) and fecundity (truncated Poisson distribution). "
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    ABSTRACT: Locally relevant conditions, such as water stress in irrigated agricultural regions, should be considered when assessing the risk of crop allele introgression into wild populations following hybridization. Although research in cultivars has suggested that domestication traits may reduce fecundity under water stress as compared to wild-like phenotypes, this has not been investigated in crop-wild hybrids. In this study, we examine phenotypic selection acting on, as well as the genetic architecture of vegetative, reproductive, and physiological characteristics in an experimental population of sunflower crop-wild hybrids grown under wild-like low water conditions. Crop-derived petiole length and head diameter were favored in low and control water environments. The direction of selection differed between environments for leaf size and leaf pressure potential. Interestingly, the additive effect of the crop-derived allele was in the direction favored by selection for approximately half the QTL detected in the low water environment. Selection favoring crop-derived traits and alleles in the low water environment suggests that a subset of these alleles would be likely to spread into wild populations under water stress. Furthermore, differences in selection between environments support the view that risk assessments should be conducted under multiple locally relevant conditions.
    PLoS ONE 07/2014; 9(7):e102717. DOI:10.1371/journal.pone.0102717 · 3.23 Impact Factor
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    • "Since the introduction of standard methods for the estimation of selection on phenotypic traits in the wild (e.g. Lande & Arnold, 1983; Mitchell-Olds & Shaw, 1987; Schluter, 1988; Shaw et al., 2008; Morrissey & Sakrejda, 2013), many studies have explored the patterns of natural selection (reviewed in Hoekstra et al., 2001; Kingsolver et al., 2001, 2012; Hereford, Hansen & Houle, 2004; Siepielski, DiBattista & Carlson, 2009). In the context of speciation with gene flow (Smadja & Butlin, 2011), hybrid zones (sensu Harrison, 1990) represent ideal systems to study the fitness of recombinant genotypes in situ (Barton & Hewitt, 1985; Abbott et al., 2013). "

    Biological Journal of the Linnean Society 01/2014; 111:391-400. · 2.26 Impact Factor
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    • "For these traits, the genetic basis is too complex to be untangled using traditional molecular genetic approaches, although genome-wide association mapping is being used to identify individual genes that contribute to the variation. Given the inability to map phenotypic variation of complex traits to the individual molecular determinants, statistical approaches based on relatedness among individuals have been used to disentangle the relative contributions of genetics and environment to phenotypic variation (Fisher 1918; Falconer & Mackay 1996), and predicting expected evolutionary responses to selection (Lande & Arnold 1983; Shaw et al. 2008). The effectiveness of formal statistical approaches for predicting response to selection without explicitly identifying the molecular mechanisms is demonstrated by the advances achieved by plant and animal breeders in the past 100 years (Moose, Dudley & Rocheford 2004). "
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    ABSTRACT: Heritability (h2) represents the potential for short-term response of a quantitative trait to selection. Unfortunately, estimating h2 through traditional crossing experiments is not practical for many species, and even for those in which mating can be manipulated, it may not be possible to assay them in ecologically relevant environments.We evaluated an approach, GCTA, that uses relatedness estimated from genomic data to estimate the proportion of phenotypic variance due to genotyped SNPs, which can be used to infer h2. Using phenotypic and genotypic data from eight replicates of experimentally grown plants of the annual legume Medicago truncatula, we examined how h2 estimates from GCTA (h2GCTA) related to traditional estimates of heritability (clonal repeatability for these inbred lines). Further, we examined how h2GCTA estimates were affected by SNP number, minor allele frequency, the number of individuals assayed and the exclusion of causative SNPs.We found that the average h2GCTA estimates for each trait made with the full data set (>5 million SNPs, 200 individuals) were strongly correlated (r = 0·99) with estimates of clonal repeatability. However, this result masks considerable variation among replicate estimates of h2GCTA, even in relatively uniform greenhouse conditions. h2GCTA estimates with 250 000 and 25 000 SNPs were very similar to those obtained with >5 million SNPs, but with 2500 SNPs, h2GCTA were lower and had higher variance than those with ≥25 k SNPs. h2GCTA estimates were slightly lower when only common SNPs were used. Excluding putatively causative SNPs had little effect on the estimates of h2GCTA, suggesting that genotyping putatively causative SNPs is not necessary to obtain accurate estimates of h2. The number of accessions sampled had the greatest effect on h2GCTA estimates, and variance greatly increased as fewer accessions were included. With only 50 accessions sampled, the range of h2GCTA ranged from 0 to 1 for all traits.These results indicate that the GCTA method may be useful for estimating h2 using data sets of a size that are available from reduced-representation genotyping but that hundreds of individuals may need to be sampled to obtain robust estimates of h2.
    Methods in Ecology and Evolution 12/2013; 4(12). DOI:10.1111/2041-210X.12129 · 6.55 Impact Factor
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