Genome-wide identification and characterization of the AP2/ERF gene family in loblolly pine (Pinus taeda L.)

The loblolly pine (Pinus taeda L.) is one of the most profitable forest species worldwide owing to its quick growth, high wood yields, and strong adaptability. The AP2/ERF gene family plays a widespread role in the physiological processes of plant defense responses and the biosynthesis of metabolites. Nevertheless, there are no reports on this gene family in loblolly pine (P. taeda). In this study, a total of 303 members of the AP2/ERF gene family were identified. Through multiple sequence alignment and phylogenetic analysis, they were classified into four subfamilies, including AP2 (34), RAV (17), ERF (251), and Soloist (1). An analysis of the conservation domains, conserved motifs, and gene structure revealed that every PtAP2/ERF transcription factor (TF) had at least one AP2 domain. While evolutionary conservation was displayed within the same subfamilies, the distribution of conserved domains, conserved motifs, and gene architectures varied between subfamilies. Cis-element analysis revealed abundant light-responsive elements, phytohormone-responsive elements, and stress-responsive elements in the promoter of the PtAP2/ERF genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of potential target genes showed that the AP2/ERF gene family might play a critical role in plant growth and development, the response to environmental stresses, and metabolite biosynthesis. Utilizing quantitative real-time PCR (qRT-PCR), we examined the expression patterns of 10 randomly selected genes from Group IX after 6 h of treatments with mechanical injury, ethephon (Eth), and methyl jasmonate (MeJA). The AP2/ERF gene family in the loblolly pine was systematically analyzed for the first time in this study, offering a theoretical basis for exploring the functions and applications of AP2/ERF genes.

How useful are genomic data for predicting maladaptation to future climate?

Methods using genomic information to forecast potential population maladaptation to climate change or new environments are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods-Gradient Forests (GFoffset) and the risk of non-adaptedness (RONA)-using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against 2- and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.

Geographic isolation and environmental heterogeneity contribute to genetic differentiation in Cephalotaxus oliveri

Evaluating the contributions of geographic distance and environmental heterogeneity to the genetic divergence can inform the demographic history and responses to environmental change of natural populations. The isolation-by-distance (IBD) reveals that genetic differentiation among populations increases with geographic distance, while the isolation-by-environment (IBE) assumes a linear relationship between genetic variation and environmental differences among populations. Here, we sampled and genotyped 330 individuals from 18 natural populations of Cephalotaxus oliveri throughout the species' distribution. Twenty-eight EST-SSR markers were applied to analyze population genetics, for the investigation of the driving factors that shaped spatial structure. In addition, we identified the outlier loci under positive selection and tested their association with environmental factors. The results showed a moderate genetic diversity in C. oliveri and high genetic differentiation among populations. Population structure analyses indicated that 18 populations were clustered into two major groups. We observed that the genetic diversity of central populations decreased and the genetic differentiation increased towards the marginal populations. Additionally, the signatures of IBD and IBE were detected in C. oliveri, and IBE provided a better contribution to genetic differentiation. Six outlier loci under positive selection were demonstrated to be closely correlated with environmental variables, among which bio8 was associated with the greatest number of loci. Genetic evidence suggests the consistency of the central-marginal hypothesis (CMH) for C. oliveri. Furthermore, our results suggest that temperature-related variables played an important role in shaping genetic differentiation.

Assessing the current genetic structure of 21 remnant populations and predicting the impacts of climate change on the geographic distribution of Phoebe sheareri in southern China

Phoebe sheareri is a valuable tree species known as "Golden Nanmu" and is one of the most important protected tree species in China. However, natural populations are decreasing because of climate change and anthropogenic factors. To evaluate the genetic diversity and structure of remnant populations and the impacts of climate change on the distribution of potential suitable habitats, we conducted a field investigation and sampled 21 P. sheareri natural populations to evaluate their genetic diversity and structure using simple sequence repeat (SSR) molecular markers. Then, we predicted the distribution of suitable P. sheareri habitats across China under future scenarios (RCP 2.6 and RCP 8.5) and periods (2050 and 2070) using multivariate modeling methods-the MaxEnt model. The results showed a medium level of genetic diversity and low inbreeding in the 21 P. sheareri natural populations, and genetic differentiation among populations was significant, with 21.2 % genetic variation among populations. The remnant populations of P. sheareri were grouped into four genetic clusters based on genetic structure; five environmental variables involving four temperature variables and precipitation seasonality (Bio12) might determine the distribution of P. sheareri populations. In the future, the suitable habitats of P. sheareri are manifested as northward migration, and the highly suitable habitats are expected to increase. Our results highlight the importance of conservation units in situ, giving priority to populations with higher genetic diversity (e.g., TMS, FJS, and THY populations); sampling strategies for ex situ conservation, breeding and reforestation should consider climate change, especially Bio1 (annual mean temperature) and Bio12 (annual precipitation). Overall, this study may provide useful genetic information for strategies for the protection, management, and utilization of P. sheareri.

Polygenic adaptation and negative selection across traits, years and environments in a long-lived plant species (Pinus pinaster Ait., Pinaceae)

A decade of genetic association studies in multiple organisms suggests that most complex traits are polygenic, i.e., they have a genetic architecture determined by numerous loci each with small effect-size. Thus, determining the degree of polygenicity and its variation across traits, environments and time is crucial to understand the genetic basis of phenotypic variation. We applied multilocus approaches to estimate the degree of polygenicity of fitness-related traits in a long-lived plant (Pinus pinaster Ait., maritime pine) and to analyze this variation across environments and years. We evaluated five categories of fitness-related traits (survival, height, phenology, functional, and biotic-stress response traits) in a clonal common-garden network, planted in contrasted environments (over 12,500 trees). Most of the analyzed traits showed evidence of local adaptation based on Q_st -F_st comparisons. We further observed a remarkably stable degree of polygenicity, averaging 6% (range of 0-27%), across traits, environments and years. We detected evidence of negative selection, which could explain, at least partially, the high degree of polygenicity. Because polygenic adaptation can occur rapidly, our results suggest that current predictions on the capacity of natural forest tree populations to adapt to new environments should be revised, especially in the current context of climate change.

Building a reference transcriptome for Juniperus squamata (Cupressaceae) based on single-molecule real-time sequencing

OBJECTIVES

Cupressaceae is the second largest family of coniferous trees (Coniferopsida) with important economic and ecological values. However, like other conifers, the members of Cupressaceae have extremely large genome (> 8 gigabytes), which limited the researches of these taxa. A high-quality transcriptome is an important resource for gene discovery and annotation for non-model organisms.

DATA DESCRIPTION

Juniperus squamata, a tetraploid species which is widely distributed in Asian mountains, represents the largest genus, Juniperus, in Cupressaceae. Single-molecule real-time sequencing was used to obtain full-length transcriptome of Juniperus squamata. The full-length transcriptome was corrected with Illumina RNA-seq data from the same individual. A total of 47,860 non-redundant full-length transcripts, N50 of which was 2839, were obtained. A total of 57,393 simple sequence repeats were identified and 268,854 open reading frames were predicted for Juniperus squamata. A BLAST alignment against non-redundant protein database was conducted and 10,818 sequences were annotated in Gene Ontology database. InterPro analysis shows that 30,403 sequences have been functionally characterized against its member database. This data presents the first comprehensive transcriptome characterization of Juniperus species, and provides an important reference for researches on the genomics and evolutionary history of Cupressaceae plants and conifers in the future.

Genomic and common garden approaches yield complementary results for quantifying environmental drivers of local adaptation in rubber rabbitbrush, a foundational Great Basin shrub

The spatial structure of genomic and phenotypic variation across populations reflects historical and demographic processes as well as evolution via natural selection. Characterizing such variation can provide an important perspective for understanding the evolutionary consequences of changing climate and for guiding ecological restoration. While evidence for local adaptation has been traditionally evaluated using phenotypic data, modern methods for generating and analyzing landscape genomic data can directly quantify local adaptation by associating allelic variation with environmental variation. Here, we analyze both genomic and phenotypic variation of rubber rabbitbrush (Ericameria nauseosa), a foundational shrub species of western North America. To quantify landscape genomic structure and provide perspective on patterns of local adaptation, we generated reduced representation sequencing data for 17 wild populations (222 individuals; 38,615 loci) spanning a range of environmental conditions. Population genetic analyses illustrated pronounced landscape genomic structure jointly shaped by geography and environment. Genetic-environment association (GEA) analyses using both redundancy analysis (RDA) and a machine-learning approach (Gradient Forest) indicated environmental variables (precipitation seasonality, slope, aspect, elevation, and annual precipitation) influenced spatial genomic structure and were correlated with allele frequency shifts indicative of local adaptation at a consistent set of genomic regions. We compared our GEA-based inference of local adaptation with phenotypic data collected by growing seeds from each population in a greenhouse common garden. Population differentiation in seed weight, emergence, and seedling traits was associated with environmental variables (e.g., precipitation seasonality) that were also implicated in GEA analyses, suggesting complementary conclusions about the drivers of local adaptation across different methods and data sources. Our results provide a baseline understanding of spatial genomic structure for E. nauseosa across the western Great Basin and illustrate the utility of GEA analyses for detecting the environmental causes and genetic signatures of local adaptation in a widely distributed plant species of restoration significance.

Breeding for Climate Change Resilience: A Case Study of Loblolly Pine (Pinus taeda L.) in North America

Earth's atmosphere is warming and the effects of climate change are becoming evident. A key observation is that both the average levels and the variability of temperature and precipitation are changing. Information and data from new technologies are developing in parallel to provide multidisciplinary opportunities to address and overcome the consequences of these changes in forest ecosystems. Changes in temperature and water availability impose multidimensional environmental constraints that trigger changes from the molecular to the forest stand level. These can represent a threat for the normal development of the tree from early seedling recruitment to adulthood both through direct mortality, and by increasing susceptibility to pathogens, insect attack, and fire damage. This review summarizes the strengths and shortcomings of previous work in the areas of genetic variation related to cold and drought stress in forest species with particular emphasis on loblolly pine (Pinus taeda L.), the most-planted tree species in North America. We describe and discuss the implementation of management and breeding strategies to increase resilience and adaptation, and discuss how new technologies in the areas of engineering and genomics are shaping the future of phenotype-genotype studies. Lessons learned from the study of species important in intensively-managed forest ecosystems may also prove to be of value in helping less-intensively managed forest ecosystems adapt to climate change, thereby increasing the sustainability and resilience of forestlands for the future.

Combining genotype, phenotype, and environmental data to delineate site-adjusted provenance strategies for ecological restoration

Despite the importance of climate-adjusted provenancing to mitigate the effects of environmental change, climatic considerations alone are insufficient when restoring highly degraded sites. Here we propose a comprehensive landscape genomic approach to assist the restoration of moderately disturbed and highly degraded sites. To illustrate it we employ genomic data sets comprising thousands of single nucleotide polymorphisms from two plant species suitable for the restoration of iron-rich Amazonian Savannas. We first use a subset of neutral loci to assess genetic structure and determine the genetic neighbourhood size. We then identify genotype-phenotype-environment associations, map adaptive genetic variation, and predict adaptive genotypes for restoration sites. Whereas local provenances were found optimal to restore a moderately disturbed site, a mixture of genotypes seemed the most promising strategy to recover a highly degraded mining site. We discuss how our results can help define site-adjusted provenancing strategies, and argue that our methods can be more broadly applied to assist other restoration initiatives.