Original Research

Soil microbial community functioning as indicators of alpine soil health in the northern Maloti–Drakensberg, South Africa

Cowan C. Mc Lean, Christiaan C. du Preez, Wijnand Swart, Elmarie Kotze, Jaco Kotze, Alec Edwards, Johan J. van Tol

Received: 29 Aug. 2025; Accepted: 24 Feb. 2026; Published: 29 June 2026

Copyright: © 2026. The Authors. Licensee: AOSIS.
This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

Abstract

The alpine soils of the northern Maloti–Drakensberg site are fundamental to carbon (C) sequestration, biodiversity conservation, and water provisioning, yet remain vulnerable to degradation from grazing and tourism pressures. Soil microbial activity is central to alpine soil functionality through its influence on nutrient cycling, organic matter (OM) turnover and structural stability, but microbial community patterns in the alpine zones of southern Africa remain poorly understood. This study provides the first coordinated quantitative characterisation of soil microbial and physicochemical properties on the Amphitheatre summit of the uThukela catchment, thereby establishing a baseline for long-term soil health monitoring. Thirty topsoil samples were analysed for monitoring their physical (texture, bulk density, aggregate stability), chemical (pH, soil organic carbon or soil organic carbon [SOC], total C, total N and active C) and biological (microbial activity and community-level physiological profiling) attributes. The soils were uniformly acidic (pH 4.9), sandy loam in texture, and exhibited relatively low bulk density (1.01 g cm⁻³) but high SOC (9.8%). Soil organic carbon, active C, total N and microbial activity showed strong correlation, indicating consistent links between organic matter pools and microbial functioning. Community-level profiling further distinguished three microbial functional groups, primarily driven by variation in SOC, active C and microbial activity, rather than by soil forms or physical properties. The study indicated that SOC and total N are the most informative soil indicators for evaluating microbial functionality in alpine soils of the northern Maloti-Drakensberg.

Conservation implications: The baseline generated in this study enables the future detection of soil changes in response to land use or climate pressures in the northern Maloti–Drakensberg. Soil organic carbon and total N emerged as key indicators of microbial community functioning, underscoring the importance of maintaining soil organic matter through sustainable grazing and tourism management to enhance the resilience of high-altitude ecosystems.

Keywords: active carbon; microbial activity; bacterial communities; soil organic carbon; soil biodiversity.

Introduction

Most of the northern Maloti–Drakensberg forms part of the only alpine region in southern Africa (Carbutt et al. 2013; Linder 1990). Alpine soils of the Maloti-Drakensberg are important for carbon (C) sequestration and biodiversity sustenance, but their most crucial role is water provisioning for South Africa, Lesotho and Namibia (Mathinya et al. 2022; Mukwada et al. 2016). However, these high-altitude ecosystems are inherently sensitive, and increasing pressures from grazing and tourism pose several risks to the sustained delivery of ecosystem services and the conservation of the region’s unique alpine biodiversity (Carbutt & Edwards 2015).

Soil degradation, defined as a reduction in soil’s capacity to perform ecological functions (Jie et al. 2002), occurs through physical, chemical or biological processes, and is often intensified by land use pressures (Alam 2014; Jie et al. 2002). In the northern Maloti–Drakensberg, anthropogenically driven overgrazing by livestock is the dominant cause of soil deterioration (Brown & Du Preez 2019). Overgrazing occurs when livestock numbers exceed the carrying capacity of rangelands, leading to vegetation loss and soil compaction (Jie et al. 2002). These pressures become concentrated during drought periods, when alpine wetlands provide a favourable grazing habitat and attract large numbers of livestock (Du Preez & Brown 2011). The Amphitheatre summit is one of the most visited tourist destinations in the region, and persistent trampling along footpaths and viewpoints contributes locally to vegetation loss (Mathinya et al. 2022). The resulting bare soil patches are highly vulnerable to cryogenic disturbance and erosion by strong winds and intense thunderstorms, which accelerate loss of topsoil (Grab & Linde 2014; Mathinya et al. 2022). This loss is of particular concern because the majority of soil microbial communities that underpin soil functioning and biodiversity remain concentrated within the upper 15 cm of the soil profile (Hao et al. 2021; Voroney 2007).

Soil microbial communities are central to alpine soil functioning because they regulate organic matter decomposition, nutrient cycling, nitrogen fixation and carbon sequestration, and contribute to soil structural stability that reduces erosion risk (Adomako, Roiloa & Yu 2022; Siebert et al. 2023; Singh & Verma 2023). Microbial activity responds rapidly to changes in temperature, moisture and nutrient availability, as well as to land-use pressures such as grazing intensity and trampling (Siebert et al. 2023). Despite their ecological importance (e.g. organic matter decomposition, mineralisation), microbial processes remain poorly understood in the alpine soils of the global South (Palomo 2017; Zucconi & Buzzini 2021; Praeg et al. 2025). A clearer understanding of how soil microbial functioning aligns with soil physicochemical properties is, therefore, essential for evaluating the vulnerability and resilience of alpine soils under increasing human pressure (Tiedje et al. 2022).

Soil health is a key determinant of ecosystem functioning because it supports biodiversity, regulates nutrient and carbon cycling and maintains water and air quality essential for plant, animal and human well-being (Doran & Zeiss 2000; Laishram et al. 2012; Lehmann et al. 2020; Seifu & Elias 2018). Soil health is commonly evaluated using a suite of physical, chemical and biological indicators that collectively describe the capacity of soil to function as a living system. However, selecting and interpreting soil health indicators can be challenging because the relevance and sensitivity of indicators vary between ecosystems and depend on the prevailing environmental and management conditions (Hubanks, Deenik & Crow 2018). Soil health indicators are particularly valuable when they are sensitive to environmental change, strongly correlated with beneficial soil functions and cost-effective to measure (Hubanks et al. 2018; Laishram et al. 2012). Minimum-dataset approaches that integrate key physical, chemical and biological variables have therefore been proposed to provide an efficient framework for evaluating soil condition and anticipating shifts in soil functionality (Hubanks et al. 2018; Lehman et al. 2015; Snakin, Krechetov & Kuzovnikova 1996). Commonly used indicators include soil pH, aggregate stability, bulk density, organic matter, soil organic carbon (SOC), total C, total N, active carbon, available nutrients, microbial biomass and microbial activity (Fausak et al. 2024). These metrics are effective in detecting early changes in alpine soils elsewhere, including studies in the Bayinbuluk grassland and the Qinghai–Tibet Plateau (Li et al. 2023; Yu et al. 2018). Despite their relevance, no coordinated quantitative baseline of soil health indicators exists for the alpine environments of)) the northern Maloti–Drakensberg. Although the importance of soil health indicators is widely recognised, no coordinated quantitative baseline exists for the alpine areas of the northern Maloti–Drakensberg National Park, largely due to the remoteness and inaccessibility of these regions. Common soil health indicators include soil pH, aggregate stability, bulk density, soil organic carbon, total C, active carbon, total N, available nutrients, microbial biomass and activity (Fausak et al. 2024). These indicators have proven particularly relevant in alpine ecosystems elsewhere, including the Bayinbuluk alpine grassland and the northeastern Qinghai–Tibet plateau, where they have been used successfully to detect early shifts in soil condition (Li et al. 2023; Yu et al. 2018), demonstrating their suitability for high-altitude environments. To our knowledge, this is the first coordinated quantitative assessment of soils, and especially soil microbial communities in an alpine environment of the northern Maloti–Drakensberg.

This study primarily aimed to establish a baseline of soil microbiology and soil properties for the Amphitheatre summit. To achieve this objective, the study (1) characterised the soils in terms of their basic physical, chemical and biological composition, (2) determined if distinct soil microbial communities could be identified based on community-level metabolic profiling and (3) examined which soil properties were associated with differences between microbial communities to evaluate their potential as indicators of alpine soil functionality.

Research methods

Study site selection and soil sampling

The study was conducted in the upper uThukela headwater catchment, located on the Amphitheatre summit of the north-eastern Maloti–Drakensberg (Figure 1). The 300 ha site forms part of the Royal Natal National Park and constitutes an altitude range from 2996 to 3282 m.a.s.l with a central longitude and latitude of 28^o 46′ 21.07″ S and 28^o 52′ 21.72″ E, respectively. Elevation directly affects most climatic variables, resulting in a general trend of higher rainfall and lower temperature with an increase in elevation. The region experiences cool, wet summers and cold winters, with mean annual rainfall of 1 200 mm –1 500 mm (Cole et al. 2017) and temperatures that average 20°C in summer and –6.3°C during winter (LMS 2013, 2021). Frost and snowfall are also common, with the closest weather station situated approximately 10 km away in the Royal Natal National Park. The site is characterised as a treeless sub-alpine vegetation belt, where two vegetation types can be distinguished, namely upland vegetation and peatlands or mires, which consist of grasses, scandent shrubs and herbaceous species (Brown & Du Preez 2019; Van As et al. 2012). Geologically, the Maloti–Drakensberg site comprises basaltic rocks deposited over softer sandstone and shale layers due to thick lava flows through a complex system of cracks or fissures (Brown & Du Preez 2019). The geology of the study site is therefore classified as basalt and non-intrusive dolerite of the Maloti–Drakensberg group (Carbutt 2019), with alpine soils that are young, poorly developed and predominantly influenced by periglacial processes. These soils are often shallow on steep slopes, with deep soils occurring on highly weatherable parent materials (Poulenard & Podwojewski 2006).

FIGURE 1: Sampling points in the upper uThukela catchment study site on the Amphitheatre summit, located on the northern section of the Maloti–Drakensberg.

The current study employed the conditioned Latin hypercube (cLHS), a model-based sampling approach, because cLHS employs a stratified random procedure that uses prior information to accurately represent the variability of environmental covariates in a feature space (Minasny & McBratney 2006). Additional sampling points were manually added to include visually distinct areas such as barren soil patches and small gullies not depicted by the cLHS. Overall, 30 topsoil samples were collected for physical, chemical and biological analysis (Figure 1).

Composite samples for biological analysis were collected within 0 cm – 15 cm, sieved in-field through a 2-mm sieve, and stored in a cooler box before transferring them to a fridge at 4°C temperature for further analysis within 5 days. The sampling depth was selected to capture the highest microbial activity, which occurs in the topsoil due to abundant organic matter and root interactions. Tools used for biological sampling were sterilised between sampling points using 70% ethanol. Separate samples for physical and chemical analysis were collected within a 0 cm – 30 cm range and dried in a dry room at 36°C for 48 h, before they were ground and passed through a 2mm sieve to obtain homogenous samples. Undisturbed core samples were collected for bulk density using a core sampler, although additional undisturbed samples were retained for aggregate stability analysis and stored in plastic containers. Composite samples for physical, chemical and biological analyses were obtained by collecting three sub-samples within a 1m radius, mixing them in a bucket and collecting one representative sample to provide a representative sample of the topsoil.

Soil classification

Soil classification was undertaken to provide a pedological context for interpreting microbial patterns, as parent material and the soil-forming environment can influence carbon accumulation and microbiological functioning in alpine regions. Each sampling point was classified at a 1.5m depth using a Thompson soil auger following the Soil Classification Working Group (2018). The soil forms were then related to an international soil classification system (IUSS working group WRB 2015) following the guidelines set in a previous study (Van Huyssteen 2020).

Soil’s physical analysis

Soil texture

The hydrometer method was used to determine the percentages of silt and clay (Bouyoucos 1962). It helps indicate aggregate formation, hydraulic conductivity, and the soil’s ability to adsorb cations.

Bulk density

Bulk density was determined using the core method as described earlier by Okalebo, Gathua and Woomer (2002). A coring metal ring with a diameter of 11 cm and a volume of 665 cm³ was driven into the soil to obtain an undisturbed sample. The soil was then placed in an oven at 105°C for 48 h and weighed afterwards to determine its dry mass (Razakamanarivo et al. 2011). The bulk density was then calculated using Equation 1:

where:

ρ_b = bulk density (g cm^-3); M_s = dry mass (g); V_t = volume of core (cm³)

Aggregate stability

Aggregate stability was determined using the wet sieving method proposed by Ekwue, Dookhoo and Chakansingh (2018), who employed four stacked sieves (2 mm, 1 mm, 0.25 mm and 0.1 mm) to segregate soil aggregates into different size classes. The sieves were arranged from large to small sizes and installed into a rotary wet sieving apparatus. The tank of the apparatus was filled with distilled water, followed by submerging the samples to allow soaking for 10 min before sieving commenced. Sieving was done at 35 strokes per minute for 17 min, and the aggregates collected from each sieve were then backwashed with distilled water into pre-weighed glass beakers. The collected samples were then oven-dried for 36 h at 100°C and weighed afterwards to determine the mass of aggregates retained on each sieve (Ekwue et al. 2018). After applying sand correction to all samples, the aggregate stability was estimated by calculating the mean weight diameter (MWD) of each soil sample as depicted by Equation 2:

where Ẋ_i denotes the arithmetic mean diameter of each size fraction (mm); W_i refers to the proportion of total water-stable aggregates in the corresponding size fraction.

Soil chemical analysis

Soil pH, total C, soil organic carbon, total N and active C

Soil pH was measured in a 1:2.5 soil/water ratio suspension on a mass basis with a pH meter (AgriLasa 2004). Due to the geology and expected low pH of the alpine soil, none of the samples contained inorganic C (Nelson & Sommers 1996), and total C content was assumed to be equivalent to SOC (Seboko et al. 2021; Kotze, Mc Lean & Van Tol 2023). Total C and total N in the soil were determined by dry combustion using the Leco TruSpec CNS analyser (Leco corporation, St. Joseph, MI, USA), which used 0.2 g soil that was combusted in a furnace up to 980°C (Kowalenko 2001). Active C was determined by potassium permanganate (KMnO₄) oxidation, a preferred method due to its speed and simplicity. This approach is strongly correlated with microbial activity, as it indicates the availability of C as an energy source for soil microbes (Okalebo et al. 2002). In brief, 2.5 g air-dried soil was placed in a 50 mL Falcon tube, and 2 mL 0.2M KMnO₄ stock solution was added, followed by 18 mL distilled water. The sample was then hand-shaken for 10 s, followed by a 2min shake on a shaker at (FMH instruments, South Africa) 120 rpm. After shaking, the sample was placed in a centrifuge for 6 min at 300 rpm before pipetting 0.5 mL of the supernatant into a second 50 mL Falcon tube, followed by adding 49.5 mL distilled water. Thereafter, the absorbance was measured at 550 nm using a spectrophotometer and active C was determined (Culman et al. 2012) using Equation 3:

Soil biological analysis

Soil microbial activity was determined using the fluorescein diacetate (FDA) method, which measures the enzymatic breakdown of FDA into fluorescein. This approach provides a broad indication of overall microbial activity in the soil but does not differentiate between microbial groups and their specific functions. As a highly sensitive soil property, microbial activity is influenced by various environmental factors, including temperature and rainfall, which can also affect the accuracy of the FDA method. Additionally, high clay content may limit its efficiency, as clay particles possess a large surface area that strongly binds organic matter and enzymes, reducing their availability for hydrolysis. Soil pH level also plays a role as it affects enzyme stability and microbial activity. The method uses 2 g of field-moist soil. In brief, soil was placed into a 50 mL Falcon tube, and 20 mL buffer (K₂HPO₄ and KH₂PO₄) solution was added, followed by 0.2 mL of FDA stock solution. The sample was then incubated at 28°C for 20 min and manually shaken every 7 min, followed by a 10min shake at 300 rpm. Thereafter, 15 mL of a 2:1 chloroform: methanol solution was added to the solution to stop the reaction. The sample was then centrifuged for 3 min at 3000 rpm to settle the soil, and the absorbance was measured at 490 nm using a spectrophotometer. The total soil microbial activity was determined (Adam & Duncan 2001) by using Equation 4:

where y refers to the concentration read from the standard graph.

Microbial community-level physiological profiling

Soil microbial community structure and activity were quantified using Biolog Ecoplates^TM, which contains 31 C substrates consisting of polymers, carbohydrates, carboxylic acids, amino acids, amines, and phenolic compounds. These C sources are utilised by different microbes, resulting in community-level physiological profiling of soil microbial communities. The composition and activity of these communities may vary within the same area, depending on how land use has affected respective areas’ soil health (Chen et al. 2024; Zabaloy et al. 2016). The procedure, as outlined by Galieva et al. (2018) and Poyraz, Sezen and Mutlu (2021), was followed with the substrate consumption rate measured over a 7-day period using a Thermo Scientific Multiskan FC microplate photometer. Results from day 4 were used due to the highest growth of average well colour development (AWCD) at this stage, indicating maximum soil microbial community metabolic activity in relation to the C substrates. Average Well Colour Development reflects the soil microbial community’s functional capacity, making day 4 the most relevant time point before potential substrate depletion (Galieva et al. 2018). Average Well Colour Development was calculated as the mean absorbance across all 31 carbon substrates after correction with the control well. Agglomerative hierarchical clustering (AHC) based on Bray–Curtis dissimilarity using Ward’s linkage was applied to group samples according to similarity in substrate utilisation patterns. The same distance matrix was used in the principal component analysis (PCA) to visualise multivariate separation between microbial communities.

Statistical analysis

Data normality was evaluated using Q–Q plots. Thereafter, each soil indicator’s mean, standard deviation and degree of variance were determined with descriptive statistical analysis. Pearson’s correlation analysis was then performed to assess the strength of the relationship between these soil indicators as the data showed normal distribution. Thereafter, principal component analysis (PCA) was performed to identify groups of similarities and dissimilarities between the Biolog Ecoplates^TM, followed by one-way analysis of variance (ANOVA) of the grouped samples using Fisher’s least significant difference (LSD) at a 5% probability level to determine the significant difference of soil indicators between these samples. The software Microsoft Excel and IBM SPSS version 27.0 were used for all statistical analyses. Because the present research was designed to establish a baseline rather than to test treatment effects, significant differences between microbial groups are not interpreted as evidence of ecosystem degradation, but rather as indicators of variability in carbon substrate utilisation to guide future monitoring.

Ethical considerations

Ethical clearance to conduct this study was obtained from the Environment and Biosafety Research Ethics Committee of the University of the Free State (No. UFS-ESD2022/0114/22).

Results

Soil properties

All topsoil samples were humified due to the mean SOC concentrations exceeding 5% and were thus classified as either Organic-O, Humic-A or Orthic-A, belonging to the Champagne, Katspruit, Nomanci, Graskop and Mispah soil forms. These soil forms were then related to the World Reference Base as a Leptic Sapric Histosol, Luvic Planosol, Leptic Umbrisol, Cutanic Leptosol and Dystric Leptosol, respectively.

Descriptive statistical analyses (e.g. mean, standard deviation [s.d.], coefficient of variance [CV]) as depicted in Table 1 indicate that the soil in this study area had a sandy loam texture due to the mean sand, silt and clay content (59.7%, 28%, 12.5%, respectively). Furthermore, by using the variability classes shown in Table 1 (low: CV < 15%; moderate: CV 15% – 5%; high: CV > 35%) (Peralta & Costa 2013), a soil indicator’s degree of variation from its mean can signify variation between sampling points. There is a high variability in clay (CV = 34%), while the mean soil pH was uniformly acidic at 4.95, as depicted by its low variability (CV = 6%) between different sampling points. A mean bulk density of 1.01 g cm⁻³ with moderate variability (CV = 24%) was also recorded, while the mean weight diameter (MWD) of 1.91 mm was relatively uniform (CV = 11%). Furthermore, SOC, total C and active C were found to be more variable (CV = 28%, 35% and 30%, respectively), while soil microbial activity showed the highest spatial variability among measured indicators (CV = 72%).

Relationships among different soil indicators

Table 2 shows results of the Pearson correlation analysis between various soil indicators and their level of significance. Positive and negative correlations were found between various soil indicators; however, only a few relationships were statistically significant. Strong positive correlations were noted among SOC, total C, total N and organic matter (p ≤ 0.01), and all four indicators demonstrated negative correlations with bulk density (p ≤ 0.01). Active C showed significant positive correlations with SOC (r = 0.52; p ≤ 0.01) and total N (r = 0.58; p ≤ 0.01), while soil microbial activity showed weaker but statistically significant relationships with SOC and organic matter (p ≤ 0.05). Soil texture (comprising sand, silt and clay) generally showed weak correlations with these biochemical variables.

Identification of microbial functional groups

Three soil microbial communities were identified from the Biolog EcoPlates™ based on their carbon–substrate utilisation profiles (Table 1-A1 and Table 2-A1, see Online Appendix 1). This result is displayed using a dendrogram, showing the agglomerative hierarchical clustering (Figure 2), which reveals three distinct microbial groups. Group 1 and Group 2 exhibit 35% dissimilarity, while Group 3 shows 62% dissimilarity from the first two groups, indicating substantial divergence in metabolic functioning. Principal component analysis (see Online Appendix 1, Table 3-A1 and Table 4-A1) further illustrated the metabolic dissimilarities among the three communities by showing clear separation along the principal components. However, only PC 1 and PC 2 were interpreted because they explained the highest proportion of variance in carbon–substrate utilisation (29.27% and 15.38%, respectively), thus providing distinct separation between microbial communities. Higher-order PCs contributed marginally to the variance but did not improve interpretation. A one-way ANOVA confirmed statistically significant difference between the three communities in their overall metabolic activity (p < 0.001), thereby providing strong statistical support for the distinction between the microbial communities identified.

FIGURE 2: Soil microbial communities: Group 1 (green), Group 2 (red) and Group 3 (blue).

Differences in soil properties among microbial groups

The differences observed between the respective microbial groups for the soil indicators are shown in Table 5-A1 (see Online Appendix 1). Soil texture, bulk density, aggregate stability and pH indicators were relatively uniform over the three groups. Still, differences in total C and N, organic matter, SOC, active C and soil microbial activity are evident, with the highest concentrations consistently observed in Group 1. Interestingly, Group 2 and Group 3 had similar values for most soil indicators, with minor differences in active C and microbial activity.

The differences found between groups for soil indicators, as indicated in Table 5-A1 (Online Appendix 1), showed that only total C, total N, organic matter and SOC were significantly different at the 5% probability level between Group 1 and Group 2. However, although the confidence is lower, a trend at the 10% probability level was observed between Group 1 and Group 3 for total C (p = 0.09), total N (p = 0.1), organic matter (p = 0.07) and SOC (p = 0.07), as well as between Group 1 and Group 2, and Group 1 and Group 3 for active C (p = 0.08, respectively), while only the microbial activity between Group 1 and Group 2 was different (p = 0.08). These differences should be viewed within the context of a baseline survey without replicated treatments and therefore represent indicative rather than confirmatory patterns.

Effects of soil form on microbial activity

A one-way ANOVA (Table 3) was done to determine statistically significant differences in microbial activity among the topsoils of the respective soil forms across the microbial groups. While microbial activity differed significantly (p < 0.01) among microbial groups, no statistically significant differences in microbial activity were found between the topsoils (i.e. organic, humic and orthic) of the soil forms (p > 0.05; Table 3). Similarly, differences in SOC and active C between these topsoils were also not statistically significant (data not shown).

Discussion

Physical, chemical and biological soil properties are widely recognised as essential indicators of soil health (Hubanks et al. 2018; Laishram et al. 2012; Lehmann et al. 2020; Wolinska et al. 2017) the way soil microbial communities respond to differences in these indicators is particularly relevant for understanding alpine soil functioning (Tiedje et al. 2022). In the present study, several soil indicators showed uniformity across the sampling area, whereas others varied substantially and were strongly related to differences in microbial activity and community structure.

Bulk density and aggregate stability were relatively homogeneous and therefore did not differentiate microbial communities across the landscape. The low bulk density observed is characteristic of mountain soils, which are typically shallow with naturally high porosity (Poulenard & Podwojewski 2006). The uniformity in aggregate stability is consistent with this and may also reflect the influence of SOC and microbial activity on aggregate formation, which act as binding mechanisms between soil particles (Gougoulias, Clark & Shaw 2013; Wu et al. 2024). These interpretations are supported by strong correlations between bulk density, SOC and microbial activity, with a statistically significant negative association between bulk density and SOC (r = –0.86, p ≤ 0.01) and a weaker but still statistically significant negative association between bulk density and microbial activity (r = –0.36, p ≤ 0.05), suggesting stronger influence of SOC on soil porosity than on microbial activity.

Soil pH level was also found uniform across sampling points (mean pH 4.95), yet microbial communities differed despite these similar acidic conditions. This result corroborates findings by Malik et al. (2018), who showed that microbial ecophysiological traits might vary substantially within similar pH ranges due to differences in carbon use efficiency rather than shifts in pH itself. In the current study, differences in SOC and active C appear more important than pH in driving microbial separation. The mean SOC content of 9.8% recorded in this study was greater than the average of 2% SOC found in South African soils (Du Preez, Van Huyssteen & Mnkeni 2011). This was expected, as alpine environments typically promote SOC accumulation through reduced organic matter decomposition under low temperatures and seasonal waterlogging conditions (Praeg et al. 2020). Furthermore, active C, which represents the readily available carbon pool for microbial metabolism, also varied substantially. However, both SOC and active C were strongly correlated with microbial activity, indicating a close coupling between carbon availability and microbial functioning in this environment.

Community-level physiological profiling revealed three microbial groups that differed markedly in their metabolic use of carbon substrates. The distinctiveness of these groups, supported by the dendrogram (Figure 2), reflects variation in the ability of microbial communities to utilise carbon resources. Group 1 exhibited significantly higher SOC, total N, active C and microbial activity than Group 2 and Group 3, indicating greater functional potential for organic matter decomposition, carbon cycling and nitrogen transformation. This finding aligns with Li et al. (2018) and Shu et al. (2023), who reported that higher microbial biomass and soil N availability promote greater mineralisation and ecosystem productivity. In contrast, Group 2 and Group 3 showed relatively similar soil properties, which corresponds with similarities in their substrate utilisation tendencies, although subtle differences in active C and microbial activity were still detected. Although soil forms differed in total concentrations of SOC, active C and microbial activity, a one-way ANOVA result indicated no significant differences in these indicators between the topsoils (organic, humic and orthic) across microbial groups. This suggests that microbial grouping was not determined by soil form but rather by carbon-substrate availability and soil biochemical properties (Korenblum et al. 2020). This highlights that SOC and total N concentrations were the most influential indicators underlying microbial functional divergence in this alpine environment.

Overall, the results demonstrate that while physical indicators such as bulk density and aggregate stability were relatively uniform across the landscape, variation in SOC, total N and active C showed a remarkable association with differences in microbial activity and substrate utilisation patterns. These findings reinforce the importance of biochemical indicators, especially SOC and total N, in regulating microbial community functioning and represent key considerations for assessing alpine soil health.

Conclusion

This study provides the first coordinated baseline of soil microbial and physicochemical indicators for the alpine environment of the northern Maloti–Drakensberg site. By integrating physical, chemical and biological soil measurements, the study demonstrated that microbial communities in this region are shaped primarily by the availability of carbon substrates rather than by soil form or topsoil type. Although the SOC concentrations across the study site were substantially higher than the South African average, variation within the carbon pool drove the formation of three distinct microbial communities. Among these, Group 1 showed the strongest functional capacity, characterised by higher SOC, active C, microbial activity, and total N, suggesting enhanced roles in organic matter decomposition, C cycling and N fixation. Group 2 and Group 3 exhibited comparatively lower functional capacity, thereby underscoring the spatial heterogeneity of microbial contributions to ecosystem functioning.

The results identify SOC and total N as the most informative soil indicators for evaluating microbial functionality in alpine soils and therefore represent priority variables for long-term soil health monitoring in the uThukela headwater catchment. Future research should investigate the taxonomic composition of the identified microbial groups and examine whether their functional profiles change under varying land use intensities or climate pressures. Furthermore, integrating microbial data with vegetation dynamics will also improve understanding of how below- and above-ground processes jointly support ecosystem resilience. From a management perspective, maintaining SOC and total N through sustainable grazing regimes and careful tourism management is essential for preserving microbial functionality and associated ecosystem services.

Overall, we can conclude that long-term monitoring of SOC and total N across alpine landscapes will provide an early-warning system for detecting soil health decline and guiding conservation interventions in the northern Maloti–Drakensberg site.

Acknowledgements

This article is based on research originally conducted as part of Cowan C. Mc Lean’s doctoral thesis titled ‘Developing a comprehensive framework to assess soil quality within selected national parks, South Africa’, submitted to the University of the Free State. The thesis is currently unpublished and is not publicly available. The thesis was supervised by Christiaan C. du Preez and Johan J. van Tol. The thesis was reworked, revised and adapted into a journal article for publication. The authors confirm that the content has not been previously published or disseminated and complies with ethical standards for original publication.

The authors declare that they have no financial or personal relationships which may have inappropriately influenced them in writing this article.

Cowan C. Mc Lean: Conceptualisation, Methodology, Formal analysis, Investigation, Writing – original draft, Data curation. Christiaan C. du Preez: Writing – review & editing, Supervision. Wijnand Swart: Conceptualisation, Software, Validation, Resources. Elmarie Kotze: Conceptualisation, Writing – review & editing. Jaco Kotze: Methodology, Investigation, Data curation. Alec Edwards: Methodology, Formal analysis, Software. Johan J. van Tol: Conceptualisation, Investigation, Visualisation, Project administration, Validation, Resources, Writing – review & editing, supervision, Funding acquisition. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and took responsibility for the integrity of its findings.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

The data that support the findings of this study are available from the corresponding author, Cowan C. Mc Lean, upon reasonable request.

The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings, and content.

References