Drought Sensitivity of Norway Spruce at the Species' Warmest Fringe: Quantitative and Molecular Analysis Reveals High Genetic Variation Among and Within Provenances

Abstract

Norway spruce (Picea abies) is by far the most important timber species in Europe, but its outstanding role in future forests is jeopardized by its high sensitivity to drought. We analyzed drought response of Norway spruce at the warmest fringe of its natural range. Based on a 35-year old provenance experiment we tested for genetic variation among and within seed provenances across consecutively occurring strong drought events using dendroclimatic time series. Moreover, we tested for associations between ≈1,700 variable SNPs and traits related to drought response, wood characteristics and climate-growth relationships. We found significant adaptive genetic variation among provenances originating from the species' Alpine, Central and Southeastern European range. Genetic variation between individuals varied significantly among provenances explaining up to 44% of the phenotypic variation in drought response. Varying phenotypic correlations between drought response and wood traits confirmed differences in selection intensity among seed provenances. Significant associations were found between 29 SNPs and traits related to drought, climate-growth relationships and wood properties which explained between 11 and 43% of trait variation, though 12 of them were due to single individuals having extreme phenotypes of the respective trait. The majority of these SNPs are located within exons of genes and the most important ones are preferentially expressed in cambium and xylem expansion layers. Phenotype-genotype associations were stronger if only provenances with significant quantitative genetic variation in drought response were considered. The present study confirms the high adaptive variation of Norway spruce in Central and Southeastern Europe and demonstrates how quantitative genetic, dendroclimatic and genomic data can be linked to understand the genetic basis of adaptation to climate extremes in trees.

Keywords: Picea abies; drought tolerance; genetic association; repeatability; single nucleotide polymorphism; wood anatomy; xylem.

Figure 1

Drought occurrence and resulting increment declines of Norway spruce at a trial site located in the Northeast of Austria. The bars show the standardized precipitation index SPI (given in standard deviations) on time scales of 1 (SPI-1) and 3 (SPI-3) months. The line plots illustrate the course of annual increment, as dimensionless ring width index (RW index). Arrows mark the three most distinct drought events which effects were analyzed in the present study.

Figure 2

Variation in drought resistance (Rt) among 11 provenances of Norway spruce within the three major drought periods (1993, 2000, and 2003 – from top to bottom). Boxes gives 1^st to 3^rd quartile, band inside boxes median, and whiskers the 1.5 interquartile range. Lower case letters above the provenance label indicate result of Duncan’s multiple comparison that was applied as post-hoc test after ANOVA (Table S5).

Figure 3

Variation in mean annual increment (ring width given in mm × 10⁻²) and mean wood density (given in g/m³) among 11 provenances of Norway spruce. Boxes gives 1^st to 3^rd quartile, band inside boxes median, and whiskers the 1.5 interquartile range. Lower case letters above the provenance label indicate result of Duncan’s multiple comparison that was applied as post-hoc test after ANOVA (Table S5).

Figure 4

Variation in trait-trait correlations among provenances. Shown are max-min differences. For example, the correlation between resilience during the drought in 1993 (Rs93) and growth response to May precipitation (CorMayP) was 0.62 for provenance B3 whereas it was -0.55 for provenance Q14 (both significant at P < 0.001 and <0.01, respectively; Figure 5). The color key in the upper left corner shows the absolute correlation difference (x-axis) as well as the frequency of occurrence (black line on y-axis). See also Figure 5 for two explanatory examples.

Figure 5

Variation in phenotypic correlations among provenances for two selected trait combinations. (A) Correlation between resilience in 1993 and growth response to May precipitation for two different provenances. (B) Correlation between recovery and maximum density for two different provenances.

Figure 6

(A) Scatterplot of Rt93 vs. Rt00 together with marginal boxplots of genotypes from marker GQ03709-C10.1.1133 associated to Scenario 1; Genotypes were colored as follows: dark gray (CC), gray (CT) and white (TT). (B) Scatterplot of Rt93 vs. Rt00 together with marginal boxplots of genotypes from marker GQ03204-K15.1.405 associated to Scenario 2; Genotypes were colored as follows: white (TT) and dark gray (CT). Dashed lines represent linear regression and boxes within the marginal boxplots represent the median (black middle line) limited by the 25^th (Q1) and 75^th (Q3). Numbers below each genotype indicates sample sizes.