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Irradiance level and elevation shape the soil microbiome communities of Coffea arabica L.

Environmental Microbiome volume 19, Article number: 75 (2024) Cite this article

Abstract

Background

The nexus plant-microbe-environment is essential to understand the ecosystem processes shaping plant health and fitness. Within this triangle, soils and associated microflora are among the key ecosystem’s drivers, underpinning plant productivity and evolution. In this study, we conducted a comprehensive analysis (physicochemical properties, enzyme activities, and taxonomic diversity) of soils under the canopy projection of Coffea arabica trees along a gradient of elevation (600, 800, and 900 m) and shade (0, 50, 100%).

Results

While shade had no influence on most parameters, altitude shaped the dynamics of microbial communities. Available phosphorus, soil organic carbon, and nitrate were significantly higher at 800 m, likely due to the higher activities of β-glucosidase and phosphatases at this altitude. Microbial biomass (carbon and nitrogen) and moisture were significantly higher at 600 and 900 m, which might be attributed to the abundance and richness of soil microorganisms. Indeed, metabarcoding analysis revealed a complex pattern of microbial consortia (bacteria, archaea, fungi) at the three altitudes, with the lowest index of richness recorded at 800 m. The highest number of Amplicon Sequence Variants was observed in bacteria, whose functional analysis revealed distinct metabolic adaptations across different altitudes. At 900 m, the main functional attributes favored the responses to environmental stimuli and microbial interactions; at 800 m, the predominant metabolic pathways were related to organic matter, fermentation, and bioremediation; and at the lower 600 m, the pathways shifted towards the breakdown of plant-derived compounds (e.g. geraniol, limonene, and pinene degradation).

Conclusion

Overall, the results indicate a higher effectiveness of the microbial consortium at 800 m, which might result in better nutrient cycling. The study highlights the importance of canopy shade species and elevation for the composition of microbial consortia in C. arabica, unveiling ecological functions beyond plant health, with implications for bio-based solutions and biotechnology.

Background

The Coffea genus comprises 130 species, from which only Coffea arabica L. (Arabica coffee) and Coffea canephora Pierre ex A. Froehner (Robusta coffee) are commercially significant [1]. These account for approximately 57% and 43% of the global coffee production, respectively [2]. Coffee is a leading commodity worldwide, involving around 100 million workers throughout its value chain [3]. However, the sustainability of the coffee sector is increasingly at risk due to climate change impacts, notably global warming and drought [4, 5]. To counteract this impact, the implementation of climate-smart strategies, such as agroforestry, is undeniably the most straight forward approach [5,6,7].

Theoretically, effective shaded coffee agroforestry systems (AFS) mitigate high temperatures and minimize evapotranspiration [7,8,9]. However, the success depends on the effective embedding of crop management practices (including soil, water, as well as compatible shade trees and coffee cultivars) within specific agro-ecological contexts [10, 11]. For example, the incidence of natural enemies of coffee biotic stressors may be either boosted or reduced depending on the vegetation structure and composition [12,13,14,15,16]. Another relevant aspect is the impact of shade which may have a negative [17], positive [18], or neutral [6] impact, depending on the interaction between genotype and environment, as well as the intensity of shade or irradiation [10, 11]. Altitude is also a crucial factor in coffee cultivation, driving the physical and chemical characteristics of coffee beans [6, 19,20,21]. Finally, the coffee microbiome emerges as a core component of the system [22,23,24,25,26], due to its preponderant role in plant evolution, health and productivity [27, 28].

Soils are among the most important reservoirs of biodiversity, hosting ca. 1/4 of living organisms in terrestrial ecosystems [29, 30]. These include bacteria, archaea, fungi, protists, and many other eukaryotes (e.g. nematodes, mites, ants, beetles, earthworms), which provide a set of supporting services (soil formation, and nutrient cycling), provision services (food, freshwater, fuel, fiber, biochemicals, genetic resources), and regulating services (climate regulation, pest and disease regulation, water regulation, remediation, and pollination) [31, 32]. Therefore, research on soil biodiversity is giving a step forward, envisioning the maximization of ecosystem goods and services, as well as the elucidation of biological, ecological and evolutionary processes [30]. In coffee, Caldwell et al. [22] reported high microbial diversity in soils from intensive, organic, and transition farms in Brazil, highlighting the potential of plant growth-promoting bacteria to improve coffee production and counteract environmental constraints. Tran [33] published a data set of the rhizosphere microbiome of C. canephora in the Central Highlands region of Vietnam, the second largest coffee producer in the world after Brazil. The reported taxonomical diversity was also considerably high, and reflected in the associated functions, particularly regarding biosynthetic processes. More recently [24], analyzed the effect of altitude on the diversity of microbial communities in the rhizosphere of C. arabica in Yunnan, the most expressive center of coffee production and trading in China. In line with the previous studies, the authors observed that microbial diversity and richness was high, and essentially driven by soil pH and altitude.

In this study, we conducted a comprehensive analysis of the soil physicochemical properties and microbial communities of C. arabica cultivated under AFS in the evergreen rainforest of Gorongosa Mountain, part of the Gorongosa National Park (GNP), Mozambique. GNP is one of the most interesting and valuable case studies for the development of climate mitigation and/or adaptation strategies [34, 35], owing to its exclusive biodiversity and anthropo-climate vulnerability [36, 37]. Specifically, we aimed to unveil the influence of elevation and canopy shade in bacteria, archaea, and fungi communities.

Materials and methods

Experimental design

The study was conducted in the Gorongosa Mountain, belonging to the Gorongosa National Park, Sofala province, Mozambique (Lat. 18º 24’ 14’’S, Long. 34º 06’ 31.5’’E). Coffea arabica plants were implanted 1.5 m apart within a row and 3 m between rows, at a density of ca. 2222 plants ha− 1. The split-plot design of [6] was used to assess the impact of altitude and/or light conditions on the coffee soil properties. This included three different altitudes (main plots): ca. 600 m (18º 30’ 53’’ S, 34º 03’ 05’’ E), ca. 800 m (18’ 30’’ 04’’ S, 34º 02’ 58’’ E), and ca. 900 m (18º 28’ 54’’ S, 34º 02’ 43” E) above sea level (a.s.l.); and three levels of light (sub-plots) per altitude: deep shade (DS, average diurnal PPFD of 127 ± 28 µmol m− 2 s− 1), moderate shade (MS, 725 ± 48 µmol m− 2 s− 1), and full Sun (FS, 1268 ± 52 µmol m− 2 s− 1). The main canopy forest trees were Khaya anthotheca (Welw.) C. DC., Erythrina lysistemon Hutch., and Albizia adianthifolia (Schumach.) W.F.Wight.

Soil sampling

At each altitude and shade level, three coffee plants were randomly selected and soil samples were collected with an auger at a depth of 0–10 cm and 10–20 cm. One sub-set was air-dried, ground, sieved (2 mm particle size) and used for physicochemical analysis. The other part was used to prepare composite samples from the two depths, stored on ice during the field collection and once in the lab kept at − 80 °C until DNA extraction.

Chemical and enzymatic analyses

Soil samples were characterized in terms of Olsen extractable phosphorus (P-Olsen), pH (in a suspension 1:2.5 with H2O), soil organic carbon (SOC), and microbial biomass carbon and nitrogen (MBC and MBN, respectively), electric conductivity, extractable potassium (Egner Rhiem K), mineral nitrogen (NO3− and NH4+) and moisture using standard protocols [38,39,40]. MBC and MBN were determined with the fumigation-extraction method using calibration values of KEC = 0.45 for C and KEN = 0.54 for N [41, 42]. The quantification of C and N based on K2SO4 extraction was conducted using a near-infrared detector for carbon, and chemiluminescence for nitrogen. In both cases, samples underwent combustion at 950 ºC in a Formac analyzer (Skalar, Breda, Netherlands).

The activities of β-glucosidase, acid phosphatase and alkaline phosphatase (phosphomonoesterases) were determined according to the protocol of [43], using p-nitrophenyl-β-D-glucopyranoside (pNG) as substrate for β-glucosidase, and p-nitrophenyl phosphate (pNP) as substrate for both phosphomonoesterases. Urease activity was determined as described by [44] without buffering. For all enzymatic activities, the absorbance values of the extracts were determined in a segmented flow analyzer system with a preliminary dialysis step to remove color and microparticle interferences. β-glucosidase, acid- and alkaline phosphatase activities were expressed in µg p-nitrophenol h− 1 g− 1 dry soil, while urease activity was expressed in mg N-NH4+ 2 h− 1 kg− 1 dry soil.

Descriptive statistics and statistical analyses for soil properties were performed using RStudio version 4.1.1 [45]. The heterogeneity of the variance was first tested, and the original data were normalized by log-transformation when necessary. A GLM analysis was used to analyze the effects of altitude, shade, and their interactions on the soil properties.

DNA extraction and amplicon sequencing

Microbial DNA was extracted from soil samples using the DNeasy PowerSoil Pro Soil DNA Isolation Kit (Qiagen, Germany City, MD, USA) following the manufacturer instructions. DNA integrity and concentration were determined by 1% agarose gel electrophoresis and fluorometric quantification using a fluorometer (Qubit 2.0, Invitrogen, CA, USA), respectively. Amplicon libraries targeting the V4 hypervariable region of the 16 S rRNA for bacteria (Bakt_341F: CCTACGGGNGGCWGCAG and Bakt_805R: GACTACHVGGGTATCTAATCC) and archaea (Bakt_341F: CCTACGGGNGGCWGCAG and Bakt_805R: GACTACHVGGGTATCTAATCC) and the ITS2 region for fungi (3 F: GCATCGATGAAGAACGCAGC and 4R: TCCTCCGCTTATTGATATGC) were used following the Amplicon Metagenomic Sequencing Library Preparation guide (http://emea.support.illumina.com) [46]. Sequencing libraries were generated using the TruSEq DNA PCR-free sample preparation kit (Illumina, San Diego, CA, USA) following the manufacturer’s instructions. The final libraries were sequenced using the Illumina Miseq300 PE to generate 300 bp paired-end reads through Macrogen sequencing services (Macrogen, Seul, Korea).

Taxonomic diversity

Paired-end sequence reads were demultiplexed using the MiSeq reporter software (Illumina Inc., CA, USA) and checked for quality using FastQC v.0.11.9 (Babraham Institute, Cambridge, UK). Paired-reads were trimmed at both 5´ and 3’ ends eliminating poor quality nucleotides, denoised, merged, and chimeric sequences using the DADA2 denoiser [47] and then incorporated into QIIME 2 [48]. The resulting Amplicon Sequence Variant (ASV) count table was depleted of singletons, and representative sequences taxonomically classified using a trained classifier of the SILVA reference (Release 132) [49]. Alpha diversity analysis was conducted based on observed ASVs, Shannon entropy and Pielou’s evenness indices, while community dissimilarity was assessed using Bray-Curtis distance, which was visualized through non-metric multidimensional scaling (NMDS) ordination. Permutational multivariate analysis of variance (PERMANAOVA) was used to test for the significance of the microbial community dissimilarity across the different gradients of altitude and shades investigated. Canonical correspondence analysis (CCA) was also performed to determine the relationship of the microbial communities to soil physicochemical parameters along the shade and altitudinal gradients. Prior to computation of microbial diversity, the counts were normalized to 11,786 (bacteria), 2,414 (archaea) and 67,302 (fungi) ASV. The detection of biomarkers across gradients of altitude and shade was performed using Linear discriminant analysis Effect Size (LEfSe) [50]. Phylotypes with an LDA score ≥ 3.0 and a False Discovery Rate (FDR)-adjusted P-value ≤ 1.0 were considered to be differentially abundant. Except stated otherwise, data analysis and visualization were performed using R software v.4.1.1 [45].

Functional prediction

Prediction of bacterial community functions was done using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States - PICRUSt2 [51]. The functional prediction was done by aligning the 16 S rRNA marker gene representative sequences to a reference multiple-sequence alignment and reference phylogeny utilizing HMMER [52], EPA-NG [53] and GAPPA [54]. Subsequently, gene families were predicted using a hidden state prediction tool – Castor [55], after a normalization of the 16 S rRNA gene copies. The predicted gene families were thereafter collapsed into KEGG pathways using MinPath [56]. Differentially abundant pathways across the different altitudes were afterward determined using LefSe (LDA score ≥ 2.0; P-value ≤ 0.05).

Results

Chemical characteristics and enzymatic activities of soils

The level of shade only affected available phosphorus (P-Olsen; P = 0.014), MBN (P = 0.014) and the level of moisture (P = 0.006) (Table S1). In contrast, altitude exhibited a more pronounced effect on soil characteristics, influencing nearly all parameters except for pH and urease activity, i.e. P-Olsen (P = 0.008), SOC (P = 0.009), MBC (P = 0.0001), MBN (P = 0.0001), electrical conductivity (P = 0.002), Egner Rhiem K (P = 0.025), NO3− (P = 0.0001), NH4+ (P = 0.0001), moisture (P = 0.0001), β-glucosidase (P = 0.008), acid phosphatase (P = 0.0001), and alkaline phosphatase (P = 0.0001) (Table S1). Overall, at 800 m there were significantly higher levels of P-Olsen, SOC, and nitrate (NO3−), when compared to the 600 m and 900 m. MBC and MBN, as well as moisture content, were found to be lower at 800 m, highlighting a distinct environmental profile at this altitude (Table 1). Electrical conductivity and NH4+ increased with altitude, while the opposite was found for Egner Rhiem K (Table 1). Apart from urease, soil enzymatic activities also exhibited sensitivity to altitude changes (Table S1), with significantly higher activities of β-glucosidase and phosphatases (acid and alkaline) at 800 m (Table 1).

Table 1 Summary of the chemical properties and enzymatic activities of Coffea arabica soils. Mean values ± SD (n = 9) are indicated. Different superscripts indicate significant differences between altitudes, for each variable (ANOVA followed by Tukey-HSD, both for a 95% of confidence)
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Coffea alpha diversity between different altitudes and shade trees

A total of 89,590 high quality reads were obtained for bacteria, 91,977 for archaea and 93,507 for fungi with high Q values and adequate GC contents (Table S2). For bacteria, the observed number of Operational Taxonomic Units (OTUs), Shannon entropy and Pielou’s evenness revealed high species richness in all locations, although no significant differences were detected between the different levels of canopy shade (Fig. 1; Table S3). However, altitude had a strong influence on the bacterial richness with significantly lower values observed at the intermediate elevation of 800 m (Fig. 1; Table S4), while archaea richness increased with altitude (Fig. 1; Table S5; Table S6). In contrast, neither shade (Table S7) nor altitude (Table S8) had an impact on fungi richness, whether considering the observed number of OTUs, Shannon entropy, or Pielou’s evenness (Fig. 1).

Fig. 1
figure 1

Observed ASVs, Shannon–Wiener index of diversity and Pielou’s evenness in soil samples of C. arabica grown under different altitudes (600 m, 800 m, and 900 m) and under different levels of canopy shading (0%, 50% and 100%) considering Bacteria, Archaea, and Fungi. Black-filled dots on the boxplots depict mean values. Comparisons between altitude and shade levels are based on Kruskal-Wallis (Bonferroni adjusted P < 0.05)

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Coffea beta diversity between different altitudes and shade levels

Nonmetric multidimensional scaling analysis revealed a strong bacteria differentiation between altitude fields (Fig. 2A). These differences in the multivariate space were significant when considering the effect of altitude (PERMANOVA R2 = 0.493, P = 0.001), but not when considering the effect of shade (PERMANOVA R2 = 0.052, P = 0.797). The same was observed in the multivariate space of archaea (Fig. 2B) where altitude had a strong effect on the community structure (PERMANOVA R2 = 0.373, P = 0.001), whereas no significant effect was detected when considering the level of shade (PERMANOVA R2 = 0.047, P = 0.745). Altitude also influenced fungi beta diversity (Fig. 2C), as significant differences were found between altitude levels (PERMANOVA R2 = 0.177, P = 0.001), but not when considering the effect of shade (PERMANOVA R2 = 0.110, P = 0.068).

Fig. 2
figure 2

Nonmetric multidimensional scaling (NMDS) analysis of Bacteria (A), Archaea (B) and Fungi (C) community differences in soil samples of C. arabica grown under different altitudes (600 m, 800 m, and 900 m)

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Altitude alone accounted for 46% of the bacterial community variation (R² = 0.02; P = 0.001). However, the effect of shade on the bacterial community was not significant (R² = 0.01; P = 0.113). In the case of the archaeal community, altitude played an even more prominent role, explaining 61% of its variation (R² = 0.01; P = 0.001), while the impact of shade remained insignificant (R² = 0.01; P = 0.277). Regarding the fungal community, altitudinal changes significantly affected its composition (R² = 0.158; P = 0.001). Additionally, canopy cover had some influence on the fungal community, though to a lesser extent (R² = 0.038; P = 0.016). Notably, there was still 81% of the variation in the fungal community that remained unexplained by either altitude or canopy cover.

To further determine the microbial community – soil physicochemical relationship, we performed a canonical correspondence analysis (CCA). The results indicated that the model for bacteria explained 33% of the community variation (adjusted R2 = 0.33; P = 0.001). Both altitude (f = 6.87; P = 0.001) and shade (f = 1.53; P = 0.05) significantly influenced the community assembly. Also, several soil physiochemical parameters, including NH4+, pH, EC, MBN, MBC and moisture significantly influenced the dissimilarities of the bacterial community along the altitudinal and shade gradients. Accordingly, there was high relative abundance of the bacterial families Flavobacteriaceae and Ligionellaceae at 900 m altitude, while Streptomycetaceae and Shingobacteriaceae were dominant at 600 m altitude (Fig. 3B).

Fig. 3
figure 3

Bacterial, archaea and fungal community differentiation explained by shade and altitude based on variation partitioning analysis (A). Canonical correspondence analysis (CCA) explaining bacterial (B), archaeal (C), and fungal (D) communities relationship to soil physicochemical parameters along a shade and altitudinal gradient. The ellipses represent a 95% confidence interval in multivariate space according to each group’s centroid

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The CCA model for the archaeal community explained 34% (adjusted R2 = 0.34; P = 0.001) of the community dissimilarities along the shade and altitudinal gradients, though only altitude (shade: f = 1.55, P = 0.09; altitude: f = 6.83; P = 0.001) had a significant influence on the archaeal community. The dominance of Methanobacteriaceae at 800 m correlated with the concentration of NO3−. Other parameters that influenced the archaeal community were similar to those influencing the bacterial community. The CCA analysis further revealed that a significantly smaller variation of the fungal community dissimilarities was explained by the constrained variables compared to bacteria and archaea (adjusted R2 = 0.16; P = 0.001). However, contrary to the archaeal community, both altitude (f = 3.04; P = 0.001) and shade (f = 1.54; P = 0.001) significantly influenced the fungal community assembly, while the influence of N-NH4+, pH, EC, MBN, MBC and moisture were consistent, regardless of microbial domain.

Coffea dominant and differentially abundant phylotypes

The composition of the microbial communities was dominated by several distinct phyla (Fig. 4). For bacterial communities, four predominant phyla were identified: Proteobacteria (24%), Verrucomicrobia (23%), Actinobacteria (19%), and Acidobacteria (15%). In contrast, the archaeal community was overwhelmingly dominated by Thaumarchaeota, which constituted 98% of its population. Among the fungi, Mucoromycota was the most prevalent (54%), followed closely by Ascomycota (40%) (Fig. 4). At this taxonomic rank, there were no differentially abundant archaea or fungi across the different altitudes and shades, but for bacteria, some phyla had distinct patterns across altitudes. For instance, higher altitudes increased the relative abundance of Acidobacteria (LDA = 5.57; adjusted P = 0.005), Proteobacteria (LDA = 5.46; adjusted P = 0.006) and Planctomycetes (LDA = 4.97; adjusted P = 0.001). At the lowest altitude of 600 m, the abundance of Bacteroidetes, Chloroflexi, Nitrospirae, and Synegistetes were significantly higher, while the biomarkers of the mid-level altitude (800 m) included Verrucomicrobia and Actinobacteria (Fig. 4).

Fig. 4
figure 4

Phylum rank taxonomic profile of the microbial communities of C. arabica grown under different altitudes (600 m, 800 m, and 900 m) and under different levels of shade from native trees (0%, 50% and 100%)

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At the genus level, bacterial communities showed a predominance of Chthoniobacter (22%), followed by Rhodoplanes (8%), Acidobacterium (6%) and Arthrobacter (5%) (Fig. 5). Nitrososphaera was the dominant archaea (85%), especially at an altitude of 600 m while the abundance of Nitrosopumilus (13%) increased at higher altitudes (Fig. 5). In the case of fungi, Linnemannia (41%) was predominant in all altitudes, followed by Mortierella (10%), Fusarium (7%) and Penicillium (5%) (Fig. 5).

Fig. 5
figure 5

Genus level taxonomic profile of the microbial communities of C. arabica grown under different altitudes (600 m, 800 m, and 900 m) and different levels of shade from native trees (0%, 50% and 100%). Only phylotypes with a relative abundance of at least 1% in any of the samples are presented

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At the genus taxonomic rank, 103 bacterial phylotypes were differentially abundant across altitudinal levels (Fig. 6). In the case of bacteria, Gaiella (LDA 5.45; FDR-adjusted P < 0.01) and Natranaerobaculum Natranaerobaculum (LDA 5.06; FDR-adjusted P = 0.03) were among the most predominant biomarker phylotypes at the lowest altitude of 600 m, Chthoniobacter (LDA 5.80; FDR-adjusted P < 0.01) and Stella (LDA 4.89; FDR-adjusted P < 0.01) were predominant biomarkers at 800 m, while Occallatibacter (LDA 5.30; FDR-adjusted P = 0.01) and Paludibaculum (LDA 4.89; FDR-adjusted P < 0.02) were predominant biomarkers at 900 m. Only four archaea phylotypes were differentially abundant at the different altitudinal levels: Nitrososphaera (LDA 6.11; FDR-adjusted P < 0.01) and Methanomassiliicoccus (LDA 5.12; FDR-adjusted P = 0.60) at 600 m and Nitrosopumilus (LDA 6.15; FDR-adjusted P < 0.01) and Methanocaldococcus (LDA 3.60; FDR-adjusted P = 0.20) at 900 m (Fig. 6). Additionally, among the classified fungal phylotypes, 30 genera were differentially abundant across altitudinal levels. Of these, Exophiala (LDA 4.27; FDR-adjusted P = 0.05) and Cladophialophora (LDA 4.21; FDR-adjusted P = 0.04) were among the predominant biomarker phylotypes at 600 m, Gliocladiopsis (LDA 4.44; FDR-adjusted P = 0.05) and Cystofilobasidium (LDA 4.12; FDR-adjusted P < 0.01) at 800 m and Podila (LDA 5.58; FDR-adjusted P = 0.01) and Cutaneotrichosporon (LDA 4.05; FDR-adjusted P < 0.01) at 900 m (Fig. 6).

Fig. 6
figure 6

Differentially abundant Bacteria, Archaea and Fungi at the genus rank, across altitudes (600 m, 800 m, and 900 m). The differentially abundant data for bacteria and fungi (LDA ≥ 2.0; FDR-adjusted P ≤ 1.0) are a subset that was sorted according to adjusted p-values, LDA scores and altitudinal groupings. The complete list of differentially abundant genera is presented in Tables S9 and SS10

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The prediction of bacterial community functions revealed significant variations across different altitudes, highlighting distinct metabolic adaptations that potentially support plant growth and productivity. As detailed in Fig. 7, several ecologically relevant KEGG pathways were identified as differentially abundant at 900 m, 800 m, and 600 m, each corresponding to specific environmental stimuli and microbial interactions, organic matter decomposition and fermentation, and the breakdown of plant-derived compounds, respectively. The LDA scores and FDR-adjusted P-values for these pathways are presented in Table S10. At the highest elevation of 900 m, bacterial communities showed a predominance of pathways associated with the response to environmental stimuli and microbial interactions. Key pathways included those related to oxidative stress response, bacterial chemotaxis, and biofilm formation.

Fig. 7
figure 7

PICRUSt2 predicted differentially abundant KEGG pathways along the altitudinal gradient (P ≤ 0.01; LDA ≥ 2.0)

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At the mid-level elevation of 800 m, the bacterial community was enriched in pathways involved in the decomposition of organic matter, fermentation processes, and bioremediation. Notable pathways included those related to amino acid and carbohydrate metabolism, butanoate and propanoate metabolism, and the degradation of aromatic compounds and chlorocyclohexane.

At the lowest elevation of 600 m, the bacterial community function was characterized by pathways related to the breakdown of complex plant-derived compounds. Pathways such as cellulose and lignin degradation, starch and sucrose metabolism, and phenylpropanoid biosynthesis were significantly represented.

Discussion

The soil microbiome encompasses diverse microorganisms residing near plant roots, playing a pivotal role in ecosystem functioning. It not only shapes the structure and composition of biodiversity but also critically influences plant health and fitness. Its interactions with plants significantly contribute to various ecosystem processes, including nutrient cycling, soil fertility, and plant disease resistance, thereby underpinning the overall health and resilience of ecosystems. Recent research has devoted special attention to the nexus plants-microbes-environment, as an essential component of climate-smart, resilient, and sustainable agriculture [57,58,59]. In this study, a comprehensive analysis of the soils under the canopy projection of Coffea arabica was conducted to evaluate the shifts in microbial composition and associated functions along a gradient of elevation (600 m, 800 m, and 900 m) and shade (0%, 50%, 100%). A set of soil variables (organic carbon, microbial biomass carbon and nitrogen, available phosphorus, extractable potassium, nitrate, ammonium, moisture, pH, electrical conductivity), and microbial enzyme activities (urease, β-glucosidase, acid- and alkaline phosphatases) were analysed. In addition, metabarcoding was performed to study microbial communities (bacteria, archaea, and fungi), and their putative functions.

Most soil parameters were highly variable (high standard deviation) and only influenced by altitude, with a particularly differentiated pattern at 800 m, where available phosphorus (P-Olsen), soil organic carbon (SOC) and nitrate (NO3−) were significantly higher, and microbial biomass carbon and nitrogen (MBC and MBN, respectively) and moisture were significantly lower when compared to 600 m and 900 m (Table 1). Considering that soil elementary composition along the elevation and shade gradients was quite homogeneous (fine clay, acidic, non-saline, non-calcareous, 8–14% organic matter; Table S11), this pattern might be driven by the vegetation composition, i.e. C. arabica, shade tree species and other natural vegetation [60, 61], which in turn shapes the microbial communities [62]. Gota et al. [63] reported that the influence of agroforestry species on soil chemical properties is specific to each species and is not altered by altitude changes. Accordingly [60], reported that shade trees were the main drivers of soil composition in cocoa agroforestry systems (AFS), improving pH, NH4+, NO3−, total C and N, biomass and P-Olsen contents. However, soil function improvement varied across AFS, being low with fruit species (Canarium and Dacryodes), moderate with tree legumes (Albizia), and high with timber trees (Milicia and Ceiba). In the present study, the canopy shade trees in the experimental plots located at 800 m were mostly legumes (Albizia adianthifolia and Erythrina lysistemon), both included on the top list of N2-fixing fertilizers [64], while at 600 m and 900 m, non-legume trees were also dominant. In addition, the differences observed between the three altitudes might also be associated with climate variations, particularly rainfall and temperature, which shape vegetation composition and structure, implying differences in the amount and composition of litter [65, 66]. This hypothesis is corroborated by the enzyme activity assays, particularly β-glucosidase, and phosphatases, which presented higher activities at 800 m, i.e. 65.73 ± 10.87, 686.61 ± 149.97, and 189.96 ± 42.47 µg p-nitrophenol g− 1 h− 1. Enzymatic activities in soils are important for the decomposition of organic matter and mineralization of nutrients, and are useful indicators of soil biological activity and deductively of soil health [67, 68].

Metabarcoding analysis revealed high species richness in bacterial communities, with the lowest indexes recorded at 800 m (Fig. 1; Table S4). This might explain the lowest MBC and MBN values obtained at this altitude. As for the soil parameters, altitude was the most important driver of bacterial communities, explaining 46% of the variation (R2 = 0.493, P = 0.001), while shade did not produce significant changes (R2 = 0.01; P = 0.113) (Figs. 2A and 3). The predominant phyla (Proteobacteria, Verrucomicrobia, Actinobacteria, and Acidobacteria) and the average Shannon diversity indexes were similar to those reported previously for Coffea spp. soils, i.e. between 6 and 7 [22, 24,25,26]. Although the abundance of various phyla varied along the elevation gradient (Fig. 4), key plant growth-promoting (PGP) functions such as, nutrient cycling, organic matter decomposition, phosphate solubilization, soil aggregation, and biocontrol, were consistently present across all clusters, in line with the studies of [69, 70]. Accordingly, the predominant genera, i.e. Chthoniobacter, Rhodoplanes, Acidobacterium, and Arthrobacter (Fig. 5), incorporated these major PGP attributes [71,72,73]. While the presence of Rhodoplanes and Arthrobacter in Coffea soils has been previously reported, the presence of Chthoniobacter and Acidobacterium seems to be exclusive of our agro-ecological system [23, 26, 74, 75]. A set of 103 biomarker phylotypes were differentially abundant across the altitude gradient (Fig. 6), from which the most prominent were: Gaiella and Natranaerobaculum at 600 m; Chthoniobacter and Stella at 800 m; and Occallatibacter and Paludibaculum at 900 m. All of them are included in the group of PGP bacteria with vital functions in forest ecosystems. The genus Gaiella is bound to microbe-microbe interactions [76, 77], playing an important role in biocontrol, e.g. of Fusarium oxysporum in tomato [78] and strawberry [79]; some members also contribute to nitrogen cycling through the reduction of nitrate to nitrite [80]. Natranaerobaculum is typical of hypersaline soda environments [81], but its occurrence in forest soils seems to be related to health biomarkers [82]. Chthoniobacter is involved in nutrient cycling and produces secondary metabolites [83, 84], associated with the control of bacterial wilt [85] and growth of beneficial bacterial communities [84]. Although Stella and Paludibaculum bacteria are not common soil taxa, their ecological function might be related to organic matter decomposition [86]. Additionally, Paludibaculum may also enhance plant chlorophyll content [87]. Occallatibacter may play a relevant function in denitrification [88] as well as soil remediation [75]. Despite the fact that the basic PGP functions are likely maintained along the elevational gradient, taking into account that P-Olsen, SOC and NO3− were significantly higher at 800 m, we hypothesize that the microbial consortium at this altitude is more effective in nutrient cycling. Interestingly, despite the presence of legume trees and unlike what has been reported in similar studies in coffee [23, 25, 26, 89], the presence of diazotrophs (Nitrogen-fixing bacteria) was not prominent in this study. However, collectively, a considerable set of symbiotic (Bradyrhizobium, Mezorhizobium, and Rhizobium) and non-symbiotic (e.g. Agrobacterium, Azospirillum, Bacillus, Burkholderia, Clostridium, Microvirga, Nitrospirillum, Paraburkholderia) genera is represented in the bacterial pool.

The richness and diversity of archaea was considerably lower than that of bacteria, and fungi. Similarly to the soil parameters and bacterial communities, the patterns of richness and diversity also varied with altitude. However, in this case, the lowest values of richness and diversity were recorded at the lowest altitude of 600 m. Thaumarchaeota, typically including ammonia-oxidizing archaea (AOA), was the dominant phylum along the altitudinal gradient, similar to the observations of [90] for alpine forest soils [22], for the coffee rhizosphere, and [91] for the Himalayas. Additionally, this is the most prominent endophytic phylum in several crops, including coffee [92]. Euryarchaeota, which is associated with methanogenesis [90], was detected only at 800 m and under full sun (0% shade) and less represented (< 1%) in all other comparison groups. The irregular and low abundance (< 10%) of this phylum has also been reported in other soils, e.g. along an elevation gradient in alpine forest soils [90], and in the rhizosphere of full-sun coffee in consociation with other food crops [22]. Both phyla are associated with a wide range of PGP functions, e.g. improvement of plant growth, tolerance to biotic and abiotic stress, and nutrient solubilization and assimilation [93]. In line with the findings of [90] and [22], in this study, Nitrososphaera (phylum Thaumarchaeota) was the most abundant genus (70–100%) in all comparison groups. Nitrosopumilus, another AOA belonging to Thaumarchaeota, was the second most abundant genus (< 30%), particularly at 800 m and 900 m. These two genera play a crucial role in the nitrogen cycle (converting NH3+ into NO2−), i.e. bioavailability, soil fertility, and environmental regulation of nitrogen.

Fungi richness and diversity was considerably high (410.26 ± 91.32 observed ASVs and 4.29 ± 1.19 Shannon indexes, on average) (Fig. 1) and within the range of the values reported for the soil microbiome of C. arabica (e.g [24, 94]. Alpha-diversity of fungi was neither affected by shade (Table S7), nor altitude (Table S8), while beta-diversity (Fig. 2C) was driven by altitude (R2 = 0.177, P = 0.001), but unaffected by shade (R2 = 0.110, P = 0.068). Mucoromycota and Ascomycota were the dominant phyla (> 90% in total) (Fig. 5). As for the other microbial domains, both phyla include soil mycorrhizal fungi, saprotrophic decomposers and endophytes, with key functions in e.g. nutrient cycling and assimilation, or plant protection [95, 96]. Linnemannia (Mucoromycota) was the predominant genus in all comparison groups. Interestingly, this genus has never been reported previously in Coffea [22,23,24,25, 73, 97,98,99]. Linnemannia and Mortierella (the second most abundant genus in our study) have been reported as strong elicitors of plant growth in maize [99], wheat [100], and Arabidopsis [101, 102], likely driven by phytohormones [102]. In the Ascomycota phylum, the most prevalent genera were Fusarium and Penicillium. These genera are linked to a broad spectrum of ecological functions, including decomposition, nutrient cycling, biocontrol, and bioremediation [103, 104]. Such functions are visible among the 30 biomarkers differentially abundant across the three altitudes, from which Exophiala and Cladophialophora (600 m), Gliocladiopsis and Cystofilobasidium (800 m), and Podila and Cutaneotrichosporon (900 m) were the predominant genera (Fig. 6). For instance, Exophiala promotes plant stress tolerance and growth [105]; Cladophialophora is associated with plant protection and productivity [106]; Gliocladiopsis enhances tolerance against biotic ad abiotic factors [107, 108]; and Cysttofilobasidium is also a potential biocontrol agent [109]. While the ecological roles of Podila and Cutaneotrichosporon remain less defined, the presence of Podila in alpine forest soils has been documented [110], and Cutaneotrichosporon shows promise in biotechnology for the food and cosmetic industries [111], as well as in biofuel and bioplastic production [112].

The KEGG pathway data across different altitudes revealed distinct metabolic adaptations of microbial communities to their respective environments (Fig. 7). Firstly, at 800 m the metabolic pathways to lipoic acid and benzoate degradation indicated a rich organic matter environment and a microbial ability to utilize diverse organic compounds [113]. This altitude also shows active fermentation processes, as evidenced by butanoate metabolism and pyruvate metabolism pathways, hinting at the decomposition of organic materials. Notably, the atrazine degradation pathway suggests a capability to bioremediate certain pollutants, reflecting potential exposure to agricultural chemicals in this mid-altitude environment [114]. This observation suggests a robust microbial activity adapted towards organic matter turnover and pollutant degradation, which aligns with the higher levels of soil organic carbon and nitrate observed at this altitude, corroborating the hypothesis formulated above regarding the higher effectiveness of the microbial consortium at this altitude.

At 900 m, the high abundance of pathways such as bacterial chemotaxis and secretion systems (Fig. 7) suggests a heightened sensitivity to environmental stimuli and an advanced capacity for microbial interactions, possibly aiding adaptation to cooler, more variable high-altitude conditions. The biosynthesis of tetracycline and ubiquinone is usually related to microbial defense and stress response, vital for survival in a potentially challenging high-altitude environment [115]. Key pathways such as oxidative phosphorylation and sulfur metabolism highlight the efficiency in energy production and nutrient cycling, crucial in nutrient-limited high-altitude conditions.

At the lowest altitude of 600 m, the dominance of pathways related to the breakdown of plant-derived compounds, such as geraniol, limonene, and pinene degradation [116] suggests a closer interaction between plants and microbes, likely due to the more direct influence of vegetation at this altitude. Basic cellular processes are represented by pathways like D-alanine metabolism and steroid biosynthesis, suggesting a diverse microbial community engaged in various ecological functions. Moreover, phosphonate and phosphinate metabolism pathways at this altitude underline roles in phosphorus cycling, essential for both plant growth and microbial activity within soil ecosystems [117]. Altogether, the microbial community at this altitude seems highly adapted to decomposing plant materials, thereby facilitating nutrient release and availability for plant uptake.

Overall, the functional prediction analysis underscores the adaptive metabolic strategies employed by bacterial communities across different altitudes. These strategies are integral to supporting the growth and productivity of C. arabica by enhancing nutrient cycling, organic matter decomposition, and resilience to environmental stressors. The differential abundance of these KEGG pathways not only reflects the unique environmental conditions at each altitude but also highlights the potential of utilizing these microbial functions for sustainable coffee cultivation and ecosystem management.

Conclusion

In summary, this study highlights the significant impact of elevation on the microbiome of Coffea arabica soils within agroforestry systems. At different elevations, particularly at 800 m with predominantly legume trees, distinct microbial communities and soil chemistry profiles emerged, suggesting elevation-specific microbial functions likely related to nutrient cycling and plant growth. Despite the variations in microbial biomass and diversity, plant growth-promoting functions remained consistent across the altitudinal gradient. The study offers valuable insights into sustainable coffee cultivation, emphasizing the role of the soil microbiome in ecosystem health. Understanding these complex microbial interactions paves the way for developing climate-smart agricultural practices that leverage natural processes for improved crop resilience and productivity, aligning with the goals of sustainable agroecosystem management.

Data availability

The datasets generated and analysed during the current study are available in the Sequence Read Archives (SRA) of the National Centre for Biotechnological Information (NCBI) under the accession number PRJNA924052 [https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA924052; https://www.ncbi.nlm.nih.gov/bioproject/924052]. The remaining data and materials are included in this article (Figures, Tables, and Supplementary information).

References

  1. Davis AP, Rakotonasolo F. Six new species of coffee (Coffea) from northern Madagascar. Kew Bull. 2021;76:497–511. https://doi.org/10.1007/s12225-021-09952-5.

    Article  Google Scholar 

  2. International Coffee Organization. Coffee Market Report. December 2023. 2023. https://icocoffee.org/specialized-reports/. Accessed Sep 2024.

  3. ICO - International Coffee Organization. Monthly coffee market report 2020/21. 2022. https://www.ico.org/Market-Report-21-22-e.asp (accessed on 26 March 2024).

  4. DaMatta FM, Avila RT, Cardoso AA, Martins SCV, Ramalho JC. Physiological and agronomic performance of the coffee crop in the context of climate change and global warming: a review. J Agric Food Chem. 2018;66:5264–74. https://doi.org/10.1021/acs.jafc.7b04537.

    Article  CAS  PubMed  Google Scholar 

  5. Cassamo CT, Draper D, Romeiras MM, Marques I, Chiulele R, Rodrigues M, et al. Impact of climate changes in the suitable areas for Coffea arabica L. production in Mozambique: agroforestry as an alternative management system to strengthen crop sustainability. Agric Ecosyst Environ. 2023;346:108341. https://doi.org/10.1016/j.agee.2022.108341.

    Article  Google Scholar 

  6. Cassamo CT, Mangueze AVJ, Leitão AE, Pais IP, Moreira R, Campa C, et al. Shade and altitude implications on the physical and chemical attributes of green coffee beans from Gorongosa mountain. Mozambique Agron. 2022;12:2540. https://doi.org/10.3390/agronomy12102540.

    Article  CAS  Google Scholar 

  7. Gomes LC, Bianchi FJJA, Cardoso IM, Fernandes RBA, Fernandes Filho EI, Schulte POR. Agroforestry systems can mitigate the impacts of climate change on coffee production: a spatially explicit assessment in Brazil. Agric Ecosyst Environ. 2020;294:106858. https://doi.org/10.1016/j.agee.2020.106858.

    Article  Google Scholar 

  8. DaMatta FM. Ecophysiological constraints on the production of shaded and unshaded coffee: a review. Field Crops Res. 2004;86:99–114. https://doi.org/10.1016/j.fcr.2003.09.001.

    Article  Google Scholar 

  9. de Carvalho AF, Fernandes-Filho EI, Daher M, de Carvalho Gomes L, Cardoso IM, Fernandes RBA, et al. Microclimate and soil and water loss in shaded and unshaded agroforestry coffee systems. Agroforest Syst. 2021;5:119–34. https://doi.org/10.1007/s10457-020-00567-6.

    Article  Google Scholar 

  10. Koutouleas A, Sarzynski T, Bertrand B, Bordeaux M, Bosselmann AS, Campa C, et al. Shade effects on yield across different Coffea arabica cultivars – how much is too much? A meta-analysis. Agron Sustain Dev. 2022b;42:55. https://doi.org/10.1007/s13593-022-00788-2.

    Article  Google Scholar 

  11. Koutouleas A, Sarzynski T, Bordeaux M, Bosselmann AS, Campa C, Etienne H, et al. Shaded-coffee: a nature-based strategy for coffee production under climate change? A review. Front Sustain Food Syst. 2022a;6:877476. https://doi.org/10.3389/fsufs.2022.877476.

    Article  Google Scholar 

  12. Perfecto I, Vandermeer J, Philpott SM. Complex ecological interactions in the coffee agroecosystem. Ann Rev Ecol Evol Syst. 2014;45:137–58. https://doi.org/10.1146/annurev-ecolsys-120213-091923.

    Article  Google Scholar 

  13. Avelino J, Allinne C, Cerda R, Willocquet L, Savary S. Multiple- disease system in coffee: from crop loss assessment to sustainable management. Ann Rev Phytopathol. 2018;56:611–35. https://doi.org/10.1146/annurev-phyto-080417-050117.

    Article  CAS  Google Scholar 

  14. Avelino J, Vilchez S, Segura-Escobar MB, Brenes-Loaiza MA, Virginio Filho EDM, Casanoves F. Shade tree chloroleucon eurycyclum promotes coffee leaf rust by reducing uredospore wash-off by rain. Crop Protect. 2020;129:105038. https://doi.org/10.1016/j.cropro.2019.105038.

    Article  CAS  Google Scholar 

  15. Gonzalez CG, Van Cauwelaert EM, Boyer D, Perfecto I, Vandermeer J, Keinrad MB. High-order interactions maintain or enhance structural robustness of a coffee agro-ecosystem network. BioRxiv. 2021;47:100951. https://doi.org/10.1016/j.ecocom.2021.100951.

    Article  Google Scholar 

  16. Newson J, Vandermeer J, Perfecto I. Differential effects of ants as biological control of the coffee berry borer in Puerto Rico. Biol Control. 2021;160:104666. https://doi.org/10.1016/j.biocontrol.2021.104666.

    Article  Google Scholar 

  17. Chen C, Liua W, Jiang X, Wua J. Effects of rubber-based agroforestry systems on soil aggregation and associated soil organic carbon: implications for land use. Geoderma. 2017;299:13–24. https://doi.org/10.1016/j.geoderma.2017.03.021.

    Article  CAS  Google Scholar 

  18. Bote AD, Struik PC. Effects of shade on growth, production and quality of coffee (Coffea arabica) in Ethiopia. J Hortic for. 2011;3:336–41.

    Google Scholar 

  19. Mintesnot A, Dechassa N. Effect of altitude, shade, and processing methods on the quality and biochemical composition of green coffee beans in Ethiopia. East Afr J Sci. 2018;12:87–100. https://doi.org/10.20372/eajs.v12i2.495.

    Article  Google Scholar 

  20. Guimarães RJ, Borem FM, Shuler J, Fara A, Romero JCP. Coffee growing and post-harvest processing. In: Farah A, editor. Coffee production, quality and chemistry. London: The Royal Society of Chemistry; 2019. pp. 150–78. https://doi.org/10.1039/9781782622437-00026.

    Chapter  Google Scholar 

  21. Paudel M, Parajuli K, Regmi S, Budhathoki S. Effect of altitude and shade on production and physical attributes of coffee in Gulmi, Syangja and Palpa districts of Nepal. J Agric Nat Resour. 2021;4:222–38. https://doi.org/10.3126/janr.v4i1.33275.

    Article  Google Scholar 

  22. Caldwell AC, Silva LCF, da Silva CC, Ouverney CC. Prokaryotic diversity in the rhizosphere of organic, intensive, and transitional coffee farms in Brazil. PLoS ONE. 2015;10:e0106355. https://doi.org/10.1371/journal.pone.0106355.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Duong B, Marraccini P, Maeght J-L, Vaast P, Lebrun M, Duponnois R. Coffee Microbiota and its potential use in sustainable crop management. A review. Front Sustain Food Syst. 2020;4:607935. https://doi.org/10.3389/fsufs.2020.607935.

    Article  Google Scholar 

  24. Ge Y, Zhang F, Xie C, Qu P, Jiang K, Du H, et al. Effects of different altitudes on Coffea arabica rhizospheric soil chemical properties and soil microbiota. Agronomy. 2023;13:471. https://doi.org/10.3390/agronomy13020471.

    Article  CAS  Google Scholar 

  25. de Sousa LP, Guerreiro-Filho O, Mondego JMC. The rhizosphere microbiomes of five species of coffee trees. Microbiol Spectrum. 2022;10:00444 – 22. https://doi.org/10.1128/spectrum.00444-22

  26. Veloso TGR, da Silva MdCS, Moreira TR, da Luz JMR, Moreli AP, Kasuya MCM, et al. Microbiomes associated with Coffea Arabica and Coffea canephora in four different floristic domains of Brazil. Sci Rep-. 2023;13:18477. https://doi.org/10.1038/s41598-023-45465-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hassani MA, Durán P, Hacquard S. Microbial interactions within the plant holobiont. Microbiome. 2018;6:58. https://doi.org/10.1186/s40168-018-0445-0.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Choi K, Khan R, Lee SW. Dissection of plant microbiota and plant-microbiome interactions. J Microbiol. 2021;59:281–91. https://doi.org/10.1007/s12275-021-0619-5.

    Article  CAS  PubMed  Google Scholar 

  29. Decaëns T, Jiménez JJ, Gioia C, Measey GJ, Lavelle P. The values of soil animals for conservation biology. Eur J Soil Biol. 2006;42:S23–38. https://doi.org/10.1016/j.ejsobi.2006.07.001.

    Article  Google Scholar 

  30. van der Bardgett RD. Belowground biodiversity and ecosystem functioning. Nature. 2014;515:505–11. https://doi.org/10.1038/nature13855.

    Article  CAS  PubMed  Google Scholar 

  31. FAO, GSBI ITPS, SCBD EC. State of knowledge of soil biodiversity - status, challenges and potentialities, Report 2020. Rome:FAO; 2020.

  32. Sokol NW, Slessarev E, Marschmann GL, Nicolas A, Blazewicz SJ, Brodie EL, et al. Life and death in the soil microbiome: how ecological processes influence biogeochemistry. Nat Rev Microbiol. 2022;20:415–30. https://doi.org/10.1038/s41579-022-00695-z.

    Article  CAS  PubMed  Google Scholar 

  33. Tran DM. Rhizosphere microbiome dataset of Robusta coffee (Coffea canephora L.) grown in the Central Highlands, Vietnam, based on 16S rRNA metagenomics analysis. Data Brief. 2022;42:108106. https://doi.org/10.1016/j.dib.2022.108106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pringle R. Upgrading protected areas to conserve wild biodiversity. Nature. 2017;546:91–9. https://doi.org/10.1038/nature22902.

    Article  CAS  PubMed  Google Scholar 

  35. Matos A, Barraza L, Ruiz-Mallén I. Linking conservation, community knowledge, and adaptation to extreme climatic events: a case study in Gorongosa National Park. Mozambique Sustain. 2021;13:6478. https://doi.org/10.3390/su13116478.

    Article  Google Scholar 

  36. Stalmans M, Victor M. Forest cover on Gorongosa mountain. Assessment of satellite imagery 2019. 2020. https://gorongosa.org/wp-content/uploads/2020/10/SerraGorongosa_2019ForestCover_15May2020.pdf. Accessed 19 Sep 2024.

  37. Intergovernmental Panel on Climate Change (IPCC). Climate change 2022 - impacts, adaptation and vulnerability. Contribution of working group II to the sixth assessment report of the intergovernmental panel on climate change. Cambridge, New York: Cambridge University Press; 2022.

    Google Scholar 

  38. Olsen SR, Cole CV, Sterling R, Watanabe FF, Dean LA. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. USDA Circular 939;1954.

  39. Egnér H, Riehm H, Domingo WR. Untersuchungen über die chemische bodenanalyse als grundlage für die beurteilung Des nährstoffzustandes Der böden. II. Chem. Extraktionsmethoden zur Phosphor- Und Kaliumbestimmung K. Lantbr Ann. 1960;26:199–215.

    Google Scholar 

  40. Claessen MEC. Manual for methods of soil analysis. 2nd ed. Rio de Janeiro: Embrapa Solos; 1997.

    Google Scholar 

  41. Joergensen RG. The fumigation-extraction method to estimate soil microbial biomass: calibration of the KEC value. Soil Biol Biochem. 1996;28:25–31. https://doi.org/10.1016/0038-0717(95)00102-6.

    Article  CAS  Google Scholar 

  42. Joergensen RG, Mueller T. The fumigation-extraction method to estimate soil microbial biomass: calibration of the KEN value. Soil Biol Biochem. 1996;28:3–37. https://doi.org/10.1016/0038-0717(95)00101-8.

    Article  Google Scholar 

  43. Tabatabai MA. Soil enzymes. In: Weaver RW, Angle JS, Bottomley PS, editors. Methods of soil analysis. Part 2: microbiological and biochemical properties. Madison, WI: Soil Science Society of America, Book Ser 5; 1994. pp. 775–833.

  44. Kandeler E. Urease activity by colorimetric technique. In: Schinner F, Kandeler E, Öhlinger R, Margesin R, editors. Methods in soil biology. Berlin: Springer-; 1995. pp. 171–4.

    Google Scholar 

  45. R Core Team. R: A language and environment for statistical computing. R foundation for statistical computing, Version 4.1.1. R Core Team, Vienna. 2021. URL https://www.R-project.org/

  46. Abdelfattah A, Malacrinò A, Wisniewski M, Cacciola SO, Schena L. Metabarcoding: a powerful tool to investigate microbial communities and shape future plant protection strategies. Biol Control. 2018;120:1–10. https://doi.org/10.1016/j.biocontrol.2017.07.009.

    Article  Google Scholar 

  47. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. https://doi.org/10.1038/nmeth.3869.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bolyen E, Rideout JR, Dillon M, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotech. 2019;37:852–7. https://doi.org/10.1038/s41587-019-0209-9.

    Article  CAS  Google Scholar 

  49. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6. https://doi.org/10.1093/nar/gks1219.

    Article  CAS  PubMed  Google Scholar 

  50. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. https://doi.org/10.1186/gb-2011-12-6-r60.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Douglas G, Maffei V, Zaneveld J, Yurgel S, Brown J, Taylor C, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotech. 2020;38:1–5. https://doi.org/10.1038/s41587-020-0548-6.

    Article  CAS  Google Scholar 

  52. Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195. https://doi.org/10.1371/journal.pcbi.1002195.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Barbera P, Kozlov AM, Czech L, Morel B, Darriba D, Flouri T, et al. EPA-ng: massively parallel evolutionary placement of genetic sequences. Syst Biol. 2019;68:365–9. https://doi.org/10.1093/sysbio/syy054.

    Article  PubMed  Google Scholar 

  54. Czech L, Barbera P, Stamatakis A. Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data. Bioinformatics. 2020;36:3263–5. https://doi.org/10.1093/bioinformatics/btaa070.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Louca S, Doebeli M. Efficient comparative phylogenetics on large trees. Bioinformatics. 2018;34:1053–5. https://doi.org/10.1093/bioinformatics/btx701.

    Article  CAS  PubMed  Google Scholar 

  56. Ye Y, Doak TG. A parsimony approach to biological pathway reconstruction / inference for genomes and metagenomes. PLOS Comp Biol. 2009;5:e1000465. https://doi.org/10.1371/journal.pcbi.1000465.

    Article  CAS  Google Scholar 

  57. Arif I, Batool M, Schenk PM. Plant microbiome engineering: expected benefits for improved crop growth and resilience. Trends Biotech. 2020;38:1385–96. https://doi.org/10.1016/j.tibtech.2020.04.015.

    Article  CAS  Google Scholar 

  58. Trivedi P, Leach JE, Tringe SG, Sa Tongmin, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21. https://doi.org/10.1038/s41579-020-0412-1.

    Article  CAS  PubMed  Google Scholar 

  59. Ajala OA, Ajibade FO, Oluwadipe OR, Nwogwu NA, Adelodun B, Guadie A, et al. Microbial impact on climate-smart agricultural practices. In: Kumar A, Singh J, Ferreira LFR, editors. Microbiome under changing climate. Duxford. Cambridge MA, Kidlington: Woodhead Publishing; 2022. pp. 203–36. https://doi.org/10.1016/B978-0-323-90571-8.00009-2.

    Chapter  Google Scholar 

  60. Sauvadet M, Saj S, Freschet GT, Essobo J-D, Enock S, Becquer T, et al. Cocoa agroforest multifunctionality and soil fertility explained by shade tree litter traits. J Appl Ecol. 2020;57:476–87. https://doi.org/10.1111/1365-2664.13560.

    Article  CAS  Google Scholar 

  61. Phour M, Sindhu SS. Soil salinity and climate change: microbiome-based strategies for mitigation of salt stress to sustainable agriculture. In: Parray JA, editor. Climate change and microbiome dynamics. Climate change management. Cham: Springer; 2023. pp. 191–243. https://doi.org/10.1007/978-3-031-21079-2_13.

    Chapter  Google Scholar 

  62. Yue H, Yue W, Jiao S, Kim H, Lee Y-H, Wei G, et al. Plant domestication shapes rhizosphere microbiome assembly and metabolic functions. Microbiome. 2023;11:70. https://doi.org/10.1186/s40168-023-01513-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Gota HG, Madalcho AB, Kerse BL, Szwagrzyk J, Solomon T. The impact of native trees, Cordia Africana and Ficus sur, and the economically valuable Manihot esculenta on soil chemical properties in an agroforestry system. Trees People. 2024;15:100471. https://doi.org/10.1016/j.tfp.2023.100471.

    Article  Google Scholar 

  64. Sileshi GW, Mafongoya PL, Akinnifesi FK, Phiri E, Chirwa P, Beedy T, et al. Agroforestry: fertilizer trees. In: Van Alfen NK, editor. Encyclopedia of agriculture and food systems. Cambridge, Massachusetts: Academic; 2014. pp. 222–34.

    Chapter  Google Scholar 

  65. Zhang H, Yuan W, Dong W, Liu S. Seasonal patterns of litterfall in forest ecosystem worldwide. Ecol Complex. 2014;20:240–7. https://doi.org/10.1016/j.ecocom.2014.01.003.

    Article  CAS  Google Scholar 

  66. Giweta M. Role of litter production and its decomposition, and factors affecting the processes in a tropical forest ecosystem: a review. J Ecol Environ. 2020;44:11. https://doi.org/10.1186/s41610-020-0151-2.

    Article  Google Scholar 

  67. Stenberg B. Monitoring soil quality of arable land: microbiological indicators. Acta Agric Scand Sect B-Soil Plant Sci. 1999;49:1–24. https://doi.org/10.1080/09064719950135669.

    Article  Google Scholar 

  68. Ezeokoli OT, Bezuidenhout CC, Maboeta MS, Khasa DP, Adeleke RA. Structural and functional differentiation of bacterial communities in post-coal mining reclamation soils of South Africa: bioindicators of soil ecosystem restoration. Sci Rep. 2020;10:1759. https://doi.org/10.1038/s41598-020-58576-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecol. 2007;88:1354–64. https://doi.org/10.1073/pnas.1215210110.

    Article  Google Scholar 

  70. Fierer N, Leff JW, Adams BJ, Nielsen UN, Bates ST, Lauber CL, et al. Effects of three regeneration methods on the growth and bacterial community diversity of Populus × euramericana. PLoS ONE. 2022;17:e0273306. https://doi.org/10.1371/journal.pone.0273306.

    Article  CAS  Google Scholar 

  71. Bill M, Chidamba L, Gokul JK, Labuschagne N, Korsten L. Bacterial community dynamics and functional profiling of soils from conventional and organic cropping systems. App Soil Ecol. 2021;157:103734. https://doi.org/10.1016/j.apsoil.2020.103734.

    Article  Google Scholar 

  72. Catania V, Bueno RS, Alduina R, Grilli E, La Mantia T, Castaldi S, et al. Soil microbial biomass and bacterial diversity in southern European regions vulnerable to desertification. Ecol Indic. 2022;145:109725. https://doi.org/10.1016/j.ecolind.2022.109725.

  73. Marian M, Licciardello G, Vicelli B, Pertot I, Perazzolli M. Ecology and potential functions of plant-associated microbial communities in cold environments. FEMS Microbiol Ecol. 2022;98:fiab161. https://doi.org/10.1093/femsec/fiab161.

    Article  CAS  PubMed  Google Scholar 

  74. Cabrera-Rodríguez A, Trejo-Calzada R, la Peña CG, Arreola-Ávila JG, Nava-Reyna E, Vaca-Paniagua F, et al. A metagenomic approach in the evaluation of the soil microbiome in coffee plantations under organic and conventional production in tropical agroecosystems. Emirates J Food Agric. 2020;32:263–70. https://doi.org/10.9755/ejfa.2020.v32.i4.2092.

    Article  Google Scholar 

  75. de Souza JP, de Araújo Pereira AP, Pedrinho A, Andreote FD, Tornisielo VL, Tizioto PC, et al. Land use and roles of soil bacterial community in the dissipation of atrazine. Sci Total Env. 2022b;827:154239. https://doi.org/10.1016/j.scitotenv.2022.154239.

    Article  CAS  Google Scholar 

  76. Sun W, Xiao E, Krumins V, Häggblom MM, Dong Y, Pu Z, et al. Rhizosphere microbial response to multiple metal(loid)s in different contaminated arable soils indicates crop-specific metal-microbe interactions. Appl Environ Microbiol. 2018;84:e00701–18. https://doi.org/10.1128/AEM.00701-18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Wang P, Kong X, Chen H, Xiao Y, Liu H, Li X, et al. Exploration of intrinsic microbial community modulators in the rice endosphere indicates a key role of distinct bacterial taxa across different cultivars. Front Microbiol. 2021;12:629852. https://doi.org/10.3389/fmicb.2021.629852.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Zhao F, Zhang Y, Dong W, Zhang Y, Zhang G, Sun Z, et al. Vermicompost can suppress Fusarium oxysporum f. sp. lycopersici via generation of beneficial bacteria in a long-term tomato monoculture soil. Plant Soil. 2019;440:491–505. https://doi.org/10.1007/s11104-019-04104-y.

    Article  CAS  Google Scholar 

  79. Lazcano C, Boyd E, Holmes G, Hewavitharana S, Pasulka A, Ivors K. The rhizosphere microbiome plays a role in the resistance to soil-borne pathogens and nutrient uptake of strawberry cultivars under field conditions. Sci Rep. 2021;11:3188. https://doi.org/10.1038/s41598-021-82768-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Albuquerque L, França L, Rainey FA, Schumann P, Nobre MF, Da Costa MS. Gaiella occulta gen. nov., sp. nov., a novel representative of a deep branching phylogenetic lineage within the actinobacteriacteria and proposal of Gaiellaceae fam. nov. and GaielOrdesNovd. nov. Syst App Microbiol. 2011;34:595–9. https://doi.org/10.1016/j.syapm.2011.07.001.

    Article  Google Scholar 

  81. Fazi S, Butturini A, Tassi F, Amalfitano S, Venturi S, Vazquez E, et al. Biogeochemistry and biodiversity in a network of saline–alkaline lakes: implications of ecohydrological connectivity in the Kenyan Rift Valley. Ecohydrol Hydrobiol. 2018;18:96–106. https://doi.org/10.1016/j.ecohyd.2017.09.003.

    Article  Google Scholar 

  82. Kim H, Park Y-H, Yang JE, Kim H-S, Kim S-C, Oh E-J, et al. Analysis of major bacteria and diversity of surface soil to discover biomarkers related to soil health. Toxics. 2022;10:117. https://doi.org/10.3390/toxics10030117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Jenkins SN, Waite IS, Blackburn A, Husband R, Rushton SP, Manning DC, et al. Actinobacterial community dynamics in long term managed grasslands. Antonie Van Leeuwenhoek. 2009;5:319–34. https://doi.org/10.1007/s10482-009-9317-8.

    Article  Google Scholar 

  84. Fu Q, Lai JL, Ji XH, Luo ZX, Wu G, Luo XG. Alterations of the rhizosphere soil microbial community composition and metabolite profiles of Zea mays by polyethylene-particles of different molecular weights. J Hazard Mater. 2022;423:127062. https://doi.org/10.1016/j.jhazmat.2021.127062.

    Article  CAS  PubMed  Google Scholar 

  85. Chen S, Qi G, Ma G, Zhao X. Biochar amendment controlled bacterial wilt through changing soil chemical properties and microbial community. Microbiol Res. 2020;231:126373. https://doi.org/10.1016/j.micres.2019.126373.

    Article  CAS  PubMed  Google Scholar 

  86. Zavarzin GA. The notion of microflora of dispersion in the carbon cycle. J Gen Biol Izv Akad Nauk USSR. 1970;31:386–93.

    Google Scholar 

  87. Yoneda Y, Yamamoto K, Makino A, Tanaka Y, Meng X-Y, Hashimoto J, et al. Novel plant-associated Acidobacteria promotes growth of common floating aquatic plants, duckweeds. Microorg. 2021;9:1133. https://doi.org/10.3390/microorganisms9061133.

    Article  CAS  Google Scholar 

  88. Truu M, Nõlvak H, Ostonen I, Oopkaup K, Maddison M, Ligi T, et al. Soil bacterial and archaeal communities and their potential to perform N-cycling processes in soils of boreal forests growing on well-drained peat. Front Microbiol. 2020;11:591358. https://doi.org/10.3389/fmicb.2020.591358.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Bullergahn VB, Menezes KMS, Veloso TGR, da Luz JMR, Castanheira LF, Pereira LL et al. Diversity of potential nitrogen-fixing bacteria from rhizosphere of the Coffea arabica L. and Coffea canephora L. 3 Biotech. 2024;14:27. https://doi.org/10.1007/s13205-023-03875-7

  90. Siles JA, Margesin R. Abundance and diversity of bacterial, archaeal, and fungal communities along an altitudinal gradient in Alpine forest soils: what are the driving factors? Microb Ecol. 2016;72:207–20. https://doi.org/10.1007/s00248-016-0748-2.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Aqeel M, Khalid N, Noman A, Ran J, Manan A, Hou Q, et al. Interplay between edaphic and climatic factors unravels plant and microbial diversity along an altitudinal gradient. Env Res. 2024;242:117711. https://doi.org/10.1016/j.envres.2023.117711.

    Article  CAS  Google Scholar 

  92. Oliveira MNV, Santos TMA, Vale HMM, Delvaux JC, Cordero AP, Ferreira AB, et al. Endophytic microbial diversity in coffee cherries of Coffea Arabica from southeastern Brazil. Can J Microbiol. 2013;59:221–30. https://doi.org/10.1139/cjm-2012-0674.

    Article  CAS  PubMed  Google Scholar 

  93. Chow C, Padda KP, Puri A. An archaic approach to a modern issue: endophytic archaea for sustainable agriculture. Curr Microbiol. 2022;79:322. https://doi.org/10.1007/s00284-022-03016-y.

    Article  CAS  PubMed  Google Scholar 

  94. Bez C, Esposito A, Musonerimana S, Nguyen TH, Navarro-Escalante L, Tesfaye K, et al. Comparative study of the rhizosphere microbiome of Coffea arabica grown in different countries reveals a small set of prevalent and keystone taxa. Rhizosphere. 2023;25:100652. https://doi.org/10.1016/j.rhisph.2022.100652.

    Article  Google Scholar 

  95. Challacombe JF, Hesse CN, Bramer LM, McCue LA, Lipton M, Purvine S, et al. Genomes and secretomes of Ascomycota fungi reveal diverse functions in plant biomass decomposition and pathogenesis. BMC Genomics. 2019;20:976. https://doi.org/10.1186/s12864-019-6358-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Pawłowska J, Okrasińska A, Kisło K, Aleksandrzak-Piekarczyk T, Szatraj K, Dolatabadi S, et al. Carbon assimilation profiles of mucoralean fungi show their metabolic versatility. Sci Rep. 2019;9:11864. https://doi.org/10.1038/s41598-019-48296-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. de Sousa LP, Guerreiro Filho O, Costa Mondego JM. Differences between the leaf mycobiome of Coffea arabica and wild coffee species and their modulation by caffeine/chlorogenic acid content. Microorg. 2021;9:2296. 10.3390/ /microorganisms9112296.

  98. Ochoa-Henriquez VH, Faggioli V, Gómez-Godínez LJ, Rivarola M, Cristancho M. Colombian coffee (Coffea arabica L.) plantations: a taxonomic and functional survey of soil fungi. Front Sustain Food Syst. 2024;8:1345383. https://doi.org/10.3389/fsufs.2024.1345383.

    Article  Google Scholar 

  99. Li F, Chen L, Redmile-Gordon M, Zhang J, Zhang C, Ning Q, et al. Mortierella Elongata’s roles in organic agriculture and crop growth promotion in a mineral soil. Land Degrad Dev. 2018;29:1642–51. https://doi.org/10.1002/ldr.2965.

    Article  Google Scholar 

  100. Johnson JM, Ludwig A, Furch ACU, Mithöfer A, Scholz S, Reichelt M, et al. The beneficial root-colonizing fungus Mortierella hyaline promotes the aerial growth of arabidopsis and activates calcium-dependent responses that restrict Alternaria brassicae-induced disease development in roots. Mol Plant Microbe Interact. 2019;32:351–63. https://doi.org/10.1094/MPMI-05-18-0115-R.

    Article  CAS  PubMed  Google Scholar 

  101. Ozimek E, Jaroszuk-Ściseł J, Bohacz J, Korniłłowicz-Kowalska T, Tyśkiewicz R, Słomka A, et al. Synthesis of indoleacetic acid, gibberellic acid and ACC-deaminase by Mortierella strains promote winter wheat seedlings growth under different conditions. Int J Mol Sci. 2018;19:30340353. https://doi.org/10.3390/ijms19103218.

    Article  CAS  Google Scholar 

  102. Vandepol N, Liber J, Yocca A, Matlock J, Edger P, Bonito G. Linnemannia elongate (Mortierellaceae) stimulates Arabidopsis thaliana aerial growth and responses to auxin, ethylene, and reactive oxygen species. PLoS ONE. 2022;17:e0261908. https://doi.org/10.1371/journal.pone.0261908.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Altaf R, Rauf CA, Naz F, Shabbir G. Surveillance and morphological characterization of Fusarium isolates associated with lentil wilt. Pak J Phytopathol. 2014;26:85–90.

    Google Scholar 

  104. Abdel-Azeem AM, Abdel-Azeem MA, Darwish AG, Nafady NA, Ibrahim NA. Fusarium: biodiversity, ecological significances, and industrial applications. In: Yadav A, Mishra S, Singh S, Gupta A, editors. Recent advancement in white biotechnology through fungi. Fungal Biology. Cham: Springer; 1980. pp. 201–61. https://doi.org/10.1007/978-3-030-10480-1_6.

    Chapter  Google Scholar 

  105. Wang Y, Wang H, Cheng H, Chang F, Wan Y, She X. Niche differentiation in the rhizosphere and endosphere fungal microbiome of wild Paris polyphylla sm. Peer J. 2020;8:e8510. https://doi.org/10.7717/peerj.8510.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Harsonowati W, Marian M, Surono, Narisawa K. The effectiveness of a dark septate endophytic fungus, Cladophialophora chaetospira sk51, to mitigate strawberry fusarium wilt disease and with growth promotion activities. Front Microbiol. 2020;11:00585. https://doi.org/10.3389/fmicb.2020.00585.

    Article  Google Scholar 

  107. Singh S, Upadhyay RS, Sharma S, Dubey OP. Endophytic gliocladiopsis sp. confers drought stress tolerance in potted wheat plants (Triticum aestivum). Braz J Microbiol. 2017;48:489–97. https://doi.org/10.1016/j.bjm.2017.01.004.

    Article  CAS  Google Scholar 

  108. Singh S, Dubey OP, Upadhyay RS, Gupta SC. Evaluation of the potential of endophytic Gliocladiopsis sp. as a biological control agent against Sclerotinia sclerotiorum in Brassica juncea. Biol Control. 2018;123:99–106. https://doi.org/10.1016/j.biocontrol.2018.05.012.

  109. Vero S, Garmendia G, Garat MF, de Aurrecoechea I, Wisniewski M. Cystofilobasidium infirmominiatum as a biocontrol agent of postharvest diseases on apples and citrus. Acta Hortic. 2011;905:169–80. https://doi.org/10.17660/ActaHortic.2011.905.

    Article  CAS  Google Scholar 

  110. Telagathoti A, Probst M, Mandolini E, Peintner U. Mortierellaceae from subalpine and alpine habitats: new species of Entomortierella, Linnemannia, Mortierella, Podila and TyrolGenla gen. Nov Stud Mycol. 2022;103:25–58. https://doi.org/10.3114/sim.2022.103.0.

    Article  CAS  PubMed  Google Scholar 

  111. Stellner NI, Rerop ZS, Mehlmer N, Masri M, Ringel M, Brük TB. Expanding the genetic toolbox for Cutaneotrichosporon oleaginosus employing newly identified promoters and a novel antibiotic resistance marker. BMC Biotechnol. 2023;23:40. oi: https://doi.org/10.1186/s12896-023-00812-7

  112. Di Fidio N, Minonne F, Antonetti C, Raspolli Galletti AM. Cutaneotrichosporon oleaginosus: a versatile whole-cell biocatalyst for the production of single-cell. Catalysts. 2021;11:1291. https://doi.org/10.3390/catal11111291.

    Article  CAS  Google Scholar 

  113. Zhou Z, Tran PQ, Breister AM, Liu Y, Kieft K, Cowley ES, et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome. 2022;10:33. https://doi.org/10.1186/s40168-021-01213-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Sandhya M, Ziqiu L, Shimei P, Wenping Z, Pankaj B, Shaohua C. Recent advanced technologies for the characterization of xenobiotic-degrading microorganisms and microbial communities. Front Bioeng Biotech. 2021;9:63205910. https://doi.org/10.3389/fbioe.2021.632059.

    Article  Google Scholar 

  115. Ahmad F, Zhu D, Sun J. Bacterial chemotaxis: a way forward to aromatic compounds biodegradation. Environ Sci Eur. 2020;32:52. https://doi.org/10.1186/s12302-020-00329-2.

    Article  Google Scholar 

  116. Ndao A, Adjallé K. Overview of the biotransformation of limonene and α-pinene from wood and citrus residues by microorganisms. Waste. 2023;1:841–59. https://doi.org/10.3390/waste1040049.

    Article  Google Scholar 

  117. Bi WX, Weng BS, Dengua Y, Hao W, Wang MK, Yan SY, et al. Responses of phosphate-solubilizing microorganisms mediated phosphorus cycling to drought-flood abrupt alternation in summer maize field soil. Front Microbiol. 2022;12:768921. https://doi.org/10.3389/fmicb.2021.768921.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to thank the Gorongosa National Park (PNG), the Mozambican Agricultural Research Institute (IIAM), and technicians Jossefo Saliva and Sional Moiane (PNG) for their support during fieldwork (Research permit PNG/DSCi/C203/2021).

Funding

This work was funded by Camões, Instituto da Cooperação e da Língua (CICL), Agência Brasileira de Cooperação (ABC), and Parque Nacional da Gorongosa (PNG) under the Triangular Project TriCafé (GorongosaCoffee), and by FCT - Fundação para a Ciência e a Tecnologia, I.P. by project references UIDB/00239/2020 of Forest Research Center, DOI https://doi.org/10.54499/UIDB/00239/2020; LA/P/0092/2020 of Associate Laboratory TERRA, DOI https://doi.org/10.54499/LA/P/0092/2020); UIDP/04035/2020 of GeoBioTec, DOI https://doi.org/10.54499/UIDB/04035/2020; UIDB/04129/2020 of LEAF, DOI https://doi.org/10.54499/UIDB/04129/2020, UIDB/00616/2020 of CQ-VR, DOI https://doi.org/10.54499/UIDB/00616/2020, UIDB/04033/2020 of CITAB, DOI https://doi.org/10.54499/UIDB/04033/2020; and Scientific Employment Stimulus - Individual Call (CEEC Individual) 2021.01107.CEECIND/CP1689/CT0001, DOI https://doi.org/10.54499/2021.01107.CEECIND/CP1689/CT0001 (IM).

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Author notes

  1. Inocência da Piedade E. Tapaça and Chinedu C. Obieze are joint-first authors that contributed equally to this work.

Authors and Affiliations

  1. Forest Research Center, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, Lisbon, 1349-017, Portugal

    Inocência da Piedade E. Tapaça, José C. Ramalho, Isabel Marques & Ana I. Ribeiro-Barros

  2. Mozambique Agricultural Research Institute (IIAM), Avenida das FPLM 2698, P.O. Box 3658, Mavalane B, Maputo, Mozambique

    Inocência da Piedade E. Tapaça

  3. Centre for Forest Research, Institute of Integrative Biology and Systems, Universite Laval, Québec, QC, G1V0A6, Canada

    Chinedu C. Obieze

  4. Bioprocess Engineering and Biotechnology Department, Federal University of Paraná, Curitiba, PR, 81531-980, Brazil

    Gilberto V. de Melo Pereira

  5. Linking Landscape, Environment, Agriculture and Food Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, Lisbon, 1349-017, Portugal

    David Fangueiro

  6. Chemistry Centre, University of Trás-os-Montes and Alto Douro, Quinta de Prados, Vila Real, 5001-801, Portugal

    João Coutinho

  7. Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes and Alto Douro, Quinta de Prados, Vila Real, 5001-801, Portugal

    Irene Fraga

  8. Department Ciências Agrárias e Biológicas (DCAB), Centro Universitário do Norte do Espírito Santo (CEUNES), Universidade Federal Espírito Santo (UFES), Rodovia BR 101 Norte, Km. 60, Bairro Litorâneo, São Mateus, ES, 29932- 540, Brazil

    Fábio L. Partelli

  9. Geoengenharias e Geotecnologias (GeoBioTec), Faculdade de Ciências e Tecnologia (FCT), Unidade de Geobiociências, Universidade NOVA de Lisboa (UNL), Caparica, Monte de Caparica, 2829-516, Portugal

    José C. Ramalho & Ana I. Ribeiro-Barros

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Contributions

I.P.E.T. - Investigation, Writing - original draft, Methodology, Visualization, Formal analysis, Data curation, Validation, Writing - review & editing. C.C.O. - Investigation, Writing - original draft, Methodology, Validation, Visualization, Writing - review & editing, Software, Formal analysis, Data curation. G.V.M.P - Writing - review & editing, Validation, Methodology, Formal analysis, Investigation. D.F. - Investigation, Methodology, Validation, Writing - review & editing, Formal analysis. J.C. - Investigation, Methodology, Validation, Writing - review & editing, Formal analysis. I.F. - Investigation, Methodology, Writing - review & editing, Formal analysis. F.L.P - Investigation, Methodology, Writing - review & editing, Formal analysis. J.C.R - Investigation, Funding acquisition, Validation, Writing - review & editing, Project administration, Resources. I.M. - Investigation, Writing - original draft, Methodology, Validation, Visualization, Writing - review & editing, Formal analysis, Supervision, Data curation. A.I.R.B. - Conceptualization, Investigation, Funding acquisition, Writing - original draft, Methodology, Validation, Writing - review & editing, Formal analysis, Project administration, Data curation, Supervision, Resources.

Corresponding authors

Correspondence to Isabel Marques or Ana I. Ribeiro-Barros.

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Research permit number: PNG/DSCi/C203/2021 issued by the scientific department of Gorongosa National Park.

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Tapaça, I.d.P.E., Obieze, C.C., Pereira, G.V.d.M. et al. Irradiance level and elevation shape the soil microbiome communities of Coffea arabica L.. Environmental Microbiome 19, 75 (2024). https://doi.org/10.1186/s40793-024-00619-9

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Keywords

Environmental Microbiome

ISSN: 2524-6372