Selective logging enhances ecosystem multifunctionality via increase of functional diversity in a Pinus yunnanensis forest in Southwest China

The impacts of selective logging on ecosystem multifunctionality (EMF) remain largely unexplored. In this study, we analyzed the response of nine variables related to four ecosystem functions (i.e. nutrient cycling, soil carbon stocks, decomposition, and wood production) to five selective logging intensities in a Pinus yunnanensis-dominated forest. We included a control group with no harvest to evaluate the potential shifts in EMF of the P. yunnanensis forests. We also assessed the relationship between above- and belowground biodiversity and EMF under these different selective logging intensities. Additionally, we evaluated the effects of biotic and abiotic factors on EMF using a structural equation modeling (SEM) approach.

Individual ecosystem functions (EFs) all had a significant positive correlation with selective logging intensity. Different EFs showed different patterns with the increase of selective logging intensity. We found that EMF tended to increase with logging intensity, and that EMF significantly improved when the stand was harvested at least twice. Both functional diversity and soil moisture had a significant positive correlation with EMF, but soil fungal operational taxonomic units (OTUs) had a significant negative correlation with EMF. Based on SEM, we found that selective logging improved EMF mainly by increasing functional diversity.

Our study demonstrates that selective logging is a good management technique from an EMF perspective, and thus provide us with potential guidelines to improve forest management in P. yunnanensis forests in this region. The functional diversity is maximized through reasonable selective logging measures, so as to enhance EMF.