'Chimes of resilience': what makes forest trees genetically resilient?

Abstract

Forest trees are foundation species of many ecosystems and are challenged by global environmental changes. We assemble genetic facts and arguments supporting or undermining resilient responses of forest trees to those changes. Genetic resilience is understood here as the capacity of a species to restore its adaptive potential following environmental changes and disturbances. Importantly, the data come primarily from European temperate tree species with large distributions and consider only marginally species with small distributions. We first examine historical trajectories of trees during repeated climatic changes. Species that survived the Pliocene-Pleistocene transition and underwent the oscillations of glacial and interglacial periods were equipped with life history traits enhancing persistence and resilience. Evidence of their resilience also comes from the maintenance of large effective population sizes across time and rapid microevolutionary responses to recent climatic events. We then review genetic mechanisms and attributes shaping resilient responses. Usually, invoked constraints to resilience, such as genetic load or generation time and overlap, have limited consequences or are offset by positive impacts. Conversely, genetic plasticity, gene flow, introgression, genetic architecture of fitness-related traits and demographic dynamics strengthen resilience by accelerating adaptive responses. Finally, we address the limitations of this review and highlight critical research gaps.

Keywords: Quaternary; effective population size; forest trees; gene flow; genetic resilience; local adaptation.

Fig. B1

Climate variations and tree extinctions during the Pliocene–Pleistocene transition. The graph represents the variation of the surface–air temperature anomaly (Δ_air(°C)) over Antarctica reconstructed from changes in benthic oxygen isotope records (δ¹⁸O). From data in De Boer et al. (2014).

Fig. 1

Genetic resilience of seven common European tree species. (a) Change in effective population size (N _e) through time (million years ago, Ma), inferred with Stairway Plot 2 (lines, with one‐sample‐per‐population). The median changes in N _e are reported. (b) A zoom‐in of (a) focusing on the 0–1.8 Ma period. Grey‐shaded rectangles delineate periods of glacial advance. (c) Species can be grouped based on their N _e trajectories. Heatmap based on Kendall's correlation coefficients computed from changes in N _e through time between each pair of species. The order of species along the x‐axis is the same as that along the y‐axis. Blue and red colors represent negative and positive correlation values, respectively. Adapted from Milesi et al. (2024).

Fig. B3

Estimating linked selection from temporal genome‐wide allelic frequency changes. See text for explanation.

Fig. 2

Ratio π ₀ : π ₄, where π ₀ is nucleotide diversity at zerofold sites (nonsynonymous) and π ₄ is nucleotide diversity at fourfold sites (synonymous), differs between combinations of mating system and lifespan. Each dot corresponds to a species. Box limits indicate the range of the central 50% of the data, and the central line marks the median value. The ratio π ₀ : π ₄ is as high in long‐term perennials as in annual selfers, suggesting that they accumulate deleterious mutations at the same rate. LTP, long‐term perennial; STP, short‐term perennial. The list of the species used to draw the figure is given in Supporting Information Table S1.

Fig. B4

Co‐ and countergradient variation for growth (a, b) and seed weight (c, d) in sessile oak along an elevational gradient in the French Pyrénées (based on data from Caignard et al., 2021).

Fig. 3

Distribution of adaptive introgressed single nucleotide polymorphisms (SNPs) across the genome and their association with environmental factors. (a) Distribution of the number of SNPs under diversifying selection in windows of introgression for Quercus acutissima 12 chromosomes. The y‐axis represents the coordinates of chromosomes, and the x‐axis represents the 12 chromosomes. (b) Density distribution of introgression windows with significant evidence of introgression on Chr. 9–12. The x‐axis shows the coordinates of each chromosome that was equally divided into 512 bins, and the y‐axis gives the number of 10‐kb sliding windows with significant evidence of introgression. Introgression is estimated by f _d , which quantifies the proportion of genomic regions affected by introgression (Martin et al., 2015). Each distribution curve corresponds to a population whose name is given at the start of the curve. (c) All four main introgressed regions in Chr. 9–12 are enriched with SNPs with frequency significantly associated with environmental/geographical factors (written in bold face), aligned vertically with each major introgression peak represented in (b, c). The size of the dots represents the mean Bayesian factor across all significant SNPs in the window. Figure adapted from Fu et al. (2022) (Creative Commons Attribution 4.0 International License).

Fig. B5

Evolvability in oaks compared with other species. Based on data from Hendry et al. (2018) and Alexandre et al. (2020). See text for further explanations.