1. Introduction
Selective thinning through human intervention is a vital management measure for adjusting forest density and, as such, it has been widely implemented in forestry production [
1,
2,
3,
4,
5]. It can directly or indirectly influence changes in the microclimatic conditions of forest stands, such as light levels, temperature, and water content [
6,
7,
8], as well as the stocks and dynamics of soil organic carbon [
9]. Soil microbial community plays a fundamental role in the process of biogeochemical cycles by controlling the transformation of organic matter [
10,
11,
12], and act as a quality indicator reflecting the effect of forest management on soil and litter in forest ecosystem [
13]. With the development of biological technologies, a series of methods for analyzing the soil microbial community have been used, such as profiling of soil phospholipid fatty acids (PLFA), community level physiological profiles (CLPPs), next-generation DNA sequencing, high-throughput sequencing technologies, and metabarcoding techniques [
14,
15,
16]. The PLFA method is particularly rapid and sensitive [
17,
18]. In addition, several PLFA biomarkers have been defined, PLFA soil profiling has been established [
19,
20,
21], and the specific ratios of PLFA groups’ responses to the shifts in environmental conditions have been determined. Therefore, the PLFA method acts as a powerful tool to provide both functional and structural information on microbial communities. It has thus become one of the most commonly used methods to study microbial community structure since the early 1990s [
20,
22,
23,
24].
Several studies have indicated that thinning or clear-cutting can significantly decrease gram-negative bacteria, fungi, and actinomycete populations [
25,
26], whereas the creation of clear-cut openings in forests may increase the richness of arbuscular mycorrhiza fungal and bacterial communities [
27]. However, other researchers have asserted that the same thinning intensity causes a decrease in the richness of soil fungal communities and an increase in the richness of bacterial communities [
28]. Therefore, thinning can influence soil microbial functional diversity in a variety of ways [
29].
In forest ecosystems, the topmost layer of the soil profile is called the organic horizon and is a key factor in the control of ecosystem productivity. It serves as a vital nutrient pool for tree growth, and an energy and nutrient source for microbial growth and activity [
30]. Research on the chemical and biological processes that occur in leaf litter is crucial for an understanding of forest soil ecology and forest management processes [
31]. As leaf litter and soil have different responses to environmental stresses, they should be considered separately when investigating changes in microbial communities [
32]. Considering the complexity of the influence of thinning on soil microbes, it is essential to explore the response of the organic horizon to thinning.
Quantitative research on the influence of thinning on litter decomposition was first reported in the first decade of the 21st century [
33]. Previous studies have indicated that thinning significantly reduces annual litter biomass [
34]; at the same time, thinning intensity influences litter decomposition rate [
33] and results in changes to the chemical composition of the organic horizon [
35,
36,
37]. Further studies have shown that the thinning intensity significantly influences the availability of substrate resources to microbes, which leads to changes in microbial richness and enzymatic activity [
38,
39]. Furthermore, thinning has been shown to significantly alter the chemical properties and enzymatic activity of litter, with different types of change being observed in litter layers of different initial compositions [
40].
The Chinese pine is a major conifer and is often planted for ecological restoration in northern China. Due to a lack of effective forest management, artificial Chinese pine plantations usually show poor growth and are unable to provide the forest ecosystem functions they were planted for. From 2000 onwards, due to an increased emphasis placed on the provision of forest ecosystem services, the adjustment of stand density through thinning has become a key measure in the management of artificial Chinese pine plantations. To determine the effects of thinning in Chinese pine plantations, Dang et al. [
41] and Ma et al. [
42] studied the changes in plant diversity and seedling regeneration after thinning, while other studies have reported the influence of thinning on soil chemical properties, soil microbes, and enzymatic activity [
41,
43,
44].
Although research on thinning has achieved substantial advances to date, the effects of thinning vary with vegetation type and climatic zone [
45]. Therefore, a favorable microenvironment for understory vegetation [
3] and soil microbes [
29] can only be created with appropriate thinning intensities within specific regions. However, at present, the influence of thinning on microbial litter populations in Chinese pine plantations remains unclear. To bridge this knowledge gap and advance the state-of-art, in this study, we explored the changes in understory vegetation, chemical properties, and microbial community compositions in leaf litter in Chinese pine plantations induced by different thinning treatments. We hypothesized that the thinning of Chinese pine plantations would change litter microbial community structures, and that the microbial communities would be a function of the understory plant communities, litter mass, and the chemical properties of the organic horizon.
2. Materials and Methods
2.1. Study Area
This study was conducted in the Badaling Forest Farm (115°55′ E, 40°17′ N), which is located in the Yanqing district, approximately 60 km from Beijing, China. The Badaling Forest Farm is situated in the Yanshan mountain range, and has an average elevation of 780 m, with minimum and maximum elevations of 1238 and 450 m, respectively. The region has a warm-temperate, semi-humid, continental monsoon climate. According to observational records from Badaling Monitoring Station (Yanqing Meteorological Bureau), the average annual precipitation and average annual temperature across the 1981–2010 period were 435 mm and 9.7 °C, respectively. The existing vegetation mainly consists of artificial forests established since the 1950s, with the Chinese pine being the dominant species. The other tree species that are present include the black locust (
Robinia pseudoacacia L.), Shantung maple (
Acer truncatum Bge), and Oriental arborvitae (
Platycladus orientalis (L.) Franco.). The understory shrubs mainly consist of the three-lobed spirea (
Spiraea trilobata var.
pubescens),
Lespedeza floribunda Bge.,
Leptopus chinensis (Bge.)
Pojark, and
Deutzia grandiflora Bge. The soil is classified as Hapli-Ustic Cambosol, according to Chinese Soil Taxonomy [
46], which is derived from a granite parent material; the soil layer thickness is 30–70 cm.
2.2. Experimental Design and Sample
A Chinese pine plantation approximately 60 years old planted on shady slopes at an elevation of approximately 700 m was selected as the experimental site. The forest had a mean height and mean diameter at breast height of 10.5 m and 15.25 cm, respectively. In August 2015, 12 plots with an area of 20 m × 30 m were established, at least 10 m apart. To minimize the effect of slope on the sample, the long side of each plot (30 m) was orthogonal to the slope.
The 12 plots were subjected to one of the following four thinning treatments:
Control plots with no thinning (henceforth, T0) had a typical stand density of 1600 individuals hm−1;
Low-intensity thinning (10% of the trees removed, henceforth T10) created plots with a density of 1440 individuals hm−1;
Medium-intensity thinning (20% of the trees removed, henceforth T20) created plots with a density of 1280 individuals hm−1;
High-intensity thinning (50% of the trees removed, henceforth T50) created plots with a density of 800 individuals hm−1.
Three replicates were established for each thinning intensity. Individual Chinese pines which possessed good external stem quality were retained during the thinning process, and the remaining trees were thinned to retain as even a distribution as possible.
In August 2017, five quadrats of 5 m × 5 m and 1 m × 1 m were randomly established in each plot to assess plant diversity within the shrub and herb layers, respectively. In each quadrat, the number, coverage, frequency, and height of each plant were investigated.
The belt transect method was used for sampling the litter. In each plot, three sample belts with a width of 50 cm were established. On each sample belt, five 20 cm × 20 cm sampling points were established 3 m apart. In the organic layer, needle leaves with a loose web-like stratification pattern, and a withered-yellow or yellow-brown appearance were classified as part of the undecomposed litter layer (henceforth L layer); needle leaves with a dark brown to brown-black appearance with traces of white mycelia and a fragmented stratification pattern were classified as part of a partially intact, partially decomposed litter layer (henceforth F layer) [
47].
After removing shrub-grass vegetation, samples of both the L layer and F layer were collected. Similar layer samples from each sample belt were mixed and weighed. A total of 24 samples were collected for analysis (4 thinning intensities × 2 litter layers × 3 replicates). Two sub-samples were obtained using the quartering method, with one part being placed in a kraft paper envelope for chemical property analysis, and the other being stored at 4 °C before being transported to the laboratory for microbial community analysis.
2.3. Laboratory Analysis
2.3.1. Chemical Properties
The water content of the litter samples was measured by weighing after drying at 70 °C for 48 h. Litter pH was determined using an FE20K pH meter (Mettler Toledo, Zurich, Switzerland), with a 1:20 ratio of litter to water (m/v). Litter organic carbon (LOC) and total nitrogen (TN) were measured using a TOC/TN analyzer (LiquiTOC II, Elementar Analysensysteme GmbH, Hanau, Germany). The litter sample was digested by sulfuric acid-hydrogen peroxide, and the total phosphorus (TP) content was determined following the molybdenum-antimony-scandium colorimetric method [
48]. The litter sample was mixed with ultrapure water using a 1:20 ratio (m/v), centrifuged and filtered using a 0.45 μm membrane filter, before the dissolved organic carbon (DOC) in the filtrate was determined using a Multi N/C 3100 analyzer (Analytik Jena AG, Jena, Germany). Ammonium nitrogen (NH
4+-N) and nitrate nitrogen (NO
3−-N) were extracted from the litter using 2 mol/L potassium chlorate solution and measured with an AA3 Continuous Flow Analyzer (Seal Analytical Corporation, Germany).
2.3.2. Microbial Community Structure
The microbial community structure of the litter sample was evaluated using the PLFA analysis method. PLFA contents of the litter samples were extracted following the procedure described by Kourtev et al. [
49]. Essentially, the lipid content of 2 g of litter was extracted using a chloroform:methanol:phosphate buffer (1:2:0.8 v/v), and lipid classes were separated by solid phase extraction (SPE) chromatography using a silica gel column. Fatty acid methyl esters were formed through mild acid methanolysis. With 19-alkyl acid as the internal standard, we used gas chromatography (Agilent 6850N, Agilent Technologies, Santa Clara, CA, USA) and the Sherlock MIS 4.5 system (MIDI company, Newark, DE, USA) to analyze the conversion of PLFA content in nmol per gram dry weight (DW) of litter samples.
We grouped total PLFAs according to specific microbial community markers: gram-positive bacteria (GP) were the sum of fatty acids i14:0, i15:0, a15:0, i16:0, i17:0, a17:0, and i18:0 [
49,
50,
51,
52,
53]; gram-negative bacteria (GN) were the sum of fatty acids 16:1ω7c, 16:1ω9c, 18:1ω5c, and 18:1ω7c [
50,
53]; total bacteria (B) were the sum of GP and GN markers together with cy17:0 and cy19:0 [
54,
55]. Fungi (FU) were the sum of fatty acids 18:2ω6c and 18:1ω9c [
20,
49,
52]; Actinobacteria (ACT) were the sum of fatty acids 10Me16:0, 10Me17:0, and 10Me18:0 [
49,
52,
53]. The PLFA labelled 16:1ω5 was used as an important marker for arbuscular mycorrhizal fungi (AMF) [
56,
57]. All of the PLFA mentioned above were used to calculate the total PLFAs (totPLFAs) of the litter microbial community.
2.4. Statistical Analysis
Species richness (R) was described according to the types of plant species or individual PLFA present. The diversity of the understory plants and individual PLFAs were calculated with the Shannon index (
H), using the following formula [
10,
40,
58]:
In Equation (1), Pi is the relative abundance of each plant and n is the number of plants detected. The diversity of the individual PLFAs was calculated using the same formula, where Pi is the relative abundance of each PLFA in the sum of all individual PLFAs, and n is the number of individually detected PLFAs.
One-way analysis of variance (ANOVA) was used for comparing plant characteristics among different thinning intensities, litter chemical properties, the diversity indices of PLFAs, and the microbial community structure and totPLFAs in different organic layers under different thinning intensities. Duncan’s test was used to determine the significance of differences between means. Two-way ANOVA (general linear model) was used to analyze the influence of different thinning intensities (T0, T10, T20, and T50), different organic layers (L layer and F layer), and the interactions of these two factors on microbial community structures. Pearson’s correlation analysis was also used to examine the correlations between litter chemical properties and microbial communities. All statistical analyses were performed using SPSS 20.0 (SPSS, Chicago, IL, USA).
Principal component analysis (PCA) was used to determine and analyze the differences between microbial community structures as a whole under different thinning intensities. Redundancy analysis (RDA) was used to investigate the relationships between the chemical properties and microbial community structures of the litter samples. Prior to RDA, the Monte Carlo permutation test was used to identify factors significantly correlated with changes in microbial community structures. These statistical analyses were performed using Canoco 5.0 for Windows (Microcomputer Power, Ithaca, NY, USA).