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Assisted migration programs, introducing new better adapted species at critical locations in our forests, have the potential to mitigate the adverse effects of climate change through the increase of forest diversity and resilience. While such measures entail ecological risks related with invasiveness of exotic species or outbreaks of new diseases, introducing close relatives of native species or populations from different parts of the species range is seen as the ecologically safer option. However, due to the similar appearance of closely related species, monitoring based on the external phenotype becomes difficult and leaves genetic screening as the only reliable, yet expensive option, limiting our ability to monitor large geographic areas. Reflectance spectroscopy has emerged as an important tool to assess plant functional trait distributions and taxonomic diversity, representing a rapid, scalable and integrated measure of the plant external and internal phenotype.Here, we examine the potential of leaf-level reflectance spectroscopy to discriminate between the subspecies European beech (Fagus sylvatica L.), and Oriental beech (Fagus sylvatica spp Orientalis (Lipsky) Greut. & Burd), which has been proposed as a potential candidate for assisted migration in European forests due to its greater genetic diversity and potentially higher drought tolerance. We investigated two European beech forests in France and Switzerland where Oriental beech from the Caucasus was introduced over 100 years ago next to European beech. Over the summers of 2021 and 2022, we measured leaf spectral reflectance and leaf morphological and biochemical traits from previously genotyped adult trees.Using least squares discriminant analysis (PLS-DA), we found that leaf spectral reflectance allowed the accurate discrimination of the two beech subspecies. In particular, we found that the short-wave-infrared (SWIR) region between 1450-1750 nm from top-of-canopy leaves provided the most accurate subspecies discrimination (BA = 0.86±0.08, k = 0.72±0.15). To provide a mechanistic basis of our findings, we estimated a suite of leaf traits based on spectra-derived indices and standard field and lab protocols. Phenotyping confirmed significant subspecies differences between traits that are known to govern light-plant interactions in the SWIR, including lignin, nitrogen in proteins, leaf mass per area and leaf thickness.Our study provides a basis for crown-level subspecies classifications from airborne or satellite-based imagery in the genus Fagus. Our findings provide an important starting point for the interpretation of variability in tree crown reflectance and the superior discrimination capacity we found for leaves at the top as compared to leaves at the bottom of the canopy, holds promise for the upscaling of the method using remote sensing.

Abies alba has a high potential for mitigating climate change in European mountain forests, yet, its natural regeneration is severely limited by ungulate browsing. Here, we simulated browsing in a common garden experiment to study growth and physiological traits, measured from bulk needles, using a randomized block design with two levels of browsing severity and seedlings originating from 19 populations across Switzerland. Genetic factors explained most variation in growth (on average, 51.5%) and physiological traits (10.2%) under control conditions, while heavy browsing considerably reduced the genetic effects on growth (to 30%), but doubled those on physiological traits related to C storage. While browsing reduced seedling height, it also lowered seedlings’ water use efficiency (decreased δ13C) and N supply by mycorrhizal fungi as indicated by an increase in δ15N. Different populations reacted differently to browsing stress, and for Height, Starch and δ15N, population differences appeared to be the result of natural selection. We found that the fastest growing populations, originating from the warmest regions, decreased their needle starch level the most as a reaction to heavy browsing, suggesting a potential genetic underpinning for a growth-storage trade-off. Further, we found that seedlings originating from mountain populations growing on steep slopes had a significantly lower N discrimination in the common garden than those originating from flat areas, indicating that they have been selected to grow on N poor, potentially drained, soils. This finding was corroborated by the fact that N concentration in adult needles was lower on steep slopes than on flat ground, strongly indicating that steep slopes are the most N poor environments. Seedlings from these poor environments generally had a low growth rate and high storage, thus might be slower to recover from browsing stress than fast growing provenances from the warm environments with developed soils, such as the Swiss plateau.

The benefits and risks of human-aided translocation of individuals within the species range, assisted gene flow (AGF), depend on the genetic divergence, on the rate and direction of hybridization, and on the climate transfer distance between the host and donor populations. In this study, we explored the use of Oriental beech (Fagus sylvatica subsp. orientalis), growing from Iran to the Balkans, for AGF into European beech populations (F. sylvatica subsp. sylvatica) that increasingly suffer from climate warming. Using samples from natural populations of Oriental and European beech and microsatellite loci, we identified 5 distinct genetic clusters in Oriental beech with a divergence (FST) of 0.15 to 0.25 from European beech. Using this knowledge, we traced the origin of 11 Oriental beech stands in Western Europe established during the 20th century. In two stands of Greater Caucasus origin, we found evidence for extensive hybridization, with 18% and 41% of the offspring having hybrid status. Climate data revealed higher seasonality with warmer and drier summers across the native Oriental beech sites in comparison to the planting sites in Western Europe. Accordingly, we found that bud burst of Oriental beech occurred four days earlier than in European beech. Overall, our results suggest that AGF of Oriental beech could increase the genetic diversity of European beech stands and may foster introgression of variants adapted to expected future climatic conditions. Our study showcases the evaluation of the benefits and risks of AGF and call for similar studies on other native tree species., Forest Ecology and Management, 531, ISSN:0378-1127, ISSN:1872-7042

Ecologists and evolutionary biologists are well aware that natural and sexual selection do not operate on traits in isolation, but instead act on combinations of traits. This long-recognized and pervasive phenomenon is known as multivariate selection, or-in the particular case where it favours correlations between interacting traits-correlational selection. Despite broad acknowledgement of correlational selection, the relevant theory has often been overlooked in genomic research. Here, we discuss theory and empirical findings from ecological, quantitative genetic and genomic research, linking key insights from different fields. Correlational selection can operate on both discrete trait combinations and quantitative characters, with profound implications for genomic architecture, linkage, pleiotropy, evolvability, modularity, phenotypic integration and phenotypic plasticity. We synthesize current knowledge and discuss promising research approaches that will enable us to understand how correlational selection shapes genomic architecture, thereby linking quantitative genetic approaches with emerging genomic methods. We suggest that research on correlational selection has great potential to integrate multiple fields in evolutionary biology, including developmental and functional biology, ecology, quantitative genetics, phenotypic polymorphisms, hybrid zones and speciation processes.

Ecologists and evolutionary biologists are well aware that natural and sexual selection do not operate on traits in isolation, but instead act on combinations of traits. This long-recognized and pervasive phenomenon is known as multivariate selection, or-in the particular case where it favours correlations between interacting traits-correlational selection. Despite broad acknowledgement of correlational selection, the relevant theory has often been overlooked in genomic research. Here, we discuss theory and empirical findings from ecological, quantitative genetic and genomic research, linking key insights from different fields. Correlational selection can operate on both discrete trait combinations and quantitative characters, with profound implications for genomic architecture, linkage, pleiotropy, evolvability, modularity, phenotypic integration and phenotypic plasticity. We synthesize current knowledge and discuss promising research approaches that will enable us to understand how correlational selection shapes genomic architecture, thereby linking quantitative genetic approaches with emerging genomic methods. We suggest that research on correlational selection has great potential to integrate multiple fields in evolutionary biology, including developmental and functional biology, ecology, quantitative genetics, phenotypic polymorphisms, hybrid zones and speciation processes.

Silver fir (Abies alba Mill.) is a keystone conifer of European montane forest ecosystems that has experienced large fluctuations in population size during during the Quaternary and, more recently, due to land-use change. To forecast the species’ future distribution and survival, it is important to investigate the genetic basis of adaptation to environmental change, notably to extreme events. For this purpose, we here provide a first draft genome assembly and annotation of the silver fir genome, established through a community-based initiative. DNA obtained from haploid megagametophyte and diploid needle tissue was used to construct and sequence Illumina paired-end and mate-pair libraries, respectively, to high depth. The assembled A. alba genome sequence accounted for over 37 million scaffolds corresponding to 18.16 Gb, with a scaffold N50 of 14,051 bp. Despite the fragmented nature of the assembly, a total of 50,757 full-length genes were functionally annotated in the nuclear genome. The chloroplast genome was also assembled into a single scaffold (120,908 bp) that shows a high collinearity with both the A. koreana and A. sibirica complete chloroplast genomes. This first genome assembly of silver fir is an important genomic resource that is now publicly available in support of a new generation of research. By genome-enabling this important conifer, this resource will open the gate for new research and more precise genetic monitoring of European silver fir forests.

Drought is increasingly considered as the most important selection pressure for forest trees in the context of climate change. We studied adaptation to drought in marginal populations of silver fir (Abies albaMill.) from the French Mediterranean Alps. Drought tolerance was assessed using proxies both from seedlings and adult trees. We measured water stress response, growth and bud break of seedlings originating from 16 populations in a greenhouse common garden experiment (N=8199) and water use efficiency viaδ13Cof adult trees of the source populationsin-situ(N=315). Further, 357 single nucleotide polymorphisms (SNPs) were used to uncover the demographic history of the populations. Demographic distances between populations were used to generate a null expectation for trait divergence, thereby detect the signature of natural selection. We found evidence for adaptive population divergence in drought tolerance across life stages. Seedlings originating from source populations with low soil water capacity resisted better to water stress in the greenhouse, and additionally, adult trees from these populations had a higher water use efficiency. Seedling growth showed an evolutionary trade-off with drought tolerance: seedlings with fast growth and high stature came from populations that had lower drought tolerance. In contrast, population divergence in bud break showed only a weak signal of adaptation, which was independent of that in drought tolerance. Variation in phenology between populations was associated with variance in temperature and drought frequency and severity at the source populations. Our results highlight the adaptive value of marginal populations, advance our understanding of the different processes that have allowed silver fir to cope with drought stress under a warming climate, and contribute to our knowledge to advise assisted migration programs.

Heterogeneous environments, such as mountainous landscapes, create spatially varying selection pressure that potentially affects several traits simultaneously across different life stages, yet little is known about the general patterns and drivers of adaptation in such complex settings. We studied silver fir (Abies alba Mill.) populations across Switzerland and characterized its mountainous landscape using downscaled historical climate data. We sampled 387 trees from 19 populations and genotyped them at 374 single-nucleotide polymorphisms (SNPs) to estimate their demographic distances. Seedling morphology, growth and phenology traits were recorded in a common garden, and a proxy for water use efficiency was estimated for adult trees. We tested whether populations have more strongly diverged at quantitative traits than expected based on genetic drift alone in a multi-trait framework, and identified potential environmental drivers of selection. We found two main responses to selection: (i) populations from warmer and more thermally stable locations have evolved towards a taller stature, and (ii) the growth timing of populations evolved towards two extreme strategies, 'start early and grow slowly' or 'start late and grow fast', driven by precipitation seasonality. Populations following the 'start early and grow slowly' strategy had higher water use efficiency and came from inner Alpine valleys characterized by pronounced summer droughts. Our results suggest that contrasting adaptive life-history strategies exist in silver fir across different life stages (seedling to adult), and that some of the characterized populations may provide suitable seed sources for tree growth under future climatic conditions.

Summary 1. Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data. Approximate Bayesian computation (ABC) is devoted to these complex models because it bypasses the evaluation of the likelihood function by comparing observed and simulated data. 2. We introduce the R package ‘abc’ that implements several ABC algorithms for performing parameter estimation and model selection. In particular, the recently developed nonlinear heteroscedastic regression methods for ABC are implemented. The ‘abc’ package also includes a cross-validation tool for measuring the accuracy of ABC estimates and to calculate the misclassification probabilities when performing model selection. The main functions are accompanied by appropriate summary and plotting tools. 3. R is already widely used in bioinformatics and several fields of biology. The R package ‘abc’ will make the ABC algorithms available to a large number of R users. ‘abc’ is a freely available R package under the GPL license, and it can be downloaded at http://cran.r-project.org/web/packages/

International audience; Understanding the forces that influence natural variation within and among populations has been a major objective of evolutionary biologists for decades. Motivated by the growth in computational power and data complexity, modern approaches to this question make intensive use of simulation methods. Approximate Bayesian Computation (ABC) is one of these methods. Here we review the foundations of ABC, its recent algorithmic developments, and its applications in evolutionary biology and ecology. We argue that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model. ABC can be a powerful tool to make inferences with complex models if these principles are carefully applied.

Knowledge of relatedness between pairs of individuals plays an important role in many research areas including evolutionary biology, quantitative genetics, and conservation. Pairwise relatedness estimation methods based on genetic data from highly variable molecular markers are now used extensively as a substitute for pedigrees. Although the sampling variance of the estimators has been intensively studied for the most common simple genetic relationships, such as unrelated, half- and full-sib, or parent–offspring, little attention has been paid to the average performance of the estimators, by which we mean the performance across all pairs of individuals in a sample. Here we apply two measures to quantify the average performance: first, misclassification rates between pairs of genetic relationships and, second, the proportion of variance explained in the pairwise relatedness estimates by the true population relatedness composition (i.e., the frequencies of different relationships in the population). Using simulated data derived from exceptionally good quality marker and pedigree data from five long-term projects of natural populations, we demonstrate that the average performance depends mainly on the population relatedness composition and may be improved by the marker data quality only within the limits of the population relatedness composition. Our five examples of vertebrate breeding systems suggest that due to the remarkably low variance in relatedness across the population, marker-based estimates may often have low power to address research questions of interest.

Templeton's letter [1] contains a reformulation of his previous arguments against some of the works cited in our review [2–7], and refers to the core of our article only marginally. Templeton's main objection to ABC is that it can produce posterior probabilities that are not ‘true’ probabilities [2–4]. The argument is as follows. Suppose that we want to test the hypothesis that some parameter q takes a given value q0 from a continuum, for example from the interval [0,1]. A model that assumes q = q0 must have a probability that is less than or equal to the probability of a more general model, e.g.