Leaf Functional Traits and Their Influencing Factors in Six Typical Vegetation Communities

Abstract

Leaf functional traits (LFTs) have become a popular topic in ecological research in recent years. Here, we measured eight LFTs, namely leaf area (LA), specific leaf area (SLA), leaf thickness (LT), leaf dry matter content (LDMC), leaf carbon content (LCC), leaf nitrogen content (LNC), leaf phosphorus content (LPC), and leaf potassium content (LKC), in six typical vegetation communities (sclerophyllous evergreen broad-leaved forests, temperate evergreen coniferous forests, cold-temperate evergreen coniferous forests, alpine deciduous broad-leaved shrubs, alpine meadows, and alpine scree sparse vegetation) in the Chayu River Basin, southeastern Qinghai-Tibet Plateau. Our aim was to explore their relationships with evolutionary history and environmental factors by combining the RLQ and the fourth-corner method, and the method of testing phylogenetic signal. The results showed that (i) there were significant differences in the eight LFTs among the six vegetation communities; (ii) the K values of the eight LFTs were less than 1; and (iii) except for LCC, all other LFTs were more sensitive to environmental changes. Among these traits, LA was the most affected by the environmental factors, followed by LNC. It showed that the LFTs in the study were minimally influenced by phylogenetic development but significantly by environmental changes. This study further verified the ecological adaptability of plants to changes in environmental factors and provides a scientific basis for predicting the distribution and diffusion direction of plants under global change conditions.

Keywords: RLQ analysis; environmental factors; leaf functional traits; phylogenetic signals.

Figure 1

Differences in leaf functional traits among plant communities. Leaf functional traits (LFTs): (a) leaf area (LA), (b) specific leaf area (SLA), (c) leaf thickness (LT), (d) leaf dry matter content (LDMC), (e) leaf carbon content (LCC), (f) leaf nitrogen content (LNC), (g) leaf phosphorus content (LPC), and (h) leaf potassium content (LKC). Community types: sclerophyllous evergreen broad-leaved forests (A), temperate evergreen coniferous forests (B), cold-temperate evergreen coniferous forests (C), alpine deciduous broad-leaved shrubs (D), alpine meadows (E), and alpine scree sparse vegetation (F). Significant differences in post hoc Dunn tests are represented by different letters above the boxplots.

Figure 2

Phylogenetic tree of typical vegetation communities in the Chayu River Basin.

Figure 3

Phylogenetic correlation of species in typical vegetation communities in the Chayu River Basin. Species names are colored according to community types, blue: sclerophyllous evergreen broad-leaved forests; green: temperate evergreen coniferous forests; purple: cold-temperate evergreen coniferous forests; red: alpine deciduous broad-leaved shrubs; yellow: alpine meadows; pink: alpine scree sparse vegetation. Black bar: no positive autocorrelation; red bar: positive autocorrelation.

Figure 4

RLQ analysis of the Chayu River Basin. Study sites and species names are colored according to community types, red: sclerophyllous evergreen broad-leaved forests; orange: temperate evergreen coniferous forests; yellow: cold-temperate evergreen coniferous forests; green: alpine deciduous broad-leaved shrubs; light blue: alpine meadows; blue: alpine scree sparse vegetation.

Figure 5

Fourth-corner analysis diagram. Red squares indicate positive relationships, blue squares indicate negative relationships, and gray squares indicate nonsignificant.

Figure 6

Study area and sample distribution map. Community types: sclerophyllous evergreen broad-leaved forests (I), temperate evergreen coniferous forests (II), cold-temperate evergreen coniferous forests (III), alpine deciduous broad-leaved shrubs (IV), alpine meadows (V), and alpine scree sparse vegetation (VI).