Increase in leaf organic acids to enhance adaptability of dominant plant species in karst habitats

Abstract

Estimation of leaf nutrient composition of dominant plant species from contrasting habitats (i.e., karst and nonkarst forests) provides an opportunity to understand how plants are adapted to karst habitats from the perspective of leaf traits. Here, we measured leaf traits-specific leaf area (SLA), concentrations of total carbon ([TC]), nitrogen ([TN]), phosphorus ([TP]), calcium ([Ca]), magnesium ([Mg]), manganese ([Mn]), minerals ([Min]), soluble sugars, soluble phenolics, lipids, and organic acids ([OA])-and calculated water-use efficiency (WUE), construction costs (CC), and N/P ratios, and searched for correlations between these traits of 18 abundant plant species in karst and nonkarst forests in southwestern China. Variation in leaf traits within and across the abundant species was both divergent and convergent. Leaf [TC], [Ca], [Min], [OA], and CC were habitat-dependent, while the others were not habitat- but species-specific. The correlations among [TN], [TP], SLA, [TC], CC, [Min], WUE, [OA], and CC were habitat-independent, and inherently associated with plant growth and carbon allocation; those between [CC] and [Lip], between [Ca] and [Mg], and between [Mg] and [WUE] were habitat-dependent. Habitat significantly affected leaf [Ca] and thus indirectly affected leaf [OA], [Min], and CC. Our results indicate that plants may regulate leaf [Ca] to moderate levels via adjusting leaf [OA] under both high and low soil Ca availability, and offer new insights into the abundance of common plant species in contrasting habitats.

Keywords: Guizhou; adaptation; calcium; mineral; nonkarst; nutrients.

FIGURE 1

Variance explained for each leaf trait investigated in this study. * and ns indicate significance and nonsignificance, respectively, in two ANOVA analyses at p < .05. Abbreviations of all leaf traits are provided in Table 2

FIGURE 2

Variations (chi‐square) of the studied leaf traits within tree species from karst to nonkarst habitats (Kruskal test). Squares with a significant difference (p < .05) are filled. Bel, Betula luminifera; Brp, Broussonetia papyrifera; Caa, Camptotheca acuminata; Ces, Celtis sinensis; Clm, Clerodendrum mandarinorum; Deo, Debregeasia orientalis; Hoa, Hovenia acerba; Lil, Ligustrum lucidum; Lig, Lindera glauca; Lif, Liquidambar formosana; Lic, Litsea cubeba; Lim, Litsea mollis; Pls, Platycarya strobilacea; Poa, Populus adenopoda; Quf, Quercus fabri; Rop, Robinia pseudoacacia; Sas, Sapium sebiferum; Tov, Toxicodendron vernicifluum. Abbreviations of all leaf traits are provided in Table 2

FIGURE 3

Correlations (Pearson's correlation coefficients, r) between the traits of 18 common tree species from karst (K) and nonkarst (NK) habitats. The abbreviations of the tree species are indicated in Table 1. Abbreviation of all leaf traits is provided in Table 2

FIGURE 4

Differences in leaf traits of the dominant plant species between karst and nonkarst habitats. Different lowercase letters indicate significant differences between habitats based on linear mixed effect models (post hoc Tukey test, p < .05). The absence of lowercase letters indicates that the effect of habitat was not significant. Boxes in each boxplot show the first and third quartiles and the median; the upper and lower whiskers indicate the largest and smallest values away from 1.5*IQR (interquartile range) of the third quartiles and first quartiles, respectively; black points in each figure are values that fell outside the whiskers. Abbreviation of all leaf traits is provided in Table 2

FIGURE 5

Correlations of the studied leaf traits derived from the idaFast function in karst (a) and nonkarst (b) habitats. The lines (both dashed and solid ones) linking two traits denote significant correlations (p < .05, black for positive and red for negative), and the effect size is shown by number close to the line. The dashed and solid lines indicate that correlations are uniform and different, respectively, between both habitats. A missing edge between two traits indicates no causal effects. Abbreviation of all leaf traits is provided in Table 2

FIGURE 6

(a) structural equation model (SEM) paths of the effects of habitat on leaf traits; (b) the standardized total effects of habitat, leaf [Ca], [Min], and [OA] on leaf construction costs (CC); (c) correlations between leaf traits of 18 dominant tree species. These blank squares indicate that the correlations between leaf traits are nonsignificant (p > .05); and (d) the a priori modeling of SEM. We removed leaf carbon concentrations when performing SEM, because of the high correlation between leaf [TC] and CC (r = 0.96, p < .001, c). Abbreviation of all leaf traits are provided in Table 2

FIGURE 7

A conceptual model explaining causal effects between the concentrations of leaf calcium ([Ca]) and organic acids ([OA])