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Article

Material Conversion, Microbial Community Composition, and Metabolic Functional Succession During Algal Sludge Composting

by
Manting Zhou
1,
Wenjing Zhu
1,
Zhenrong Zheng
1,
Hainan Wu
1,*,
Haibing Cong
1 and
Shaoyuan Feng
2
1
College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225009, China
2
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(19), 2904; https://doi.org/10.3390/w17192904
Submission received: 24 August 2025 / Revised: 24 September 2025 / Accepted: 6 October 2025 / Published: 8 October 2025
(This article belongs to the Special Issue Innovative Green Technologies for Wastewater Treatment and Sludge Valorization Towards a Circular Economy)
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Abstract

Although bacterial and fungal communities play essential roles in organic matter degradation and humification during composting, their composition, interactions, abiotic compost properties, and succession patterns remain unclear. In this study, the succession of bacterial and fungal communities during algal sludge composting was explored using 16S and ITS rRNA amplicon sequencing. The compost rapidly entered the thermophilic phase (>50 °C) within the first phase. During the composting process, the diversity of bacterial and fungal communities did not show a significant response to the different composting phases. The physicochemical parameters and microbial community structures changed significantly during the thermophilic and cooling phases, particularly in the former, and gradually stabilized as the compost matured. Integrated random forest and network analyses suggested that the bacteria genera Geobacillus and Parapedobacter, along with the fungus genus Gilmaniella, could serve as potential biomarkers for different composting phases. The functional activity of the bacterial communities was obviously higher during the thermophilic phase than during the other phases, while fungal activity remained relatively high during both the thermophilic and cooling phases. Structural Equation Modeling (SEM) further indicated that bacterial communities primarily mediated nitrogen transformation and humification processes, while fungal communities mainly contributed to humification. These results cast a new light on understanding about microbial function during aerobic algal sludge composting.

1. Introduction

The rapid industrialization has brought about rapid economic growth, but it has also led to the production of large amounts of polluting waste [1]. Globally, three primary solid waste management strategies are currently implemented based on regional economic conditions and resource availability: landfill, incineration disposal, and compost [2]. Among these, composting is a cost-effective technology for organic solid waste treatment and resource recovery [3]. During this process, complex organic matter is converted into stable humus through the metabolic activities of microorganisms, thereby achieving the harmless treatment of pollutants [4]. Therefore, a thorough analysis of the microbial community succession during the composting process is the basis for optimizing the operational parameters and enhancing the quality of the final product.
The microbial community structure exhibits distinct dynamic succession patterns during the composting process, with bacteria and fungi forming the core functional groups. According to Langarica-Fuentes et al. [5], bacterial communities typically dominate in the initiation phase of most composting processes, while fungal communities usually proliferate in the relatively cold maturation phase. This phase-specific succession of microbial communities reflects the adaptive response of different functional microbiota to changes in environmental conditions in the compost pile. In order to investigate this mechanism in detail, many studies have been carried out on the composting of different mixtures of biomass as feedstocks. For instance, in a mixed chicken manure-rice bran composting system, Mao et al. [6] found that fungal community diversity peaked during the thermophilic phase, while bacterial community diversity peaked during the initial stages of composting, followed by a gradual decrease. This divergence was accompanied by distinct functional adaptations: bacteria exhibited an enhanced capacity for catabolizing carbohydrates at the thermophilic phase, whereas fungi exhibited a significant increase in carboxylic acid and amino acid metabolism potential during the maturity phase [6]. In a co-composting system of mung bean hulls and corn stover, Zhang et al. [7] found through time-series analysis that the bacterial and fungal community structures had different succession patterns during the composting process and that specific biomarkers existed in each. Specifically, bacterial communities exhibited phase-specific functional preferences and were primarily responsible for the rapid decomposition of perishable organic matter during the initial phases of composting. As composting progresses to the thermophilic phase, fungal communities that prefer to degrade complex lignocellulosic substrates gradually dominate, and community interactions drive the degradation process [7]. However, most of the current studies focus on the respective structural changes and phase-specific functional characteristics of different microbial communities, while the study of the interaction mechanism between fungal and bacterial communities is still relatively insufficient. Therefore, the systematic analysis of the dynamic evolution of different microbial communities during the composting process and the elucidation of the interactions between fungal and bacterial communities are of great theoretical and practical importance for optimizing the composting process.
Microbial communities vary significantly in different composting feedstocks, and analyzing them in specific materials is directly useful for optimizing the composting process. Algae rapidly proliferate in eutrophic water systems by utilizing excess nutrients, posing risks to drinking water safety [8,9]. Currently, the main physical pretreatment method for algae involves direct salvage of the floating algae, followed by mechanical press filtration, which produces a large amount of algal sludge [10]. Due to the high nutrient content, algal sludge has been treated through composting in some studies [11]. Zhang et al. [12] evaluated the feasibility of using algal sludge in aerobic co-composting with livestock manure and straw. Jiang et al. [13] found that the carbon/nitrogen (C/N) ratio significantly affected both the degree of feedstock degradation and the degradation efficiency of microcystins during algal sludge composting; optimizing such key parameters can effectively regulate the quality of the final product. Si et al. [14] reported that adding B. subtilis inoculants during algal composting can increase the heating rate of the pile, promote the transformation of organic matter, accelerate the humification process, and improve the quality of the resulting compost. Although existing studies have preliminarily revealed the effects of process parameters and microbial community structure in algal sludge composting, the interactions between bacterial and fungal communities and their specific roles in this process are still not well understood.
Lake Taihu, China’s third-largest freshwater lake, experiences seasonal large-scale algal proliferation every summer due to eutrophication. The salvaged algal sludge requires further treatment. This study therefore conducted aerobic composting experiments using freshly collected algal sludge from Lake Taihu. The primary goals of this study were as follows: (i) to analyze the alterations in physicochemical properties throughout the composting process; (ii) to explore the succession of bacterial and fungal structure and function during the algal composting process; and (iii) to examine the relationship between microbial communities and physicochemical parameters during the composting process. The methodological framework presented here may serve as a valuable reference for microbial optimization in composting of other materials.

2. Materials and Methods

2.1. Composting Process and Sample Collection

The algal sludge used in the compost was collected from Taihu Lake. After belt-pressed filtration, the sludge had a moisture content of approximately 85%. The compost mixture, consisting of algal sludge, mushroom residue, and mature compost in a 15:7.5:1 volume ratio, was placed in a >100 L aerobic bioreactor (Figure S1). Air was pumped to the pile from the bottom to maintain high oxygen levels. The composting experiment lasted 49 days, and samples were taken on days 0.5, 1, 2, 3, 4, 5, 7, 9, 14, 19, 25, 31, 39, and 49. The pile was manually turned on Days 5 and 16. Sampling for subsequent physicochemical analysis and DNA extraction was conducted at a depth of 10 cm below the pile surface.

2.2. Physicochemical Analysis

Temperature measurements were taken daily using a thermometer at a depth of 10 cm within the compost pile and in the surrounding air. Simultaneously, a gas detector (K-600, Henan Kailu Electronic Technology Co., Ltd., Zhengzhou, China) was employed to measure the oxygen (O2) concentration. Total organic carbon (TOC) content was analyzed via the loss-on-ignition method. For total nitrogen (TN), the Kjeldahl digestion method was applied. The germination index (GI) was calculated following the method of Chang et al. [15], using the following formula: GI = (treatment germination rate × treatment root length)/(control germination rate × control root length). Humic (HA) and fulvic (FA) acids were isolated via an NaOH-Na4P2O7 extraction and subsequently analyzed by an organic carbon analyzer.

2.3. DNA Extraction, Sequencing, and Sequence Analysis

The DNA from the compost samples was obtained using the Fast DNA SPIN Kit for Soil (MP Biomedicals, Irvine, CA, USA). The extracted DNA was preserved at −80 °C until further molecular analysis. The universal primer sets (341F and 806R) targeting the V3–V4 hypervariable region of the 16S rRNA gene were selected [16]. For fungal community analysis, the ITS1 region was amplified with primers ITS1F and ITS2R [17]. The triplicate PCR products for each reaction were pooled and purified for each sample. The mixture was sequenced with the MiSeq PE250 Sequencer (Illumina, San Diego, CA, USA) at Shanghai BIOZERON Biotechnology Co., Ltd. (Shanghai, China). Raw sequences were imported to the platform QIIME2. The dataset generated in this study has been deposited in the NCBI Sequence Read Archive under the accession code PRJNA1017365.

2.4. Statistical Analyses

The vegan package in R (version 4.3.3) was used to assess the α-diversity (Shannon, Evenness, and Pd_faith indexes) of bacterial and fungal communities, as well as to perform non-metric multidimensional scaling (NMDS). Random forest modeling (randomForest packages) was applied to identify the changes in bacterial and fungal genus-level abundances against the composting time. Bacterial functional characteristics were assessed using the FAPROTAX database (https://github.com/yongxinliu/EasyMicrobiome, accessed on 17 March 2025). Fungal functions were predicted using the FUNGuild database based on the ITS data (https://github.com/UMNFuN/FUNGuild, accessed on 20 March 2025).
The interaction between bacterial communities and fungal communities was investigated by building molecular ecological networks via the Molecular Ecological Network Analyses Pipeline [18], with network visualization performed in Gephi 0.9.2. To reduce network complexity, only bacterial and fungal genera with an average relative abundance of at least 0.02% were included. After threshold scanning through the RMT-based approach, the network was built using a similarity threshold of 0.72. The topological roles of each node—peripherals, connectors, module hubs, and network hubs—were defined by calculating within-module connectivity (Zi) and among-module connectivity (Pi). Connectors, module hubs, and network hubs were considered key taxa due to their important role in maintaining network stability. Module hubs referred to keystone species with high connectivity within specific functional modules. Connectors are keystone species that connect different modules within the microbial network.
A structural equation model (SEM) was employed to analyze both direct and indirect relationships between bacterial and fungal communities and physicochemical parameters throughout the composting process of algal sludge. The SEM analysis was conducted using Amos 24.0 (IBM SPSS, Armonk, NY, USA), where maximum likelihood estimation was used to determine path coefficients and model fit indices. Model validity was assessed with fit indices such as the incremental fit index (IFI), chi-square/degrees of freedom ratio (χ2/DF), and comparative fit index (CFI). Prior to SEM construction, principal component analysis (PCA) was applied to reduce the dimensionality of the microbial community data. The first principal component (PC1) with cumulative variance ≥ 50% was extracted for subsequent path analysis. Based on presumed causal relationships, the model was structured with temperature as the first layer and GI as the final layer.

3. Results and Discussion

3.1. Variations of Physicochemical Properties

GI is a key indicator for evaluating the maturity of compost, effectively reflecting the stability of the compost and its phytotoxicity [19]. In this study, the GI value of the algal sludge composting increased gradually within 25 days (Figure 1b), reaching 133% on Day 25. This indicated that well-fermented compost with almost no phytotoxicity can be obtained [20]. The GI value remained relatively stable after 25 days, indicating that the compost had entered the maturation phase. Based on the dynamic changes in compost temperature and GI over time, the process can be divided into three phases: the thermophilic phase (days 0.5, 1, 2, 3, and 4), the cooling phase (days 5, 7, 9, 14, and 19), and the maturation phase (days 25, 31, 39, and 49) (Figure 1a). The temperature rose sharply to approximately 75 °C within the first day, marking the onset of the thermophilic phase, and remained above 50 °C for around 4 days. The temperature increased rapidly as mesophilic microorganisms decomposed easily biodegradable organic matter, releasing heat in the process [21]. Then, as microbial growth and metabolic activity slowed, the compost entered the cooling phase. Finally, during the maturation phase, the temperature stabilized at around 23 °C. An adequate supply of oxygen is crucial for maintaining microbial metabolic activity throughout the composting process [22], with a recommended oxygen content exceeding 10% [23]. Notably, despite the highest aeration rate being applied on the first day coinciding with the peak temperature, the oxygen content within the pile reached its lowest level at this time. This indicates that the microbial community’s general metabolic activity was at its strongest at this time, with large amounts of oxygen being consumed through aerobic respiration.
As algal feedstock is rich in nitrogen, the initial TN content was high. As the composting progressed, TN content decreased from 36.41 g/kg to 28.25 g/kg on day 49 (Figure 1d). The TN content decreased quickly during the thermophilic phase, a phenomenon attributed to the extensive breakdown of proteinaceous compounds. This process generates NH4+-N, leading to subsequent NH3 volatilization and a net loss of nitrogen [24]. Similarly, the TOC content decreased rapidly in the thermophilic phase as readily degradable carbon was consumed through intense microbial metabolism [25] (Figure 1e). The reduction in TOC slowed down when the temperature dropped; this may be because microbial and enzyme activity weakened when the temperature decreased. The fluctuation in TN and TOC concentrations observed during the cooling phase can be attributed to the continuous decomposition in the total mass and volume of the compost pile, as the overall mass of the pile decreased faster than the nutrients themselves were lost at certain points, leading to a temporary relative enrichment in the remaining material (Figure 1d,e).
The contents and ratios of HA and FA are well-established indicators of compost maturity [26]. During the thermophilic phase, the HA content increased rapidly, while the content of FA decreased gradually (Figure 1f). This may be because microbes prefer to use FA with a simple structure and low molecular weight, converting the organic matter into the more structurally stable HA in the process [27]. The HA/FA ratio surpassed 2 after day 25 in this experiment, indicating compost maturity, a result that supports the widely referenced maturity threshold of >1.6 [28]. These observations indicate that the changes in physicochemical properties during composting were primarily concentrated in the thermophilic and cooling phases, especially in the former, and stabilized in the maturation phase.

3.2. Microbial Succession During Composting

The Shannon, Evenness, and Faith’s PD indices were used to represent the α-diversity of the microbial communities during the composting process. The Shannon and Evenness indices of the bacterial communities showed a slight increase during the cooling phase (Figure 2a). This rise likely occurred because the decreasing temperature allowed more bacterial species to grow and multiply, promoting the colonization of diverse ecological niches and forming a more complex community [29]. Overall, however, the diversity of bacterial and fungal communities did not show significant differences during the composting process.
NMDS was used to assess the successional dynamics of microbial communities. During the thermophilic and cooling phases, the distributions of both bacterial and fungal communities at different time points were dispersed in NMDS, suggesting obvious changes in the microbial communities during these phases (Figure 2c,d). Compared to the thermophilic phase, differences in bacterial and fungal communities decreased during the cooling phase. In the maturation phase, the bacterial and fungal communities at different time points showed clear clustering, which was particularly pronounced for bacterial communities. This demonstrates that the changes in the microbial communities in the pile gradually decreased after the thermophilic phase and stabilized during the maturation phase, particularly for the bacterial communities (Figure 2c). This trend in microbial communities aligned with the changes in physicochemical parameters. The observed stabilization of the bacterial communities was likely due to the depletion of the labile organic compounds pathway [29]. In contrast, fungal communities showed a weaker clustering tendency than bacterial communities in the maturation phase, suggesting their composition continued to undergo dynamic changes. This indicates that fungal communities likely play a major role in degrading residual recalcitrant organic matter later in the process [30].
To explore shifts in community composition in greater detail, the relative abundances of bacterial and fungal phyla were compared (Figure 2e,f). Distinct shifts in composition were observed across the different composting phases. Overall, Proteobacteria (24.7–57.8%), Firmicutes (4.1–59.9%), Bacteroidetes (1.4–38.9%), Actinobacteria (6.7–19.3%), and Chloroflexi (1.1–17.2%) were identified as the dominant bacterial phyla during composting (Figure 2e). Proteobacteria, a key phylum involved in lignocellulose degradation, exhibited the highest average relative abundance and remained stable throughout all phases [31]. The relative abundance of Firmicutes was significantly higher during the thermophilic phase than during the other two phases, which can be attributed to their ability to form thermoresistant endospores and thrive at high temperatures [32]. Bacteroidetes could transform lignocellulose into short-chain fatty acids [33]. The relative abundance of Bacteroidetes was high initially (38.9%) but sharply declined to 1.4% by day 3, indicating limited thermotolerance. The relative abundance of Actinobacteria, which can degrade recalcitrant polymers like lignin and chitin [34,35], remained relatively constant across phases. The relative abundance of Chloroflexi was higher at the maturation phase compared to other phases.
In terms of fungal communities, Ascomycota was dominant throughout all phases of composting (84.8–99.6%). Ascomycota are common saprophytes that play a dominant role in involvement in lignocellulose decomposition and humification throughout the composting process. Their ecological success is attributed to high thermotolerance, enabling them to maintain >80% viability at 60 °C, and a robust capacity for organic matter degradation [36].
To link microbial community composition to the composting timeline, we employed a random forest model to regress the relative abundances of bacterial and fungal genera against process time (Figure 3a). The top 48 microbial biomarkers in terms of explanatory rate (p > 0.05) were identified by regressing the relative abundances against the composting time. Among these key taxa, 41 were bacteria and 7 were fungi. Most of the biomarker taxa showed a high relative abundance during the corresponding composting process (Figure 3b).
During thermophilic phases, thermophilic microbes become dominant. Bacterial genera such as Geobacillus (0.41–8.82%) and Thermobacillus (0.03–5.59%) are known to participate in cellulose and protein degradation by secreting heat-stable enzymes [37,38]. The fungal genus Myceliophthora (0.04–27.76%) also thrived, playing a key role in the initial breakdown of complex substrates like lignocellulose [39]. As composting temperatures decline in the cooling phase, the abundance of these thermophiles gradually decreases. Conversely, the relative abundance of Marivirga increased from 0.08% to 16.57% during the cooling phase. This genus plays a role in degrading biopolymers like polysaccharides and proteins, which is consistent with its significant enrichment of enzymes, including carbohydrate-active types [40]. Upon entering the maturation phase, the relative abundance of plant-pathogenic Fusarium species decreased obviously [41], consistent with the known ability of mature composts to suppress soil-borne diseases caused by fungal pathogens [42]. These results demonstrate that the microbial community composition differed distinctly across the various phases of algal sludge composting, reflecting the shifting functional demands of each stage.

3.3. Interactions Between Bacterial and Fungal Communities

A network was constructed to analyze the interactions between bacteria and fungi during algal sludge composting. The resulting network consisted of 297 nodes (bacteria and fungi) and 3732 edges (Figure 4a). The interactions were predominantly among bacteria (91.3%), followed by bacteria-fungi (8.5%), then those among fungi (0.3%). The network analysis revealed that positive and negative correlations within the bacterial communities accounted for 46.2% and 53.8%, respectively, indicating that competitive interactions were slightly more prevalent than cooperative relationships. These positive and negative correlations reflect complex microbial behaviors such as cooperation, competition, and symbiosis [43].
Seventeen keystone species, belonging to three types (Module hubs, Connectors, and Network hubs), were identified in the co-occurrence network (Figure 4b). Among these, the two bacterial genera, Paracoccus and Altererythrobacter, were identified as module hubs. Paracoccus contributes to the nitrogen cycle through denitrification [7], while Altererythrobacter possesses functional genes for dissimilatory nitrate reduction to ammonium (DNRA) [44]. In addition, Bacilli was identified as network hubs. This group is known for its ability to form thermotolerant endospores and degrade diverse organic matter.
Fourteen notes were classified as Connectors, including twelve bacterial genera (Geobacillus, Parapedobacter, Brumimicrobium, Comamonas, Microcystis, Thermomonas, Nitratireductor, Aeromonas, Salinispora, Pseudohongiella, Gemmobacter, and Cellvibrionaceae_uncultured) and two fungal genera (Gilmaniella and Trichurus) (Figure 4b). As the connectors link different modules within the microbial network, they are likely to perform a wide range of ecological functions. Geobacillus, known for its thermotolerance, primarily degrades lignocellulose [45]. Parapedobacter and Brumimicrobium participate in polysaccharide decomposition and humification [46,47]. Nitratireductor, Comamonas, and Thermomonas are all involved in nitrogen transformation during composting [48]. Salinispora produces antimicrobial compounds, and Aeromonas can suppress pathogen growth [49,50]. Pseudohongiella and Cellvibrionaceae decompose recalcitrant organic matter [51,52]. The fungi also play key roles: Trichurus secretes keratinases to hydrolyze complex organics, accelerating decomposition and humification [53], and Gilmaniella exhibits thermotolerance and produces antifungal metabolites [54].
Notably, the intersection of results from random forest and network analyses highlighted Geobacillus, Parapedobacter, and Gilmaniella as key taxa. Their significant temporal dynamics (random forest) and central topological role as connectors (network) jointly confirm their status as potential biomarkers for specific composting phases, potentially reflecting the functional shifts that characterize each stage of the process.

3.4. Microbial Metabolic Functions During Composting

Shifts in microbial abundance during composting suggested concomitant changes in community functional potential. To evaluate this, FAPROTAX and FUNGuild were used to further evaluate the potential functions of the bacterial and fungal communities during the composting process. In summary, the functional potential related to energy sources, the nitrogen cycle (excluding nitrogen fixation), and the carbon cycle was significantly elevated in the thermophilic phase compared to other phases. This indicates a probable increase in both the abundance and metabolic activity of the pertinent bacterial taxa during this period. The enhanced nitrogen cycle functions in the thermophilic phase may be associated with Paracoccus, Burkholderia, and Comamonas. These genera are potentially involved in denitrification and showed high or moderately high abundances in the thermophilic phase (and, for Burkholderia, also in the maturation phase) [55,56,57]. In contrast, the functional potential for nitrogen fixation was higher in the cooling and maturation phases. This shift corresponded with an increase in the abundance of Sinorhizobium, a genus known for its role in nitrogen fixation, during the later stages of composting [58].
The elevated functional potential for carbon cycling during the thermophilic phase was likely linked to key genera, including Thermobifida, Sphingobacterium, Geobacillus, and Micromonospora. Thermobifida and Sphingobacterium contribute to cellulose decomposition [59,60]. Geobacillus and Micromonospora were also highly abundant in the thermophilic phase. They are potentially involved in organic matter mineralization and may participate in methylotrophy, which would further enhance the carbon metabolic activities [61,62]. Furthermore, the increased potential for fermentation functions was associated with the high abundance of Bacillus, a genus known for its role in both cellulose decomposition and fermentation. The peak in nitrogen and carbon cycle functional potential during the thermophilic phase coincided with the most rapid decreases in TN and TOC content, underscoring the critical role of this phase in driving organic matter decomposition.
Regarding the fungal communities, the FUNGuild analysis revealed that individual fungal OTUs could be assigned to multiple trophic modes (Figure 5b). Saprotrophic fungi exhibited relatively high abundances during the thermophilic and cooling phases, indicating their sustained role in organic matter decomposition throughout the compost maturation process. The proportions of parasites (including fungal and lichen parasites) and pathotrophs (such as animal and plant pathogens) gradually decreased. This trend suggests that the composting process effectively reduces the abundance and potential threat of these harmful microorganisms.
In terms of functional dynamics, the predicted functional potential of bacterial communities was highest during the thermophilic phase. In contrast, fungal communities maintained a significant functional presence during both the thermophilic and cooling phases.

3.5. Relationship Between Physicochemical Characteristics and Microorganisms During Composting

Based on these findings, we developed a structural equation model (SEM) to elucidate the causal relationships between microbial communities (bacteria and fungi), physicochemical properties, and composting outcomes (Figure 6). The analysis indicated that temperature exerted a significant and direct effect on microbial communities (p < 0.001) [63]. Specifically, a positive correlation was observed between temperature and fungal communities (p < 0.001), while a negative correlation was found between temperature and bacterial communities (p < 0.001), suggesting a potential complementary relationship between them. This finding implies that temperature may play a role in regulating microbial community succession, which could, in turn, influence nitrogen transformation and humification processes, contributing to the overall maturity of the compost. Bacterial communities were negatively correlated with TN content (p = 0.001) but positively correlated with the humification index (HA/FA) (p = 0.002). This suggests that the bacterial communities may have a significant effect on the loss of TN and the humification process. In contrast, the fungal communities were negatively correlated with HA/FA (p = 0.012) but had no significant effect on nitrogen loss (p = 0.947). As expected, both the germination index (GI) and the HA/FA ratio increased progressively as composting progressed, confirming the production of a stable and mature compost.

4. Conclusions

This study investigated the dynamic changes in physicochemical parameters and microbial communities during the aerobic composting of algal sludge. The physicochemical parameters and the structures of both bacterial and fungal communities changed most significantly during the thermophilic and cooling phases, particularly the former, and gradually stabilized upon maturation. The integration of random forest and network analyses identified the bacteria Geobacillus (0.41–8.82%) and Parapedobacter (0.01–2.93%), along with the fungus Gilmaniella (0.93–48.24%), as key taxa whose abundances shifted significantly (p < 0.05) throughout composting and which occupied critical roles in the co-occurrence network. These taxa are, therefore, proposed as potential biomarkers for distinct composting phases. The predicted functional potential related to energy sources, the nitrogen cycle (excluding nitrogen fixation), and the carbon cycle was significantly elevated in the thermophilic phase compared to the other phases. Saprotroph was the main trophic mode of fungal communities, with its relative abundance being significantly higher in the thermophilic and cooling phases than in the maturation phase. SEM further indicated that bacterial communities may primarily mediate nitrogen transformation and the humification process, and the fungal communities primarily contribute to humification. Together, these findings suggest that bacterial and fungal community succession was closely linked to dynamic changes in compost properties, offering a potential explanation for the alternating patterns of taxa abundance observed throughout algal sludge composting.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17192904/s1. Figure S1: Schematic diagram of compost reactor. The bioreactor was constructed from 10 mm thick polyvinyl chloride material, with an effective volume of >100 L (Φ60 cm × height 60 cm).

Author Contributions

Conceptualization, H.C.; Methodology, H.W., H.C. and S.F.; Formal analysis, M.Z., W.Z., Z.Z. and H.W.; Investigation, M.Z., W.Z., Z.Z. and H.W.; Data curation, H.W.; Writing—original draft, M.Z., W.Z. and Z.Z.; Writing—review and editing, H.W.; Visualization, M.Z., W.Z. and Z.Z.; Supervision, H.C. and S.F.; Project administration, H.C.; Funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the National Natural Science Foundation of China [No. 52200205] and the National Key Research and Development Program of China [Nos. 2022YFC3203604 and 2022YFC3203602].

Data Availability Statement

The dataset generated in this study has been deposited in the NCBI Sequence Read Archive under the accession code PRJNA1017365.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Dynamic changes of temperature (a); germination index (b); O2 (c); TN (d); TOC (e); and HA, FA, and HA/FA (f) during the composting process.
Figure 1. Dynamic changes of temperature (a); germination index (b); O2 (c); TN (d); TOC (e); and HA, FA, and HA/FA (f) during the composting process.
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Figure 2. The changes in bacterial (a,c,e) and fungal (b,d,f) communities during composting. Changes in bacterial (a) and fungal (b) community α-diversity (Shannon, Evenness, and Pd_faith indexes). The NMDS of bacterial (c) and fungal (d) community composition. The relative abundances of bacterial communities (e) and fungal communities (f) at the phyla level.
Figure 2. The changes in bacterial (a,c,e) and fungal (b,d,f) communities during composting. Changes in bacterial (a) and fungal (b) community α-diversity (Shannon, Evenness, and Pd_faith indexes). The NMDS of bacterial (c) and fungal (d) community composition. The relative abundances of bacterial communities (e) and fungal communities (f) at the phyla level.
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Figure 3. Microbial biomarkers associated with the composting progression, identified by random forest analysis. (a) Ranking of the top 48 genera based on their importance for predicting changes across the composting process (** p < 0.01; * p < 0.05). (b) The relative abundances of the top 48 predicted biomarkers relative to the composting process.
Figure 3. Microbial biomarkers associated with the composting progression, identified by random forest analysis. (a) Ranking of the top 48 genera based on their importance for predicting changes across the composting process (** p < 0.01; * p < 0.05). (b) The relative abundances of the top 48 predicted biomarkers relative to the composting process.
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Figure 4. Network analysis based on the co-occurrence of the bacterial and fungal communities during composting (a). Putative keystone species identified based on the node topological roles in networks under (b). The topological role of each genus was determined according to the scatter plot of Zi and Pi (module hub, Zi ≥ 2.5; connector, Pi ≥ 0.62; network hub, Zi ≥ 2.5 and Pi ≥ 0.62).
Figure 4. Network analysis based on the co-occurrence of the bacterial and fungal communities during composting (a). Putative keystone species identified based on the node topological roles in networks under (b). The topological role of each genus was determined according to the scatter plot of Zi and Pi (module hub, Zi ≥ 2.5; connector, Pi ≥ 0.62; network hub, Zi ≥ 2.5 and Pi ≥ 0.62).
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Figure 5. Variations in bacterial and fungal functional profiles during composting. (a) Bacterial community function (energy source, C-cycle, and N-cycle) annotated by FAPROTAX. (b) Fungal community function with an average relative abundance greater than 1% annotated by FUNGuild.
Figure 5. Variations in bacterial and fungal functional profiles during composting. (a) Bacterial community function (energy source, C-cycle, and N-cycle) annotated by FAPROTAX. (b) Fungal community function with an average relative abundance greater than 1% annotated by FUNGuild.
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Figure 6. SEM illustrates the hypothesized causal relationship between bacterial and fungal communities, T, compost humification (HA/FA), TN, and GI during the composting. Red and blue arrows denote significant positive and negative path coefficients, respectively (*** p < 0.001; ** p < 0.01; * p < 0.05).
Figure 6. SEM illustrates the hypothesized causal relationship between bacterial and fungal communities, T, compost humification (HA/FA), TN, and GI during the composting. Red and blue arrows denote significant positive and negative path coefficients, respectively (*** p < 0.001; ** p < 0.01; * p < 0.05).
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MDPI and ACS Style

Zhou, M.; Zhu, W.; Zheng, Z.; Wu, H.; Cong, H.; Feng, S. Material Conversion, Microbial Community Composition, and Metabolic Functional Succession During Algal Sludge Composting. Water 2025, 17, 2904. https://doi.org/10.3390/w17192904

AMA Style

Zhou M, Zhu W, Zheng Z, Wu H, Cong H, Feng S. Material Conversion, Microbial Community Composition, and Metabolic Functional Succession During Algal Sludge Composting. Water. 2025; 17(19):2904. https://doi.org/10.3390/w17192904

Chicago/Turabian Style

Zhou, Manting, Wenjing Zhu, Zhenrong Zheng, Hainan Wu, Haibing Cong, and Shaoyuan Feng. 2025. "Material Conversion, Microbial Community Composition, and Metabolic Functional Succession During Algal Sludge Composting" Water 17, no. 19: 2904. https://doi.org/10.3390/w17192904

APA Style

Zhou, M., Zhu, W., Zheng, Z., Wu, H., Cong, H., & Feng, S. (2025). Material Conversion, Microbial Community Composition, and Metabolic Functional Succession During Algal Sludge Composting. Water, 17(19), 2904. https://doi.org/10.3390/w17192904

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