Selective logging effects on plant functional traits depend on soil enzyme activity and nutrient cycling in a Pinus yunnanensis forest

Abstract

Selective logging has a wide impact on the remaining trees and soils which in turn influences total ecosystem function. Lots of studies focus on effects of selective logging on changing of plant functional traits, but the mechanism underlying these effects is still unclear. Thus, we studied 6 plant functional traits including leaf N concentration (LNC), leaf P concentration (LPC), leaf N:P ratio (N:P), wood density (WD), specific leaf area (SLA), and leaf dry mass content (LDMC) typically associated with resource acquisition and allocation. We measured these traits across 13 tree species in 52 plots under 5 selective logging intensities in a Pinus yunnanensis forest and aimed to test how selective logging and soil factors affect community weighted mean (CWM) of plant functional traits. The results revealed significant positive correlations between LNC and LPC, LNC and SLA, LPC and SLA, N:P and LDMC. Significant negative correlations were found between LNC and LDMC, LPC and N:P, LPC and LDMC. Only soil β-glucosidase activity (SGA), soil phosphatase activity (SPA), soil total nitrogen (STN), soil total phosphorus (STP) and soil hydrolysable nitrogen (SHN) had a significant relationship with one or several of the 6 plant functional traits. Compared with the control group, there was little significant difference in all plant functional traits across the different selective logging intensities. However, for plant functional traits, selective logging significantly increased the LNC, LPC and SLA. For soil factors, selective logging significantly increased the SGA, soil urease activity (SUA), SPA, soil moisture (SM), STN, STP and SHN. Based on structural equation models, we found that although selective logging has no direct effect on plant functional traits, it can indirectly affect plant functional traits through soil factors, mainly soil enzyme activity and nutrient cycling. Meanwhile, the mechanism of selective logging affecting different functional traits may be varied. Our findings provide a theoretical basis for the scientific management of P. yunnanensis forest.