Revegetation projects face the major challenge of sourcing optimal plant material. This is often done with limited information about plant performance and increasingly requires factoring resilience to climate change. Functional traits can be used as quantitative indices of plant performance and guide seed provenancing, but trait values expected under novel conditions are often unknown. To support climate-resilient provenancing efforts, we develop a trait prediction model that integrates the effect of genetic variation with fine-scale temperature variation. We train our model on multiple field plantings of Arabidopsis thaliana and predict two relevant fitness traits-days-to-bolting and fecundity-across the species' European range. Prediction accuracy was high for days-to-bolting and moderate for fecundity, with the majority of trait variation explained by temperature differences between plantings. Projection under future climate predicted a decline in fecundity, although this response was heterogeneous across the range. In response, we identified novel genotypes that could be introduced to genetically offset the fitness decay. Our study highlights the value of predictive models to aid seed provenancing and improve the success of revegetation projects.

Identification of population structure is a common goal for a variety of applications, including conservation, wildlife management, and medical genetics. The outcome of these analyses can have far reaching implications; therefore, it is important to ensure an understanding of the strengths and weaknesses of the methodologies used. Increasing in popularity, the discriminant analysis of principal components (DAPC) method incorporates combinations of genetic variables (alleles) into a model that differentiates individuals into genetic clusters. However, users may not have a full understanding of how to best parameterize the model. In this issue of Thia (Molecular Ecology Resources, 2022) looks under the hood of the DAPC. Using simulated data, he demonstrates the importance of careful parameter selection in developing a DAPC model, what the implications are for over-fitting the model, and finally, how best to evaluate the results of DAPC models. This work highlights the issues that can arise when over-parameterizing the DAPC model when gene flow is high among clusters and provides important guidelines to ensure researchers are making conclusions that are biologically relevant.

The analysis of genomic data can be an intimidating process, particularly for researchers who are not experienced programmers. Commonly used analyses are spread across many programs, each requiring their own specific input formats, and so data must often be repeatedly reorganized and transformed into new formats. Analyses often require splitting data according to metadata variables such as population or family, which can be challenging to manage in large data sets. Here, we introduce snpR, a user-friendly data analysis package in R for processing SNP genomic data. snpR is designed to automate data subsetting and analyses across categorical metadata while also streamlining repeated analyses by integrating approaches contained in many different packages in a single ecosystem. snpR facilitates iterative and efficient analyses centred on a single R object for an entire analysis pipeline.

Genome introgression is one of the driving forces that can increase species and genetic diversity and facilitate the adaptive evolution of organisms and biodiversity conservation. However, the genomic introgression and its contribution to biodiversity of macrofungi are still unclear. The genus Ganoderma is a typical macrofungal group that plays crucial roles in forest ecosystem as saprophytic organisms and plant pathogens, and is also involved in human health as medicinal mushrooms. Most public Ganoderma genomes are fragmented, and reference genomes and whole-genome information of diverse germplasm resources for many Ganoderma species are lacking, thus hindering functional and evolutionary genomic investigations among Ganoderma species. In this study, we provide high-quality genomes of 10 Ganoderma species and whole-genome variants data of 224 individuals from various ecoregions, enabling us to infer the phylogeny of Ganoderma species and their historical population dynamics. Based on whole-genome variants, widespread and genome-wide introgression among Ganoderma species is revealed. Genes with significant introgression signals were related to stress response, digestive absorption, and secondary metabolite synthesis, factors that may contribute to environmental adaptation and important biocomponent metabolism. CYP512U6, an essential functional gene in the CYP450 family related to Ganoderma triterpene synthesis, was detected with significant introgression and selection signals combined with Ganoderma metabolomic analysis, indicating that both ancient gene exchange and recent domestication have contributed to the categories and content of secondary metabolites of Ganoderma. The reference genomes, whole-genome variants, and metabolite profiles could serve as abundant and valuable genetic resources for evolution, ecology, and conservation investigations of Ganoderma species and other macrofungi.