A C++ Template Library for Efficient Forward-Time Population Genetic Simulation of Large Populations

fwdpp is a C++ library of routines intended to facilitate the development of forward-time simulations under arbitrary mutation and fitness models. The library design provides a combination of speed, low memory overhead, and modeling flexibility not currently available from other forward simulation tools. The library is particularly useful when the simulation of large populations is required, as programs implemented using the library are much more efficient than other available forward simulation programs.     

Is there a trade-off between egg weight and clutch size in wild Lesser Snow Geese (Anser c. caerulescens)?

Previous studies of arctic nesting geese suggest that laying is limited by the size of a female's body reserves and that larger eggs contain more nutrients. These observations imply a life-history trade-off between egg size and clutch size which may give rise to a negative genetic correlation between the two characters. We estimated the genetic correlation between egg weight and clutch size using measurements from mothers and their daughters in a wild population of Lesser Snow Geese Anser caerulescens caerulescens. Between 65 and 80 % of the variance in egg weight is attributable to differences between individuals, and heritability of egg weight is about 60 %. In contrast, 10–20 % of the variance in clutch size is attributable to differences between individuals, and heritability of clutch size is about 15 %. The genetic correlation coefficient between egg weight and clutch size ranges from 0.09 to 0.32 and does not differ significantly from zero. We discuss the possible reasons for the lack of the expected negative genetic correlation.     

The effect of a population bottleneck on the evolution of genetic variance/covariance structure

It is well known that standard population genetic theory predicts decreased additive genetic variance (V(a) ) following a population bottleneck and that theoretical models including interallelic and intergenic interactions indicate such loss may be avoided. However, few empirical data from multicellular model systems are available, especially regarding variance/covariance (V/CV) relationships. Here, we compare the V/CV structure of seventeen traits related to body size and composition between control (60 mating pairs/generation) and bottlenecked (2 mating pairs/generation; average F = 0.39) strains of mice. Although results for individual traits vary considerably, multivariate analysis indicates that V(a) in the bottlenecked populations is greater than expected. Traits with patterns and amounts of epistasis predictive of enhanced V(a) also show the largest deviations from additive expectations. Finally, the correlation structure of weekly weights is not significantly different between control and experimental lines but correlations between necropsy traits do differ, especially those involving the heart, kidney and tail length.     

Understanding and using quantitative genetic variation

Quantitative genetics, or the genetics of complex traits, is the study of those characters which are not affected by the action of just a few major genes. Its basis is in statistical models and methodology, albeit based on many strong assumptions. While these are formally unrealistic, methods work. Analyses using dense molecular markers are greatly increasing information about the architecture of these traits, but while some genes of large effect are found, even many dozens of genes do not explain all the variation. Hence, new methods of prediction of merit in breeding programmes are again based on essentially numerical methods, but incorporating genomic information. Long-term selection responses are revealed in laboratory selection experiments, and prospects for continued genetic improvement are high. There is extensive genetic variation in natural populations, but better estimates of covariances among multiple traits and their relation to fitness are needed. Methods based on summary statistics and predictions rather than at the individual gene level seem likely to prevail for some time yet.     

Evolutionary potential in the wild: more than meets the eye

The genus Aquilegia consists of 60–70 perennial plant species widely distributed throughout the northern hemisphere. Its flowers have a delicate and ornamental appearance that makes them a favourite of gardeners. In this genus, adaptive radiations for both floral and vegetative traits have occurred. These adaptive radiations, and the key phylogenetic placement of Aquilegia between Arabidopsis and rice, make this genus a ‘model system’ for plant evolution ( Kramer 2009 ). In this issue, Castellanos et al. (2011) use a marker‐based method to infer heritability for floral and vegetative traits in two Aquilegia species. Layered on top of this are estimates of the strength of natural selection. This novel joint estimation of heritability and selection in the wild showed that vegetative traits, compared to floral traits, have the highest evolutionarily potential. Evolutionary potential is the most important quantity to measure in wild populations. It combines inheritance and strength of selection and predicts the potential for populations to adapt to changing environments. The combination of molecular techniques with species in natural environments makes this work a model for molecular ecological investigations.     

Brief communication: preliminary radiocarbon dates from Florida crania in Hrdlička's gulf states catalog

Ale? Hrdli?ka produced a tremendous amount of data in his career, much of which was published in a series of catalogs by the US National Museum. The Gulf States catalog, for example, contains raw craniometric data for over 700 individuals from the state of Florida alone. However, many of these skeletons are poorly sourced by Hrdli?ka, thus limiting their utility in modern bioarchaeological analyses where context is critical. In particular, the age of the skeletal material is often based solely on associated material culture and information on the sites themselves is not presented by Hrdli?ka. To address this impasse we attempted radiocarbon dates for 10 of the largest Florida sites published in the Gulf States catalog. In addition, archival data in the form of unpublished field notes and personal correspondence were accessed to better contextualize the radiocarbon dates and to provide some guidance on the degree of temporal variability at the sites. Eight AMS radiocarbon dates were successful. Archival data was of variable quality per site. In some cases very little is known about the provenience of the specimens. In other cases, however, individual burials could be allocated to specific strata within specific mounds. The relevance of using published raw data is discussed with respect to the Howells and Boas Immigrant datasets and the impact the dissemination of these resources has had on the discipline. Am J Phys Anthropol 145:163–167, 2011. © 2011 Wiley‐Liss, Inc.     

Were neandertal and modern human cranial differences produced by natural selection or genetic drift?

Most evolutionary explanations for cranial differences between Neandertals and modern humans emphasize adaptation by natural selection. Features of the crania of Neandertals could be adaptations to the glacial climate of Pleistocene Europe or to the high mechanical strains produced by habitually using the front teeth as tools, while those of modern humans could be adaptations for articulate speech production. A few researchers have proposed non-adaptive explanations. These stress that isolation between Neandertal and modern human populations would have lead to cranial diversification by genetic drift (chance changes in the frequencies of alleles at genetic loci contributing to variation in cranial morphology). Here we use a variety of statistical tests founded on explicit predictions from quantitative- and population-genetic theory to show that genetic drift can explain cranial differences between Neandertals and modern humans. These tests are based on thirty-seven standard cranial measurements from a sample of 2524 modern humans from 30 populations and 20 Neandertal fossils. As a further test, we compare our results for modern human cranial measurements with those for a genetic dataset consisting of 377 microsatellites typed for a sample of 1056 modern humans from 52 populations. We conclude that rather than requiring special adaptive accounts, Neandertal and modern human crania may simply represent two outcomes from a vast space of random evolutionary possibilities.