Soil quality and C-S-R fungal communities in monoculture plantations after conversion from subtropical forest

Abstract

Intensive clear cutting of natural forests and conversion to monoculture plantations are ongoing worldwide, leading to degradation of soil quality and microbial functions. Here, we compared soil quality index (SQI) and fungal community in a natural forest (Forest) and four 5-year-old monoculture plantations, including Camellia oleifera (Oil), Amygdalus persica (Peach), Myrica rubra (Berry) and Cunninghamia lanceolata (Fir), in a subtropical region of China. After conversion, soil pH rose up to 0.31, but organic carbon, total nitrogen, sucrase, acid protease, glutaminase and phosphatase activities decreased by 83%, 59%, 43%, 31%, 64%, 66% and 77%, respectively, in the plantations. Correspondingly, the SQI dropped by 65%. High-throughput sequencing of the ITS1 region demonstrated an increase in α-diversity and a striking difference in β-diversity following conversion. Changes in the dominant fungal taxa following forest conversion to plantations was interpreted by Grime’s C-S-R life history framework. Conversion increased the fungal groups with stress-tolerant (S) and ruderal (R) strategies - mainly copiotrophic saprophytes, such as Ascomycota and Zygomycota, but decreased the fungal groups with competitor (C) strategies - mainly oligotrophic saprophytes and mycorrhizal fungi, such as Basidiomycota. Genera affiliated to those phyla including Pseudophialophora, Rhytisma increased, but Russula decreased. Redundancy analysis and structural equation modeling indicated that the diversity and composition of fungal communities changed with soil quality degradation, which were mainly driven by increased soil pH, decreased available carbon and nutrients (N, P), and related enzymes activities.

Analyzing the Land Surface Temperature Response to Urban Morphological Changes: A Case Study of the Chengdu–Chongqing Urban Agglomeration

“…Similar to Chen et al. (2022), we obtained the 1 km spatial resolution fusion land cover data in 2020 (Figure 1b) from the Resource and Environment Science and Data Center (Xu et al., 2018; https://doi.org/10.12078/2018070201) and reclassified the land use and land cover data into 24 categories according to the International Geosphere‐Biosphere Project classification scheme of the USGS to meet the classification standard in the WRF model. Finally, results of the model simulations were output every 1 hr.…”

“…The NCEP FNL data sets used for WRF simulations were obtained from https://doi.org/10.5065/D6M043C6 [Dataset] (National Centers for Environmental Prediction et al., 2000). The land use and land cover data sets in this work were obtained from https://doi.org/10.12078/2018070201 [Dataset] (Xu et al., 2018). The urban canopy parameters data set of 60 Chinese cities are available at https://doi.org/10.7910/DVN/VZH1QY [Dataset] (Sun et al., 2021), and the FROM‐GLC10 data are available at http://data.starcloud.pcl.ac.cn/zh/resource/1 [Dataset] (Gong et al., 2019).…”