Reduced microbial stability in the active layer is associated with carbon loss under alpine permafrost degradation

Significance

Permafrost degradation may induce soil carbon (C) loss, critical for global C cycling, and be mediated by microbes. Despite larger C stored within the active layer of permafrost regions, which are more affected by warming, and the critical roles of Qinghai–Tibet Plateau in global C cycling, most previous studies focused on the permafrost layer and in high-latitude areas. Here, we demonstrate in situ that permafrost degradation alters the diversity and potentially decreases the stability of active layer microbial communities. Additionally, these changes are associated with soil C loss and potentially a positive C feedback. This study provides insights into microbial-mediated mechanisms responsible for C loss within the active layer in degraded permafrost, aiding in the modeling of C emission under future scenarios.

Abstract

Permafrost degradation may induce soil carbon (C) loss, critical for global C cycling, and be mediated by microbes. Despite larger C stored within the active layer of permafrost regions, which are more affected by warming, and the critical roles of Qinghai-Tibet Plateau in C cycling, most previous studies focused on the permafrost layer and in high-latitude areas. We demonstrate in situ that permafrost degradation alters the diversity and potentially decreases the stability of active layer microbial communities. These changes are associated with soil C loss and potentially a positive C feedback. This study provides insights into microbial-mediated mechanisms responsible for C loss within the active layer in degraded permafrost, aiding in the modeling of C emission under future scenarios.

As a major soil carbon (C) reservoir, permafrost stores ∼50% of the global C pool, although it occupies only 24% of the Earth’s land area (1–3). In recent decades, climate warming has triggered extensive permafrost degradation (4, 5), characterized by increases in soil temperature, permafrost layer (permanently frozen layer) thaw, and an increase in the thickness of the seasonally unfrozen active layer (6) (SI Appendix, Fig. S1). Past studies have shown that permafrost degradation causes major changes in aboveground vegetation characteristics (4) and belowground soil properties (7), as well as microbial community composition (8) and function (9, 10) and microbially mediated biogeochemical transformations (11), thereby threatening the stability of permafrost ecosystems (12) and their capacity to store C (9, 13). Importantly, under permafrost degradation, soil organic matter (SOM) may become more accessible to microbial decomposition (14), possibly triggering a positive C feedback to climate change (15, 16). Despite this knowledge, our understanding of microbial responses to permafrost degradation and how they link to ecosystem C cycling remains limited, which hinders our ability to predict how permafrost ecosystems will respond to future climate warming.

Permafrost supports a diversity of microbial life, especially in the active layer where more abundant and active microbial communities are harbored, compared to those in the permanently frozen deeper layers (17, 18). Past studies have indicated that microbial community composition (8, 9), diversity (17, 19), and activity (14, 18, 20) of both the active and frozen layers in permafrost regions are strongly influenced by permafrost thaw or increases in temperature due to climate changes. However, the majority of these studies have been performed in high-latitude permafrost regions, such as the Alaskan Arctic (8), Canadian High Arctic (18), Antarctica (19), Greenland (21), and Siberia (22), rather than in high-altitude permafrost regions. The Qinghai–Tibet Plateau (QTP) has the largest extent of high-altitude mountain permafrost in the world (23) that encompasses 1.06 × 10⁶ km² and stores ∼74% of the soil C present in the QTP region (13). Furthermore, QTP permafrost is experiencing warming at a level that is approximately double the global average (24), with mean annual soil temperatures (MATs) and active layer thickness (ALT) increasing significantly since the mid-1950s (6), leading to a decrease in alpine permafrost of 23.8% (24, 25). Considering the vital role of the QTP as Asia’s water tower and regulator for regional and even global climate (26), such permafrost degradation poses a major risk to future climate change on the QTP and beyond.

Soils in the QTP permafrost generally have different characteristics from those of high-latitude ecosystems. They are relatively ice poor, with lower SOM (27), and subject to higher evapotranspiration and solar radiation (6). Soils with these unique characteristics will likely harbor distinct microbial communities that respond differently to permafrost degradation from those of high-latitude soils. Moreover, according to projections based on Earth system models, the active layer of the QTP permafrost will lose 5 and 10% of soil C by 2100 under moderate and high representative scenarios (RCP4.5 and RCP8.5), respectively (13). In fact, the QTP soil C pool in the top layer (0 to 1 m), which is more active and directly influenced by climate warming, is even greater than that in the deeper layers (28, 29). Despite this, virtually nothing is known about how the active layer microbial community composition in these high-altitude ecosystems respond to permafrost degradation or what the implications of these changes are for ecosystem C storage.

The capacity of soil microbial communities to remain stable or recover from disturbances depends on their diversity and the complex interactions among community members (30–32). Network-based approaches have been increasingly used to explore interconnections among microbial community members, investigate relationships with their surrounding environment (33–35), and interrogate their stability based on topological properties (e.g., complexity, centrality, and modularity under disturbance) (36, 37). Based on theoretical expectations, increased node connectivity (38), centrality (39), and complexity (40), but lower modularity, are associated with reduced network stability. Furthermore, theoretical studies argue that network robustness could be measured by natural connectivity after “attacking” edges (41) or nodes (42), with a greater resistance of natural connectivity under attacking being indicative of a more robust or stable network (43). Nevertheless, microbial network property changes and its stability in response to permafrost degradation remain largely elusive.

Here, we examined how the diversity, composition, and network structure of active layer microbial communities respond to permafrost degradation in alpine ecosystems of the QTP. We also investigated whether permafrost degradation promotes destabilizing properties in microbial networks, with potential consequences for ecosystem C cycling. Importantly, our study was based on space-for-time analysis, whereby we sampled different permafrost types, ranging from stable permafrost (SP) to extremely unstable permafrost (EUP), that represented a temporal series of permafrost degradation (44). We tested the following hypotheses. First, we hypothesized that permafrost degradation decreases soil microbial diversity in the active layer. This hypothesis was based on past work showing that microbial diversity (e.g., richness and evenness) decreases significantly with permafrost thawing in a high-latitude ecosystem (8), although this remains untested in alpine ecosystems, as studied here. Second, we hypothesized that permafrost degradation weakens the stability of active layer microbial community by increasing its sensitivity to environmental change and decreasing its network stability. This hypothesis was based on past work showing that active layer microbial functional communities are vulnerable to climate warming in a high-latitude permafrost (45) and that soil microbial community similarity declines under climate warming in grassland ecosystems (46). Specifically, permafrost degradation is expected to increase the sensitivity of microbial communities to environmental change, based on a greater turnover rate in the decay relationship between microbial community similarity and environmental distance. Previous studies have shown that higher turnover rates are linked to decreased community stability under environmental disturbance (47–49). Based on theoretical expectations, permafrost degradation is also expected to promote destabilizing properties in microbial co-occurrence networks, as evidenced by increased node connectivity, intensified centrality and complexity, and decreased modularity of microbial networks (38–40). Moreover, we conducted a robustness test to measure the resistance of a network through natural connectivity changes under node or edge attacking (41, 42). Finally, we hypothesized that decreased microbial diversity and stability under permafrost degradation are linked to changes in soil C storage.

These hypotheses were tested using a combination of in-depth analysis of active layer microbial communities and their co-occurrence networks along an extensive gradient of permafrost degradation, ranging from SP to EUP, on the western part of the Qilian Mountains, northeast margin of the QTP, China (SI Appendix, Fig. S2 and Table S1). Soils were sampled from a series of sites classified as lightly degraded permafrost, including stable and substable stages (S-SSP), and severely degraded permafrost, with unstable and extremely unstable stages (U-EUP).

Results

Vegetation and Environmental Variables.

Kobresia and Carex genera of the Cyperaceae family dominated the plant community in lightly degraded permafrost, while severely degraded permafrost was dominated by the Stipa genus of the Poaceae family (see details in ref. 50). Aboveground or belowground plant biomass was significantly greater in lightly degraded permafrost than in severely degraded permafrost (SI Appendix, Table S2). Climatic and abiotic properties of the active layer differed significantly between lightly degraded and severely degraded permafrost (SI Appendix, Table S2). Precipitation (Pre), soil water content (SWC), SOM, total nitrogen (TN), soil C:N, and soil concentrations of water-soluble organic C (WSOC) were all greater in lightly than severely degraded permafrost, whereas ALT and soil and air temperature were higher in severely than lightly degraded permafrost. No significant differences were observed for litter biomass, soil pH, redux potential (Eh), porosity, or sand and clay contents between lightly and severely degraded permafrost.

Microbial Diversity and Community Composition.

For alpha diversity, both bacterial and archaeal richness (number of detected operational taxonomic units [OTUs]), bacterial evenness, and Shannon diversity were significantly lower in severely degraded permafrost than lightly degraded permafrost, while fungal richness, fungal and archaeal evenness, and Shannon diversity did not change (SI Appendix, Fig. S3). Bacterial and fungal dissimilarities increased significantly with permafrost degradation (SI Appendix, Fig. S4 and Fig. 1). Significant shifts in microbial community composition between lightly and severely degraded permafrost ecosystems were detected using the Adonis test (all P < 0.001) and nonmetric multidimensional scaling (NMDS) analysis, which showed that bacterial, fungal, and archaeal communities formed distinct clusters by permafrost degradation class (SI Appendix, Fig. S4). While the dominant phyla of microbial communities (>5%) were similar in lightly and severely degraded permafrost (SI Appendix, Fig. S5), the abundances of some dominant bacterial phyla differed significantly between them (SI Appendix, Table S3). In particular, permafrost degradation increased the abundances of Actinobacteria (from 6.2 to 17.2%) and Gemmatimonadetes (from 3.4 to 5.4%) and decreased the abundances of Proteobacteria (from 32.2 to 22.9%) and Verrucomicrobia (from 6.7 to 4.6%). The dominant archaeal phylum of Parvarchaeota was reduced from 5.7 to 0.3% by permafrost degradation, whereas all dominant fungal phyla did not change.

Fig. 1.

Relationships between SOC density and community dissimilarity. Bacterial, fungal, and archaeal community dissimilarities were based on the Bray–Curtis distance, in which larger values indicate that there are larger variances within microbial communities. By ANOVA test, difference of SOC density and bacterial and fungal community dissimilarities were lower in severely degraded permafrost than lightly degraded permafrost (shown in boxplots). S-SSP represents lightly degraded permafrost, including SP and SSP, while U-EUP represents severely degraded permafrost, including unstable permafrost and EUP. Asterisks indicate the statistical significance (***P < 0.001; **P < 0.01; and *P < 0.05).

Decay Relationship of Microbial Community Similarities over Environmental Distance.

We found that decay rates were higher in severely than lightly degraded permafrost for microbial community similarities over increased environmental distance (Euclidean distance of pairwise samples based on matrix of measured environmental variables) (Fig. 2, all P < 0.001 and SI Appendix, Table S4). Thus, microbial community similarities declined more sharply in severely degraded permafrost per unit change of environmental distance, reflecting the same extent of environmental disturbance. Moreover, compared to those for bacterial and archaeal communities, the decay rate for fungal community was lowest (SI Appendix, Fig. S6 and Table S5).

Fig. 2.

Relationship between microbial community similarity and environmental distance. The bacterial, fungal, and archaeal community similarities (based on [1 − the Bray–Curtis distance]) are shown in relation to environmental distance, such that larger values indicate that there are less variances within microbial communities. Permafrost degradation promoted the turnover rates of bacterial, fungal, and archaeal communities. S-SSP represents lightly degraded permafrost (SP and SSP), while U-EUP represents severely degraded permafrost (unstable permafrost and EUP). Asterisks indicate the statistical significance (***P < 0.001; **P < 0.01; and *P < 0.05).

We then used the partial Mantel test to decipher drivers for these significant decay relationships between microbial community similarities and environmental distance (Fig. 3). Certain soil physicochemical factors (i.e., soil temperature, sand content, and porosity), climate elements (i.e., air temperature and Pre), and permafrost characteristics (ALT) were responsible for all significant decay relationships of bacterial, fungal, and archaeal community similarities over environmental distance. In addition, the soil physicochemical factor (i.e., water content and SOM) was responsible for both fungal and archaeal decay relationships, while vegetation properties (i.e., aboveground biomass [AGB] and belowground biomass [BGB]) and soil C:N were identified to be important for archaeal and fungal decay relationships, respectively.

Fig. 3.

Correlations between environmental factors and microbial community composition. The bacterial, fungal, and archaeal community composition based on Bray–Curtis distance is related to each environmental factor by partial Mantel test. Line width corresponds to the partial Mantel’s r statistic, and line color denotes the statistical significance based on 999 permutations. Pairwise comparisons of environmental factors are also shown, with a color gradient denoting Pearson’s correlation coefficient, and these factors are synthesized into four groups based on attribute of data surveyed. Asterisks indicate the statistical significance (***P < 0.001; **P < 0.01; and *P < 0.05).

Microbial Co-occurrence Network and Its Stability.

Bacterial, fungal, and archaeal co-occurrence networks were constructed based on Spearman correlations among OTUs to investigate microbial interconnections along the gradient of permafrost degradation (Fig. 4A). We found that node connectedness (degree), centrality (eigenvector), and complexity (linkage density) of bacterial and fungal network nodes increased significantly by 351.0 to 491.9%, 119.7 to 186.2%, and 334.3 to 368.6%, respectively, in the severely degraded permafrost, compared to lightly degraded permafrost (Fig. 5A). Increases in these properties for nodes suggested lower network stability. This was confirmed by changes in the properties characterizing the whole bacterial or fungal network structure, which displayed a 27.2 to 45.0% decrease in diameter, a 25.4 to 55.4% decrease in modularity, and a 20.9 to 23.0% increase in transitivity with permafrost degradation (Fig. 5B). In contrast, node connectedness and centrality, and network diameter, transitivity, and modularity, did not alter or changed little with permafrost degradation for archaeal networks, although node complexity increased. Similar results were obtained using SparCC (51) for network construction (SI Appendix, Fig. S7).

Fig. 4.

Co-occurrence networks and robustness analysis for microbial communities between lightly and severely degraded permafrost. (A) In microbial networks, nodes represent individual OTUs whose color and size are positively correlated with the node degree; edges represent significant Spearman correlations (R > 0.75 and P < 0.05), which red lines indicate as positive correlations, and blue lines indicate as negative correlations. (B) Robustness analysis is shown as the relationships between microbial natural connectivity and the proportion of removed nodes, such that larger shifts upon the same proportion indicate that there are less robustness or stability within microbial networks. S-SSP represents lightly degraded permafrost, including SP and SSP, while U-EUP represents severely degraded permafrost, including unstable permafrost and EUP. Asterisks indicate the statistical significance (***P < 0.001; **P < 0.01; and *P < 0.05).

Fig. 5.

Multiple network properties of microbial co-occurrence networks. (A) Network node properties, including node connectedness (degree), centrality (eigenvector), and complexity (linkage density), were compared between lightly and severely degraded permafrost. (B) Other network properties of bacterial, fungal, and archaeal networks consisted of diameter, transitivity, and modularity. S-SSP represents lightly degraded permafrost, including stable and SSP, while U-EUP represents severely degraded permafrost, including unstable permafrost and EUP. Asterisks in A indicate the statistical significance (***P < 0.001; **P < 0.01; and *P < 0.05).

Resistance of microbial networks to disturbances was also tested by altering the amplitude of natural connectivity with the removal of nodes (Fig. 4B and SI Appendix, Fig. S8B) or edges (SI Appendix, Fig. S8 A and C). Nodes and edges were removed in a decreasing order of nodes’ betweenness or edges’ weight. We found that the natural connectivity of bacterial and fungal networks decreased to a greater degree with a greater fluctuation in severely than lightly degraded permafrost by removing the same proportion of nodes or edges (SI Appendix, Table S6), indicating weakened resistance.

Linking Environmental Variables, Microbial Community, and Soil C Storage.

Variance partitioning analysis based on a canonical correlation analysis model (P < 0.05) showed that environmental variables explained 61.8, 47.0, and 61.5% of the variation in bacterial, fungal, and archaeal community compositions, respectively (SI Appendix, Fig. S9). Among the environmental variables, certain soil physicochemical properties (i.e., soil temperature, SWC, SOM, WSOC, C:N, sand and clay contents, porosity, pH, and Eh) explained about 19.3 to 23.7% of the variation in bacterial, fungal, and archaeal community composition. Vegetation properties (i.e., AGB, BGB, and litter biomass) or climatic factors (i.e., air temperature and Pre) explained only 5.3 to 6.2% of the variance in the compositions of bacterial, fungal, and archaeal communities, while ALT only explained 3.0 to 3.4% of the variance.

Linkages among the active layer soil C storage represented by soil organic C (SOC) density (grams of C in top 20 cm ⋅ m⁻²), microbial community composition, and other environmental variables were examined using structural equation modeling (SEM, all P > 0.05, all root mean square error of approximation [RMSEA] = 0.00; Fig. 6). The SEM revealed that 59.4 to 68.0% of the variance in SOC storage along the gradient of permafrost degradation was explained directly by bacterial, fungal, and archaeal community compositions represented by nonmetric multidimensional scaling 1 (NMDS1), soil physicochemical, and climatic variables represented by the first component of principal component analysis (PC1) scores. Among them, soil physicochemical variables had the higher positive effects on SOC density (77.4%). For components of the microbial community, climatic variables explained the largest proportion of bacterial community composition (66.9%), while soil physicochemical variables accounted for the largest proportion of fungal (55.3%) and archaeal (90.8%) community composition.

Fig. 6.

Effects of environmental variables and microbial communities on SOC density by structural equation model. In A, single-headed arrows indicate the hypothesized direction of causation. Black solid lines indicate significant positive relationships, while black dotted lines indicate significant negative relationships. Gray arrows indicate insignificant relationships. The arrow width is proportional to the strength of the relationship. Rectangles represent the first component from the principal component analysis, conducted for the climate elements (air temperature and Pre), soil physiochemical factors (SOM, WSOC, C:N, sand and clay content, Por, pH, Eh, SWC, and soil temperature), and vegetation properties (AGB, BGB, and litter biomass); ALT represented permafrost characteristics. (B) Bar graphs are the standardized effects from SEM on the SOC density. Asterisks indicate the statistical significance (***P < 0.001; **P < 0.01; and *P < 0.05).

Interestingly, changes in the active layer soil C storage were amplified by a greater extent of the active layer microbial composition shift represented by increasing microbial community dissimilarity in severely but not lightly degraded permafrost (Fig. 1). Based on linear regression models, each 10% microbial community dissimilarity shift was associated with a 0.1 to 1.5% change in active layer soil C storage in severely degraded permafrost (R² = 0.37, 0.31, and 0.26 for bacteria, fungi, and archaeal communities, respectively), while shifts in microbial community composition were not linked to changes in soil C storage in lightly degraded permafrost. Moreover, active layer soil C storage was significantly negatively correlated with the active layer bacterial or archaeal community richness, evenness, and Shannon indices, and each 10% increase in their richness, evenness, and Shannon diversity, were associated with 1.9 to 2.8%, 3.4 to 16.5%, and 2.7 to 27.1% of SOC loss, respectively (SI Appendix, Fig. S10), in severely but not lightly degraded permafrost. In contrast, the active layer fungal community richness was not linked to the active layer soil C storage in either severely or lightly degraded permafrost.

Discussion

Our results demonstrate that permafrost degradation alters the diversity and potentially decreases the stability of active layer microbial communities with environmental disturbance in alpine ecosystems of the QTP. We found that permafrost degradation decreased bacterial richness, evenness, and Shannon diversity, as well as archaeal richness, and increased bacterial and fungal dissimilarities and the sensitivity of bacterial, fungal, and archaeal communities to environmental disturbance, as evidenced by higher turnover rates in the distance–decay relationship between soil bacterial, fungal, or archaeal community similarity and environmental distance in severely degraded permafrost. This finding advances on past work by showing that active layer microbial communities are sensitive to environmental changes under climate warming in alpine ecosystems, as previously shown in high-latitude permafrost (45) and under climate warming in grassland ecosystems (46). Microbial communities with higher turnover rates in their distance–decay relationships over spatial, temporal, or environmental distance have been linked to decreased community stability (47–49), which is likely due to the rapid elimination and replacement of species in reassembling processes of microbial populations (52).

We also found that permafrost degradation promoted destabilizing properties in bacterial and fungal networks, including increased node connectedness (degree), centrality (eigenvector), and complexity (linkage density). Past studies have found that increased node connectivity (38), centrality (39), and complexity (40) are associated with reduced network stability. Moreover, robustness tests based on natural connectivity also showed that permafrost degradation reduced the robustness of bacteria and fungi networks to node or edge attacking. These network analyses originate from graph theory (53) or social network analysis (54) and have previously been used to explore the stability of microbial networks in response to disturbances (37, 55). Previous studies have shown that climate warming decreases microbial community temporal stability in grassland ecosystems in Inner Mongolia, China, and the Southern Great Plains, United States (56, 57). However, this evidence suggests that permafrost degradation decreases the stability of active layer microbial communities with disturbance in alpine ecosystems.

Interestingly, the richness, Shannon diversity, and evenness of the fungal community were not affected by permafrost degradation. This finding follows past studies showing that fungi are more resistant than other components of the microbial community, such as bacteria, to disturbances (e.g., desiccations) (37, 58, 59), likely because of their special physiological features [e.g., spore reproduction, active dispersal through airborne spores, and aerial hyphae (60)]. Nevertheless, the stability of the fungal community, measured in terms of various network properties, decreased with permafrost degradation. Of particular note, the robustness test showed that the natural connectivity of fungal co-occurrence networks only decreased after >16.5% nodes were attacked but remained stable before that point in severely degraded permafrost. This suggests that active layer fungal networks are resistant to minor disturbances, but they are less stable under intensified disturbance of severe permafrost.

Decreased stability of active layer microbial communities under permafrost degradation was likely due to their lower resistance to harsh environmental conditions in alpine ecosystems of the QTP. Microbes inhabiting harsh habitats are known to have narrow ecological niches and to be sensitive to environment change (61), which may contribute to their instability. As revealed by our partial Mantel test, higher temperatures and increased ALT were the primary factors responsible for shifts in microbial community composition under permafrost degradation, driving significant decay relationships of microbial community similarity over environmental distance. The importance of these factors in shaping microbial communities has been shown in previous studies conducted in alpine permafrost (28, 62). While higher temperatures generally stimulate microbial growth and metabolism (63, 64), temperature increases under permafrost degradation in the alpine ecosystem might restrain, or even kill, some microorganisms adapted to low temperatures by changing their metabolic activity and damaging their physiology (6, 61, 65). We also found that thickening of the active layer under permafrost degradation reduced water availability, which in turn may affect oxygen and resource availability and physical connectivity within soils (66). Accompanying reductions in water availability, we detected declines in aboveground and belowground plant biomass and soil nutrient content (e.g., TN) in severely degraded permafrost, which likely led to harsher living conditions for microorganisms (65). It is also possible that declines in bacterial and archaeal community richness with permafrost degradation contributed to the observed decreased stability of the active layer microbial community, given that higher diversity typically promotes community resistance to disturbance (66–70), although the mechanistic basis for such relationships in microbial communities remains unclear (32).

Reductions in the stability of microbial communities with permafrost degradation could potentially lead to abrupt ecosystem state shifts due to nonlinear relationships between microbial communities and ecosystem properties (71) and/or cascading impacts of these property changes (72). In particular, decreased stability of the microbial community in permafrost could lead to a positive C–climate feedback (15, 16), posing a potential risk to ecosystem C sequestration (73). In our study, the reduced the stability of active layer microbial communities with permafrost degradation was associated with greater shifts in microbial community composition. We also found that greater shifts in active layer microbial community compositions associated with the severe permafrost degradation, as indicated by increased community dissimilarity, amplified soil C change, likely because of stimulated microbial activity and accelerated C decomposition (15, 45). In contrast, smaller shifts in the active layer microbial communities in lightly degraded permafrost were not related to a change in the active layer soil C storage. Our data suggest that each 10% dissimilarity increase in microbial community composition is associated with 0.1 to 1.5% change in active layer soil C storage in severely degraded but not lightly degraded permafrost, thereby potentially contributing to a positive C feedback of alpine permafrost on the QTP. As such, our findings point to a potential mechanism for active layer soil C loss on the QTP, which has been projected by Earth system models (13) and has important implication for C–climate feedbacks.

It is important to point out that we assessed microbial stability via single time-point measurements over a gradient of environmental disturbance rather than directly using temporal investigations of microbial communities along the disturbance gradient. Nevertheless, by combining a space-for-time sampling approach (74) based on different stages of permafrost degradation (44) with a comprehensive assessment of microbial properties associated with reduced stability, we believe that our results provide important implications into changes in the stability of microbial communities under permafrost degradation and association with soil C loss. However, further investigations are needed to fully explore how permafrost degradation influences the stability of microbial communities over space and time in alpine ecosystems of the QTP and consequences for C feedbacks.

Taken together, our findings reveal that permafrost degradation in alpine ecosystems alters the diversity and potentially decreases the stability of active layer microbial communities in alpine ecosystems of the QTP. We show that permafrost degradation not only increases the sensitivity of microbial communities to environmental changes but also promotes destabilizing properties in active layer microbial networks, which might trigger cascading effects on ecosystem properties, especially ecosystem C storage and C feedbacks.

Materials and Methods

Site Characterization.

The sampling region (38.2 to 40.0° N, 96.2 to 99.0° E, and 3,600 to 4,100 m above sea level) was located in the Shule River headwater region on the western part of Qilian Mountains, northeast margin of the QTP, China (SI Appendix, Fig. S2). Controlled by westerly winds, this region belongs to the continental arid desert climate, with a low annual mean air temperature from −2.0 to −4.1 °C and Pre from 360 to 510 mm. The major vegetation types include alpine marsh meadow, alpine meadow, and alpine steppe (44), belonging to high-mountain (alpine) ecosystem (75). Alpine permafrost covers 73% of the Shule River Basin (76). A defining system developed for alpine permafrost on the QTP (75), where permafrost was divided into six types based on the manually measured ground temperatures (MAGT) values at a depth of 15 m, was adopted. These six types included highly stable permafrost (MAGT < −5.0 °C), SP (−5.0 °C < MAGT < −3.0 °C), substable permafrost (SSP, −3.0 °C < MAGT < −1.5 °C), transitional permafrost (−1.5 °C < MAGT < −0.5 °C), unstable permafrost (−0.5 °C < MAGT < 0.5 °C), and EUP (MAGT > 0.5 °C) (44, 75). Importantly, we used a methodology of space-for-time analysis, whereby the spatial pattern of different permafrost types was taken to represent a temporal series of different stages of permafrost degradation. As shown in SI Appendix, Fig. S2, our sampling sites are distributed in the Shule River headwater region on the western part of Qilian Mountains, northeast margin of the QTP, China. According to the classification system developed for alpine permafrost on the QTP (75; SI Appendix, Fig. S1), these sites were divided into different permafrost types, ranging from SP to EUP, based on the ground temperature ranges ordering from low to high at a depth of 15 m. These ground temperature ranges well represent different stages of permafrost degradation (44). Since the ground temperature at 15 m depth has risen by +0.18 °C/decade on the QTP (77, 78) and +0.17 °C/decade in our study area (unpublished monitoring data), shifts between permafrost types could be taken to represent a broad temporal series of permafrost degradation. In this study, we focused on comparing lightly degraded permafrost (S-SSP, including stable and substable stages containing S1 and S3 sites) and severely degraded permafrost (U-EUP, including unstable and extremely unstable stages containing S5, S6, and S7 sites) (SI Appendix, Fig. S1 and Table S1).

Sample Collection and Measurement.

Soil samples were collected at the end of July and in early August 2017. Six replicated soil samples were taken from each site randomly at 0- to 20-cm depth by compositing five soil cores (3.8-cm diameter) for each replicate. Soils stored at 4 °C were used to measure the following: 1) WSOC by ultrapure water extracts and determined by the potassium dichromate-sulfuric acid oxidation technique and 2) soil pH and Eh by composite electrodes connected to a PHBJ-260 pH meter (INESA) with a soil:water ratio of 1:5. Air-dried soils were used to measure the following: 1) SOC and TN by the Walkley–Black dichromate oxidation method and micro-Kjeldahl procedures, respectively, and 2) soil particle size (sand, silt, and clay) and specific gravity (SG) were determined by the wet sieving and pycnometer methods, respectively. In addition, soil bulk density (BD) was collected using a cutting ring (100 g ⋅ cm⁻³), and soil porosity (Por) was calculated by the following equation: Por (%) = (1 − BD/SG) × 100%. SOC density was calculated by the following equation: SOC density = ${\displaystyle \sum }_{i=1}^n{h}_{i}B{D}_{i}SO{C}_i(1-C_i)/100$, where h_i, BD_i, SOC_i, and C_i were soil thickness (centimeter), BD (gram ⋅ centimeter⁻³), SOC (gram ⋅ kilogram⁻¹), and volume percentage of the soil particle fraction >2 mm at soil layer i (0 to 20 cm), respectively. SOM was calculated by the following equation: SOM = SOC × 1.724, where 1.724 is the constant coefficient that coverts SOC to SOM. Frozen soils were used for microbial analyses.

The living plant AGB, litter biomass (Litter), and BGB were harvested in three quadrats (50 × 50 cm) at each site. AGB samples only consisted of fresh grass, while the litter samples consisted of shedding and dead grass. BGB samples were sampled by soil core (4.8-cm diameter) and passed through a 2-mm sieve after removing impurities. AGB, litter, and BGB samples were dried at 80 °C until a constant weight was obtained.

Soil temperature (converted to MATs) and SWC were automatically measured by the Hydra Probe II soil sensors (Stevens) connected to a CR1000× datalogger (Campbell Scientific). Air temperature (mean annual air temperature [MATa]) at 1.5-m high was measured by use of an HMP155A Air Temperature probe (Vaisala) connected to a CR1000× datalogger. Annual Pre was measured by use of a weighing Pre gauge (Geonor, T-200B) or manual recording.

ALT, the distance between the soil surface and the top of permafrost table, was derived by three ways: 1) borehole drilling, 2) manual trial boring, and 3) simulation by the Stefan equation (79).

Sequencing for Microbial Communities and Bioinformatics.

Total DNA was extracted from soils using the PowerSoil DNA Isolation Kit (Qiagen), following the manufacturer’s instruction. Universal primers were used to amplify the 16S ribosomal RNA genes of bacteria (515F: GTGCCAGCMGCCGCGGTAA, 806R: GGACTACHVGGGTWTCTAAT), archaea (Ar915aF: AGG​AAT​TGG​CGG​GGG​AGC​AC, Ar1386R: GCG​GTG​TGT​GCA​AGG​AGC), and fungal internal transcribed spacer (ITS) genes (ITS1: CTT​GGT​CAT​TTA​GAG​GAA​GTA​A, ITS2: GCT​GCG​TTC​TTC​ATC​GAT​GC). Amplicons were sequenced with the Illumina MiSeq Platform (Illumina) using the paired-ends model (PE250 and PE300) at BGI-Wuhan, China.

In each sample, raw sequences were quality trimmed using Quantitative Insights into Microbial Ecology (QIIME; version 1.8.0) and paired-end reads were merged to tags using Fast Length Adjustment of Short Reads (FLASH; version 1.2.11). The singletons have been removed and then the tags were clustered into OTUs at a 97% threshold by USEARCH (version 7.0.1090) (80). After removing chimeric sequences, the bacteria and archaea OTUs were taxonomically assigned with the Greengenes ribosomal database (V13-8) (81) and fungi OTUs were taxonomically assigned with the UNITE database (V20140910) (82) using the Ribosomal Database Project Classifier (V2.2) with confidence values >0.8. The clean tags were mapped to OTU representative sequences to obtain the OTUs and species abundance profiles. Random subsampling of 51,000, 25,500, and 14,500 sequences per bacteria, fungi, and archaea sample, respectively, was performed to generate OTU tables.

Statistical Analysis.

All statistical analyses were performed in the R software (V3.5.3; http://www.r-project.org/), and all figures were generated by “ggplot2” R package. Phyla relative abundances less than 1% in all samples were combined as “Others,” and phyla with abundances >5% were defined as dominant phyla. Richness index (alpha diversity) was calculated using the “diversity” function (vegan package). One-way ANOVA was used to test the effects of permafrost degradation on measured variables. The “metaMDS” function in the vegan R package was used to assess microbial community compositions. The dissimilarity test was carried out by nonparametric multivariate statistical tests with the “xadonis” function (999 permutations) in the vegan R package. Microbial community similarities or dissimilarities (beta diversity) were calculated by the Bray–Curtis index via the “vegdist” function in the vegan R package, while environmental distance was calculated by the Euclidean distance based on matrix of measured environmental variables, including SOM, WSOC, C:N, Sand, Clay, Por, pH, Eh, SWC, MATs, AGB, BGB, Litter, MATa, Pre, and ALT.

Co-occurrence networks were constructed for bacterial, fungal, and archaeal communities by Spearman correlations using the “corr.test” function in the psych R package. Different network construction methods may lead to different information. SparCC was found to have the highest performance among a series of methods tested (51), which was also used to construct the co-occurrence network in this study. Both Spearman correlation and SparCC results were filter by the thresholds r > 0.75 and false discovery rate < 0.05. Network graphs were generated by using the “igraph” R package, and network parameters were extracted, including nodes, edges, degree, eigenvector centrality, complexity (linkage density; degree/node), diameter, transitivity, and modularity. Network natural connectivity was estimated by “attacking” nodes (40) or edges (39) in the static network. Importantly, robustness test was a powerful method to measure the network stability (more specifically resistance) through natural connectivity changes against node or edge removal, organized in a decreasing order of nodes’ betweenness or edges’ weight.

Because strong collinearity occurred among particular environmental factors, we used cluster analysis to assess the collinearity or redundancy of environmental variables (83) by the varclus procedure in the Hmisc R package before further analyses. Only one variable was selected for those clustered closely (Pearson’s R² > 0.7) as the representative variable (SI Appendix, Fig. S11). The partial Mantel test was carried out using the “mantel.partial” function (999 permutations) in the vegan R package to evaluate relationships between microbial community and environmental variables. The explanations of environmental factors were assessed by using partitioning of microbial composition variance analysis based on canonical correlation analysis. A structural equation model was constructed by AMOS 21.0 (Amos Development Corporation). The fit of the suitable model was judged by the χ² test (0 ≤ χ² ≤ 2 and 0.05 ≤ P ≤ 1.00) and the RMSEA (0 ≤ RMSEA ≤ 0.05 and 0.10 ≤ P ≤ 1.00). Additionally, the Bollen–Stine bootstrap test was used to confirm the model fit (0.10 ≤ Bootstrap P ≤ 1.00). The soil, vegetation, and climate variables were represented by their PC1 scores, which explained 41.02, 99.77, and 99.99% variance of corresponding environmental groups. Microbial composition was represented by nonmetric multidimensional scaling 1, the first component of NMDS analysis.

Data Availability

The data that support the findings of this study have been deposited into China National GeneBank Sequence Archive (CNSA, https://db.cngb.org/cnsa/) of China National GeneBank DataBase (CNGBdb) with accession number CNP0001030.