Adaptation to drought is coupled with slow growth, but independent from phenology in marginal silver fir (Abies alba Mill.) populations

Drought is one of the most important selection pressures for forest trees in the context of climate change. Yet, the different evolutionary mechanisms, and their environmental drivers, by which certain populations become more drought tolerant than others is still little understood. We studied adaptation to drought in 16 silver fir (Abies alba Mill.) populations from the French Mediterranean Alps by combining observations on seedlings from a greenhouse experiment (N = 8,199) and on adult tress in situ (N = 315). In the greenhouse, we followed half‐sib families for four growing seasons for growth and phenology traits, and tested their water stress response in a "drought until death" experiment. Adult trees in the field were assessed for δ13C, a proxy for water use efficiency, and genotyped at 357 SNP loci. SNP data was used to generate a null expectation for seedling trait divergence between populations in order to detect the signature of selection, and 31 environmental variables were used to identify the selective environment. We found that seedlings originating from populations with low soil water capacity grew more slowly, attained a smaller stature, and resisted water stress for a longer period of time in the greenhouse. Additionally, adult trees of these populations exhibited a higher water use efficiency as evidenced by their δ13C. These results suggest a correlated evolution of the growth‐drought tolerance trait complex. Population divergence in bud break phenology was adaptive only in the second growing season, and evolved independently from the growth‐drought tolerance trait complex. Adaptive divergence in bud break phenology was principally driven by the inter‐ and intra‐annual variation in temperature at the geographic origin of the population. Our results illustrate the different evolutionary strategies used by populations to cope with drought stress at the range limits across a highly heterogeneous landscape, and can be used to inform assisted migration programs.