Variations in leaf economics spectrum traits for an evergreen coniferous species: Tree size dominates over environment factors

Abstract

1. Many leaf traits strongly vary with tree size and environmental factors, but the importance of these factors to intraspecific variations of leaf traits in forest trees has rarely been simultaneously evaluated.2. We measured needle longevity and specific leaf area (SLA) and nitrogen (N) content of every needle age (0-to 4-year old) for 65 individuals with 0.3-100 cm diameter at breast height (DBH) for an evergreen coniferous species, Pinus koraiensis Sieb. et Zucc., in Northeast China. We simultaneously evaluated the effects of tree size (DBH or tree height) and environment factors (light intensity, soil N content and water availability) on the needle longevity, SLA, foliage N content as well as the slopes of regressions of SLA and foliage N content against needle age.3. All of the studied leaf traits and slopes of regressions of SLA and foliage N content against needle age were significantly related to tree size. Tree height had a greater impact on SLA and area-based leaf N content (N area ), whereas DBH was more important for needle longevity and mass-based leaf N content (N mass ). The environment variables, light intensity, soil N content and water availability, were rather minor factors for trait variations compared with tree size. Significant influence of light intensity was found only on needle longevity, and soil N and water availability had no effects on the leaf traits.4. Our study clearly showed that tree size is an important driver of intraspecific variations in the key leaf traits of P. koraiensis in a natural forest. We also emphasize the importance of DBH or tree height varies depending on leaf traits, suggesting various mechanisms of size effects on the intraspecific variations in leaf traits. We suggest that ecological significance of leaf trait variations needs reconsideration incorporating tree size effect.

Effects of ontogenetic stage and leaf age on leaf functional traits and the relationships between traits in Pinus koraiensis

“…LMA and LDMC increased and leaf N and P contents decreased significantly with increasing leaf age (Fig. 1), which is consistent with the widely observed patterns (Han et al 2008;Liu et al 2020). Furthermore, the various patterns of leaf functional traits with leaf age were similar in all the ontogenetic stages.…”

“…The total needle volume was determined by the volume displacement method (Chen et al 1997) for each leaf age group. The total needle area for each leaf age group was calculated using the equation from Liu et al (2020). The sample needles were then dried at 65 °C for 72 h and weighed to obtain the leaf dry mass (LDM, g).…”

“…The growth development and trait characteristics of leaves directly influence the basic behavior and functioning of a plant (Poorter et al 2008;Falster et al 2018). Previous studies have suggested that the expression of leaf traits, i.e., leaf thickness, leaf mass per area (LMA), mass-based leaf nitrogen (N) content, and phosphorus (P) content, may vary by ontogenetic stage (England and Attiwill 2005;Juárez-López et al 2008;Palow and Kitajima 2012;Liu et al 2020;). However, previous studies on functional traits have generally focused on a certain ontogenetic stage (Azuma et al 2019) or comparisons based on young and mature individuals (i.e., young trees and canopy trees) (Greenwood et al 2008;Mediavilla and Escudero 2009;Palow and Kitajima 2012;Kuusk et al 2018), with fewer studies based on all ontogenetic stages.…”

“…For evergreen coniferous trees with long leaf longevity, resource investment, allocation patterns, and trait characteristics vary with leaf age (Reich et al 1991;Pantin et al 2012;Mediavilla et al 2014;Liu et al 2020). For example, studies have shown that LMA and leaf dry matter content (LDMC) increase and N decreases with leaf age (Niinemets and Lukjanova 2003;Niinemets et al 2006;Han et al 2008;Liu et al 2020). Details of the various patterns of leaf functional traits by leaf age are helpful for better understanding the adaptive strategies used by plants to maximize carbongain, which is the basis for explaining the mechanisms that maintain community structure and optimize productivity (Kikuzawa and Lechowicz 2011).…”

Light spectra modify nitrogen assimilation and nitrogen content in Quercus variabilis Blume seedling components: A bioassay with 15N pulses

“…9.4 64-bit, SAS Institute Inc., Cary, NC, USA). Shapiro-Wilk test was conducted on data using the univariate procedure, and logarithm transformation plus 1.0 was used for data with abnormal distribution to meet the normality requirement [48]. The effects of LED spectra (blue, red, and green) and 15 N pulse (pulse vs control) were tested using two-way analysis of variance (ANOVA) using a split-block model with the placement of three replicated blocks (n = 3) as the random factor in the mixed procedure.…”