Biocrusts Mediate the Niche Distribution and Diversity of Ammonia-Oxidizing Microorganisms in the Gurbantunggut Desert, Northwestern China
Biological soil crusts, or biocrusts, are diverse communities of microorganisms found on the surface of drylands worldwide. These communities play a crucial role in nutrient cycling within these ecosystems . Biocrusts contribute to over 40% of global biological nitrogen (N) fixation and account for 7% primary productivity, thus significantly impacting terrestrial N fixation . However, due to the fragile nature of biocrusts and their high sensitivity to disturbances on the soil surface , our current understanding of how desert soil N processes respond to biocrust disturbance is limited. This knowledge gap is partly attributed to insufficient insights into responses of biocrust communities to interference. Ammonium oxidation (AO), the initial step in nitrification, plays a pivotal role in nitrogen (N) cycling within biocrusts by regulating concentrations of stable soil ammonium N (NH 4 + _N) and unstable nitrites (NO 2 − _N) and nitrates (NO 3 − _N) . Moreover, AO significantly impacts the utilization of available N, nitrate leaching, and greenhouse gas emissions of nitrous oxide (N 2 O) and nitric oxide (NO) in desert soils . This process is carried out by ammonia-oxidizing archaea (AOA) and bacteria (AOB) . Similar to trends observed in other ecosystems, AOA and AOB demonstrate niche differentiation along various soil-trait dimensions in deserts . For example, AOA are more prevalent than AOB in Antarctic valleys characterized by harsh desert conditions, such as extremely low soil C/N ratios and exceptionally high soil electrical conductivity . Additionally, Liu et al.  have shown that AOA abundance is notably higher in oligotrophic soils during freeze–thaw periods in the Gurbantunggut Desert; this suggests that AOA represent a prominent nitrifying group in barren desert soils . The contribution of N by biocrusts is believed to exert a significant impact on N transformation and ammonia-oxidizing communities in drylands, where nitrogen, along with water, is a crucial limiting factor . Evidence suggests that N fixation in biocrusts is closely associated with ammonia oxidation processes . Areal rates of actual ammonia oxidation measured by Johnson et al.  ranged from 19 to 46 µmol N m −2 h −1 , which closely align with the input rates of N 2 fixation (6.5–48 µmol C 2 H 2 m −2 h −1 ) observed in intact biocrusts. Importantly, biocrust-mediated nitrification, unlike nitrification that causes N loss in other ecosystems, is a potential mechanism to reduce N loss in N-restricted areas . Furthermore, there exists a strong correlation between the abundance of ammonia-oxidizing microorganisms and biocrust type, suggesting that these microorganisms drive the succession of biocrusts and are associated with ecological processes within desert ecosystems involving other communities . Biocrust ammoxidation is strongly influenced by soil factors, including NH 4 + _N, pH, conductivity, soil moisture, and oxygen . For example, Johnson et al.  have proposed that the ammoxidation process in biocrusts is primarily restricted to a thin subsurface layer (1–3 mm thick) as a consequence of substrate availability and oxygen constraints in deeper soils. Liu et al.  have illustrated that rates of biocrust ammoxidation in the Gurbantunggut Desert are closely associated with soil moisture and NH 4 + _N during freeze–thaw periods. Light crusts dominated by Microcoleus vaginatus Gom. generally exhibit lower N fixation rates compared with dark crusts dominated by Nostoc commune Vauch. and/or Scytonema species ; however, light crusts may demonstrate higher ammoxidation rates when direct coupling occurs between photosynthesis and ammoxidation processes . Although the regulation of ammonia oxidation by biocrusts has mainly been observed within individual desert ecosystems, we propose that disturbances to biocrust communities can influence the distribution of ammonia-oxidizing microorganisms through changes in abiotic physical and chemical parameters as well as alterations in biological interactions within the desert environment. One promising approach for comprehending the impact of interference on biocrust communities is through network analysis . Biocrust microbial species establish ecological relationships, encompassing mutualism and competition, which, along with niche preferences and other factors such as community assembly processes, shape their microbial abundances . Recent studies have addressed disturbances in biocrusts that alter their microbial species relationships and function. For instance, Rong et al.  found that long-term addition of N leads to a dense and interconnected bacterial cooccurrence network that accentuates a functional shift from networks dominated by phototrophic taxa (e.g., Nostocaceae, Xenococcaceae, Phormidiaceae, and Scytonemataceae) to ammonia-oxidizing ones (e.g., Nitrosomonadaceae and Nitrospiraceae). According to Li and Hu , the number of bacteria nodes in biocrust networks decreases while eukaryotes nodes increase as precipitation increases across northern China; furthermore, rare bacteria mainly dominate in areas with low precipitation, whereas Proteobacteria and Chloroflexi are the most abundant co-occurring species in regions with high precipitation. The edges in microbial networks are often interpreted as representing biotic interactions, such as cross-feeding of byproducts or competition for nutrient/niche resources . In addition to providing insights into taxonomic connections between organisms, analysis of microbial association networks reveals niches occupied by specific taxa and facilitates the identification of keystone species and alternative community configurations . Despite these findings mentioned above, our current understanding of how perturbations affect species interactions among ammonia-oxidizing microorganisms in biocrusts remains limited. The concomitant production of N and carbon (C) in conjunction with biocrust and hydrological impacts results in vertical stratification of desert soils . Some evidence indicates that the biocrust layer establishes a direct nutrient linkage between biocrusts and drylands, thus functioning as a zone of high nutrient deposition, transformation, and availability . In contrast, deeper layers of desert soils primarily derive their C and N from microbial communities, which generally exhibit less microbial biomass . Variations in soil depth are also significantly influenced by soil moisture levels . Seasonal nutrient inputs into biocrust follow a pulsating pattern associated with precipitation events and/or snowfall cover . Compared to summer-long dry-dormancy conditions, winter snow cover greatly alleviates nutrient and moisture limitations on biocrusts in deserts while promoting active nitrogen cycling within the crust layer . Dew may also have important implications for the maintenance and persistence of desert ecosystems. There is evidence that biocrusts promote N accumulation in response to dew events in dryland soils . Consequently, the dynamics of ammonia oxidation communities during winter and summer play an integral role in shaping biocrust N transformations in drylands. In this study, we combined quantitative analysis of ammonia-oxidizing microorganism abundance, metagenomic targeting, and microbial community analysis of dominant AOA to investigate the impact of interference on nitrifying communities in biocrusts. We examined two seasons: summer, which is characterized by low microbial activity, and winter, when previous observations have indicated peak nitrogen levels in biocrusts . Additionally, we explored the influence of soil depth on the abundance of ammonia-oxidizing microorganisms and studied whether niche separation is driven by biocrust interference, soil depth, or both. Our main hypothesis was that seasonal and/or depth differences in AOA and AOB abundance are mediated by the presence of biocrusts and that niche separation of ammonia-oxidizing microorganisms is thus biocrust specific. Given prior evidence highlighting the pivotal role of AOA as key drivers of ongoing geochemical changes in deserts  and their dominance within nitrifying groups, we specifically investigated the effects of biocrust interference on community structure and species interactions within the AOA population.   Study Area and Biocrust-Removal Treatments The experiment was conducted in the Gurbantunggut Desert (44.62° N, 88.26° E, 570 m), where well-developed biocrusts are distributed across inter-dune areas . The mean annual temperature in the study area is 6.6 ± 0.8 °C. Annual precipitation ranges from 70 to180 mm, with a mean annual precipitation of 129 mm. Summers in the desert are hot and dry, with potential annual evaporation exceeding 2000 mm. Condensation occurs 325 days a year, with an average daily input of 0.077 mm. During winter, however, approximately 20 cm of snow covers the soil surface, thus providing ample water during the thaw season. Dominant shrub species in this desert ecosystem include Haloxylon persicum Bunge ex Boiss. & Buhse and Haloxylon ammodendron (C. A. Mey) Bunge ex Fenzl. In October 2015, biocrust-removal manipulations were conducted at four sites with well-developed biocrusts to investigate the effects of biocrust removal on ammonia-oxidizing microbial communities. All sites were located approximately 30 m from each other. Two treatments were carried out at each study site: intact-biocrust and biocrust-removal treatments. The study sites were flat and, before treatment, exhibited an even distribution of well-developed biocrusts dominated by a cyanobacterial-lichen crust type without prior disturbance in the field . Eight 1.5 × 1.5-m plots were established at sites. Four plots with naturally occurring biocrust were left undisturbed, and the biocrust in the remaining four plots was gently removed with a shovel. The spacing between quadrangles was > 1 m to minimize marginal effects. To assess the abundance and community structure of ammonia-oxidizing microorganisms, biannual sampling was conducted in February (winter) and July (summer) of 2019, i.e., approximately 4 years after the biocrust removal treatment. Soil Samples and Environmental Variables Soil volumetric water content and temperature were measured with an EcH 2 O instrument (5TM, LI-COR, Lincoln, NE, USA) at 5 cm below the soil surface of each plot. To determine soil gravimetric water content, fresh soil samples were initially weighed and then dried at 105 °C for 48 h before being reweighed . Samples were collected on 20 July 2019—during the hot, dry period of summer—and on 10 February 2019—during late winter under a snowpack of approximately 18 cm, when environmental conditions had long remained constant because of snow cover. Soil cores with a ring stainless sampler (5 cm diameter, 20 cm height) were collected from each plot at every study site. Composite samples from different depths (0–2 cm, 2–5 cm, 5–10 cm, and 10–20 cm) were pooled separately within each plot using the crossing five-spot method. A total of 64 soil samples were obtained from sites (2 treatments × 4 replicates × 2 seasons × 4 depths). The soil samples were stored in a portable refrigerator and transported to the laboratory. Each sample was homogenized and subdivided into three parts for subsequent analyses. Portions for genomic DNA extraction and inorganic N determination were stored at − 80 °C and 4 °C, respectively, whereas the portion used for physicochemical characterization was air-dried and sieved through a mesh with a pore size of < 2 mm to remove coarse rock fragments. The following soil physicochemical properties were measured: soil total carbon (TC) and nitrogen (TN) content, NH 4 + _N concentration, NO 3 − _N concentration, pH, and soil electrical conductivity (EC). Soil pH and EC were determined using a 1:5 mixture of soil sample and deionized water. Soil total carbon content was quantified with a TOC analyzer (Multi N/C 3100, Analytik Jena, Germany). TN was measured by the CuSO 4 -Se powder diffusion method . Soil NH 4 + _N and NO 3 − _N concentrations were determined by shaking the samples with a 0.01 M CaCl 2 solution in a 5:1 solution/soil ratio for 1 h followed by colorimetric analysis on an Auto-Analyzer 3 (Seal AA3, Bran + Luebbe, Norderstedt, Germany). DNA Extraction, Quantitative PCR Analysis, and High-Throughput Sequencing The abundance of AOA and AOB amoA genes in desert soil was quantified using quantitative PCR (qPCR). Soil DNA was extracted from 0.75 g of soil samples utilizing a MoBio Powersoil DNA isolation kit (MoBio Laboratories, CA, USA). The purity and quality of the genomic DNA were evaluated through agarose gel electrophoresis on 1% agarose gels and by measuring the A260/280 ratio on a NanoDrop spectrophotometer (Thermo Fisher Scientific, MA, USA). The archaeal amoA gene region was amplified using primers Arch-amoAF/Arch-amoAR (Supplementary Table ) , and the bacterial amoA gene region was amplified with primers amoA-1F/amoA-2R (Supplementary Table ) . qPCR amplifications were performed on a CFX96 Optical Real-Time Detection system (Bio-Rad Laboratories, Hercules, CA, USA). Standard curves were generated by diluting linear plasmids of amoA genes amplified from archaeal or bacterial strains (Supplementary Fig. ). Real-time qPCR assays were carried out in 20-μL reaction volumes containing 10 μL of TB Green Premix Ex Taq (Takara Biotech, China), 1-μL of each primer, and 1.0-μL template DNA. Despite its inherent variability, qPCR enables comparative analysis of relative abundance across different soil samples. The amplification efficiency observed in this study ranged from 90 to 99%, with an R 2 value exceeding 0.99. Each qPCR experiment was independently repeated twice for each soil sample. The cycle threshold (Ct) method was used to determine initial template concentrations of AOA and AOB amoA genes; formula  from these Ct values were then utilized to convert and calculate copy numbers of target genes in desert soil: 1 [12pt]{minimal} 				 				  				  				  				 				 				 				 				$$= C V}{0.75 (1-)}$$ Copy numbers = X n × C × V 0.75 × 1 - SM In the equation, X represents the initial template concentration of AOA and AOB amoA genes; n denotes the amount of template DNA used (ng); C and V respectively stand for the original concentration (ng/μL) and volume (μL) of extracted 0.75 g soil DNA; SM refers to soil moisture content. We conducted sequencing of AOA amoA genes in top-layer soil samples from intact-biocrust and biocrust-removal treatments. To facilitate multiplexing, a 10-bp barcode unique to each sample was incorporated between the sequencing adapter and the 5′ end of the forward primers. The amplification of AOA amoA regions followed established protocols . PCR amplifications were carried out in 25-μL reaction volumes, comprising 12.5 μL of 2 × Taq Master Mix, 1-μL each of forward and reverse primers (10 μM), 2-μL template DNA (10 ng μL), and 8.5-μL ddH 2 O using the following thermal-cycling protocol: 95 °C for 3 min, followed by 32 cycles of 94 °C for 60 s, 55 °C for 60 s, and 72 °C for 60 s, with a final extension of 72 °C for 10 min. Two independent PCR amplifications were performed for each sample to minimize biases introduced during PCR reaction. Construction of PCR libraries was conducted using a NEXTFLEX Rapid DNA-Seq kit (Bioo Scientific, USA). Sequencing was performed on the Illumina MiSeq platform (Illumina, CA, USA) with 300-bp paired-end reads. Sequence Processing and Taxonomic Identification The generated sequences were utilized to identify the predominant AOA taxa in both biocrust-intact and biocrust-removed soils, thereby facilitating the detection of species interactions that could potentially influence ecosystem processes. Raw reads were assigned to different samples based on their barcodes and trimmed for sequence quality using Qiime v.1.8.0 software. Putative chimaeras were identified and removed with Flash v1.2.11 ( https://ccb.jhu.edu/software/FLASH/index.shtml ). The pyrosequencing data underwent screening with the following parameters: quality score > 20; sequence length > 200 and < 350; maximum homopolymers = 10; no ambiguous bases; and no mismatched bases in primers. Operational taxonomic units (OTUs) for the amoA gene were classified at a nucleotide sequence similarity cutoff of 97% using Uparse v7.1 ( http://drive5.com/uparse/ ). OTU representative sequences for the amoA gene were annotated using RDP classifier v2.2 ( http://sourceforge.net/ projects/rdp-classifier), with a confidence threshold of 0.7, against the fgr/amoA database (Release7.3 http://fungene.cme.msu.edu/ ). Re-sampling to an equal sequencing depth prior to downstream analysis yielded 9676 sequences per sample for subsequent analysis. The sample amoA gene sequences clustered into 47 AOA OTUs. The original sequence data files have been deposited in the National Center for Biotechnology Information Sequence Read Archive under accession number PRJNA1056783. Data Analysis The effects of biocrust treatment, soil depth, and season on the abundance of AOA and AOB amoA genes and soil physicochemical properties were assessed by using three-way analysis of variance (ANOVA) in R. The number of copies of AOA and AOB amoA genes in all soil samples was compared using paired T -test (Rstatix v.0.7.2). Significant differences in Shannon and Pielou indices of archaeal ammonia communities between biocrust-intact and biocrust-removed soils were analyzed by one-way ANOVA (Ggsignif v.0.6.4) . In cases where the normality assumption was violated, a Kruskal–Wallis test (Agricolae v.1.3–7)  was employed to determine statistically significant differences among the medians of three or more independent groups. Stepwise multivariate regression analysis and redundancy analysis (Vegan v.2.6–8)  were performed to identify key factors influencing the abundances of AOA and AOB amoA genes. Permutational multivariate analysis of variance (PERMANOVA) based on Bray–Curtis dissimilarities (999 permutations) was utilized to investigate whether beta diversity differed between dominant AOA communities under different biocrust treatments . PCoA plots (Ape v.5.8)  based on Bray–Curtis similarity matrices were generated using the R package ggplot2 (v 3.5.1) . Pairwise Wilcoxon signed-rank tests with Benjamini–Hochberg-adjusted P -values  were applied to assess differences in beta diversity between intact-biocrust and biocrust-removal treatments. The relevant data and R code for this manuscript have been deposited in a public GitHub repository ( https://github.com/liuxin5116/NDDAOM ). Phylogenetic Analysis of Archaeal amoA Genes The initial compilation of a reference database for archaeal amoA sequences involved selection of FunGene’s existing sequences along with additional ones obtained through BLASTN searches using representative samples from prior studies as queries . More than 99% of the total collection of 35 reference sequences resulted from PCR or high-throughput sequencing amplifying gene fragments ranging between 593 and 651 bp . To ensure data quality, sequence fragments ≥ 272 bp underwent trimming via the MEGA v7.0 software to eliminate partial codons at both ends . Manual inspection of alignments excluded non-specifically annotated archaeal amoA genes and those containing previously undetected frameshifts. The final curated database comprised 82 unique sequences representing 82 OTUs. Phylogenetic trees were constructed utilizing neighbor-joining under the K2P model in IQ-TREE v1.5.380. Ten independent tree inferences were conducted, with selection made based on stability following Minh et al.’s methodology . Ultrafast bootstrap37 and SH-aLRT38 support values derived from the IQ-TREE analysis relied on 1000 replicates according to Minh et al. . Community Network Co-occurrence Models We developed network co-occurrence models based on maximal information coefficient (MIC). MICs were calculated using the Minerva package (v 1.5.10)  in R to evaluate both linear and non-linear relationships between paired variables. Subsequently, we improved the network analysis section by incorporating the Spearman correlation results. The valid or robust co-occurrence connections of amoA gene with MICs of > 0.6 and statistically significant ( P value < 0.01) were identified; the Spearman correlation was screened for MIC values that were not equal to 0 and |R-corr|> 0.6 using the Hmisc package(v 5.1–3)  To describe the topology of the resulting network, we assessed the following measures: average node connectivity, average path length, diameter, cumulative degree distribution, clustering coefficient, and modularity. The vegan package (v 2.4.6.26)  is also used to calculate the correlation of OTU in each submodule with the environmental factors. All statistical analyses were carried out in the R environment ( http://www.r-project.org ) using ggClusterNet  and igraph packages (v.2.0.3) . The resulting network was compared against its randomized version using igraph to assess nonrandom patterns. Structural attributes such as clustering coefficient and characteristic path length in this network were compared with those in a random network having equal nodes and edges.   The experiment was conducted in the Gurbantunggut Desert (44.62° N, 88.26° E, 570 m), where well-developed biocrusts are distributed across inter-dune areas . The mean annual temperature in the study area is 6.6 ± 0.8 °C. Annual precipitation ranges from 70 to180 mm, with a mean annual precipitation of 129 mm. Summers in the desert are hot and dry, with potential annual evaporation exceeding 2000 mm. Condensation occurs 325 days a year, with an average daily input of 0.077 mm. During winter, however, approximately 20 cm of snow covers the soil surface, thus providing ample water during the thaw season. Dominant shrub species in this desert ecosystem include Haloxylon persicum Bunge ex Boiss. & Buhse and Haloxylon ammodendron (C. A. Mey) Bunge ex Fenzl. In October 2015, biocrust-removal manipulations were conducted at four sites with well-developed biocrusts to investigate the effects of biocrust removal on ammonia-oxidizing microbial communities. All sites were located approximately 30 m from each other. Two treatments were carried out at each study site: intact-biocrust and biocrust-removal treatments. The study sites were flat and, before treatment, exhibited an even distribution of well-developed biocrusts dominated by a cyanobacterial-lichen crust type without prior disturbance in the field . Eight 1.5 × 1.5-m plots were established at sites. Four plots with naturally occurring biocrust were left undisturbed, and the biocrust in the remaining four plots was gently removed with a shovel. The spacing between quadrangles was > 1 m to minimize marginal effects. To assess the abundance and community structure of ammonia-oxidizing microorganisms, biannual sampling was conducted in February (winter) and July (summer) of 2019, i.e., approximately 4 years after the biocrust removal treatment.   Soil volumetric water content and temperature were measured with an EcH 2 O instrument (5TM, LI-COR, Lincoln, NE, USA) at 5 cm below the soil surface of each plot. To determine soil gravimetric water content, fresh soil samples were initially weighed and then dried at 105 °C for 48 h before being reweighed . Samples were collected on 20 July 2019—during the hot, dry period of summer—and on 10 February 2019—during late winter under a snowpack of approximately 18 cm, when environmental conditions had long remained constant because of snow cover. Soil cores with a ring stainless sampler (5 cm diameter, 20 cm height) were collected from each plot at every study site. Composite samples from different depths (0–2 cm, 2–5 cm, 5–10 cm, and 10–20 cm) were pooled separately within each plot using the crossing five-spot method. A total of 64 soil samples were obtained from sites (2 treatments × 4 replicates × 2 seasons × 4 depths). The soil samples were stored in a portable refrigerator and transported to the laboratory. Each sample was homogenized and subdivided into three parts for subsequent analyses. Portions for genomic DNA extraction and inorganic N determination were stored at − 80 °C and 4 °C, respectively, whereas the portion used for physicochemical characterization was air-dried and sieved through a mesh with a pore size of < 2 mm to remove coarse rock fragments. The following soil physicochemical properties were measured: soil total carbon (TC) and nitrogen (TN) content, NH 4 + _N concentration, NO 3 − _N concentration, pH, and soil electrical conductivity (EC). Soil pH and EC were determined using a 1:5 mixture of soil sample and deionized water. Soil total carbon content was quantified with a TOC analyzer (Multi N/C 3100, Analytik Jena, Germany). TN was measured by the CuSO 4 -Se powder diffusion method . Soil NH 4 + _N and NO 3 − _N concentrations were determined by shaking the samples with a 0.01 M CaCl 2 solution in a 5:1 solution/soil ratio for 1 h followed by colorimetric analysis on an Auto-Analyzer 3 (Seal AA3, Bran + Luebbe, Norderstedt, Germany).   The abundance of AOA and AOB amoA genes in desert soil was quantified using quantitative PCR (qPCR). Soil DNA was extracted from 0.75 g of soil samples utilizing a MoBio Powersoil DNA isolation kit (MoBio Laboratories, CA, USA). The purity and quality of the genomic DNA were evaluated through agarose gel electrophoresis on 1% agarose gels and by measuring the A260/280 ratio on a NanoDrop spectrophotometer (Thermo Fisher Scientific, MA, USA). The archaeal amoA gene region was amplified using primers Arch-amoAF/Arch-amoAR (Supplementary Table ) , and the bacterial amoA gene region was amplified with primers amoA-1F/amoA-2R (Supplementary Table ) . qPCR amplifications were performed on a CFX96 Optical Real-Time Detection system (Bio-Rad Laboratories, Hercules, CA, USA). Standard curves were generated by diluting linear plasmids of amoA genes amplified from archaeal or bacterial strains (Supplementary Fig. ). Real-time qPCR assays were carried out in 20-μL reaction volumes containing 10 μL of TB Green Premix Ex Taq (Takara Biotech, China), 1-μL of each primer, and 1.0-μL template DNA. Despite its inherent variability, qPCR enables comparative analysis of relative abundance across different soil samples. The amplification efficiency observed in this study ranged from 90 to 99%, with an R 2 value exceeding 0.99. Each qPCR experiment was independently repeated twice for each soil sample. The cycle threshold (Ct) method was used to determine initial template concentrations of AOA and AOB amoA genes; formula  from these Ct values were then utilized to convert and calculate copy numbers of target genes in desert soil: 1 [12pt]{minimal} 				 				  				  				  				 				 				 				 				$$= C V}{0.75 (1-)}$$ Copy numbers = X n × C × V 0.75 × 1 - SM In the equation, X represents the initial template concentration of AOA and AOB amoA genes; n denotes the amount of template DNA used (ng); C and V respectively stand for the original concentration (ng/μL) and volume (μL) of extracted 0.75 g soil DNA; SM refers to soil moisture content. We conducted sequencing of AOA amoA genes in top-layer soil samples from intact-biocrust and biocrust-removal treatments. To facilitate multiplexing, a 10-bp barcode unique to each sample was incorporated between the sequencing adapter and the 5′ end of the forward primers. The amplification of AOA amoA regions followed established protocols . PCR amplifications were carried out in 25-μL reaction volumes, comprising 12.5 μL of 2 × Taq Master Mix, 1-μL each of forward and reverse primers (10 μM), 2-μL template DNA (10 ng μL), and 8.5-μL ddH 2 O using the following thermal-cycling protocol: 95 °C for 3 min, followed by 32 cycles of 94 °C for 60 s, 55 °C for 60 s, and 72 °C for 60 s, with a final extension of 72 °C for 10 min. Two independent PCR amplifications were performed for each sample to minimize biases introduced during PCR reaction. Construction of PCR libraries was conducted using a NEXTFLEX Rapid DNA-Seq kit (Bioo Scientific, USA). Sequencing was performed on the Illumina MiSeq platform (Illumina, CA, USA) with 300-bp paired-end reads.   The generated sequences were utilized to identify the predominant AOA taxa in both biocrust-intact and biocrust-removed soils, thereby facilitating the detection of species interactions that could potentially influence ecosystem processes. Raw reads were assigned to different samples based on their barcodes and trimmed for sequence quality using Qiime v.1.8.0 software. Putative chimaeras were identified and removed with Flash v1.2.11 ( https://ccb.jhu.edu/software/FLASH/index.shtml ). The pyrosequencing data underwent screening with the following parameters: quality score > 20; sequence length > 200 and < 350; maximum homopolymers = 10; no ambiguous bases; and no mismatched bases in primers. Operational taxonomic units (OTUs) for the amoA gene were classified at a nucleotide sequence similarity cutoff of 97% using Uparse v7.1 ( http://drive5.com/uparse/ ). OTU representative sequences for the amoA gene were annotated using RDP classifier v2.2 ( http://sourceforge.net/ projects/rdp-classifier), with a confidence threshold of 0.7, against the fgr/amoA database (Release7.3 http://fungene.cme.msu.edu/ ). Re-sampling to an equal sequencing depth prior to downstream analysis yielded 9676 sequences per sample for subsequent analysis. The sample amoA gene sequences clustered into 47 AOA OTUs. The original sequence data files have been deposited in the National Center for Biotechnology Information Sequence Read Archive under accession number PRJNA1056783.   The effects of biocrust treatment, soil depth, and season on the abundance of AOA and AOB amoA genes and soil physicochemical properties were assessed by using three-way analysis of variance (ANOVA) in R. The number of copies of AOA and AOB amoA genes in all soil samples was compared using paired T -test (Rstatix v.0.7.2). Significant differences in Shannon and Pielou indices of archaeal ammonia communities between biocrust-intact and biocrust-removed soils were analyzed by one-way ANOVA (Ggsignif v.0.6.4) . In cases where the normality assumption was violated, a Kruskal–Wallis test (Agricolae v.1.3–7)  was employed to determine statistically significant differences among the medians of three or more independent groups. Stepwise multivariate regression analysis and redundancy analysis (Vegan v.2.6–8)  were performed to identify key factors influencing the abundances of AOA and AOB amoA genes. Permutational multivariate analysis of variance (PERMANOVA) based on Bray–Curtis dissimilarities (999 permutations) was utilized to investigate whether beta diversity differed between dominant AOA communities under different biocrust treatments . PCoA plots (Ape v.5.8)  based on Bray–Curtis similarity matrices were generated using the R package ggplot2 (v 3.5.1) . Pairwise Wilcoxon signed-rank tests with Benjamini–Hochberg-adjusted P -values  were applied to assess differences in beta diversity between intact-biocrust and biocrust-removal treatments. The relevant data and R code for this manuscript have been deposited in a public GitHub repository ( https://github.com/liuxin5116/NDDAOM ).   amoA Genes The initial compilation of a reference database for archaeal amoA sequences involved selection of FunGene’s existing sequences along with additional ones obtained through BLASTN searches using representative samples from prior studies as queries . More than 99% of the total collection of 35 reference sequences resulted from PCR or high-throughput sequencing amplifying gene fragments ranging between 593 and 651 bp . To ensure data quality, sequence fragments ≥ 272 bp underwent trimming via the MEGA v7.0 software to eliminate partial codons at both ends . Manual inspection of alignments excluded non-specifically annotated archaeal amoA genes and those containing previously undetected frameshifts. The final curated database comprised 82 unique sequences representing 82 OTUs. Phylogenetic trees were constructed utilizing neighbor-joining under the K2P model in IQ-TREE v1.5.380. Ten independent tree inferences were conducted, with selection made based on stability following Minh et al.’s methodology . Ultrafast bootstrap37 and SH-aLRT38 support values derived from the IQ-TREE analysis relied on 1000 replicates according to Minh et al. .   We developed network co-occurrence models based on maximal information coefficient (MIC). MICs were calculated using the Minerva package (v 1.5.10)  in R to evaluate both linear and non-linear relationships between paired variables. Subsequently, we improved the network analysis section by incorporating the Spearman correlation results. The valid or robust co-occurrence connections of amoA gene with MICs of > 0.6 and statistically significant ( P value < 0.01) were identified; the Spearman correlation was screened for MIC values that were not equal to 0 and |R-corr|> 0.6 using the Hmisc package(v 5.1–3)  To describe the topology of the resulting network, we assessed the following measures: average node connectivity, average path length, diameter, cumulative degree distribution, clustering coefficient, and modularity. The vegan package (v 2.4.6.26)  is also used to calculate the correlation of OTU in each submodule with the environmental factors. All statistical analyses were carried out in the R environment ( http://www.r-project.org ) using ggClusterNet  and igraph packages (v.2.0.3) . The resulting network was compared against its randomized version using igraph to assess nonrandom patterns. Structural attributes such as clustering coefficient and characteristic path length in this network were compared with those in a random network having equal nodes and edges.   AOA and AOB Abundances in Desert Soil AOA were significantly more abundant than AOB in both biocrust-intact and biocrust-removed soils. The number of archaeal amoA gene copies ranged from 7 × 10 3 –9 × 10 4 copies g −1 dry soil, which was markedly higher than that of the bacterial amoA gene (1 × 10 2 –3 × 10 4 copies g −1 dry soil) ( P < 0.001, n = 64; Fig. and Supplementary Table ). AOA were more dominant than AOB in biocrust-intact soils at depths of 2–20 cm, with the highest AOA to AOB ratio (6549-fold) observed in 5–10-cm soils during summer. In both winter and summer, AOA abundance decreased owing to the presence of biocrusts. In biocrust-intact soil, the abundance of AOA in the topmost layer (7 × 10 3 –4 × 10 4 copies g −1 dry soil) was lower than that at depths of 2–20 cm (1 × 10 4 –9 × 10 4 copies g −1 dry soil) ( P < 0.05). Removal of biocrusts significantly increased the abundance of both AOA and AOB in topsoil (0–2 cm). Furthermore, bacterial amoA gene abundance significantly increased between summer (< 500 copies g −1 ) and winter (800–13,511 copies g −1 dry soil), which suggests that N-rich conditions during winter promoted the abundance of AOB. AOB abundance at all soil depths following biocrust removal was markedly increased compared with that of soil with undisturbed biocrust during both winter and summer ( P < 0.05; Fig. and Supplementary Table ). Stepwise multivariate regression and redundancy analysis revealed that soil temperature ( P < 0.001), NO 3 − _N ( P < 0.001), and total carbon content ( P < 0.001) were the most relevant factors affecting the abundances of both AOA and AOB, respectively, accounting for 29.87%, 16.08%, and 8.09% of the total conditioned effect. Among these factors, AOA was positively correlated with soil temperature, while AOB abundance was negatively influenced by NO 3 − _N, total carbon, and soil temperature (Fig. and Supplementary Table ). Community Structure of Dominant AOA The removal of biocrust significantly enhanced both the diversity and evenness of AOA communities ( P < 0.001; Fig. a). The manipulation of biocrust had a substantial influence on the composition of AOA communities, as demonstrated by PCoA results illustrating clear differentiation between communities subjected to biocrust removal and those associated with undisturbed biocrust (Fig. b). Both undisturbed-biocrust soil and disturbed soils resulting from biocrust removal displayed alterations in AOA community structure; however, the seasonal AOA community variation retained a signature of the biocrust treatment from which they were derived while exhibiting spatial separation based on seasonality or soil conditions (Fig. b). PERMANOVA analysis corroborated these observations suggesting that changes induced by disturbances due to interference with biocrust had a greater impact on AOA community ( R 2 = 0.69, F = 29.08, df = 1, P = 0.001) dynamics than seasonal effects did ( R 2 = 0.02, F = 0.76, df = 1, P > 0.05). The AOA identified in this study, consisting of AOA taxa within Nitrososphaera and the Nitrososphaera sister cluster , exhibited a significant response to biocrust removal (Figs. and ). For example, the relative abundance of OTU42 decreased with biocrust removal (from 79.32% ± 0.06 in biocrust-intact soils to 48.65% ± 0.07 in biocrust-removed soils; Supplementary Fig. ). Conversely, the abundance of OTU26 was notably enriched in biocrust-removed soils (relative abundance of 5.61% ± 0.03 in intact biocrust and 26% ± 0.08 in biocrust-removed soils; P < 0.001; Supplementary Fig. ). In the phylogenetic tree, amoA genes were primarily divided into two basal lineages: 18 of the defined OTUs belonged to the Nitrososphaera cluster, while 2 were assigned to the Nitrososphaera sister cluster, with the remaining 27 designated as unclassified OTUs. The unclassified and Nitrososphaera sister cluster AOA OTUs tended to inhabit soils with intact biocrusts, whereas the Nitrososphaera cluster had a broader distribution range. In both biocrust-intact and biocrust-removed soils, most AOA diversity was associated with the Nitrososphaera cluster. Two OTUs belonging to the Nitrososphaera sister cluster, however, were exclusive to biocrust-intact soils (Fig. and Supplementary Fig. ). Responses of AOA Species Interactions to Biocrust Removal On the basis of the above clustering patterns, four co-occurrence networks were constructed based on significant correlations among AOA OTUs (correlation coefficient for MIC values r > 0.6, P < 0.01). As shown in Fig. and Table , network characteristics varied among groups. Biocrust removal led to less complex but more interconnected AOA networks. The number of nodes, as well as other topological features such as average degree and modularity, was lower in the AOA networks derived from the biocrust-removal treatment; however, both winter and summer community networks displayed lower average path length. Notably, the biocrust-removal summer-sampled AOA network had the low average degree (2.284) and lowest average path length (2.284) but the highest clustering coefficient (0.460). These findings suggest that drastic changes during winter caused by biocrust removal exacerbate dynamics among AOA species interactions. Furthermore, co-occurrence networks associated with biocrust removal had fewer positive correlations compared with intact biocrusts (76.47–77.78% vs. 78.95–81.82%, respectively).   AOA were significantly more abundant than AOB in both biocrust-intact and biocrust-removed soils. The number of archaeal amoA gene copies ranged from 7 × 10 3 –9 × 10 4 copies g −1 dry soil, which was markedly higher than that of the bacterial amoA gene (1 × 10 2 –3 × 10 4 copies g −1 dry soil) ( P < 0.001, n = 64; Fig. and Supplementary Table ). AOA were more dominant than AOB in biocrust-intact soils at depths of 2–20 cm, with the highest AOA to AOB ratio (6549-fold) observed in 5–10-cm soils during summer. In both winter and summer, AOA abundance decreased owing to the presence of biocrusts. In biocrust-intact soil, the abundance of AOA in the topmost layer (7 × 10 3 –4 × 10 4 copies g −1 dry soil) was lower than that at depths of 2–20 cm (1 × 10 4 –9 × 10 4 copies g −1 dry soil) ( P < 0.05). Removal of biocrusts significantly increased the abundance of both AOA and AOB in topsoil (0–2 cm). Furthermore, bacterial amoA gene abundance significantly increased between summer (< 500 copies g −1 ) and winter (800–13,511 copies g −1 dry soil), which suggests that N-rich conditions during winter promoted the abundance of AOB. AOB abundance at all soil depths following biocrust removal was markedly increased compared with that of soil with undisturbed biocrust during both winter and summer ( P < 0.05; Fig. and Supplementary Table ). Stepwise multivariate regression and redundancy analysis revealed that soil temperature ( P < 0.001), NO 3 − _N ( P < 0.001), and total carbon content ( P < 0.001) were the most relevant factors affecting the abundances of both AOA and AOB, respectively, accounting for 29.87%, 16.08%, and 8.09% of the total conditioned effect. Among these factors, AOA was positively correlated with soil temperature, while AOB abundance was negatively influenced by NO 3 − _N, total carbon, and soil temperature (Fig. and Supplementary Table ).   The removal of biocrust significantly enhanced both the diversity and evenness of AOA communities ( P < 0.001; Fig. a). The manipulation of biocrust had a substantial influence on the composition of AOA communities, as demonstrated by PCoA results illustrating clear differentiation between communities subjected to biocrust removal and those associated with undisturbed biocrust (Fig. b). Both undisturbed-biocrust soil and disturbed soils resulting from biocrust removal displayed alterations in AOA community structure; however, the seasonal AOA community variation retained a signature of the biocrust treatment from which they were derived while exhibiting spatial separation based on seasonality or soil conditions (Fig. b). PERMANOVA analysis corroborated these observations suggesting that changes induced by disturbances due to interference with biocrust had a greater impact on AOA community ( R 2 = 0.69, F = 29.08, df = 1, P = 0.001) dynamics than seasonal effects did ( R 2 = 0.02, F = 0.76, df = 1, P > 0.05). The AOA identified in this study, consisting of AOA taxa within Nitrososphaera and the Nitrososphaera sister cluster , exhibited a significant response to biocrust removal (Figs. and ). For example, the relative abundance of OTU42 decreased with biocrust removal (from 79.32% ± 0.06 in biocrust-intact soils to 48.65% ± 0.07 in biocrust-removed soils; Supplementary Fig. ). Conversely, the abundance of OTU26 was notably enriched in biocrust-removed soils (relative abundance of 5.61% ± 0.03 in intact biocrust and 26% ± 0.08 in biocrust-removed soils; P < 0.001; Supplementary Fig. ). In the phylogenetic tree, amoA genes were primarily divided into two basal lineages: 18 of the defined OTUs belonged to the Nitrososphaera cluster, while 2 were assigned to the Nitrososphaera sister cluster, with the remaining 27 designated as unclassified OTUs. The unclassified and Nitrososphaera sister cluster AOA OTUs tended to inhabit soils with intact biocrusts, whereas the Nitrososphaera cluster had a broader distribution range. In both biocrust-intact and biocrust-removed soils, most AOA diversity was associated with the Nitrososphaera cluster. Two OTUs belonging to the Nitrososphaera sister cluster, however, were exclusive to biocrust-intact soils (Fig. and Supplementary Fig. ).   On the basis of the above clustering patterns, four co-occurrence networks were constructed based on significant correlations among AOA OTUs (correlation coefficient for MIC values r > 0.6, P < 0.01). As shown in Fig. and Table , network characteristics varied among groups. Biocrust removal led to less complex but more interconnected AOA networks. The number of nodes, as well as other topological features such as average degree and modularity, was lower in the AOA networks derived from the biocrust-removal treatment; however, both winter and summer community networks displayed lower average path length. Notably, the biocrust-removal summer-sampled AOA network had the low average degree (2.284) and lowest average path length (2.284) but the highest clustering coefficient (0.460). These findings suggest that drastic changes during winter caused by biocrust removal exacerbate dynamics among AOA species interactions. Furthermore, co-occurrence networks associated with biocrust removal had fewer positive correlations compared with intact biocrusts (76.47–77.78% vs. 78.95–81.82%, respectively).   Biocrusts have a significant impact on nitrogen-limited desert ecosystems by effectively regulating reactive nitrogen pools, surpassing the overall community’s influence. Our study has revealed that biocrust removal notably increases AOA and AOB abundance, leading to a less intricate network with higher cluster coefficients. These findings suggest that biocrust interference, particularly during winter, is associated with more dynamic communities involved in ammonia oxidation. Two lines of evidence indicate that biocrusts have an impoverishing effect on AOA abundance. First, AOA abundance was lower in the top layer of biocrust-intact soil (7 × 10 3 –4 × 10 4 copies g −1 dry soil; C/N = 14–18) compared with that in deeper (2–20 cm) soils (1 × 10 4 –9 × 10 4 copies g −1 dry soil; C/N = 16–74) ( P < 0.05). Second, removal of biocrusts resulted in increased AOA abundance (2 × 10 4 –7 × 10 4 copies g −1 dry soil; C/N = 23–54) ( P < 0.01). We propose that the altered distribution of AOA and AOB due to interference disrupts the fertile island effect created by biocrusts in this region . For instance, Ferrenberg et al.  observed that trampling caused a shift from lichen or dark cyanobacterial crusts to a light cyanobacterial crust. The nitrogen fixation rate of dark cyanobacterial and/or lichen crusts is generally three to four times higher than that of light cyanobacterial crusts . Because biocrust N fixation and ammonia oxidation processes occur simultaneously, various forms of N, including nitrate (NO 3 − _N), ammonium ion (NH 4 + _N), amides, peptides, and free amino acids, are restrictively released upon biocrust removal , a phenomenon that may further affect the niches occupied by ammonia oxidizers. In our previous study at the same sites within the Gurbantunggut Desert, we observed a significant reduction in potential nitrification upon removal of biocrusts . Our results collectively demonstrate that biocrusts play a crucial role in shaping the assemblage of ammoxidation communities and in driving nitrification processes in desert ecosystems. Importantly, our results highlight the significant interaction between biocrusts and environmental factors (e.g., soil temperature, TC, and NO 3 − _N content) (Fig. ), which strongly influences nitrification dynamics. Our findings suggest that ammonia-oxidizing archaea are predominant in desert ecosystems, indicating their potential significant role in nitrification processes within desert soils (Fig. ). More broadly, AOA may serve as a nitrogen source derived from organic matter mineralization for desert plants. Particularly, biocrust-intact soils may promote N accumulation in response to dew events during summer in the Gurbantunggut Desert . Adapted to low-nutrient environments, AOA, as chemoautotrophs, primarily inhabit subsurface layers of biocrusts and exhibit higher AOA/AOB ratios during summer. The affinity of both AOB and AOA towards ammonia and oxygen levels plays a crucial role in niche differentiation among these organisms. Notably, the remarkably high specific affinity for reduced nitrogen exhibited by AOA suggests that Nitrososphaera -like ammonia-oxidizing archaea can effectively compete with heterotrophic AOB. Furthermore, ammonia-oxidizing Thaumarchaeota utilize a modified version of the 3HP/4HB cycle originally described in Crenarchaeota for CO 2 fixation—the most highly energy-efficient pathway discovered thus far . Collectively, these characteristics confer upon AOA a competitive advantage over AOB specifically within desert environments. Although the harsher soil conditions in summer were positively correlated with AOA abundance, our findings demonstrate that stable snow cover, coupled with reduced N limitation in winter, enhanced the abundance of AOB. The pronounced seasonal abundance patterns of AOA and AOB observed in the desert can be attributed to elevated soil temperatures promoting a higher number of AOA amoA gene copies and a higher soil temperature and NO 3 − _N content, in turn leading to decreased AOB amoA gene abundance (Fig. ). In the Gurbantunggut Desert, situated in the hinterlands of Central Asia, seasonal snowfall plays a crucial role as a water source supporting short-lived vegetation communities. We propose that winter snow cover alleviates water and nitrogen substrate limitations, thereby promoting, to some extent, an increase in AOB abundance . Biocrust removal significantly increased AOB abundance in both winter and summer in this study, possibly by eliminating the competition between AOB and biocrust communities for ammonia substates following crust disturbance . Although biocrust removal reduces the nutrient content of surface soils in the Gurbantunggut Desert compared with subcutaneous soil removal (5–20 cm), the topsoil of biocrust-removed soils is rich in NH 4 + -N, which may rapidly enhance AOB abundance through moisture coupling during winter. In summer, Central Asian deserts are typically microbially dormant with low nutrient availability including accessible nitrogen; we found that under these conditions, AOA have a competitive advantage over AOB in regard to resources as well as stronger resistance to biocrust interference . Importantly, biocrust removal had more significant effects on both AOA and AOB abundances during the winter season, which further highlights the indispensable regulatory role of these organisms in desert ecosystems . Moreover, considering the participation of biocrusts in carbon cycle during winters within desert ecosystems, the idea that ammonia-oxidizing communities, such as those involved in methane oxidation, may correlate with specific carbon functional groups in desert soil is plausible . This observation prompts us to reassess the ecological significance of desert ammonia-oxidizing microorganisms aside from their involvement in nitrification. As primarily reflected by the reduced number of nodes, average degree, and associated edges of the network and a higher clustering coefficient (Table ), our findings suggest that biocrust removal results in less complex, more unstable AOA networks in temperate desert soil. Previous studies have shown that a higher clustering coefficient corresponds to a more dynamic and active community . Evidence exists that external disturbances, such as nitrogen deposition and drought, weaken species interconnectedness in biocrusts . Our study reveals that physical disturbances caused by biocrust removal contribute to a decreased interconnectedness among species. Conversely, biocrust removal results in increased clustering coefficients in AOA networks during both summer and winter seasons, indicating enhanced dynamics. This finding implies that biocrust removal does not promote functional synergy among AOA species . The observed less complex but more dynamic interconnectedness can be attributed to multiple non-mutually exclusive factors. Firstly, disruption of biocrusts altered soil conditions directly impacting AOA distributions and interactions through changes in moisture level, substrate availability, and oxygen content. Secondly, long-term interference disrupted functional redundancy within the original intact-crust AOA network, which exhibited lower connectivity but a longer average path length. This reduced functional redundancy may strengthen reciprocity among species involved in AOA network dynamics following disturbance events. Thirdly, interference with biocrusts compelled N-limited systems to exhibit greater gain in ammonia oxidation efficiency for an equivalent amount of transformed N compared with that in less N-limited intact biocrusts . Within AOA networks associated with biocrust disturbance, negative correlations/competition appeared to prevail. We hypothesize that competition enhances the adaptability of the AOA network to biocrust disturbance. Furthermore, competition may allow dominant AOA OTUs to outcompete non-dominant ones, as evidenced by significant differences in hub nodes between intact-biocrust and biocrust-removed networks. These findings suggest that perturbations in biocrusts are linked to altered AOA communities, which results in auto-adaption of the AOA assemblage to the disturbed environment. To further investigate the response characteristics and ecological adaptation strategies of dominant AOA to biocrust disturbance, we assessed AOA community metrics in both biocrust-intact and biocrust-removed soils. We found that biocrust removal increased AOA diversity and that AOA community composition was sensitive to crust disturbance (Figs. and ). Phylogenetic analysis revealed that the 18 most abundant OTUs, all belonging to the Nitrososphaera cluster, were widely distributed in both intact and disturbed biocrust soils. Additionally, two OTUs from the Nitrososphaera sister cluster were exclusively found in crusted soil, suggesting potential specific ecological associations between individual AOA species and other members of the biocrust community. As judged by Bray–Curtis dissimilarities, we observed that biocrusts significantly reduced AOA diffusion. The biocrust-imposed limitation on AOA diversity and diffusion may be attributed to niche constraints, such as differences in oxygen availability and nutrient levels or balance constraints during crust formation, as well as their functional associations with other community groups . Nevertheless, removal of biocrusts substantially alters AOA community structure and potentially exerts a greater impact on overall functioning than solely affecting nitrification processes alone (Fig. ; Table ).   Our observations revealed a dominance of AOA as the primary ammonia-oxidizing microorganisms in the Gurbantunggut Desert soil, exhibiting a significantly higher abundance compared to AOB. Significant differences were observed in the abundance of ammonia-oxidizers between biocrust-covered and biocrusts-removed desert soils from winter to summer. Notably, the abundance of AOA was significantly higher than that of AOB in summer; however, removal of biocrusts led to a substantial increase in the abundance of AOB in both winter and summer. Phylogenetic analysis unveiled Nitrososphaera cluster members as prevailing constitutes within the archaeal ammonia-oxidizing community responsible for ammonia oxidation; nevertheless, disruption caused by biocrust removal exerted notable influence on their composition and species interactions. The AOA network associated with intact biocrusts exhibited a higher average degree, number of edges, and modularity in both winter and summer compared with those related to biocrust removal; however, the clustering coefficient was higher for the AOA network associated with biocrust removal. This result suggests that interference to biocrusts during winter, when water and nutrient limitations on ammonia oxidation are largely alleviated, is linked to more dynamic ammonia-oxidizing communities. Furthermore, our findings indicate that biocrusts primarily interacted with soil temperature, TC, and NO 3 − _N to influence differential distribution patterns between AOA and AOB based on their efficiency in utilizing ammonia. Therefore, these discoveries further support the crucial role played by biocrusts in nitrogen fixation as well as their regulatory function in controlling ammonia oxidation processes in desert soil, particularly during winter—a critical period for nitrogen transformation. These findings may have broader implications for ecological processes involving other microbial communities and higher ephemeral plant lamella using snowmelt water in deserts.   Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 2190 KB)   