Predicting Spatial Distribution of Plant Functional Traits in a Forest-Steppe Zone

Abstract

We investigated the response mechanisms of plant functional traits to environmental factors at the community level in order to elucidate the adaptive and survival strategies of plants in different environmental gradients. 184 vegetation sampling plots were laid by stratified random sampling in the Saihanba region of Hebei, China. Three functional traits (leaf nitrogen content, LNC; specific leaf area, SLA; leaf dry matter content, LDMC) were measured and the community-level weighted means of the trait values were calculated by the species coverage values. Climate and terrain data were generated from the climate model ClimateAP and using ArcGIS. Finally, eight environmental factors, including climate, topographical, and soil factor, were recorded and the association with functional traits was analysed using a generalized additive model. Model testing indicated a good predictability for the SLA and LDMC while a relatively poor predictability was seen with LNC. Environmental factors that significantly impacted SLA included elevation, degree-days above 0°C, mean annual precipitation and total soil nitrogen content. In contrast, LDMC was significantly influenced by elevation, total soil nitrogen and phosphorous content while LNC was affected by elevation and degree-days above 0°C. High values of SLA and LNC were found in areas at lower elevations. The distribution of high LDMC values indicated that plant leaves have a relatively high tolerance and resistance to stress, which was better for plant to grow in the adverse environment. At the community level, clarifying plant functional traits distribution and their changes with environmental gradients is useful for the potential vegetation restoration.