Plot information and plot data of Keteleeria davidiana forest communities in China

Keteleeria davidiana is a relict and endemic species found in China, and K. davidiana forests are distributed in Shaanxi, Sichuan, Hubei, Hunan, Guizhou and Yunnan. These forests play important roles in soil improvement, water conservation, material and medicinal functions. To provide a scientific basis for the conservation and sustainable utilization of K. davidiana germplasm resources, we obtained data from 48 forest plots where K. davidiana is the primary dominant species through field investigations. Based on the principles of community-ecological classification and the latest revision of the Chinese vegetation classification system, we classified the forests into Association Groups and Associations, and described the characteristics of each Association Group and Association, highlighting their community composition and structure. The main results are as follows: (1) We classified the 48 plots into 3 Association Groups, including K. davidiana - Shrub - Herb Association Group, K. davidiana - Herb Association Group and K. davidiana + broadleaf tree - Herb Association Group, and these three Association Groups were further divided into 7 Associations, including K. davidiana - Myrsine africana - Cyrtomium fortunei Association, K. davidiana - Camellia oleifera - Liriope spicata Association, K. davidiana - L. spicata Association, K. davidiana - Carex breviculmis Association, K. davidiana - Diplopterygium glaucum Association, K. davidiana - Isachne globosa Association, K. davidiana + Liquidambar formosana - Iris confusa Association. (2) In both the K. davidiana - Shrub - Herb Association Group and the K. davidiana - Herb Association Group, K. davidiana was the sole dominant species in the arbor layer. (3) In the surveyed K. davidiana forest, a significant number of young K. davidiana trees were present in the shrub layer, indicating good regeneration. In this study, we provide the original survey data of all plots and specify the sources of existing data.