Revegetation projects seeking to restore degraded ecosystems face a major challenge in sourcing appropriate plant material, as identifying plants adapted to future climates requires knowledge of plant performance under novel conditions. In order to support climate-resilient provenancing efforts, we develop a quantitative trait model that integrates genetic and microenvironmental variation. We train our model with multiple natural plantings of Arabidopsis thaliana and predict days-to-bolting and fecundity across the species' European range. Model prediction accuracy was high for days-to-bolting and moderate for fecundity, with the majority of trait variation being explained by temperature variation. Concerningly, fecundity was predicted to decline under future conditions, although this response was heterogeneous across regions, and could be offset through the introduction of specific genotypes. Our study highlights the value of predictive models to aid seed provenancing and improve the success of revegetation projects.

The genetic composition of founding populations plays a key role in determining invasion success. Despite this fact, the role of genetic variation on the potential distribution of invaders has rarely been investigated. Here, we integrate genomic data into ecological niche models (ENMs) to predict the distribution of globally invasive common ragweed (Ambrosia artemisiifolia) to Australia. We identified three genetic clusters of ragweed and used these clusters to construct separate ENMs. The predicted distribution of ragweed in Australia changed depending on the genetic composition and continent of origin of the source population. By quantifying this change, we identified source populations most likely to expand the ragweed distribution. As prevention remains the most effective method of invasive species management, our work provides a valuable way of ranking the threat posed by different populations to better inform management decisions.

Revegetation projects face the major challenge of sourcing the optimal plant material. This is often done with limited information about plant performance and increasingly requires to factor resilience to climate change. Functional traits can be used as quantitative indices of plant performance and guide provenancing, but trait values expected under novel conditions are often unkown. 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 accuracies were 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.

Mapping the genes underlying ecologically-relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterisation of a species' genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine through simulation, the sampling scheme that maximises the power to map the genetic basis of a complex trait in an outbreeding species across an idealised landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that QTL detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost-effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.

Mapping the genes underlying ecologically-relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterisation of a species' genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling. Here we determine through simulation, the sampling scheme that maximises the power to map the genetic basis of a complex trait across an idealised landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that QTL detection power and prediction accuracy are higher when performing a shallow sampling of more populations over the landscape which is done best using pool sequencing. Populations should be collected from areas of high genetic diversity and we recommend against sampling from the margins of the species' range. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.