What ‘animal models’ can tell ornithologists about the genetics of wild populations. J Ornithol 148:S633-S642

J. Ornithol 12/2007; 148:633-642. DOI: 10.1007/s10336-007-0191-8

ABSTRACT Good estimates of the genetic parameters of natural populations, such as heritability, are essential for both understanding
how genetic variation is maintained and estimating a population’s evolutionary potential. Long-term studies on birds are especially
amenable for calculating such estimates because of the ease with which pedigrees can be inferred. Recent ‘animal model’ methodology,
originally developed by animal breeders to identify animals of high genetic merit, has been applied to natural bird populations
of known pedigree. Animal models are more powerful than traditional analyses such as parent–offspring regression because they
use all of the available pedigree information simultaneously. In doing so, they can accommodate common phenomena like selection
and inbreeding and are especially suitable for the complex and incomplete pedigrees typical of natural populations. Animal
models not only provide a better way of estimating genetic and environmental variance components, they also allow individual
phenotypes to be separated into their genetic and environmental components. Here we aim to provide the interested ornithologist
with an accessible entry into the vast and sometimes daunting quantitative genetics literature and, in particular, into the
literature on the animal model. We outline not only the possibilities offered by the animal model for the accurate estimation
of genetic parameters in the wild but also associated potential pitfalls and limitations. On the whole, we aim to provide
an accessible and up-to-date overview of the rapidly developing and exciting field of evolutionary genetics applied to long-term
studies of wild bird populations.

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    • "molecularly assigned paternities). In wild populations , the presence of environmental effects shared by related individuals (Wilson et al. 2010) and issues related to data quality (Quinn et al. 2006; Postma & Charmantier 2007), misassigned paternities (Charmantier & R eale 2005) or imperfect detection (Cam 2009; Papaı¨x et al. 2010) can, however, generate biases or decrease statistical power when estimating heritability . The estimation of heritability in wild populations therefore requires accounting for these specificities, in particular unbalanced sampling designs (Kruuk 2004). "
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    ABSTRACT: 1. Estimating heritability of traits in wild populations is a major prerequisite to understand their evolution. Until recently, most heritability estimates had been obtained using parent-offspring regressions. However, the popular-ity of animal models, that is, (generalized) linear mixed models assessing the genetic variance component based on population pedigree information, has markedly increased in the past few years. Animal models are claimed to perform better than parent–offspring regressions mainly because they use full between-individual relatedness information and they allow explicit modelling of the environmental effects shared by individuals. However, the differences between heritability estimates obtained using both approaches are not straight forward, and the fac-tors influencing these differences remain unclear. 2. We performed a simulation study to evaluate and compare the accuracy and precision of estimates obtained from parent–offspring regressions and animal models using both Frequentist (REML, PQL) and Bayesian (MCMC) estimation methods. We explored the influence of (i) the presence and type of shared environmental effects (non-transgenerational or transgenerational), (ii) the distribution of the phenotypic trait considered (Gaussian or binary trait) and (iii) data quantity and quality (sample size, pedigree connectivity) on heritability estimates obtained from the two approaches for different levels of true heritability. 3. In the absence of shared environmental effects, the animal model using the REML method performed best for a Gaussian trait, while the animal model using MCMC was more appropriate for a binary trait. For low quantity and quality data, and a binary trait, the parent–offspring regression yielded very imprecise estimates. 4. Estimates from the parent–offspring regression were not influenced by a non-transgenerational shared envi-ronmental effect, whereas estimates from animal models in which environmental effects are ignored were affected by both non-transgenerational and transgenerational effects. 5. We discuss the relevance of each approach and estimation method for estimating heritability in wild popula-tions. Importantly, because most effects fitted in animal models are, in fact, non-transgenerational (including environmental maternal effects), we advocate a systematic comparison between parent–offspring regression and animal model estimates to detect potentially missing non-transgenerational environmental effects.
    Methods in Ecology and Evolution 03/2013; 4(3):260-275. DOI:10.1111/2041-210X.12011 · 5.32 Impact Factor
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    • "phenotypic resemblance between close relatives and more distant relatives, who are less likely to live under similar environmental conditions (Postma and Charmantier 2007). Nonetheless, failing to properly account for such shared environmental effects is known to bias estimates of parameters derived from " animal models " (Kruuk and Hadfield 2007), and it has become common practice to account for certain kinds of shared environmental effects (e.g., parental identity, nest, group, or region of study area) by incorporating these into models as fixed or random effects (e.g., Kruuk et al. 2001; MacColl and Hatchwell 2003; Charmantier et al. 2004; Wilson et al. 2005; Kruuk and Hadfield 2007). "
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    ABSTRACT: Social structure, limited dispersal, and spatial heterogeneity in resources are ubiquitous in wild vertebrate populations. As a result, relatives share environments as well as genes, and environmental and genetic sources of similarity between individuals are potentially confounded. Quantitative genetic studies in the wild therefore typically account for easily captured shared environmental effects (e.g., parent, nest, or region). Fine-scale spatial effects are likely to be just as important in wild vertebrates, but have been largely ignored. We used data from wild red deer to build "animal models" to estimate additive genetic variance and heritability in four female traits (spring and rut home range size, offspring birth weight, and lifetime breeding success). We then, separately, incorporated spatial autocorrelation and a matrix of home range overlap into these models to estimate the effect of location or shared habitat on phenotypic variation. These terms explained a substantial amount of variation in all traits and their inclusion resulted in reductions in heritability estimates, up to an order of magnitude up for home range size. Our results highlight the potential of multiple covariance matrices to dissect environmental, social, and genetic contributions to phenotypic variation, and the importance of considering fine-scale spatial processes in quantitative genetic studies.
    Evolution 08/2012; 66(8):2411-26. DOI:10.1111/j.1558-5646.2012.01620.x · 4.66 Impact Factor
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    • "For example, in the Lesser Snow Goose, a species with substantial variation in gosling size according to feeding conditions in the rearing habitat (Cooch et al. 1991, 1993), Cooch (2002) hypothesized that ''selection may operate on the environmental component of body size, not on additive genetic variance of body size.'' Future studies will benefit from recent methodological development of statistical models combining the quantitative genetics 'animal model' (Postma and Charmantier 2007) and capture–mark–recapture models (i.e., model designed to handle missing data resulting from non-detection of animals that are alive and present in the study area; Papaı¨x et al. 2010). "
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    ABSTRACT: Lindström (in Trends Ecol Evol 14:343–347, 1999) synthesized knowledge about “early development and fitness in birds and mammals”, interesting tracks and challenges for future studies. Today, there is unambiguous evidence that Lindström’s first statement holds in long-lived birds: “It is obvious that adverse environmental conditions might have immediate effects […].” However, whether there are “long-term fitness consequences of conditions experienced during early development” (Lindström’s second statement) is unclear for long-lived birds. The extent to which the disadvantage of frail individuals at independence is expressed predominantly in terms of higher mortality and disappearance from the population before recruitment, or persists after recruitment, is still an open question. Due to the rarity of relevant data and the fact that most studies are retrospective, heterogeneity in methods and timescales hampers the identification of general patterns. Nevertheless, several studies have provided evidence of a relationship between early conditions and future reproductive parameters, or lifetime reproductive success. Evidence from large mammals suggests substantial long-term individual and population effects of early conditions, including trans-generational maternal effects. Evidence from short-lived birds also suggests long-term individual consequences, and maternal effects have been documented in long-lived ones. Despite logistical and financial difficulties inherent in long-term studies, they are the only way of addressing Lindström’s second statement. Existing long-term longitudinal datasets should be re-analyzed using recently developed capture–mark–recapture models handling state uncertainty and unobservable heterogeneity in populations. Statistical methods designed to estimate lifetime reproductive success or incorporate pedigree information in standard situations of studies of wild vertebrates with imperfect detection offer new opportunities to assess long-term fitness consequences of early development in long-lived birds. KeywordsLife history evolution–Longitudinal studies–Long-term effects–Population dynamics
    Journal of Ornithology 09/2011; 152(1):187-201. DOI:10.1007/s10336-011-0707-0 · 1.93 Impact Factor
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