Abstract
The leaf economics spectrum (LES) is the leading theory of plant ecological strategies based on functional traits, which explains the trade-off between dry matter investment in leaf structure and the potential rate of resource return, revealing general patterns of leaf economic traits investment for different plant growth types, functional types, or biomes. Prior work has revealed the moderating role of different environmental factors on the LES, but whether the leaf trait bivariate relationships are shifted across climate regions or across continental scales requires further verification. Here we use the Köppen–Geiger climate classification, a very widely used and robust criterion, as a basis for classifying climate regions to explore climatic differences in leaf trait relationships. We compiled five leaf economic traits from a global dataset, including leaf dry matter content (LDMC), specific leaf area (SLA), photosynthesis per unit of leaf dry mass (Amass), leaf nitrogen concentration (Nmass), and leaf phosphorus concentration (Pmass). Moreover, we primarily used the standardized major axis (SMA) analysis to establish leaf trait bivariate relationships and to explore differences in trait relationships across climate regions as well as intercontinental differences within the same climate type. Leaf trait relationships were significantly correlated across almost all subgroups (P < 0.001). However, there was no common slope among different climate zones or climate types and the slopes of the groups fluctuated sharply up and down from the global estimates. The range of variation in the SMA slope of each leaf relationship was as follows: LDMC–SLA relationships (from −0.84 to −0.41); Amass–SLA relationships (from 0.83 to 1.97); Amass–Nmass relationships (from 1.33 to 2.25); Nmass–Pmass relationships (from 0.57 to 1.02). In addition, there was significant slope heterogeneity among continents within the Steppe climate (BS) or the Temperate humid climate (Cf). The shifts of leaf trait relationships in different climate regions provide evidence for environmentally driven differential plant investment in leaf economic traits. Understanding these differences helps to better calibrate various plant-climate models and reminds us that smaller-scale studies may need to be carefully compared with global studies.
Highlights
It is well-known that plant functional traits have the potential to explain species’ adaption strategies and the response of plants to environments (Westoby and Wright, 2006)
Pmass showed the greatest variation at the species level (CV = 69.8%), while specific leaf area (SLA) showed the greatest variation at the site level (CV = 56.2%)
The relationships we described at the species level for leaf economic traits with larger sample sizes were very consistent with those described in previous global dataset GLOPNET (Wright et al, 2004), and these relationships were validated at the site level (Table 4)
Summary
It is well-known that plant functional traits have the potential to explain species’ adaption strategies and the response of plants to environments (Westoby and Wright, 2006). Leaf traits are considered to be sensitive and important indicators of environmental changes and useful in explaining how plants are responding to the current climate change of great concern (Garnier et al, 2001; Poorter et al, 2009; Salazar Zarzosa et al, 2021). Quantifying the relationship between leaf functional traits and climate is the key to explaining what traits make plants suitable for living in specific climatic regions and have the potential to predict the response of communities and ecosystems to future climate change (Heilmeier, 2019). Plant functional traits tend not to vary independently (Reich et al, 1997, 2003; Wright et al, 2001, 2004), which shows covariation or coordination caused by the allocation of limited resources to balance different needs (Candeias and Fraterrigo, 2020). Based on the framework of “economics spectrum,” wood (Chave et al, 2009), root (Roumet et al, 2016), seed (Saatkamp et al, 2019), flower (Roddy et al, 2021), and whole plant economics spectrums (Reich, 2014; Díaz et al, 2016) have been proposed in recent years
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