Biodiverse Compounds from Angiosperms and Gymnosperms: A Chemical, Nutritional, and Microbiological Approach

Abstract

Biodiverse composts obtained through composting are widely used in regenerative agriculture due to their ability to improve soil quality, crop growth, and productivity, primarily by promoting beneficial microorganisms. These composts result from the decomposition of mixtures containing nitrogenous and plant biomass. During plant biomass preparation, litter serves as a source of beneficial microorganisms, which transition from endophytes to decomposers. This study tested the hypothesis that the type of litter influences the composition of bacterial and fungal communities in biodiverse composts, thereby affecting species abundance and diversity. To this end, litter from the tree species Handroanthus impetiginosus (Angiosperm—AC) and Pinus elliottii (Gymnosperm—GC) was evaluated in compost preparation, also investigating the impact of litter type on the concentration of macronutrients, chemical parameters (such as organic carbon, cation exchange capacity—CEC; carbon/nitrogen ratio—C/N; organic matter—OM; pH, and humic substances fractions, including humic and fulvic acids), and microbiological quality (assessed by Microbial Biomass Carbon—MBC). The microbial composition of composts prepared with both AC and GC litter was more influenced by the composting method than by plant origin, with bacterial genera such as Thermobacillus (representing 1.27% and 1.23% of the genera present in AC and GC, respectively) and thermotolerant species, adapted to the high temperatures of the thermophilic phase, being notably present. GC litter favored a higher abundance of bacterial (pi = 0.027) and fungal species (pi = 0.042), despite the antimicrobial properties of P. elliottii. In contrast, AC compost accumulated higher levels of macronutrients and OM (39.5%), reflecting the efficacy of specific fungi in decomposition, particularly species from the phyla Chytridiomycota and Zoopagomycota, identified exclusively in this compost. MBC analysis indicated that composts reach optimal efficiency and nutritional quality between 60 and 90 days of maturation, suggesting that this period is the most suitable for leveraging the resident microbiota and producing high-quality composts for agricultural use.

1. Introduction

Composting is an essential practice for sustainable agriculture, contributing significantly to improving soil quality and creating a balanced agricultural environment [1,2]. By transforming organic waste into compost, this technique increases soil organic matter, promoting structural improvements, greater water retention, and enhanced nutrient exchange capacity [3,4]. Furthermore, the compost produced is rich in beneficial microorganisms that promote soil health, fostering biodiversity and ecological balance [5], and is therefore considered biodiverse compost. These microorganisms can be extracted using techniques such as the Soil Food Web, which generates extracts or concentrated teas of beneficial microbiota that have been shown to effectively promote growth and increase the productivity of various crops [6,7]. Additionally, the use of compost aims to reduce the application of chemical fertilizers to crops, promoting fertility naturally and helping to reduce pollution and contamination of water resources [8]. Thus, composting stands out as a valuable tool for regenerative and resilient agriculture.

The composting process involves several interdependent stages, each involving different groups of microorganisms, with each group performing essential functions in the degradation of organic matter [9]. The initial phase, known as the thermophilic phase, occurs when mesophilic microorganisms (moderate-temperature bacteria and fungi) begin to decompose organic waste, generating heat [10]. The subsequent aerobic or accelerated phase is characterized by the predominance of thermophilic microorganisms, which are capable of degrading more complex compounds. These microorganisms, such as thermophilic bacteria, accelerate the transformation of organic material, reducing the volume of waste and increasing the compost temperature to levels that favor the elimination of pathogens [11]. In the final maturation stage, the temperature gradually decreases, and psychrophilic microorganisms take over the decomposition of the remaining materials, transforming them into humus [12,13]. Throughout all stages, microbial diversity and selection are critical to ensuring efficient decomposition, pathogen control, and the formation of high-quality, nutrient-rich compost for the soil [9]. Thus, some species are adapted to specific phases, while others can persist throughout the entire process.

Several studies indicate that the structure of the microbial community in biodiverse composts can vary throughout the composting process, influenced by various factors. Temperature is one of the most important factors, as it directly affects microbial activity and diversity [14]. Moisture also plays a crucial role, as inadequate moisture levels can limit microbial activity; very low moisture inhibits microorganism proliferation, while excess moisture reduces aeration and favors anaerobic conditions, impairing aerobic decomposition [15,16]. Aeration, or oxygen supply, influences the abundance of aerobic microorganisms, such as bacteria and fungi, which are essential for efficient decomposition and maintaining the ideal temperature [17]. Another important factor is the presence of antimicrobial agents in the composted materials, such as pesticides or contaminants, which can inhibit specific microbial groups, altering the community composition and slowing down the composting process [18,19].

The composition of the organic waste used in composting, such as the proportion of carbon (C) and nitrogen (N), also plays a fundamental role, as the imbalance between these elements can affect the activity of different microbial groups, influencing the rate of decomposition [20,21]. In the preparation of composts using the Soil Food Web technique, nitrogenous biomass, typically composed of cattle manure (10%), is combined with plant biomass, including grass (30%) and a mixture of wood shavings and plant litter (60%). Considering this context, we hypothesize that the type of litter used influences the composition of bacterial and fungal communities in biodiverse composts, impacting microbial abundance and diversity.

Studies indicate that, after plant death, microorganisms present in the tissues of angiosperms and gymnosperms cease to act as endophytes and begin to function as saprophytes, releasing enzymes that facilitate the decomposition of organic matter [22,23]. Therefore, litter serves as an important source of beneficial microorganisms that promote plant growth and become part of the resident microbiota in biodiverse composts. In these composts, these microorganisms mineralize nutrients that were previously unavailable to plants, improving nutrient acquisition and ensuring greater crop productivity [24,25]. Considering this potential, we evaluated the use of litter from two tree species (Angiosperm x Gymnosperm) in the preparation of biodiverse composts and tested the influence of litter type on the chemical and nutritional characteristics, as well as the microbiological quality, of the final product during the maturation phase. This contrast was chosen because these terrestrial plant groups have distinct evolutionary histories [26,27] and, consequently, harbor different endophytic microbiomes [28]. Our hypothesis is that litter can influence the dynamics of the microbiome and, consequently, the quality of the final compost produced.

2. Materials and Methods

2.1. Preparation of Biodiverse Compounds

Two types of biodiverse composts were prepared independently: AC (Angiosperm-based compost) and GC (Gymnosperm-based compost). The woody base for the AC compost consisted of leaf and branch litter from Handroanthus impetiginosus (purple ipê), while the GC compost used Pinus elliottii (pine) litter. All plant material collection procedures adhered strictly to ethical and regulatory guidelines. The specimens were obtained exclusively from a private, cultivated area on the Brasilanda farm (17°30′14.79″ S, 51°16′34.31″ W, located in the rural area of the municipality of Montividiu, Goiás, Brazil, at an average altitude of 905 m) and did not involve the collection of any species classified as threatened or endangered. Furthermore, the collection was non-destructive, focusing solely on litterfall (dry leaves and branches), ensuring no live plants were harmed. Collection was conducted under the official permit CMRV DPPGI 7/2023, issued on 1 September 2023 by the Directorate of Post-Graduation, Research, and Innovation of the IFGoiano (Rio Verde campus). Voucher specimens have been deposited in the Instituto Federal Goiano Herbarium (Handroanthus impetiginosus IFRV-2449; Pinus elliottii IFRV-2448). At the herbarium, the taxonomic identification was performed by the botanist, Dr. Gisele Cristina de Oliveira Menino. Following taxonomic confirmation, the biodiverse compounds were prepared directly in the farm area designated for composting.

The composts were prepared following the methodology described by Ingham [29] and Ingham and Slaughter [30], based on the Soil Food Web technique. Each mixture consisted of 60% woody base, 30% fresh plant biomass, and 10% high-nitrogen biomass. The woody base was a combination of wood shavings and litter (Handroanthus impetiginosus for the AC compost and P. elliottii for the GC compost). The fresh plant biomass was composed of Andropogon minarum (Capim-açú), while the nitrogenous biomass was represented by cattle manure (2% de N—7.13 g dm⁻³) (Figure 1). Although the woody base was specific to each compost, the other inputs remained constant between the two treatments, allowing for the isolation of the effect of the litter type on the evaluated parameters.

Figure 1. Preparation of biodiverse composts based on Angiosperm and Gymnosperm litter. Mixture of cattle manure, Andropogon minarum (Capim-açu) plant biomass, and wood shavings + litter to form compost piles, followed by the creation of composting windrows.

An initial 1200 L of each compost (AC and GC) were prepared. The inputs were homogenized and moistened until reaching 50% moisture content, according to the method described by Scheu et al. [31]. The homogeneous mixtures were packaged separately in cylindrical screen structures with a 25 mm mesh, forming two compost piles, each measuring 1.5 m in height and 3 m in diameter. The piles were left to rest for 30 days, encompassing the mesophilic, thermophilic, and maturation phases of the composting process. During this period, temperature and humidity were monitored daily, using a specific composting thermometer and the Soil Food Web method [31], respectively. The temperature was maintained at 35 ± 2 °C, while humidity was controlled between 40% and 45%. Whenever necessary, the piles were turned to dissipate heat and were moistened to adjust the moisture level.

After the initial composting period, during the maturation phase, the composts were transferred to the ground and arranged in windrows. During this stage, the moisture content was monitored daily and maintained at 50%. To enrich the composts, 1 L of seaweed extract from Ascophyllum nodosum (Stingray^®, Koppert, Piracicaba, Brazil) was added to each windrow. Metabarcoding, chemical, and nutritional analyses were performed on samples collected from the windrows during the maturation phase.

2.2. Genetic Data for Metabarcoding Analysis

The total microbial community was evaluated in samples of the biodiverse composts AC and GC, collected after 30 days of maturation. The samples were stored in liquid nitrogen until analysis. DNA extraction was performed using the PowerSoil Pro Kit (QIAGEN, Venlo, The Netherlands). The concentration and purity of the extracted DNA were assessed by electrophoresis in 1.0% (w/v) agarose gels.

For amplification of the V3–V4 region of the 16S rRNA gene of bacteria and archaea, the primers S-DBact-0341-b-S-17 (5′-CCTACGGGNGGCWGCAG-3′) and S-D-Bact-0785-a-A-21 (5′-GACTACHVGGGTATCTAATCC-3′) were used [32]. Amplification of the fungal 18S rRNA gene region was performed using primers Euk1391F (5′-GTACACACCGCCCGTC-3′) and EukBR (5′-TGACCTTCTGCAGGTTCACCTAC-3′) [33,34]. The PCR products were visualized on a 1.5% (w/v) agarose gel.

After quantification, qualification, pooling, and purification of the PCR products, the samples were prepared for sequencing using the KAPA Library Quantification Kit for Illumina (Roche, Wilmington, NC, USA). Sequencing was performed using the MiSeq platform (Illumina, San Diego, CA, USA), with an average coverage of approximately 300,000 reads per sample.

2.3. Bioinformatics Analysis

The analysis of 16S and 18S rRNA sequencing data was performed using the automated pipeline nf-core/ampliseq (version 2.10.1; https://github.com/nf-core/ampliseq, accessed on 1 August 2025) [35], built on the Nextflow platform (version 24.04.5) and executed with the Singularity containerization profile to ensure computational reproducibility, traceability, and portability. The input consisted of paired-end FASTQ files from multiple sequencing runs, which were organized in separate directories and processed with the multiple_sequencing_runs parameter to accurately model run-specific error profiles. The quality of raw reads was initially assessed using FastQC (version 0.12.0) [36], followed by the removal of primer sequences using Cutadapt (version 2.7) [37], with primer sequences specified according to the targeted rRNA gene. Subsequent steps included denoising, dereplication, and chimera removal using DADA2 (version 1.16) [38]. For 16S data, forward and reverse reads were truncated at positions 233 and 229, respectively, based on manual inspection of quality profiles. For 18S data, reads were truncated at positions 150 (forward) and 142 (reverse) to eliminate low-quality regions. Reads that did not meet defined length thresholds were discarded. Truncated reads were then merged with a minimum overlap of 20 bp to generate exact amplicon sequence variants (ASVs).

Taxonomic classification was performed using two curated reference databases: SILVA (v.138) for 16S sequences, targeting bacterial and archaeal taxa, and PR2 (Protist Ribosomal Reference Database) for 18S sequences, aimed at identifying microbial eukaryotes. ASVs were further assigned to operational taxonomic units (OTUs) using a Naïve Bayes classifier trained on preprocessed databases, as implemented in QIIME2 (version 2025.10) [39]. ASVs identified as of mitochondrial, or chloroplast origin were excluded from subsequent analyses. Only ASVs with a minimum abundance of five reads in at least one sample were retained for downstream analyses. A metadata file formatted according to QIIME2 specifications was used to associate samples with experimental and biological variables, enabling multivariate analyses and the generation of taxonomic bar plots.

2.4. Diversity Estimation

The taxonomic assignments obtained within the different groups were used to determine the Shannon-Wiener diversity index (H’). Equation (1):

$$H^{\prime }=-\sum _{i=1}^{S}pi\ln pi$$

(1)

where S = the species richness within a specific taxonomic group (species, genus, family, order, and others); and pi = the relative abundance of species within a specific taxonomic group, calculated by the proportion of species in the group divided by the total number of species.

2.5. Chemical and Nutritional Analysis of Biodiverse Compounds

The chemical and nutritional composition of the biodiverse composts AC and GC was analyzed in samples collected after 30 days of maturation. The concentrations of the macronutrients nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S) were evaluated, along with chemical parameters including organic carbon, cation exchange capacity (CEC), carbon/nitrogen (C/N) ratio, pH, organic matter (OM), organic carbon (%), and fractions of humic substances, such as humic and fulvic acids (%). The analyses followed the protocol established by Teixeira et al. [40].

Organic carbon and OM concentrations were determined using the Walkley-Black method [41], which consists of titration following wet oxidation of organic carbon using potassium dichromate (K₂Cr₂O₇) in an acidic medium. To determine the fractions of humic substances present in the composts, an extraction method using a diluted alkaline solution was employed [42]. The humic fractions were separated based on their solubility in an acidic medium (pH 1), resulting in an insoluble fraction corresponding to humic acids and a soluble fraction corresponding to fulvic acids.

2.6. Microbial Biomass Carbon (MBC) Analysis of Biodiverse Compounds

Microbial biomass carbon (MBC) was evaluated using the fumigation and extraction method, following the protocol described by Silva et al. [43]. Samples of the composts were collected at different maturation periods (10, 30, 60, and 90 days) under aseptic conditions. The samples were placed in sterilized Falcon tubes and stored at −80 °C until processing. Collections were performed from 30 cm deep layers of the windrows, with each sample consisting of 20 g of material, and evaluations were performed in triplicate for each compost. Each replicate consisted of one sample intended for fumigation and one non-fumigated sample.

For processing, the samples were transferred to glass vials with lids. The samples intended for fumigation received 1 mL of alcohol-free chloroform (CHCl₃) and were stored in the dark for 24 h. After this period, the vials were placed in a fume hood and left open until the chloroform had completely evaporated. Both fumigated and non-fumigated samples were used in subsequent steps, with vials without compounds included as a control.

Fifty mL of potassium sulfate (K₂SO₄) solution (0.5 mol L⁻¹, pH = 6.5 ± 0.2) were added to the vials to obtain the extract. The samples were shaken for 30 minutes and then left to stand for the same period to allow decantation. The supernatant was separated from the solid fraction by filtration using filter paper with a porosity of 28 µm. The determination of microbial biomass carbon was performed in the fumigated and non-fumigated extracts, as well as in the control vials. For this purpose, the following reagents were sequentially added to 8 mL of the extract: 2 mL of K₂Cr₂O₇ 0.006 mol L⁻¹, 10 mL of sulfuric acid (H₂SO₄) (98%), and 5 mL of orthophosphoric acid (H₃PO₄) (85%). After the mixture was cooled, 70 mL of distilled water was added, followed by additional cooling. Before titration, 4 drops of diphenylamine ((C₆H₅)₂NH; 1 g diluted in 100 mL of concentrated sulfuric acid) were added, causing a color change from yellow to violet.

The titration was performed using a solution of ammoniacal ferrous sulfate ((NH₄)₂Fe(SO₄)₂·6H₂O) at a concentration of 0.033 mol L⁻¹, with constant stirring. At the end of the process, the color of the samples changed from violet to green, indicating the endpoint of the titration. The microbial biomass carbon (MBC—mg kg⁻¹) was calculated using Equation (2).

$$MBC={C\mathrm{k}\mathrm{c}}^{-1}$$

(2)

where C = flux obtained from the difference between the amount of C (mg kg⁻¹), given by Equation (3), and Kc = correction fator (0.33) [44]:

$$C=\frac{\left(Vb-Va\right)·M·0.003·V1·{10}^6}{Wd·V2}$$

(3)

where Vb = volume of ammoniacal ferrous sulfate used in the titration of the control solution; Va = volume of ammoniacal ferrous sulfate used in sample titration; M = exact molarity of ammoniacal ferrous sulfate; V1 = extractor volume (K₂SO₄); V2 = pipetted aliquot of the extract for titration; 0.003 = milliequivalent of carbon; and Wd (g) = dry weight of the compound (final weight after drying 20 g of moist compound in a forced circulation oven for 48 h at 70 °C).

2.7. Statistical Analyses

The relative abundance (pi) and diversity (H’) data at each taxonomic level, along with the MBC data and chemical and nutritional parameters of the composts, were subjected to analysis of variance (ANOVA). The F-test was applied at the 5% significance level to assess the effect of the different types of litter (AC and GC). When statistically significant differences were detected, the means for AC and GC were compared using Student’s t-test, also at the 5% significance level. The effect size of litter type on pi and H’ of the composts was quantified using Cohen’s effect size (d_s), calculated with the cohensD function from the effsize package [45]. The d_s values were classified as small (≤0.2), medium (>0.2 to ≤0.5), and large (>0.5), according to Sullivan and Feinn [46]. Additionally, the effect of maturation time on MBC was evaluated using Tukey’s test at the 5% significance level. All statistical procedures were performed using R software (version 4.3.0) [47].

3. Results

3.1. Microbial Diversity

Sequencing analyses of the 16S and 18S rRNA regions revealed differences in yield between the compounds prepared with AC and GC litter. For the 16S region, after quality control and filtering procedures, samples from the AC group exhibited between 117,884 and 131,887 reads, corresponding to a volume of 54.6 to 61.1 million base pairs (Mbp). The average per sample was 124,198 reads and 57.6 Mbp. In the GC group, the number of reads ranged from 114,902 to 118,571, with a volume of 53.2 to 55.0 Mbp, resulting in an average of 116,866 reads and 54.2 Mbp per sample. In the 18S region, a similar trend was observed. The AC samples presented 110,501 to 115,229 reads, totaling 337,101 reads, with an individual volume ranging from 33.7 to 34.7 Mbp and an average of 112,367 reads and 34.3 Mbp per sample. The GC samples ranged from 59,766 to 97,949 reads, totaling 250,951 reads, with a volume ranging from 18.7 to 29.9 Mbp, and an average of 83,650 reads and 25.8 Mbp. These results demonstrate good sequencing performance across all samples and suggest greater efficiency in the recovery of microbial DNA in the compounds enriched with angiosperm litter, both in terms of the number of reads and base pair coverage.

Figure S1 shows the percentage distribution of unique and duplicate reads in the Bacteria (a) and Fungi (b) domains, before and after quality analysis. In the bacterial libraries, a notable variation in the proportion of unique reads was observed among the samples. Notably, some samples from the GC group (e.g., GC-3.2) presented more than 30% of unique reads, suggesting greater microbial complexity or lower sequence redundancy in these libraries. The AC group samples exhibited proportions of unique reads ranging from 10% to 25%, values still indicating satisfactory microbial diversity. For the fungal libraries, the proportion of unique reads was considerably lower compared to the bacterial domain, remaining below 15% in all samples. This pattern may be associated with the greater redundancy inherent in the ITS or 18S regions amplified by PCR, which is often observed in communities dominated by a few taxa. Nevertheless, the values obtained were considered adequate for diversity analyses, with the GC group generally presenting slightly higher proportions of unique reads compared to the AC group.

3.2. Composition and Diversity of Bacterial Communities

The analysis of bacterial phyla composition in the biodiverse compounds AC and GC revealed a predominance of the phylum Proteobacteria in both samples, representing 30.32% of the sequences in AC and 28.53% in GC. Other significant phyla included Bacteroidota, Actinobacteriota, Firmicutes, and Planctomycetota, with modest variations in relative abundances between the compounds. In the AC compound, Actinobacteriota exhibited the highest relative abundance (11.48%), while in the GC compound, this value was slightly lower (10.05%). The phyla Bacteroidota and Myxococcota had similar frequencies in both compounds, while Patescibacteria was more abundant in GC (2.18%) than in AC (1.54%). Three phyla were exclusive to the GC group, while only one was exclusive to the AC group (Figure S2a). Alpha diversity, as estimated by the Shannon index (H’), was slightly higher in GC (2.39) than in AC (2.32). However, when comparing the relative abundances of the different phyla, the differences between the compounds were negligible, indicating no significant effect of phylum type on these samples (Figure 2a).

Figure 2. Relative abundance (pi) of different bacterial phyla, classes, orders, and families within specific taxonomic groups observed in samples of biodiverse compounds constituted by Angiosperm (AC) and Gymnosperm (GC) litter. The data are presented for Phylum (a), Class (b), Order (c), and Family (d). The black horizontal bars within the boxplots represent the median relative abundance, calculated as the proportion of occurrences of a given taxonomic group divided by the total number of occurrences. The vertical bars indicate the maximum and minimum values, while points outside the box are considered outliers. Above the boxes, the values represent the results of Student’s t-test (p < 0.05) and the effect size, as indicated by Cohen’s d_s index.

The distribution of bacterial classes revealed a predominance of Alphaproteobacteria, Gammaproteobacteria, and Bacteroidia in both compounds. In the AC compound, Alphaproteobacteria was the most abundant class (17.05%), followed by Gammaproteobacteria (13.46%) and Bacteroidia (8.10%). In the GC compound, Alphaproteobacteria also stood out (16.25%), with Gammaproteobacteria (12.48%) and Bacteroidia (8.71%) presenting similar frequencies to those in AC. Some classes, such as Polyangia and Planctomycetes, were also well represented in both samples. The GC samples had four exclusive classes, while the AC samples had three exclusive classes (Figure S2b). The Shannon diversity index (H’) values were similar between the samples, with GC having a value of 3.00 and AC having a value of 2.96. When comparing the relative abundances of the different classes, the differences between AC and GC were minimal, indicating that the effect of class type was negligible (Figure 2a,b).

The samples from the biodiverse compounds AC and GC exhibited a wide diversity of bacterial orders, with differences in relative abundance between the groups. In the AC compound, the most abundant orders were Rhizobiales (9.03%), Burkholderiales (5.21%), and Polyangiales (5.73%), while in the GC compound, some of these same orders were also prevalent, including Rhizobiales (8.98%), Pirellulales (5.22%), and Burkholderiales (4.57%). Although most of the orders were shared between the two compounds, seven exclusive orders were identified in GC and three in AC (Figure S3a). The Shannon diversity index (H’) values were very similar between the samples, with values of 4.02 for GC and 4.01 for AC, reflecting comparable taxonomic profiles. These similarities were also evident in the relative abundances of the different orders, with the effect of the order having minimal impact on the differentiation between the two compounds (Figure 2c).

At the family taxonomic level, a diverse composition was also observed. In the AC compound, the most abundant families were Pirellulaceae (4.28%), Paenibacillaceae (3.10%), and Microscillaceae (2.66%), while in the GC compound, the most abundant families were Microscillaceae (2.90%), Haliangiaceae (1.99%), and Hyphomicrobiaceae (1.82%) (Figure S3b). A significant proportion of sequences could not be assigned to any known family, representing 25.31% in AC and 28.16% in GC, highlighting the presence of poorly characterized or undescribed microorganisms. Both compounds also contained a substantial number of exclusive families, with 14 exclusive to AC and 7 exclusive to GC. However, the H’ values were very similar between the samples, being 4.44 for AC and 4.42 for GC. Similarities in the relative abundance of families were also observed, with the effect of family having a negligible impact on the differentiation between the two compounds (Figure 2d).

The biodiverse compounds AC and GC exhibited considerable bacterial diversity at the genus level, with a substantial number of taxa shared between the two compounds. However, taxonomic classification at the genus level was not possible for most sequences, with 52.49% unclassified in AC and 55.75% in GC. In the AC compound, the most predominant genus was Haliangium (1.56%), followed by Steroidobacter (1.34%) and Thermobacillus (1.27%). In the GC compound, the most frequent genera included Haliangium (1.97%), Thermobacillus (1.23%), and Bdellovibrio (1.15%) (Figure S4a). Despite the many shared genera, a considerable number of unique genera were present in the different compounds, with 27 exclusive genera in AC and 13 in GC. Consequently, the Shannon diversity index (H’) was slightly higher in AC (3.89) compared to GC (3.80). The relative abundances of the genera, however, were similar between the two compounds, with the effect of genus being negligible in distinguishing between the AC and GC samples (Figure 3a).

Figure 3. Relative abundance (pi) of different bacterial genera and species within a specific taxonomic group and mean Shannon index (H’), observed in samples of biodiverse compounds constituted by Angiosperm (AC) and Gymnosperm (GC) litter. Genus (a), Species (b), and Mean Shannon index (H’) (c). The mean H’ values were calculated based on the diversity values observed across the various taxonomic levels. The black horizontal bars within the boxplots represent the median relative abundance, calculated as the proportion of occurrences of a given taxonomic group divided by the total number of occurrences. The vertical bars indicate the maximum and minimum values, while points outside the box are considered outliers. Above the boxes, the values represent the results of Student’s t-test (p < 0.05) and the effect size, as indicated by Cohen’s d_s index.

At the bacterial species level, a significant predominance of unidentified sequences was observed in both compounds, accounting for 98.98% in AC and 97.11% in GC. This highlights the limitations of current taxonomic databases for identifying bacterial species in complex environments, such as the organic compounds analyzed. Among the identified species, a relatively low diversity was noted, with several species occurring in both samples, including Actinomadura keratinilytica, Bosea thiooxidans, Cellulomonas massiliensis, Chelativorans composti, Clostridium isatidis, Cohnella xylanilytica, Desulfotomaculum ferrireducens, Hungateiclostridium straminisolvens, Hyphomicrobium facile, Hyphomicrobium zavarzinii, Lysinibacillus halotolerans, Lysinibacillus massiliensis, Microvirga lotononidis, Pseudoclostridium thermosuccinogenes, Pseudonocardia thermophila, Pseudoxanthomonas kaohsiungensis, Pseudoxanthomonas suwonensis, Pseudoxanthomonas taiwanensis, Rummeliibacillus pycnus, Solibacillus silvestris, Sphaerobacter thermophilus, Sporocytophaga myxococcoides, Streptomyces spongiae, Streptomyces thermocoprophilus, Thermobifida fusca, Thermobispora bispora, Thermoclostridium caenicola, Thermonomospora curvata, Thermopolyspora flexuosa, Tuberibacillus calidus, and Turicibacter sanguinis (Figure S4b). The number of exclusive species was 8 in AC and 3 in GC, with H’ values being slightly higher in AC (3.70) compared to GC (3.57). The relative abundances of the species differed between the compounds, with a medium effect observed for the Species variable (Figure 3b).

Furthermore, when considering the combined mean diversity across all taxonomic levels, the AC and GC compounds displayed similar values. The type of litter did not significantly influence the alpha diversity indices (Figure 3c), suggesting that the origin of the litter had a limited effect on the global microbial diversity within the biodiverse compounds.

3.3. Composition and Diversity of Fungal Communities

The analysis of fungal communities in the biodiverse compounds AC and GC revealed a predominance of the phyla Ascomycota and Basidiomycota in both samples. In the AC compound, Ascomycota was the most representative phylum, comprising 52.27% of the fungal community, followed by Basidiomycota (31.82%), Mucoromycota (11.36%), Chytridiomycota (2.27%), and Zoopagomycota (2.27%). In the GC compound, Ascomycota presented an even higher frequency (61.29%), while Basidiomycota and Mucoromycota were present in equal proportions (19.35% each) (Figure S5a). The phyla Chytridiomycota and Zoopagomycota were observed exclusively in the AC compound, leading to greater phyla diversity in this group (H’ = 1.12) compared to the GC group (H’ = 0.93). Although the phyla abundances varied, the differences in pi between the compounds were not significant, although a medium Phylum effect was observed (Figure 4a).

Figure 4. Relative abundance (pi) of different fungal phyla, classes, orders and families within specific taxonomic groups observed in samples of biodiverse compounds constituted by Angiosperm (AC) and Gymnosperm (GC) litter. Phylum (a), Class (b), Order (c), and Family (d). The black horizontal bars within the boxplots represent the median relative abundance, calculated as the proportion of occurrences of a given taxonomic group divided by the total number of occurrences. The vertical bars indicate the maximum and minimum values, while points outside the box are considered outliers. Above the boxes, the values represent the results of Student’s t-test (p < 0.05) and the effect size, as indicated by Cohen’s d_s index.

At the class level, the fungal community structure showed notable differences. In AC, Agaricomycetes was the dominant class (25.58%), followed by Dothideomycetes (18.60%) and Eurotiomycetes (11.63%). Other classes such as Mortierellomycetes and Sordariomycetes exhibited intermediate frequencies (9.30% each), while the remaining classes occurred in smaller proportions (<5%). In GC, the diversity of fungal classes was more balanced, with Agaricomycetes, Dothideomycetes, and Mortierellomycetes each comprising approximately 13% of the community. Eurotiomycetes and Sordariomycetes were represented similarly at 10.26%, followed by Lecanoromycetes (7.69%) and Pezizomycetes (5.13%) (Figure S5b). Additionally, four exclusive fungal classes were observed in the GC compound. Despite these differences, H’ values were similar between the two compounds (H’ = 2.23 for GC and H’ = 2.21 for AC). The relative abundance of the classes in both compounds was comparable, with the Class variable showing a small effect on the differentiation between the AC and GC groups (Figure 4b).

The evaluation of fungal orders revealed differences in the composition and distribution of communities across the biodiverse compounds. In the AC compound, Pleosporales was the predominant order (14.63%), followed by Mortierellales (9.76%), Eurotiales, and Sebacinales (7.32% each). In the GC compound, Mortierellales was the most abundant order (16.13%), followed by Pleosporales (12.90%) and Eurotiales and Teloschistales (9.68% each). Although the samples shared some dominant fungal orders, eight unique orders were observed in AC (Figure S6a), resulting in greater diversity in this group (H’ = 2.99) compared to GC (H’ = 2.60). The relative abundances of the different orders were generally similar across the compounds, but the effect of the Order variable was considered moderate, contributing to the observed differences (Figure 4c).

Regarding the fungal families present in the compounds, in AC, Mortierellaceae was the most representative family (9.52%), followed by Aspergillaceae (7.14%) and Chaetomiaceae, Hydnodontaceae, Orbiliaceae, Psathyrellaceae, Serendipitaceae, and Teloschistaceae (4.76% each). In the GC compound, Mortierellaceae was also the most abundant family (16.13%), followed by Aspergillaceae and Teloschistaceae (9.68% each), while Chaetomiaceae and Psathyrellaceae showed intermediate representation (6.45%) (Figure S6b). These results indicate a significant overlap of families between the two compounds. Despite this, the AC compound presented 10 exclusive families, while GC had only one, leading to higher H’ values in AC (3.33) compared to GC (2.87). The average relative abundances of the families differed between the compounds, with higher pi values observed for the families in GC. Thus, the effect of the Family variable on pi was considered medium (Figure 4d).

At the genus level, both compounds exhibited high diversity, with shared genera such as Aspergillus, Exophiala, and Pestalotiopsis. In the AC compound, the most frequent genera were Mortierella, Penicillium, Psathyrella, Serendipita, Trechispora, and Xanthoria (4.76% each). In the GC compound, Mortierella was the most abundant genus (13.33%), followed by Penicillium and Xanthoria (6.67% each) (Figure S7a). Despite the predominance of Mortierella in both compounds, AC hosted 14 exclusive genera, while GC contained only 3 exclusive genera. Thus, the genus diversity was higher in AC (H’ = 3.54) compared to GC (H’ = 3.12). The compounds showed different abundances for the genera, with the highest average abundances observed in GC. Furthermore, the effect of the Genus variable on pi was considered medium (Figure 5a).

Figure 5. Relative abundance (pi) of different fungal genera and species within a specific taxonomic group and mean Shannon index (H’), observed in samples of biodiverse compounds constituted by Angiosperm (AC) and Gymnosperm (GC) litter. Genus (a), Species (b), and H’ (c). The mean H’ values were calculated based on the diversity values observed across the various taxonomic levels. The black horizontal bars within the boxplots represent the median relative abundance, calculated as the proportion of occurrences of a given taxonomic group divided by the total number of occurrences. The vertical bars indicate the maximum and minimum values, while points outside the box are considered outliers. Above the boxes, the values represent the results of Student’s t-test (p < 0.05) and the effect size, as indicated by Cohen’s d_s index.

The analysis of fungal species in the AC and GC compounds revealed a broad diversity, with some species shared between the groups, such as Aspergillus fumigatus, Exophiala lecanii-corni, and Xanthoria parietina, albeit with variations in their relative frequencies. In the AC compound, most species were found at low frequencies (2.38%), with the exceptions of Psathyrella casca and Serendipita vermifera, which occurred in higher proportions (4.76%). Additionally, a category of unclassified sequences represented 19.05% of the total, suggesting the presence of a significant portion of the fungal community that remains unidentified. In the GC compound, the distribution followed a similar pattern, with Psathyrella casca being the most frequent species (6.45%), while most other species occurred at a frequency of 3.23% (Figure S7b). The proportion of unclassified sequences was even higher in GC (25.81%), reinforcing the limitations of taxonomic databases for environments rich in microbial diversity, such as the organic compounds analyzed. Thirteen unique species were observed in the AC compound, while only three were found in GC. The pi values of the species were higher in the GC compound, with a large effect of species on defining abundance, indicating their significance in differentiating between the compounds (Figure 5b).

In general, when evaluating the overall diversity across different taxonomic levels of fungi, the average diversity values were similar for the two compounds, and the type of compound (AC or GC) had a minor effect on defining the H’ values (Figure 5c).

3.4. Chemical and Nutritional Composition of Biodiverse Compounds

The analysis of the mean percentage concentrations of macronutrients in the biodiverse compounds AC and GC revealed differences between the groups, particularly for the macronutrients N, K, Ca, and S, which exhibited higher values in the AC compound. The mean nitrogen (N) content was 2.33% in AC, while in GC it was 2.07%. Potassium (K) had an average of 1.83% in AC and 1.33% in GC. Calcium (Ca) was also more abundant in AC, with an average of 1.87%, compared to GC, which had an average of 1.22%. Sulfur (S) had an average of 0.25% in AC and 0.21% in GC. On the other hand, although the mean phosphorus (P) values were higher in AC (0.9%) compared to GC (0.7%), no significant difference was observed between the compounds. Similarly, the mean magnesium (Mg) concentrations were similar in AC (0.55%) and GC (0.45%) (Figure 6a–f).

Figure 6. Percentages of macronutrients (N, P, K, Ca, Mg, and S) observed in samples of biodiverse compounds constituted by Angiosperm (AC) and Gymnosperm (GC) litter. Nitrogen (a), Phosphorus (b), Potassium (c), Calcium (d), Magnesium (e), and Sulfur (f). The black horizontal bars within the boxplots represent the median concentration. The vertical bars indicate the maximum and minimum values, and points outside the box represent outliers. Above the boxes, the values show the results of Student’s t-test (p < 0.05).

The chemical characteristics, including CEC, percentage of organic carbon, C/N ratio, and percentage of OM, also showed differences between the compounds, with AC exhibiting the highest average values. The average CEC was 1100 mmolc kg⁻¹ in AC, whereas in GC, the average was 820 mmolc kg⁻¹. The percentage of organic carbon was 22.9% in AC and 11.46% in GC. Similarly, the average C/N ratio was 10.05 in AC and 5.57 in GC. Additionally, the percentage of organic matter was significantly higher in AC (39.5%) compared to GC (19.7%) (Figure 7a–d).

Figure 7. Percentages of chemical parameters (CEC, OC, C/N, and OM) observed in samples of biodiverse compounds constituted by Angiosperm (AC) and Gymnosperm (GC) litter. Cation Exchange Capacity (a), Organic Carbon (b), C/N Ratio (c), and Organic Matter (d). The black horizontal bars within the boxplots represent the median concentration. The vertical bars indicate the maximum and minimum values, and points outside the box represent outliers. Above the boxes, the values show the results of Student’s t-test (p < 0.05).

The concentrations of humic and fulvic extracts, as well as the pH values, also varied according to the type of biodiverse compound. Higher concentrations of humic acids (27.93%) and pH (7.73) were observed in the AC compound samples, compared to GC (5.16% and 7.33, respectively). The concentrations of fulvic extracts were higher in the GC samples (23.01%) compared to AC (12.41%) (Figure 8a–c).

Figure 8. Percentages of chemical parameters (humic extracts, fulvic extracts and pH) observed in samples of biodiverse compounds constituted by Angiosperm (AC) and Gymnosperm (GC) litter. Humic extracts (a), fulvic extracts (b) and pH (c). The black horizontal bars within the boxplots represent the median concentration. The vertical bars indicate the maximum and minimum values, and points outside the box represent outliers. Above the boxes, the values show the results of Student’s t-test (p < 0.05).

The carbon fractions attributed to microbial biomass showed similar average values between the compounds, with 156.25 mg kg⁻¹ in AC and 178.87 mg kg⁻¹ in GC. However, when analyzing the content of this carbon throughout the maturation period of the compounds, we found that, in AC, the 90-day samples exhibited the highest average MBC contents (310.78 mg kg⁻¹), while in GC, the highest average contents were observed at 60 days of maturation (262.28 mg kg⁻¹) (Figure 9).

Figure 9. Microbial biomass carbon (MBC) observed in samples of biodiverse compounds constituted by Angiosperm (AC) and Gymnosperm (GC) litter, collected at 30, 60, 90, and 300 days of maturation. The black horizontal bars within the boxplots represent the median concentration. The vertical bars indicate the maximum and minimum values, and points outside the box represent outliers. Above the boxes, the letters indicate the differences obtained by the Tukey test (p < 0.05), comparing maturation times, and below the boxes, the values display the results of Student’s t-test (p < 0.05), comparing the compounds.

4. Discussion

4.1. Biodiverse Compounds Derived from AC and GC Exhibit Similar Bacterial Composition and Diversity Values

We observed the presence of several microbial groups shared between AC and GC, thus refuting the hypothesis that the type of litter predominantly influences the composition of the bacterial community in biodiverse composts. We suggest that this composition is primarily determined by the compost preparation method, which acts as a selective factor with greater influence than the plant source used. During the thermophilic phase of compost production, specific bacterial groups can be selected due to their affinity for or resistance to temperatures above 35 °C [48]. High temperatures play a fundamental role in selecting specific bacterial groups, favoring the growth of thermophilic and thermotolerant microorganisms, which possess physiological and biochemical adaptations essential for survival under extreme conditions. This contributes to the diversity and stability of microbial communities under intense heat [49]. Studies have shown that the structure of bacterial communities is linked to thermal responses [50] and suggest that composts harbor a highly dynamic diversity, rich in mesophilic and heat-adapted saprophytic species [51,52,53].

The genera Bacillus and Thermus are frequently described as key components of the microbiota in composts due to their production of thermostable enzymes [54]. In the AC and GC composts, we observed the presence of Bacillus, but the genus Thermobacillus stood out due to its high predominance. Bacteria of the genus Thermobacillus are capable of surviving and proliferating in high-temperature environments, with optimal growth temperatures typically ranging from 50 °C to 70 °C. These microorganisms are notable for their ability to degrade complex organic matter, making them crucial in the composting process, especially in the initial stages when high temperatures prevail [55,56]. Studies demonstrate that the presence of Thermobacillus in compost-derived compounds is linked to its ability to produce enzymes essential for the decomposition of organic waste, such as hemicellulolytic and lignocellulolytic enzymes [57,58,59]. These thermotolerant bacteria can also synthesize complex waste-degrading carboxylesterases [60]. Finore et al. [54] highlight the importance of cellulases, hemicellulases, lignin-modifying enzymes, and esterases during the thermophilic phase of the composting process.

4.2. The Type of Compound Affected the Abundance of Bacterial and Fungal Species, Being Superior in GC

Contrary to expectations, we observed that the abundance of bacterial and fungal species increased when GC litter was used. This was unexpected, as the leaves and wood of P. elliottii contain antimicrobial substances with significant biological properties [61]. Among the various compounds with antibacterial and antifungal activities identified, flavonoids, terpenes, and phenols are prominent [62,63]. Germacrene D, the main constituent of the essential oil of the leaves of this plant, exhibits antibacterial and repellent activity [64]. Similarly, the oleoresin of P. elliottii and its primary compound, dehydroabietic acid, demonstrate effective antibacterial activity, including against multidrug-resistant bacteria [65]. Ouyang et al. [66] demonstrated that leaf extracts of P. elliottii can efficiently inhibit the proliferation of fungi such as Coriolus versicolor, Gloeophyllum trabeum, and Polyporus vaporaria Fr. This explains the traditional use of P. elliottii in folk medicine and material preservation practices.

The observed effect on the abundance of both bacterial species and fungal phyla, orders, families, genera, and species resulted in a greater microbial population in the GC compound samples. However, the AC compound exhibited greater taxonomic richness, with a higher number of exclusive taxonomic categories across the phyla, order, family, genus, and species levels. Although this richness was significant, it was associated with lower relative abundances, which may have contributed to the reduced overall average abundance in AC. Thus, it is possible that the bioactive secondary metabolites present in P. elliottii litter act as selective agents, favoring the colonization of adapted endophytic microorganisms [67]. On the other hand, the microbial diversity observed in AC may reflect a broader, albeit less dominant, microbial community, which also influences the final microbial composition during the composting process.

4.3. Decomposition by Specific Microbial Groups Contributed to the Improvement of the Chemical and Nutritional Characteristics, as Well as to the Acceleration of the Maturation Process in AC

AC samples accumulated more macronutrients, organic carbon, organic matter, and humic extracts compared to GC, which can be explained by the decomposing activity of specific fungal phyla present only in AC. Fungi play a fundamental role in composting processes, being essential for the decomposition of organic matter and nutrient cycling. They are responsible for breaking down complex compounds, such as cellulose, lignin, and chitin, which are difficult for other microorganisms, such as bacteria, to degrade [68]. We also observed a high cation exchange capacity (CEC) in AC, indicating the presence of saprophytic microorganisms capable of improving compost structure and contributing to the final quality of the product [69]. The CEC of AC reflected the presence of cations such as Ca²⁺, Mg²⁺, K⁺, Na⁺, H⁺, and Al³⁺, and can be defined by the total amount of cations capable of neutralizing negative charges. Colloidal materials, such as humus or clay, contribute to the increase in CEC, which, together with the concentration of OM, is used to assess the level and quality of compost maturation [70].

During the thermophilic phase of composting, pH increases due to the release of bases and the consumption of organic acids. In this phase, humic acids, fulvic acids, and humin are produced from the activity of microbial biomass. Humic and fulvic acids tend to stabilize the pH near neutrality, contributing to microbial activity as these acids buffer soil pH by releasing carbon dioxide [71]. Humic acids represent the stable fraction, being insoluble in highly acidic media due to the protonation of functional groups, which leads to the precipitation of macromolecules [72]. Fulvic acids contain many oxygenated functional groups, allowing their solubility in both basic and acidic media [73]. The maturation of a compost can be indicated by the higher ratio of humic acids to fulvic acids. Thus, it is concluded that, in terms of maturation, the AC compound demonstrated better quality than GC. Fulvic acids are the first to be synthesized during the maturation stage, while humic acids are produced last, being more complex and stable substances. The greater quantity of humic acids at the end of maturation serves as an indicator of compost stability [74].

Chytridiomycota and Zoopagomycota were observed exclusively in the AC compost, suggesting a potential effect of the litter on the composition of the microbial community. This influence was corroborated by the d_s values, though relatively low. Chytridiomycota belong to one of the most primitive groups of fungi and are generally aquatic; however, some species are capable of colonizing terrestrial environments, such as litter and OM in compost. In the composting process, Chytridiomycota play a role in the decomposition of OM, especially during the early stages of decomposition when moisture remains relatively high. Roberts et al. [75] proposed that chytrids act as facilitators in the decomposition ecosystem through osmotrophic feeding, which modifies the bacterial communities of particulate organic matter. Chytrids feed saprophytically via rhizoids that secrete extracellular enzymes to degrade OM [76]. Although generally less abundant than Ascomycota or Basidiomycota, they can act synergistically with other decomposers, particularly in aerobic and humid environments. Additionally, some Chytridiomycota species have demonstrated the ability to degrade complex compounds such as cellulose, hemicellulose, pectin, chitin, and keratin, which are found in insects and other detritus, thus complementing the action of other microorganisms in composting [77].

The phylum Zoopagomycota represents the first divergent clade of zygomycete fungi. It includes a single class, Zoopagomycetes, and a single order, Zoopagales [78]. Members of this group are predominantly parasites or predators of protozoa, nematodes, and even other fungi, including genera such as Syncephalis, Piptocephalis, and Dimargaritales [79,80]. Some species are endoparasites, while others act as ectoparasites, producing haustoria that penetrate host bodies and absorb nutrients. However, some species are considered generalist saprobes [81]. These fungi are most commonly found in environments rich in OM and microscopic organisms, such as soil and decaying plant debris. Mahongnao et al. [82] also observed the presence of Zoopagomycota in the fungal microbiome of litter-based composts. However, despite the detection of species from this phylum in the AC samples, it was not possible to classify them into higher taxonomic levels, reflecting the scarcity of genetic information available for this group. This is due to the fact that many Zoopagomycota species cannot be cultured separately from their host organisms in axenic cultures, which complicates the process of obtaining pure DNA for molecular studies [83].

In the AC compost, we highlight the presence of Conidiobolus brefeldianus, an entomopathogenic species with potential applications in biological pest control [84,85]. Additionally, the species Sebacina incrustans, an ectomycorrhizal fungus that symbiotically associates with plant roots, was observed [86,87,88]. Also noteworthy is the genus Orbilia, which includes fungi that prey on nematodes. The species Peziza echinospora, Phallus impudicus, Sistotrema brinkmannii, Trechispora alnicola, and Trechispora farinacea are recognized as saprophytes, commonly found in wood decomposition processes [89,90,91]. The genus Peziza includes many species that grow in soil, decaying wood, or excrement, and they play crucial roles in nutrient cycling [92]. Fungi from the genera Sistotrema and Trechispora are known for their ability to colonize dead wood and other plant materials [93,94]. Therefore, these saprophytic species make a significant contribution to improving the nutritional quality of compost produced from AC litter.

The frequency of Psathyrella casca and Serendipita vermifera in both AC and GC compounds is particularly noteworthy. Species of the genus Psathyrella have been isolated from various environments such as soil, sawdust piles, decomposing logs, cattle manure, and leaf litter, demonstrating their potential role in decomposition processes [95]. Serendipita vermifera, a species from the order Sebacinales, is known for its ability to form specialized interactions with plant roots [96]. Sarkar et al. [97] showed that an endophytic isolate of Serendipita vermifera obtained from Hordeum vulgare can act as a protective rhizospheric barrier against pathogen attack. This protective effect was also observed in Arabidopsis thaliana plants inoculated with Serendipita vermifera [98].

Therefore, although the AC and GC compounds exhibit distinct characteristics in terms of their fungal composition, the presence of shared species and genera, such as Mortierella, Penicillium, and Xanthoria, suggests ecological similarities between the two compounds. Nevertheless, the differences observed in the overall fungal microbiome composition appear to be influenced by variations in the nutritional content of the compounds, which in turn affect microbial selection throughout the maturation process.

4.4. MBC Can Be More Effectively Utilized When Biodiverse Compounds Are Allowed to Mature for an Average Period of 60 to 90 Days

The microorganisms that constitute biodiverse composts represent the microbial biomass (MBC), and its concentration fluctuates during the different phases of compost formation. Various factors, including temperature, humidity, pH, and ammonia concentrations, can influence the microbial population [99,100]. In our study, we found that MBC was higher in the samples analyzed at 60 and 90 days of maturation in both AC and GC composts. Villar et al. [101] reported that microbial biomass concentration is highest during the initial phase of maturation and tends to decrease progressively over time.

During the thermophilic phase of composting, the temperature rises due to the biological activity generating heat. At this stage, the microbial population decreases, as many microorganisms cannot survive temperatures above 60 °C. Only thermophilic and endospore-forming microorganisms, which can tolerate high temperatures, predominate. As the temperature drops and the cooling phase begins, some of the thermophilic microbiota disappear. However, this cooling phase favors the recolonization of other microorganisms, forming the mesophilic microbiota [25,102], which leads to an increase in microbial biomass.

Once the bio-oxidative phase is completed, easily degradable nutrients are depleted, leaving behind more complex carbon molecules. This shifts the dominance to microorganisms capable of breaking down these complex substances. As the maturation phase progresses and organic matter and nutritional resources become scarcer, microbial populations diminish due to competition between groups. López-González et al. [25] also noted that the abundance of bacterial communities tends to decrease as composting progresses. Therefore, we suggest that the resident microbiota in AC and GC composts should be utilized most effectively during the initial months of maturation, specifically between 60 and 90 days.

Understanding the taxonomic composition and nutritional characteristics of biodiverse composts is crucial for optimizing the composting process and improving the final products. By gaining insights into the bacterial and fungal diversity associated with different types of litter, such as AC and GC, we can identify the microbial groups most effective in decomposing waste and cycling nutrients. This enhances the compost’s nutritional profile, including a higher accumulation of macronutrients, organic matter, and humic substances, which ultimately improves soil fertility. Furthermore, analyzing microbial biomass dynamics during the 60 to 90-day maturation phase is key to maximizing efficiency in producing high-quality composts. Understanding variations in microbial communities allows producers to adjust maturation times and conditions to enhance decomposition and stabilization, resulting in more biodiverse, nutritious, and beneficial products for soil and plant health.

This research effectively demonstrates the robustness of the Soil Food Web composting method, revealing that the composting process exerts a selective pressure capable of standardizing core bacterial communities regardless of the botanical source used. The identification of exclusive fungal phyla in AC and its superior humification process (higher HA/FA ratio) represent significant strengths, providing a biological basis for selecting litter to improve nutritional quality. However, certain limitations must be considered. The low taxonomic resolution at the species level for bacteria indicates that a vast portion of the microbial ‘dark matter’ within the compost remains uncharacterized, potentially harboring functional taxa not yet described in databases. Future perspectives should focus on the long-term field application of these biodiverse composts, evaluating whether the observed microbial richness effectively translates into soil pathogen suppressiveness and enhanced crop resilience.

5. Conclusions

The results of this study demonstrate that the microbial composition of biodiverse composts prepared with AC and GC litter is more strongly influenced by the composting method than by the plant origin of the base material. The significant presence of thermotolerant microorganisms, particularly from the genus Thermobacillus, highlights the selective influence of the thermophilic phase on the bacterial community structure, regardless of the litter type used. Despite the antimicrobial properties of P. elliottii, GC composts exhibited a greater abundance of bacterial and fungal species, suggesting that microorganisms may have adapted to, or been selected by, the bioactive metabolites present in the litter. Conversely, AC compost stood out for its higher taxonomic richness and superior accumulation of macronutrients and OM, reflecting the effective role of specific fungi in decomposition and the production of more stable, nutritionally enriched compost. The microbial biomass analysis revealed that the composts achieved greater efficiency and nutritional quality during the 60 to 90-day maturation period, suggesting that this phase is the optimal window for utilizing resident microbiota to produce high-quality compost for agricultural applications. Overall, the findings of this study emphasize the importance of adopting an integrated approach that considers the microbial, chemical, and functional aspects of organic composts. This approach not only enhances composting practices but also contributes to the promotion of regenerative agriculture and the implementation of more sustainable soil management strategies.