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Article

The phoD-Harboring Microorganism Communities and Networks in Karst and Non-Karst Forests in Southwest China

by
Min Chen
1,2,†,
Hanlian Qin
2,†,
Yueming Liang
2,3,*,
Dan Xiao
4,5,
Peidong Yan
3,
Mingshan Yin
6 and
Fujing Pan
1,*
1
Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control, College of Environmental and Engineering, Guilin University of Technology, Guilin 541006, China
2
Karst Dynamics Laboratory, Ministry of Natural Resources, Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin 541004, China
3
Guangxi Key Laboratory of Superior Timber Trees Resource Cultivation, Guangxi Forestry Research Institute, Nanning 530002, China
4
Key Laboratory of Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
5
Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences, Hechi 547000, China
6
Huashan Forest Farm, Hechi 547000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2024, 15(2), 341; https://doi.org/10.3390/f15020341
Submission received: 4 December 2023 / Revised: 29 January 2024 / Accepted: 5 February 2024 / Published: 9 February 2024
(This article belongs to the Section Forest Ecology and Management)
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Abstract

:
Phosphorous (P) limitation is common not only in tropical rainforest and savanna ecosystems, but also in karst forest ecosystems. Soil phoD-harboring microorganisms are essential in soil P cycles, but very little information is available about them in karst ecosystems. A total of 36 soil samples were collected from two types of forest ecosystems (karst and non-karst) over two seasons (rainy and dry), and the diversity and community structure of soil phoD-harboring microorganisms were measured. The contents of available P (AP), soil total P (TP), microbial biomass P (MBP) and the activity of alkaline phosphatase (ALP) in karst forest soils were higher than those in non-karst forest soils, whereas the contents of CaCl2-P, citrate-P, enzyme-P and the activity of acid phosphatase (ACP) were the opposite. Soil AP content was significantly higher in the rainy season than in the dry season, whereas ALP activity was the opposite. The community structure of phoD-harboring microorganisms was more influenced by forest-type than season. The network connectivity was higher in non-karst forests than in karst forests. Two dominant orders, Burkholderiales and Rhizobiales, were the keystone taxa in these networks in two forests, and their relative abundances were higher in non-karst forests than in karst forests. The microorganic diversity indices (e.g., Shannon–Wiener, Evenness, Richness, and Chao1) were substantially higher in karst than in non-karst forests. These indices were positively correlated with the contents of SOC and TN in the two forests; meanwhile, richness and evenness indices were positively correlated with citrate-P, HCl-P, and TP in non-karst forests. Structural equation modelling results showed that the relative abundance of phoD-harboring microorganisms was mainly influenced by pH and AP, with direct affection of soil AP, pH, and ALP activity, and indirect affection of ALP activity through affecting AP. These findings highlight that the P cycle is mainly regulated by the diversity of phoD-harboring microorganisms in karst forest ecosystems, whereas it is mainly regulated by dominant taxa in non-karst forest ecosystems. In future, regulating the interaction networks and keystone taxa of phoD-harboring microorganisms may be critical to alleviating P limitations in karst forest ecosystems.

1. Introduction

Phosphorous (P) is an essential element for plant growth and development [1,2], originating mainly from the weathering of the parent rock [3]. Soil P is divided into inorganic and organic P, accounting for 30%–65% and 30%–70% of the total soil P, respectively [4,5]. Organic P consists mainly of nucleic acids, nucleotides, inositol phosphates, phospholipids, sugar phosphates, phosphoprotein and phosphonate esters [6]. Inorganic P, including orthophosphate, pyrophosphate and metaphosphate, is mainly fixed in the bedrock, soil and sediment in the form of various phosphate ores (apatite, red phosphorite, aluminite phosphate) [7]. In inorganic P, only 5% of soluble inorganic P can be directly absorbed by plants and microorganisms, and thus can easily lead to limitations in plant growth in most ecosystems, especially in forest soils [8,9]. Alkaline phosphatase (ALP) is an extracellular enzyme that is mainly produced by living soil microbes [10]. ALP can mineralize organic P into soluble inorganic P in soil [11,12], which is encoded by the phoD gene. The phoD gene is known as the “starvation gene”, and its expression is stimulated in low-available P soils. For instance, the relative abundances of phoD-harboring microorganisms are high in low-available P soils of ecosystems from the Asian and European regions, and Bradyrhizobium genera are dominant in these ecosystems [13,14]. This indicates that phoD-harboring microorganisms play an important role in the P cycle, especially in poor soil-available P ecosystems [15,16].
Karst landscapes cover 15% of the total global land area and are characterized by high rock exposures and shallow soils [17]. The total P (TP) content and microbial activity are higher in the 0–30 cm soil layer [18,19]; thus, available P in karst ecosystems is sensitive to environmental changes. Over the past few decades, high-intensity human activities (e.g., inappropriate reclamation) in karst ecosystems have resulted in substantial losses of soil P nutrients [20]. Simultaneously, the calcareous soils in karst ecosystems are characterized by higher calcium (Ca) and pH [13] than the red soils in non-karst ecosystems from the same latitude. Therefore, P in karst soils mainly exists in the form of a stable P–Ca bound state [21], which is not easy to use in regard to plants and soil microorganisms. This may lead to more serious growth restrictions for the available P in karst ecosystems than in non-karst ecosystems [22]. Soil pH can directly or indirectly affect bacterial communities [23,24]. For example, the relative abundances of Acidobacteria were dominant in pH < 5 soils, while the relative abundances of Actinobacteria were rich in pH > 5 soils [23]. Soil pH is significantly different between karst and non-karst soils, which will alter the diversity and structure of phoD-harboring microorganisms. However, related information is rare.
Microbial networks have attracted increased attention as an indicator of the functional stability of ecosystems [25,26]. The connection numbers and closeness in the network can be used to evaluate microorganisms’ functions in an ecosystem [27]. For example, Hu et al. [28] have reported that stronger positive correlations in the networks among phoD-harboring microorganisms, suggesting P starvation can lead to higher cooperation interactions. The amount of connectivity in the networks increases with vegetation recovery in the European region [29], indicating that vegetal coverture plays an important in influencing underground soil microorganism interactions. Furthermore, Pan et al. [30] have found that different tree species also influence the connectivity and complexity of bacterial–fungal networks. In addition, the results of Li et al. [31] indicate that more complex microbial networks were found during the dry season than in the rainy season. Significant differences in vegetation types and soil TP content in non-karst forest ecosystems and karst forest ecosystems could influence the connection numbers and closeness of phoD-harboring microorganism networks.
This study aimed to investigate the diversity and structure of phoD-harboring microorganisms during the rainy and dry seasons in a typical karst forest ecosystem using high-throughput sequencing, as well as to provide a theoretical support in regard to regulating the diversity and structure of phoD-harboring microorganisms for vegetation restoration in these fragile ecosystems. Red soil from a non-karst forest ecosystem was used for comparison, which is the most widespread soil type in subtropical China. The soil types in the karst and non-karst forests were classified as limestone and laterite soils, respectively, according to the FAO–UNESCO Soil Classification System [11]. We hypothesized that (1) soil pH can affect the diversity and structure of phoD-harboring microorganisms through altering available P, and (2) that more complex phoD-harboring microorganism networks and enhanced connectivity were found in non-karst forests with lower available phosphorus compared to karst forests.

2. Materials and Methods

2.1. Site Description and Experimental Design

The study was conducted in the Mulun Nature Reserve (25°06′–25°12′ N and 107°53′–108°05′ E) and Huashan Forest Farm (25°06′ N, 108°15′ E), Guangxi Zhuang Autonomous Region, southwestern China (Figure 1). Both the Mulun Nature Reserve and Huashan Forest Farm have subtropical climates. The average annual temperature is 15.0–19.8 °C, and the average annual precipitation is 1530–1820 mm. Rainfall during the study period (1 January to 31 December 2020) was 1240 mm (Figure S1). The rainy season began in April and continued until mid-September; the dry season was from mid-September to December and January to March (Figure S1) [32]. The maximum temperature was 35.5 °C (August) and the minimum temperature was 14.2 °C (December). Forest soil collected from the Mulun Nature Reserve consisted of karst soil, while the soil chosen for comparison from the Huashan Forest Farm consisted of non-karst soil. According to the international soil classification system for naming soils and creating legends for soil maps (WRB, 2014), as well as the studying of our team [33], soil in the karst region is defined as lithosol (a type of leptosol) formed from a dolostone or limestone base, while soil in the non-karst region is defined as ferralsol, which is the most representative of non-karst forest soil in this region with the same latitude.
These two types of forests were approximately 30 years old, according to the reserve materials of the Mulun Reserve and Huashan Forest Farm, and originated from the natural restoration of forests after returning farmland (mainly used for growing corn) to forests. Nine plots (20 m × 20 m) for each type of forest, at least 50 m apart, were established. All the plots showed minor differences in geographical location. In total, 18 plots (two type forests × nine replicate plots) were established. The dominant understory species in karst forests included Celtis biondii (Cannabaceae), Cleidion bracteosum (Euphorbiaceae), Cryptocarya chinensis (Lauraceae), Cyclobalanopsis glauca (Fagaceae), Loropetalum chinense (Hamamelidaceae), Miliusa chunii (Annonaceae), and Pteroceltis tatarinowii (Cannabaceae) [34,35]. The dominant understory species in non-karst forests included Pinus massoniana (Pinaceae), Schefflera heptaphylla (Araliaceae), Ficus tikoua (Moraceae), Vernonia solanifolia (Asteraceae), Evodia lepta (Rutaceae), and Rhodomyrtus tomentosa (Myrtaceae) [36].

2.2. Field Sampling

Sampling was conducted in August (rainy season) and November (dry season), 2020. Soil samples were screened using a 2 mm sieve to remove stones, animals, roots, and plant materials. Six soil samples (20 cm depths) were excavated from each plot and mixed into a composite sample. Each sample was divided into three portions. One sub-sample (10 g) was immediately stored at −80 °C for sequencing of the phoD gene. One sub-sample was kept at 4 °C for the analysis of enzyme activity, microbial biomass P (MBP), and soil P components. CaCl2-P, citrate-P, HCl-P, and MBP contents were determined within 4 weeks of collection and enzyme activity was determined within 2 weeks. The remaining sub-samples were used for soil physical and chemical property analyses after air drying.

2.3. General Soil Parameters

Soil pH was determined using a Mettler Toledo 320 pH meter (Delta 320; Mettler-Toledo Instruments Ltd., Shanghai, China). Samples were prepared in a 1:2.5 ratio of soil–water. Soil TN was determined using the Kjeldahl method and a flow-injection instrument (FIASTAR 5000, FOSS, HII1ERD, Hillerød, Denmark) [11]. Soil-organic carbon (SOC) content was examined using the K2Cr2O7-H2SO4 oxidation-reduction titration method. Soil total P (TP) was extracted using acid digestion with an H2SO4 + HClO4 solution and then determined using a Visible Spectrophotometer. Available P was extracted with 0.5 M NaHCO3 and measured using the ammonium molybdate method, then analyzed by a Visible Spectrophotometer (V-5800, Shanghai Metash Instruments Co., Ltd., Shanghai, China) [37]. ExMg2+ and ExCa2+ were displaced via compulsive exchange in 1 M ammonium acetate at pH 7.0 and analyzed using inductively coupled plasma atomic emission spectroscopy (ICP-AES, Optima 7000 DV, Perkin Elmer Technologies, Waltham, MA, USA) [16]. Soil MBC and MBP were determined using chloroform fumigation, followed by leaching. Soil MBC content was determined using an automatic total organic carbon analyzer (TOC-Vwp, Shimadzu Co., Kyoto, Japan), while soil MBP content was determined using a Visible Spectrophotometer [38].
The biologically based P (BBP) extraction protocol proposes a new biologically based approach to evaluating the availability of P in complex ecosystems (including CaCl2-P, citrate-P, enzyme-P, and HCl-P) via selecting extractants to simulate four common and important plant rhizosphere-mediated P acquisition mechanisms: (1) root interception (CaCl2-P), (2) organic acid complexation (citrate-P), (3) enzymatic hydrolysis (enzyme-P), and (4) protolection-induced acidification (HCl-P) [39]. These four P fractions were defined as bioavailable P. Each P component was extracted with 10 mL extractor (0.01 M CaCl2 for CaCl2-P, enzyme extractant for enzyme-P, 0.01 M citric acid for citrate-P, and 1 M HCl for HCl-P), respectively. The enzyme extractant consisted of three enzymes (6 mg acid phosphomonesterase (Sigma Aldich, Tokyo, Japan), 0.2 mg alkaline phosphomonesterase (Sigma Aldich, Tokyo, Japan), and 2 mg phytase (Sigma Aldich, Tokyo, Japan)) which were filled with water to 1 L, measured using the malachite green method, and analyzed by a Power Wave-XS microplate Spectrophotometer (Infinite M200 PRO, Tecan, Switzerland).
The ACP and ALP activities were determined using a fluorescence spectrophotometer [40]. The specific steps involved 0.4 g of fresh soil being weighed and placed in a 100 mL sterilized centrifuge tube. An amount of 50 mL of sodium acetate (sodium bicarbonate) buffer solution was added and comprehensively mixed using a high-speed homogenizer and a vortex instrument. An amount of 200 μL of soil suspension was quickly extracted into 96-well microplates, and 50 μL of standard, buffer, and substrate solutions were sequentially added. The microplates were cultured in a dark room at 20 °C for 4 h. At the end of the culture, 10 μL of 1 M sodium hydroxide was added to each well to stop the reaction. The samples were immediately placed on an enzyme marker, and the fluorescence value was measured using a microplate fluorometer (Infinite M200 PRO, Tecan, Switzerland) at an excitation wavelength of 365 nm and an emission wavelength of 450 nm. After the negative control and quenching correction had been conducted, the enzyme activity was calculated and expressed as nmol·h−1 g−1.

2.4. DNA Extraction and Illumina Sequencing

In accordance with the manufacturer’s instructions, soil DNA was extracted from 0.5 g frozen soil using the FastDNA SPIN kits (MP Biomedicals, Cleveland, OH, USA). A Nanodrop ND-1000 UV/vis spectrophotometer (NanoDrop Technologies, Wilmington, NC, USA) was utilized to measure the quality and quantity of the extracted DNA, which was then examined with a 1% (w/v) agarose gel, while a primer set for phoD-F733 (5′-CAGTGGGACGACCACGAGGT-3′)/phoD-R1083 (5′-GAGGCCGATCGGCATGTCG-3′) was employed to target the phoD gene [41]. Amplification of each sample was performed in triplicate in a 25-μL reaction, including 0.3 μL Ex Taq (Takara Shuzo Foods Co., Ltd., Beijing, China), 2.5 μL 10 × Ex Taq buffer (Mg2+ plus), 2 μL DNA, 0.5 μL of each primer, and 19.2 μL double distilled H2O. A polymerase chain reaction was performed under the following cycling conditions: cycling at 95 °C for 3 min, followed by cycling at 95 °C for 20 s, 57 °C for 40 s, 72 °C for 60 s, and finally at 72 °C for 5 min. PCR products were purified using a TIANquick Midi Purification Kit (TIANGEN, Beijing, China). Finally, the constructed amplicon library was sequenced on an Illumina Nova 6000 platform (Magigene Co., Ltd., Guangzhou, China).

2.5. Analysis of Illumina Sequencing Data

Raw sequences were processed using the QIIME platform [42]. Raw sequences were quality-screened, and average quality scores lower than 30, sequences shorter than 200 bp, or those containing ambiguous bases were discarded. Subsequently, the UCHIME v9.0 method was used on the QIIME 1 platform to remove sequences with chimeras. The blast-x method was used for sequence alignment (Table S5). The nucleotide sequences were then converted to amino acid sequences to remove sequences that did not match the phoD gene or that had termination codons, which were identified using the FrameBot tool of the RDP function, the gene pipeline (http://fungene.cme.msu.edu/FunGenePipeline/, accessed on 19 October 2023). A total of 6,419,304 clean sequences were obtained after trimming and filtering. The number of sequences in each sample ranged from 107,068 to 56,206. The operational taxonomic units (OTUs) accounting for less than 0.005% were removed [43]. Subsequently, the taxonomic assignment of each OTU was performed using BLAST in the Fun-Gene database [11,44], and all sequences were deposited in the NCBI database (accession number: PRJNA1024954).

2.6. Statistical Analysis

The normal distribution of each index was checked using SPSS 26.0. Independent sample t-tests and the least significant difference method (LSD) were used to analyze the differences in soil physicochemical properties and phosphatase activity between the two forest types during the rainy and dry seasons. The correlations between soil nutrient and phosphatase activity and phoD-harboring microbial community structures were analyzed using the “corrplot” package in R (R v4.3.1). A similarities (ANSOM) analysis and charting was performed using the Biozeron Cloud Platform (http://www.Cloud.biomicroclass.com/Cloud, accessed on 1 October 2023). The structure of phoD-harboring microorganisms in the two forest types in the rainy and dry seasons was visualized using principal coordinate analysis (PCoA). Important factors influencing the phoD-harboring microbial community structure were analyzed using a random forest model.
When constructing phoD-harboring bacterial networks, OTUs with a relative abundance of <0.1% of total samples were excluded to minimize bias caused by rare species [45]. The corresponding nodes, edges, network densities, and centralities of the co-occurrence network were determined using the SparCC method. The SpiecEasi, igraph, phyloseq, WGCNA, RColorBrewer, RMThreshold, and Hmisc packages in R (R v4.3.1) were used to analyze the environmental factors and OTU data, and the corresponding nodes, edges, network density, and centrality of the co-occurrence network graph were obtained. Based on degrees greater than 50, betweenness centrality lower than 0.12, and closeness centrality higher than 0.44 [46], core taxa were selected and imported into the visualization software, gephi v 0.9.7, to generate network images.
The causal relationship between soil forest type, soil nutrients, phosphatase activity, pH, available P, and the relative abundance of phoD-harboring microorganisms was tested using the structural equation model (SEM). To simplify the SEM model, the PCoA first-axis fraction of each sample was used to represent the soil nutrients. The best-fit model was derived using the maximum likelihood method based on model fit, namely the chi-square test (χ2), goodness of fit index (GFI), and approximate root-mean-square error (RMSEA) [47]. The model fit was iteratively improved by removing or adding relationships between the observed variables in the preceding model.

3. Results

3.1. Soil P Fractions and Phosphatase Activity

The contents of available P (AP), total P (TP), microbial biomass P (MBP) and activity of ALP in karst forest soils were higher than those in non-karst forest soils (p < 0.05), whereas the contents of CaCl2-P, citrate-P, enzyme-P and ACP activities were lower (Figure 2). In karst forest soils, the MBP and HCl-P contents and ALP and ACP activities were significantly higher in the rainy season than in the dry season, whereas the AP, TP, CaCl2-P, citrate -P, and enzyme-P contents were substantially higher in the dry season than in the rainy season. In non-karst forest soils, ALP and ACP activities were significantly higher in the rainy season than in the dry season, but the contents of AP, TP, MBP, CaCl2-P, citrate-P, and HCl-P decreased (Figure 2).

3.2. Diversity and Community Structure of phoD-Harboring Microorganisms

The Shannon–Wiener, Evenness, Richness, and Chao1 indices of phoD-harboring microorganisms in karst soils were significantly higher than those regarding non-karst forest soils, whereas the Simpson indices of non-karst soils were substantially higher than those regarding karst forest soils (Table 1). The Shannon–Wiener, Simpson, Evenness, Richness, and Chao1 indices of phoD-harboring microorganisms were similar across seasons with the karst forest soils. The Shannon–Wiener and Simpson indices of phoD-harboring microorganisms in the rainy season were higher in non-karst forest soils (Table 1).
Rhizobiales and Burkholderiales were the dominant orders in karst and non-karst forest soils, proving to be higher in the non-karst forest soils than in the karst forest soils (Figure 3). In karst forest soils, the relative abundance of Rhizobiales (Rainy = 23.7%, Dry = 21.6%) was the highest, followed by the relative abundance of Burkholderiales (Rainy = 2.95%, Dry = 2.99%). In non-karst forest soils, the relative abundance of Burkholderiales (Rainy = 37.7%, Dry = 36.7%) was the highest, followed by the relative abundance of Rhizobiales (Rainy = 29.3%, Dry = 36.9%). No statistically significant seasonal differences were observed between the two dominant taxa in the two forest soil types (Figure 3). Streptomycetales, as a non-dominant group, was significantly higher in karst (1.22%) than in non-karst forest soils (0.038%), but there were no differences between seasons. Furthermore, many species in the karst forest soils were unclassified (Rainy = 69.2%, Dry = 71.5%).
The results of the PCoA analysis showed that the community structures of phoD-harboring microorganisms were significantly different between the two forest soil types but not substantially different between the two seasons (Figure 4). Thus, changes in phoD-harboring microbial community structure were caused mainly by forest soil (ANSOM, R = 1, p = 0.001) and not by season (ANSOM, R = −0.037, p = 0.871) (Table S2).

3.3. Network Connectivity of phoD-Harboring Microorganisms

A bacterial community co-occurrence network was modeled at the order level. The prediction results of the co-occurrence network model indicated that the co-occurrence network of phoD-harboring microorganisms in karst forest soils produced 670 edges and 63 nodes, while that in non-karst forest soils produced 1597 edges and 94 nodes, which was more abundant than that in karst forest soils (Table 2). The degree, path length, density, and clustering coefficient of phoD-harboring microbial networks in the non-karst forest soils were 1.67-fold, 1.07-fold, 1.16-fold and 1.13-fold higher than those in the karst forest soils, respectively. Positive interactions in the microorganic network (58.96%) in karst forest soils were higher than those in non-karst forest soils (51.47%). Negative interactions in the microorganic network (41.04%) in karst forest soils were lower than those in non-karst forest soils (48.53%). The positive interactions between phoD-harboring microorganic species were higher compared with negative interactions regardless of forest soils (Figure 5). Most bacterial species (nodes) in the microorganic network in karst forest soils belonged to Rhizobiales (23.81%) and Burkholderiales (12.7%). Most bacterial species (nodes) in non-karst forest soils belonged to Rhizobiales (26.67%) and Burkholderiales (13.33%). The results indicated that the microorganic networks of both forest soils were dominated by Rhizobiales and Burkholderiales, suggesting that there may be a strong interaction between them (Figure 5). The phoD-harboring microbial network structure was more closely connected in the non-karst forest soils compared to karst forest soils; however, the network structure did not differ markedly between seasons (Figures S2 and S3).
According to the two standards, eight OTUs were identified as core functional bacteria: the karst forest soils contained Burkholderiales OTUs (13, 132, 154, 158) and unclassified Proteobacteria OTUs (21, 144, 194, 34) (Figure 6a). The non-karst forest soils contained the Burkholderiale OTU 63), Rhizobiales OTUs (13539, 69, 99, and 80), and unclassified Proteobacteria OTUs (40, 383, 51) (Figure 6b). In the two forest soil types, there were positive and negative interactions between the core functional bacteria, P components, and phosphatase activity. phoD-harboring microorganisms’ core-functional taxa (Burkholderiales and Rhizobiales) had greater connectivity with P components and phosphatase in non-karst forest soils.

3.4. Correlation between Diversity and Structure of phoD-Harboring Microorganisms and Soil Physicochemical Parameters

In karst forest soils, the richness index of phoD-harboring microorganisms was strongly positively correlated with SOC and TN, while the Shannon–Wiener index had a strong positive correlation with pH (Figure 7a). In non-karst forest soils, the richness index of phoD-harboring microorganisms was strongly positively correlated with ALP, citrate-P, SOC, TN, and HCl-P, while the evenness index was strongly positively correlated with TP (Figure 7b). In karst forests, soil AP was positively correlated with MBP, ALP, ACP, and TP. In the non-karst forest, soil AP content and ACP activity, CaCl2-P and MBP were strongly negatively correlated (Figure 7a,b).
In the karst forest soils, ALP, TN, MBC, SOC, ExCa2+, TP, and AP mainly affected the community structure of phoD-harboring microorganisms (Figure 7c). In the non-karst forest soils, TN, ExCa2+, ExMg2+, ALP, and pH substantially affected the community structure (Figure 7d).
The SEM results indicated that P and pH directly affected the abundance of phoD-harboring microorganisms in both karst and non-karst forest soils, while the relationship was negative in karst but positive in non-karst forest soils (Figure 8a,b). In non-karst forest soils, pH also indirectly affected the abundance of phoD-harboring microorganisms in the soil AP (Figure 8b). ALP activity directly affected the abundance of phoD-harboring microorganisms or indirectly affected the abundance of phoD-harboring microorganisms in soil AP (Figure 8a,b). Forest soil type indirectly affected the abundance of phoD-harboring microorganisms by influencing the soil AP (Figure 8c).

4. Discussion

4.1. Difference Patterns of Soil P Fractions between Karst and Non-Karst Forest Soils

In the present study, the AP content of karst forest soils was significantly higher than that of non-karst forest soils, which were consistent with the previous study of karst and non-karst areas in the Guizhou province of China [48]. The following suggests some possible reasons for this. First, higher contents of soil total P and microbial biomass P were found in karst forest soils, which were approximately three and four times higher than those in non-karst soils, respectively (Figure 2b). Microbial biomass P is easily converted into inorganic phosphorus, which increases the AP content [49,50]. Second, higher alkaline phosphatase activity in karst forest soils can mineralize (decompose) more organic P into inorganic P and further increase the AP content [22]. Third, soil pH affects phosphorus adsorption, which increases in the pH range of 3–7. However, the amount of phosphorus adsorbed decreased in the pH range of 7–9 [51,52]. Therefore, higher soil pH (~pH 7) in karst forest soils is conducive to the release of P.
AP was significantly higher in the dry season than in the rainy season in karst and non-karst forest soils, which was consistent with the findings of Lopez-Gutierrez et al. in northeastern Venezuela [53]. This is because suitable temperatures and humidity in the rainy season increased the growth and activity of microbes and plants [54], which promoted the preservation of inorganic phosphorus in the microorganism body and further decreased the AP content [55]. Furthermore, P leaching was lower in the dry season than in the rainy season, which decreased the loss of P in the dry season [56].

4.2. Communities and Networks of phoD-Harboring Microorganisms in Non-Karst and Karst Forest Soils

Burkholderiales and Rhizobiales were dominant in both karst and non-karst forest soils, which is consistent with previous research [57]. Burkholderiales and Rhizobiales were mainly distributed in soils with low AP and played key roles in increasing AP [58]. This is supported by the current results, in which a higher relative abundance of these two taxa was found in non-karst forest soils with low soil AP.
Burkholderiales and Rhizobiales have nitrogen-fixation potential, and the relative abundance of these two taxa accounted for 70% and 25% of the total relative abundance in non-karst forest soils and karst forest soils, respectively. The phenomenon is closely related to tree growth in non-karst soils being limited by low nitrogen and P content [59], whereas tree growth in karst soils is limited by low AP content [22]. It is worth mentioning that ~70% of species in karst forest soils are unclassified, which is closely related to few studies on the diversity and structure of phoD-harboring microorganisms in this study area, or the database is not complete enough. In future studies, it will be necessary to strengthen the relevant studies, in-depth analyses and excavation of related microorganisms.
The microbial co-occurrence network may suggest the existence of interactions between microorganisms [60]. The number of nodes, edges, and degrees in a network reveals its complexity [61]. The nodes, edges, and degree of connectedness of the phoD-harboring microbial network in the current study were 1.49-times, 2.38-times, and 1.67-times in non-karst than in karst forest soils, respectively (Table 2; Figure 5), indicating phoD-harboring microorganism network interactions may be more complex in non-karst than in karst forest soils. A possible reason for this is that the phoD-harboring microorganisms, especially the dominant species, strengthen cooperation among species in soil with a low P content [29,51]. This accelerates the P cycle and promotes the adaptation of plants and microorganisms to low-P stress [62]. The negative interaction of phoD-harboring microbial networks in non-karst forest soils (48.53%) was higher than that in karst forest soils (41.04%). This could be an indication that the phoD-harboring microorganisms had a strong intraspecific competition in non-karst forest soils, which made the functionally redundant groups disappear and reduced the consumption of AP.

4.3. Diversities of phoD-Harboring Microorganisms in Non-Karst and Karst Forest Soils

The Shannon–Wiener and Evenness indices of phoD-harboring microorganisms in karst forest soils were significantly higher than that in non-karst forest soils (Table 1). This is closely related to the dominant taxa in both forest soils. The relative abundances of Burkholderiales and Rhizobiales dominated in present study, accounting for 70.3% and 25.62% in non-karst and karst forest soils (Figure 3), respectively. The relative abundance of Burkholderiales and Rhizobiales in non-karst forest soils was 2.74-times that of karst forest soils. Streptomycetales, as a non-dominant species [63], were significantly higher in karst (1.22%) than in non-karst forest soils (0.038%). Thus, dominant species in non-karst forest soils inhibited the growth of non-dominant species, resulting in low Shannon–Wiener and Evenness indices of phoD-harboring microorganisms [64,65]. Lower Shannon–Wiener and Evenness indices of phoD-harboring microorganisms indicated that microbial communities in non-karst forest ecosystems were unstable and weak in response to environmental changes. Additionally, the dominant species in the non-karst forest soils exhibited a stronger correlation with the P fraction and phosphatase activity comparing with the karst forest soils. This indicates that dominant species play a role in improving AP in non-karst forest ecosystems with low AP. Seasonal variation had no marked effect on the diversity index of phoD-harboring microorganisms in karst forest soils, but it did have a marked effect on non-karst forest soils. This suggests that phoD-harboring microorganisms were more active in the soil P cycle and more sensitive to seasonal changes in non-karst forest ecosystems, which is closely related to their low AP [65,66].

4.4. Factors Affecting the Diversity and Structure of phoD-Harboring Microorganisms and Their Implications for Future Management

The community structures of the phoD-harboring microorganisms in the two forest soils were influenced by a multitude of environmental factors [67,68]. In the present study, AP content influenced the diversity and structure of phoD-harboring microorganisms. This result is in agreement with that of a previous study [69,70]. The diversity of phoD-harboring microorganisms was higher, but the AP content was lower in non-karst forest soils than in karst forest soils. These results indicate that phoD-harboring microorganisms play a key role in the low availability of P in soils. The phoD-harboring microorganisms regulate ALP secretion, which can mineralize organic P into inorganic P and further increase AP to alleviate the stress of P limitation on plant growth [71]. Low P availability stimulates the expression of phoD-harboring microorganisms, which accelerates the synthesis of phosphatase and further increases AP [11].
Soil pH is an important factor affecting the diversity and community structure of phoD-harboring microorganisms [15,66,69,72]. One explanation is that soil pH can directly affect the growth of phoD-harboring microorganisms [73]. Another explanation is that pH indirectly influences phoD-harboring microorganism communities through effecting other functional microorganisms.
A higher calcium content reduces the communication and movement abilities of some microbial taxa [65,74]. In the present study, the ExCa2+ content in karst forest soils was higher than that in non-karst forest soils [70], and a stronger effect of ExCa2+ content on the abundance of phoD-harboring microorganism taxa was observed in karst forest soils. These phenomena can partly explain why the relative abundance of Burkholderiales was lower in the karst soils compared to the non-karst forest soils.

5. Conclusions

Two particular orders, Rhizobiales and Burkholderiales, were dominant in karst and non-karst forest soils, whereas the relative abundance of these two orders was markedly different. Higher available P content and alkaline phosphatase activities in karst forest soils were accompanied by higher diversity indices (Shannon–Wiener, Evenness, Richness, and Chao1) in phoD-harboring microorganisms. The structure of phoD-harboring microorganisms differed between the two forest soils and was influenced by soil pH and available P content. These results supported our first hypothesis. The higher network connection density between phoD-harboring microorganism taxa in non-karst forests compared to karst forests might suggest a greater contribution of this microorganism to the P cycle. These results supported our second hypothesis that phoD-harboring microorganisms increase their cooperation in response to low P soils. These findings highlight the specific profiles of phoD-harboring microorganism communities in karst forest ecosystems and indicate that these microorganisms play an important role in the P cycle of this ecosystem. Overall, this study explored the adaptation strategies to low P stress in two forest soil types from the perspective of phoD-harboring microorganisms. Future research should focus on the regulation of interaction networks and keystone taxa in karst forest soils.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15020341/s1, Figure S1: Meteorological conditions of the study site in 2020; Figure S2: The interaction network of phoD-harboring microorganism communities in karst forest during rainy (a) and dry (b) seasons; Figure S3: The interaction network of phoD-harboring microorganism communities in non-karst forest during rainy (a) and dry (b) seasons; Table S1: Basic physical and chemical properties of karst and non-karst soils in different seasons; Table S2: Similarity analysis of the phoD-harboring Microbial community structure by different forest types and seasons (ANOSM); Table S3: Key taxas of microbial network co-occurrence analysis in karst forest; Table S4: Key taxas of microbial network co-occurrence analysis in non-karst forest; Table S5: The identity value, percentage of identical matches, and expect value of each OTU.

Author Contributions

F.P. and Y.L. designed and conceived the study. F.P., Y.L., H.Q., P.Y., M.Y., D.X. and M.C. conducted the experiments and field sampling. M.C. and H.Q. analyzed the data. M.C. wrote the manuscript. F.P. and Y.L. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by the National Natural Science Foundation of China [grant numbers U20A2011; 32271730; 42261011] and the Guang-xi Key Laboratory of Superior Timber Trees Resource Cultivation [grant numbers 2020-B-04-04].

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to unreasonable request.

Acknowledgments

We thank Qian Qian and Runyang Zhang for their assistance in field sampling, as well as the Collaborative Innovation Center for Water Pollution Control and Water Safety in the Karst Area and the Guangxi Engineering Research Center of Comprehensive Treatment for Agricultural Non-Point Source Pollution for the parameters measuring.

Conflicts of Interest

Author Mingshan Yin is employed by the Huashan Forest Farm. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The Huashan Forest Farm had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. The study was conducted in the Mulun Nature Reserve and Huashan Forest Farm, Guangxi Zhuang Autonomous Region, southwestern China.
Figure 1. The study was conducted in the Mulun Nature Reserve and Huashan Forest Farm, Guangxi Zhuang Autonomous Region, southwestern China.
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Figure 2. Seasonal changes of available P (AP) (a), total P (TP) (b), microbial biomass P (MBP) (c), CaCl2–P (d), citrate–P (e), enzyme–P (f), HCl–P (g) contents and ALP (h)/ACP (i) activities in karst and non-karst forest soils. Note: Capital letters indicate significant differences between forest soil types, while lowercase letters indicate significant differences between seasons (p < 0.05).
Figure 2. Seasonal changes of available P (AP) (a), total P (TP) (b), microbial biomass P (MBP) (c), CaCl2–P (d), citrate–P (e), enzyme–P (f), HCl–P (g) contents and ALP (h)/ACP (i) activities in karst and non-karst forest soils. Note: Capital letters indicate significant differences between forest soil types, while lowercase letters indicate significant differences between seasons (p < 0.05).
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Figure 3. Taxonomic of phoD-harboring microorganisms at order level at different seasons in karst and non-karst forest soils.
Figure 3. Taxonomic of phoD-harboring microorganisms at order level at different seasons in karst and non-karst forest soils.
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Figure 4. PCoA analysis of phoD–harboring microorganisms community structures during different seasons in karst and non-karst forest soils.
Figure 4. PCoA analysis of phoD–harboring microorganisms community structures during different seasons in karst and non-karst forest soils.
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Figure 5. Interaction network diagrams of phoD-harboring microbial communities in karst forest soils (a) and non-karst forest soils (b). The nodes of the co-occurrence network are colored according to different species at order level, the red edge represents the positive interaction between two nodes, and the blue edge represents the negative interaction between two nodes. The number value indicates the OTU number.
Figure 5. Interaction network diagrams of phoD-harboring microbial communities in karst forest soils (a) and non-karst forest soils (b). The nodes of the co-occurrence network are colored according to different species at order level, the red edge represents the positive interaction between two nodes, and the blue edge represents the negative interaction between two nodes. The number value indicates the OTU number.
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Figure 6. Correlation diagram of phoD-harboring microorganic core-functional taxa and phosphatase and P component networks in karst forest soils (a). Correlation diagram of phoD-harboring microorganic core-functional taxa and phosphatase and P component networks in non-karst forest soils (b). The red edge represented positive interactions between two nodes, the blue edge represented negative interactions between two nodes. The number value indicates the OTU number. Core functional bacteria were defined as follows: (1) the relative abundance of OTUs must be >0.1%; (2) OTUs with degree greater than 50, betweenness centrality lower than 0.12, and closeness centrality higher than 0.44. AP, available P; TP, total P; MBP, microbial biomass P.
Figure 6. Correlation diagram of phoD-harboring microorganic core-functional taxa and phosphatase and P component networks in karst forest soils (a). Correlation diagram of phoD-harboring microorganic core-functional taxa and phosphatase and P component networks in non-karst forest soils (b). The red edge represented positive interactions between two nodes, the blue edge represented negative interactions between two nodes. The number value indicates the OTU number. Core functional bacteria were defined as follows: (1) the relative abundance of OTUs must be >0.1%; (2) OTUs with degree greater than 50, betweenness centrality lower than 0.12, and closeness centrality higher than 0.44. AP, available P; TP, total P; MBP, microbial biomass P.
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Figure 7. Relationships among soil physical–chemical properties, phosphatase activity and phoD-harboring microbial diversity in karst (a) and non-karst forest soils (b). Environmental factors affecting phoD-harboring microbial community structures in karst (c) and non-karst forest soils (d). Note: * means significant (p < 0.05), ** means highly significant (p < 0.01), *** means extremely significant (p < 0.001). ALP, alkaline phosphatase; ACP, acid phosphatase; SOC, Soil organic carbon; MBP, microbial biomass P; AP, available P; MBC, microbial biomass C.
Figure 7. Relationships among soil physical–chemical properties, phosphatase activity and phoD-harboring microbial diversity in karst (a) and non-karst forest soils (b). Environmental factors affecting phoD-harboring microbial community structures in karst (c) and non-karst forest soils (d). Note: * means significant (p < 0.05), ** means highly significant (p < 0.01), *** means extremely significant (p < 0.001). ALP, alkaline phosphatase; ACP, acid phosphatase; SOC, Soil organic carbon; MBP, microbial biomass P; AP, available P; MBC, microbial biomass C.
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Figure 8. SEM showed the relationship between phoD-harboring microorganisms, AP and environmental variables in karst forest soils (a) and non-karst forest soils (b), as well as the relationship between soil nutrients and phosphatase, pH, the relative abundance of phoD-harboring microorganisms, and AP under different forest soil types (c). The red arrow indicates a positive path coefficient; the blue indicates a negative path coefficient. * means significant (p < 0.05), ** means highly significant (p < 0.01), *** means extremely significant (p < 0.001). ALP, alkaline phosphatase; ACP, acid phosphatase.
Figure 8. SEM showed the relationship between phoD-harboring microorganisms, AP and environmental variables in karst forest soils (a) and non-karst forest soils (b), as well as the relationship between soil nutrients and phosphatase, pH, the relative abundance of phoD-harboring microorganisms, and AP under different forest soil types (c). The red arrow indicates a positive path coefficient; the blue indicates a negative path coefficient. * means significant (p < 0.05), ** means highly significant (p < 0.01), *** means extremely significant (p < 0.001). ALP, alkaline phosphatase; ACP, acid phosphatase.
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Table 1. Diversity indices of phoD-harboring microorganisms.
Table 1. Diversity indices of phoD-harboring microorganisms.
Forest Soil TypeSeasonShannon-WienerSIMPSONEvennessRichnessChao 1
Karst forest soilRainy5.776 ± 0.077 Aa0.015 ± 0.001 Ba0.087 ± 0.005 Aa3776 ± 143 Aa3778 ± 142 Aa
Dry5.890 ± 0.095 Aa0.013 ± 0.000 Ba0.101 ± 0.010 Aa3749 ± 112 Aa3751 ± 112 Aa
Non-karst forest soilRainy4.829 ± 0.096 Ba0.045 ± 0.002 Ab0.059 ± 0.008 Ba2329 ± 113 Ba2332 ± 113 Ba
Dry4.510 ± 0.106 Bb0.061 ± 0.002 Aa0.043 ± 0.005 Ba2307 ± 141 Ba2310 ± 141 Ba
Note: Capital letters indicate significant differences between forest soil types and lowercase letters indicate significant differences between seasons (p < 0.05).
Table 2. Network parameters and keystone species in karst and non-karst forest soils.
Table 2. Network parameters and keystone species in karst and non-karst forest soils.
NetworkNodes/EdgesAvg. DegreeAvg. Path LengthDensityDiameterClust.
Coeff.
Karst forest soil63/67021.2701.730.34340.686
Non-karst forest soil94/159735.4891.8430.39940.744
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Chen, M.; Qin, H.; Liang, Y.; Xiao, D.; Yan, P.; Yin, M.; Pan, F. The phoD-Harboring Microorganism Communities and Networks in Karst and Non-Karst Forests in Southwest China. Forests 2024, 15, 341. https://doi.org/10.3390/f15020341

AMA Style

Chen M, Qin H, Liang Y, Xiao D, Yan P, Yin M, Pan F. The phoD-Harboring Microorganism Communities and Networks in Karst and Non-Karst Forests in Southwest China. Forests. 2024; 15(2):341. https://doi.org/10.3390/f15020341

Chicago/Turabian Style

Chen, Min, Hanlian Qin, Yueming Liang, Dan Xiao, Peidong Yan, Mingshan Yin, and Fujing Pan. 2024. "The phoD-Harboring Microorganism Communities and Networks in Karst and Non-Karst Forests in Southwest China" Forests 15, no. 2: 341. https://doi.org/10.3390/f15020341

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