Postglacial phylogeography, admixture, and evolution of red spruce (Picea rubens Sarg.) in Eastern North America.

Climate change is a major evolutionary force that can affect the structure of forest ecosystems worldwide. Red spruce (Picea rubens Sarg.) has recently faced a considerable decline in the Southern Appalachians due to rapid environmental change, which includes historical land use, and atmospheric pollution. In the northern part of its range, red spruce is sympatric with closely related black spruce (Picea mariana (Mill.) B.S.P.), where introgressive hybridization commonly occurs. We investigated range-wide population genetic diversity and structure and inferred postglacial migration patterns and evolution of red spruce using nuclear microsatellites. Moderate genetic diversity and differentiation were observed in red spruce. Genetic distance, maximum likelihood and Bayesian analyses identified two distinct population clusters: southern glacial populations, and the evolutionarily younger northern populations. Approximate Bayesian computation suggests that patterns of admixture are the result of divergence of red spruce and black spruce from a common ancestor and then introgressive hybridization during post-glacial migration. Genetic diversity, effective population size (Ne) and genetic differentiation were higher in the northern than in the southern populations. Our results along with previously available fossil data suggest that Picea rubens and Picea mariana occupied separate southern refugia during the last glaciation. After initial expansion in the early Holocene, these two species faced a period of recession and formed a secondary coastal refugium, where introgressive hybridization occurred, and then both species migrated northward. As a result, various levels of black spruce alleles are present in the sympatric red spruce populations. Allopatric populations of P. rubens and P. mariana have many species-specific alleles and much fewer alleles from common ancestry. The pure southern red spruce populations may become critically endangered under projected climate change conditions as their ecological niche may disappear.

Genetic signatures of natural selection in response to air pollution in red spruce (Picea rubens, Pinaceae).

One of the most important drivers of local adaptation for forest trees is climate. Coupled to these patterns, however, are human-induced disturbances through habitat modification and pollution. The confounded effects of climate and disturbance have rarely been investigated with regard to selective pressure on forest trees. Here, we have developed and used a population genetic approach to search for signals of selection within a set of 36 candidate genes chosen for their putative effects on adaptation to climate and human-induced air pollution within five populations of red spruce (Picea rubens Sarg.), distributed across its natural range and air pollution gradient in eastern North America. Specifically, we used FST outlier and environmental correlation analyses to highlight a set of seven single nucleotide polymorphisms (SNPs) that were overly correlated with climate and levels of sulphate pollution after correcting for the confounding effects of population history. Use of three age cohorts within each population allowed the effects of climate and pollution to be separated temporally, as climate-related SNPs (n = 7) showed the strongest signals in the oldest cohort, while pollution-related SNPs (n = 3) showed the strongest signals in the youngest cohorts. These results highlight the usefulness of population genetic scans for the identification of putatively nonneutral evolution within genomes of nonmodel forest tree species, but also highlight the need for the development and application of robust methodologies to deal with the inherent multivariate nature of the genetic and ecological data used in these types of analyses.

A simple method for estimating genetic diversity in large populations from finite sample sizes.

BACKGROUND: Sample size is one of the critical factors affecting the accuracy of the estimation of population genetic diversity parameters. Small sample sizes often lead to significant errors in determining the allelic richness, which is one of the most important and commonly used estimators of genetic diversity in populations. Correct estimation of allelic richness in natural populations is challenging since they often do not conform to model assumptions. Here, we introduce a simple and robust approach to estimate the genetic diversity in large natural populations based on the empirical data for finite sample sizes. RESULTS: We developed a non-linear regression model to infer genetic diversity estimates in large natural populations from finite sample sizes. The allelic richness values predicted by our model were in good agreement with those observed in the simulated data sets and the true allelic richness observed in the source populations. The model has been validated using simulated population genetic data sets with different evolutionary scenarios implied in the simulated populations, as well as large microsatellite and allozyme experimental data sets for four conifer species with contrasting patterns of inherent genetic diversity and mating systems. Our model was a better predictor for allelic richness in natural populations than the widely-used Ewens sampling formula, coalescent approach, and rarefaction algorithm. CONCLUSIONS: Our regression model was capable of accurately estimating allelic richness in natural populations regardless of the species and marker system. This regression modeling approach is free from assumptions and can be widely used for population genetic and conservation applications.

Protocol: A high-throughput DNA extraction system suitable for conifers.

BACKGROUND: High throughput DNA isolation from plants is a major bottleneck for most studies requiring large sample sizes. A variety of protocols have been developed for DNA isolation from plants. However, many species, including conifers, have high contents of secondary metabolites that interfere with the extraction process or the subsequent analysis steps. Here, we describe a procedure for high-throughput DNA isolation from conifers. RESULTS: We have developed a high-throughput DNA extraction protocol for conifers using an automated liquid handler and modifying the Qiagen MagAttract Plant Kit protocol. The modifications involve change to the buffer system and improving the protocol so that it almost doubles the number of samples processed per kit, which significantly reduces the overall costs. We describe two versions of the protocol: one for medium-throughput (MTP) and another for high-throughput (HTP) DNA isolation. The HTP version works from start to end in the industry-standard 96-well format, while the MTP version provides higher DNA yields per sample processed. We have successfully used the protocol for DNA extraction and genotyping of thousands of individuals of several spruce and a pine species. CONCLUSION: A high-throughput system for DNA extraction from conifer needles and seeds has been developed and validated. The quality of the isolated DNA was comparable with that obtained from two commonly used methods: the silica-spin column and the classic CTAB protocol. Our protocol provides a fully automatable and cost effective solution for processing large numbers of conifer samples.