Analyzing the impact of variations in land use and elevation on selected soil microbial indices and spatial distribution

Abstract

Soil biological characteristics are highly sensitive to land use changes, making them valuable indicators of soil quality. This study assesses the effects of three land use types (agriculture, rangeland, and forest) and elevation variations on soil microbial parameters and their spatial distribution in the Khaneghah region. Standard physicochemical and biological properties of the soil were measured on a total of 72 soil samples collected using systematic and random sampling techniques. Spatial distribution maps of the biological indices were generated using geostatistical techniques, specifically the Kriging method, within a geographic information system (GIS). The results revealed significantly higher values for microbial biomass carbon (MBC = 900 mg C_mic-CO₂ kg⁻¹), nitrogen (MBN = 8.97 mg N_mic kg⁻¹), basal respiration (BR = 25.1 mg C-CO₂ g⁻¹ day⁻¹), and the total microbial population (MPN = 0.63 × 10⁹ cells g⁻¹) in forest soils compared to rangeland and agricultural soils. The alignment between land use maps and biological index maps reinforced these findings. Although the correlations between biological indices and physicochemical properties were generally weak (positive or negative), organic matter content, field capacity moisture, and silt percentage exhibited a slight positive correlation with most of the microbial indices evaluated. The comparison of soil microbial indices with the digital elevation model map indicated higher levels of MBC, MBN, BR, and MPN at elevated regions. However, the microbial quotient and metabolic quotient (qCO₂) did not show significant changes with increasing elevation. The study also confirmed the effectiveness of Kriging interpolation in mapping specific soil microbial indices, as the correlation between Kriging estimates and measured values at sampling points exceeded 0.2, demonstrating statistical significance at a 5% confidence level.

Introduction

Soil microbial biomass (SMB) is a living component of soil organic matter that plays a crucial role in regulating and functioning within the soil system. Although constituting only 1 to 3% of the total soil organic matter, it serves as a source and reservoir of plant nutrients (Raiesi & Beheshti, 2015; Sicardi, García-Préchac and Frioni, 2004). SMB is considered one of the fundamental biological indicators in soil quality assessment (Arshad & Martin, 2002; Doran & Parkin, 1994; Islam & Weil, 2000). It responds more rapidly than other soil components to environmental changes caused by alterations in soil organic matter content (Powlson & Jenkinson, 1976). MBC is a dynamic component of the soil ecosystem, primarily indicating the amount of carbon present in bacterial and fungal cells per unit of dry soil. This parameter constitutes 1% of the soil’s organic carbon and serves as a vital carbon reservoir influencing feedbacks to climatic conditions (van Gestel et al., 2018). The MBC is essential for various soil functions, including nutrient cycling, enzymatic activity, soil dynamics, and overall stability. It serves as a bio-indicator for evaluating and modeling soil ecosystem health and fertility. Consequently, MBC is crucial for quantifying the microbial community and has been widely measured in diverse global studies (Patoine, 2022). Furthermore, MBN is crucial for preserving soil fertility, serving as a biologically active nitrogen pool in the soil ecosystem (Deng et al., 2000). Despite constituting only 0.5 to 3.15% of total soil organic nitrogen, MBN significantly influences the transition between organic and mineral nitrogen pools. It plays an essential role in regulating the absorption of crucial nutrients by plants (Anderson & Domsch, 2006). The ratio of MBN to total soil nitrogen indicates the nitrogen supply for soil microflora. The MBN size varies with agricultural management practices and can serve as an early indicator of changes in soil nitrogen stability due to its high sensitivity to environmental shifts (Li et al., 2018). Among the soil microbial indices, the ratio of MBC to soil organic carbon, known as the microbial quotient (q_mic), indicates the availability of carbon for soil microflora. This ratio serves as an indicator of the dynamic nature of organic materials under various land uses (Liu et al., 2018). The q_mic, along with the ratio of MBN to total soil nitrogen, serves as a responsive parameter for monitoring the dynamics and alterations of soil organic matter resulting from changes in land use. The metabolic quotient qCO₂, a physiological parameter, is employed to assess the qualitative impact of land use on SMB, serving as an indirect measure of the overall energy efficiency of the soil microbial population. Typically, qCO₂ provides more information than SMB and BR about the soil’s biological status (Babur, 2022). The type of land use, influenced by various crop sequences, irrigation practices, and soil management strategies, significantly affects soil biological activities and processes (Sparling et al, 1994; Caravaca et al., 2002; Templera et al. 2005; Cochran et al., 2007; Li et al., 2018).

Soltani Toularoud and Asghari (2021) found significantly higher values for all biological parameters, such as MBC, in forest lands compared to rangelands during their assessment. Nanganoa et al. (2019) found that various land uses negatively affected soil macrofauna and selected soil physicochemical indicators. Forested areas showed the highest macrofauna count and maximum organic matter content. Khormali et al. (2009) found a considerable reduction in BR after deforestation and conversion to cultivated land. Furthermore, carbon and nitrogen content, along with fungal population, were significantly higher in all sloped forest locations compared to adjacent deforested cultivated lands. In Maharjana et al.’s (2017) study, the total organic C and N content, as well as SMB, were significantly higher in organic agriculture compared to conventional agriculture and forest. Additionally, findings from Wardle’s research (1992) revealed that the q_mic in agricultural soils was lower than in forest and rangeland soils. Changes in topography and elevation directly impact soil moisture, temperature, water flow, and the deposition of substances. These alterations can indirectly affect plant performance and the distribution pattern of organic matter, subsequently influencing soil microbial indicators. Variations in these environmental factors can lead to shifts in microbial biomass, activity, and community composition, thereby affecting overall soil health and fertility (Tsui et al., 2004; Alemayehu, 2007; Moges & Holden, 2008; Yimer et al., 2008; Karimzadeh et al., 2022).

The soil inherent characteristics are inherently changeable due to soil-forming factors such as parent material, vegetation cover, and climate. These changes can also be influenced by agricultural management practices (Wei et al., 2006). Understanding and predicting the spatial variability of soil properties is crucial for agricultural land management and necessitates a substantial amount of georeferenced data (Athira & Subaida, 2023). Many studies have extensively explored spatial changes in soil properties using geostatistical methods and have created maps illustrating their spatial distribution. In the study conducted by Karimzadeh et al. (2022), interpolation using the Kriging geostatistical method demonstrated considerable accuracy due to the high correlation between measured and estimated soil properties at various points. Ghorbanzadeh et al. (2019) investigated the spatial changes and frequency distribution of microbial indices and other soil properties in the ShafaRud forest, northern Iran, and observed the spatial diversity of the studied soil features and biological indices on mapped plots. Navidi et al. (2009) discovered that co-kriging, particularly with a limited sample size, surpassed two alternative methods in estimating soil properties such as copper, zinc, manganese, lead, organic carbon, and lime. Furthermore, for estimating other soil parameters, the inverse distance method with various powers showed the least error, solidifying its position as the preferred interpolation method. Seifi and Mirzaei, (2017) found that in their study area, ordinary kriging (spherical model) performed best for estimating cadmium and copper concentrations, while ordinary kriging (exponential model) excelled in predicting zinc concentrations.

Soil’s biological characteristics, particularly microbial features, prove more responsive to ecosystem changes than other attributes, serving as crucial indicators of soil quality (Geisseler & Horwath, 2009). Despite the thorough investigation into how land use and topography affect the soil physicochemical properties, there is a notable lack of studies within the country focusing on their impact on microbial indicators. This study in Ardabil Province’s Namin County assesses the impact of land use and topography on MBC, MBN, q_mic, qCO₂, and the correlation with soil physicochemical properties in Khanegah village. The research also compares variogram models and produces spatial distribution maps for the microbial parameters studied.

Materials and methods

Study area and soil sampling

This research covered a substantial 208-hectare area around Khanegah village in Namin County, Ardabil Province (Fig. 1), with geographic coordinates approximately 48°32′36″ E longitude and 38°25′25″ N latitude. The highest and lowest elevations in the study area were 1704 and 1417 m above sea level, respectively. The climate is semi-arid, averaging 9.4 °C, with annual precipitation of 291 mm. Light rain throughout most months, influenced by proximity to northern forests, imparts a climate resembling semi-humid condition.

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Fig. 1

Location of study area and sampling points in different land uses

The area’s moderately hilly terrain, with slopes and elevation changes, impacts water runoff, soil erosion, and microclimate, affecting soil moisture and agricultural yields. The land consists of cultivated fields and untouched pastures, with some areas farmed using traditional methods like terracing or contour plowing. Lighter green areas with scattered bushes suggest uncultivated regions or land that has reverted to its natural state. Varied vegetation, including dense shrubs and trees, hints at past deforestation for agriculture. This combination of vegetation signals a risk of soil degradation, especially given the steep terrain, if not properly managed. Overall, the region’s topography plays a crucial role in shaping land use and ecological patterns.

Seventy-two soil samples were collected during autumn using a systematic grid with approximately 200-m random spacing (Fig. 1). While the grid provided structure, randomizing the sampling points within each cell improved representativeness and ensured comprehensive area coverage, minimizing the influence of underlying soil property patterns. Two samples, one for physicochemical analyses and another for biological experiments, were taken from depths of 0 to 30 cm. After transfer to the lab, the first set of samples was air-dried, sieved (2 mm), and stored in labeled plastic containers. The second set, intended for biological assessments, was immediately placed in labeled plastic bags with ice packs and stored in a refrigerator at 4 °C in the dark until experiments were conducted.

Assessing soil physicochemical and microbial properties

In order to measure certain physicochemical properties of the prepared soil samples, including soil texture class, it was conducted using the Bouyoucos hydrometer method (Beretta et al., 2014), total porosity (Reynolds et al., 2009), field capacity moisture (Hillel., 2012), and organic carbon using the Walkley and Black wet oxidation method (Jha et al., 2014). pH was directly measured in a soil:water ratio 1:2 suspension, and electrical conductivity was in the suspension extract (Aboukila & Norton, 2017), and calcium carbonate equivalent was determined by titration (Page et al., 1982).

The total microbial population was determined by the most probable number (MPN) method (Panen & Breg, 1998). Microbial biomass carbon (Sparling and West 1988) and nitrogen (Brookes et al., 1985) were measured using the fumigation-extraction method. Microbial basal respiration was measured using the method of collecting freely released CO₂ in sodium hydroxide (NaOH) and titrating the remaining amount with hydrochloric acid (Anderson, 1982). Metabolic quotient (mg CO₂-C_resp mg⁻¹ C_mic h⁻¹) was calculated by dividing the BR (mg C-CO₂ g⁻¹ soil day⁻¹) by the amount of MBC (mg C_mic-CO₂ kg⁻¹ soil) (Anderson, 1982). Microbial quotient (mg C_mic-CO₂ g⁻¹ C_org soil) was calculated by dividing MBC by soil organic carbon (Anderson & Domsch, 1989).

Assessing the spatial distribution of microbial indices

The spatial distribution of microbial indices (MPN, MBC, MBN, BR, q_mic, and qCO₂) was determined using Kriging. Kriging is a geostatistical interpolation technique used to estimate values at unsampled locations based on nearby observed data (Oliver & Webster, 1990). The process involves the following key steps:

1. Variogram modeling: The variogram quantifies the spatial variability of a variable by measuring the average squared differences between values at pairs of locations, as a function of the distance between them. This provides insights into the spatial correlation of the data (e.g., R², RSS, C/(C₀ + C)), indicating that closer points tend to have more similar values. Various variogram models (such as spherical, exponential, and Gaussian) are fitted to the data to capture the underlying spatial structure (Gringarten & Deutsch, 2001).

2. Weight estimation: For each unsampled location, Kriging assigns weights to the surrounding observed data points. These weights are determined by solving a system of equations that minimizes the estimation variance. The spatial correlation between the unsampled location and the observed data is reflected in the weights, with closer points generally receiving higher weights (Van Beers & Kleijnen, 2004).

3. Interpolation: Using the calculated weights, Kriging generates an estimated value for each unsampled location. This estimate is a linear combination of the observed values, weighted according to their spatial relationships to the prediction location.

In this study, the coordinates of sampling points were recorded using a Global Positioning System (GPS) (Fang et al., 2012). The experimental variograms of microbial indices were plotted using GS + Geostatistics software (Iduma & Uko, 2017). Parameters such as threshold, range, and nugget were determined, and the best-fitting variogram model was selected. Kriging was then employed to interpolate and estimate microbial indices at unsampled locations. Soil microbial index distribution maps were generated using geostatistical Kriging methods within a GIS environment (Iduma & Uko, 2017). To select the best-fitting model for the semivariogram, we used criteria including maximum R², minimum RSS, maximum semivariogram range, and the maximum structured fraction of the semivariogram (C/(C₀ + C)), which corresponds to the minimum nugget-to-sill ratio (Iduma & Uko, 2017).

Additionally, a high-precision 10-m digital elevation model (DEM) was created from a 1:50,000 scale topographic map of Iran using Global Mapper software. This DEM was obtained by digitizing and processing polygon data to ensure a detailed and accurate representation of the terrain. The resulting DEM was exported for further spatial analysis within a GIS environment, serving as a robust foundation for our geostatistical studies (Guo and Gifford, 2002; Fang et al., 2012).

Statistical analysis

In this study, a completely randomized design was employed. Mean comparisons across three land use types (agricultural, rangeland, and forest) were conducted using the unpaired T-test method, implemented in SPSS (V.19). Data normality was assessed using the Shapiro–Wilk test (1965), and graphs were created using Excel. To evaluate the effectiveness of the kriging method in estimating microbial indices, the correlation between estimated and quantified values was calculated and assessed in SPSS19. Furthermore, the Pearson correlation between microbial indices and the measured physicochemical properties was also determined using SPSS19.

Results and discussion

Some descriptive statistics of measured soil physicochemical and biological properties on the study area are presented in Table 1. Descriptive statistics of soil properties revealed that the majority of soils in the study area belong to the loamy texture class. The pH values ranged from 5.4 to 6.9; organic carbon content varied between 0.6 and 4.6%, and calcium carbonate content ranged from 0.67 to 16.67%. The maximum electrical conductivity measured was less than 2 dS m⁻¹, indicating low salinity in the region’s soils. The average values for MBC and MBN, BR, and q_mic were 881 mg C_mic-CO₂ kg⁻¹, 95.9 mg N_mic kg⁻¹, 1.21 mg C-CO₂ g⁻¹ day⁻¹, and 5.4 mg C_mic-CO₂ g⁻¹ C_org, respectively. The variation in soil microorganism abundance ranged from 5.97 × 10⁷ to 1.07 × 10⁹ per gram of dry soil, and the qCO₂ varied from 0.84 to 1.54 mg CO₂-Cg⁻¹ C_mic h⁻¹.

Table 1. Descriptive statistics of measured variables

Property	Unit	Min.	Max.	Mean	C.V.	Skewness	Kurtosis
pH		5.4	6.93	6.33	0.05	–0.46	0.11
EC	dS m-1	0	0.69	0.25	0.16	0.64	–0.57
CaCO3	%	0.67	16.67	88.01	0.42	0.21	–0.51
OC		0.58	4.6	1.91	0.75	0.98	3.3
Sand		6.7	59.32	31.34	0.4	0.04	–0.71
Silt		20.78	62.55	42.25	0.25	0.2	–0.8
Clay		11.43	42.44	26.39	0.28	1.21	–0.69
Porosity		28.38	67.21	44.54	0.12	0.92	2.42
qFC		1.69	5.9	3.37	0.33	0.63	–0.61
MBC	(mg Cmic-CO2 kg−1)	637	1468	881	0.15	1	2.01
MBN	(mg Nmic kg−1)	81.67	146.4	95.9	0.08	2.02	1.35
BR	(mg C-CO2g−1d−1)	0.78	1.95	1.12	0.26	0.37	–0.61
MPN	No. cells g−1	5.97×107	1.07×109	5.2×109	0.67	–0.58	–1.37
θmic	(mg Cmic-CO2 g−1 Corg)	1.94	12.56	5.4	0.3	1.3	1.15
qCO2	(mg C-CO2g-1Cmic.h-1)	0.84	1.54	1.15	0.13	0.32	–0.41

C.V., Coefficient of variation, pH,Acidity, EC: Electrical Conductivity, CaCO_3,Calcium Carbonate Equivalent, OC, Organic Carbon, θ_FC, Field Capacity Moisture, MBC, Microbial Biomass Carbon, MBN, Microbial Biomass Nitrogen, BR, Basal Respiration, q_mic,Microbial Quotient, qCO_2,Metabolic Quotient.

The impact of land use type on soil microbial indices

The highest MBC recorded was 901 mg C_mic-CO₂ kg⁻¹ in forest soils, exceeding values in agricultural (883 mg C_mic-CO₂ kg⁻¹) and rangeland soils (834 mg C_mic-CO₂ kg⁻¹) (Fig. 2A). In contrast to the mentioned index, MBN content in rangeland soils (94.2 mg N_mic kg⁻¹) was higher than in agricultural soils (93.1 mg N_mic kg⁻¹), without statistical significance. The peak value for MBN (97.8 mg N_mic kg⁻¹) was also observed in forest soils (Fig. 2B). The maximum and minimum BR values (1.25 and 1.14 mg C-CO₂ g⁻¹ day⁻¹, respectively) were observed in forest and agricultural soils, exhibiting a significant difference at a 5% level (Fig. 2D). Forest land use had a significantly higher MPN (5.63 × 10⁹ cells g⁻¹ soil) compared to the other two land uses (Fig. 2C). The highest q_mic was found in agricultural soils (2.88 mg C_mic-CO₂ g⁻¹ C_org), with no significant difference compared to rangeland use (Fig. 2E). The measured values of the qCO₂ were almost identical across the three land uses, with no statistically significant difference observed (Fig. 2F). Lepcha and Devi (2020) confirmed that land use, soil depth, and seasonal variations significantly influence soil physicochemical and biological characteristics. Consistent with these findings, research shows that soil microorganisms are highly sensitive to land use changes due to their reliance on various soil properties, such as organic carbon content, nutrient availability, pH, bulk density, porosity, and moisture. These factors have a profound impact on the microbial community’s structure, abundance, and activities (Padiab and Abbasi-Kalo, 2020; Sui et al., 2019). The type of land use—whether forest, rangeland, or agricultural—has a pronounced effect on SMB, MBC, and MBN (Evangelou et al., 2021; Kara & Bolat, 2008; Leeuwen et al., 2017). Forest soils typically show higher values for these microbial indicators compared to other land uses. However, when forests are converted to agricultural land, there is a notable decline in SMB, MBC, and MBN (Pandey et al., 2010). The elevated organic matter content in forest and rangeland soils plays a key role in boosting MBC, MBN, MPN, and BR in these ecosystems. Research suggests that converting forest and rangeland areas to agricultural use speeds up the decomposition process, reduces organic carbon inputs, and leads to a significant drop in soil organic matter content. This transformation has negative consequences, particularly for the physicochemical and microbial properties of the soil ecosystem (Brady and Weil, 2008). The findings of this study, along with other research, suggest that the high bioavailability of carbon in agricultural soils promotes a dynamic and thriving microbial community (Evangelou, 2021). The elevated q_mic in agricultural land use can be attributed to practices like tillage and plowing, which enhance soil aeration, providing more oxygen for microbial growth and activity. Additionally, breaking up soil clods and aggregates increases the bioavailability of organic matter, making it more accessible to heterotrophic microorganisms. These factors promote greater microbial growth and activity, leading to higher microbial indices, including MBC. Moreover, the application of chemical fertilizers supplies readily available carbon sources, further boosting microbial activity, increasing MBC, and subsequently raising the microbial quotient in agricultural soils (de Cassia Lima Mazzuchelli et al., 2020). In contrast, forests store high levels of carbon, conversion to agriculture can decrease carbon inputs, and tillage can expose protected carbon to decomposition, leading to a decline in soil organic carbon (Verchot, 2010). The high q_mic in rangeland soils is linked to high productivity, dense root accumulation in surface soil, and annual regrowth (Evangelou, 2021). Furthermore, the plant community and products present in the rhizosphere are stimulated by grazing (Liu et al., 2018), which, in itself, can lead to the dynamism and mobility of the microbial community in the soil (Frank et al., 1995). In contrast, forest soils contain substantial amounts of phenols and other components that might inhibit microbial growth and activity (Priha and Oksa, 2001; Nsabimana et al., 2004). The qCO₂ is a valuable parameter for assessing stress in soil ecosystems. In soils characterized by increased microbial biomass, the qCO₂ typically exhibits a lower value (Anderson, 1994; Sparling, 1997). Stressful conditions prompt microorganisms to enhance energy consumption for biomass maintenance (Anderson, 2003). The study revealed a notably higher qCO₂ in agricultural soils compared to two other land uses, potentially attributed to the extensive use of pesticides and soil compaction resulting from agricultural machinery traffic (Kaiser et al., 1995; Wardle & Parkinson, 1992).

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Fig. 2

Impact of different land uses (forest, rangeland, and agricultural) on soil microbial indices. Dissimilar letters indicate a significant difference at the 5% probability level. Microbial biomass carbon (A), microbial biomass nitrogen (B), microbial population (C), basal respiration (D), microbial quotient (E), and metabolic quotient (F)

MBC and MBN demonstrated significant positive correlations with all microbial indices (excluding qCO₂) at a 1% probability level (Table 2). The most robust correlation (r = 0.91) was observed between MBC and MBN. All assessed microbial indices displayed meaningful correlations with basal respiration and the soil microbial population. The qCO₂ showed a significant and positive correlation (p ≤ 0.01) only with BR and MPN. MBC and MBN showed positive and significant correlations with, OC%, and silt (p ≤ 0.05), as well as with the agricultural threshold soil moisture (p ≤ 0.01). There were notable and positive correlations observed between BR and OC%, as well as θ_FC at a significance level of p ≤ 0.01, and soil porosity percentage at a significance level of p ≤ 0.05. The MPN exhibited a significant and positive correlation (p ≤ 0.05) solely with θ_FC. There were significant and positive correlations between BR and OC%, as well as θ_FC (p ≤ 0.01), and soil porosity percentage (p ≤ 0.05). The MPN showed a significant and positive correlation (p ≤ 0.05) only with θ_FC. pH, Acidity, EC, Electrical Conductivity, CaCO_3, Calcium Carbonate Equivalent, OC, Organic Carbon, θ_FC, Field Capacity Moisture, MBC, Microbial Biomass Carbon, MBN, Microbial Biomass Nitrogen, BR, Basal Respiration, MPN, Microbial Population q_mic, Microbial Quotient, qCO_2, Metabolic Quotient.

Table 2. Pearsons correlation coefficient between selected biological indicators and other soil characteristics

Properties	MBC	MBN	BR	MPN	qmic	qCO2
MBC	1
MBN	0.91**	1
BR	0.81**	0.64**	1
MPN	0.36**	0.31**	0.39**	1
qmic	0.29*	0.31**	0.28*	0.30*	1
qCO2	0.19ns	0.03ns	0.60**	0.22ns	0.14ns	1
OC%	0.29*	0.28*	0.11**	0.03ns	0.26*	–0.00ns
CaCO3	0.05ns	0.04ns	0.12ns	–0.12 ns	0.15ns	0.16ns
Porosity%	0.06ns	0.01ns	0.06*	–0.09 ns	0.11ns	–0.05ns
Clay%	–0.10ns	–0.10ns	–0.08ns	0.13 ns	–0.14ns	–0.03ns
Sand%	–0.11ns	–0.09ns	–0.12ns	0.00 ns	0.64**	–0.07ns
Silt%	0.25*	0.30*	–0.20ns	–0.10 ns	–0.68**	0.10ns
pH	–0.04ns	–0.01ns	–0.07	–0.013 ns	0.19ns	–0.14ns
EC	–0.10ns	–0.16ns	-0.16 ns	–0.06ns	0.01ns	0.21ns
θFC%	0.43**	0.59**	0.37**	0.14*	0.07ns	0.00ns

ns, *, ** indicate non-significant (p>0.05), significant at a probability level of 0.05 (p<0.05) and 0.01(p<0.01), respectively.

In the soil ecosystem, factors like organic matter quantity, moisture levels, soil structure, and permeability, combined with essential material provision and optimized conditions for temperature, water, oxygen concentration, and absence of toxic compounds, boost microorganism quantity and activity. This promotes their growth and development. The study highlights a positive correlation between soil microbiological indicators and certain physicochemical properties. Fierer et al. (2009) found a notable correlation between the MBC and MBN with OC% and total nitrogen. Padali et al. (2022) demonstrated a mutual relationship and positive correlation between the physicochemical characteristics of the soil MBC and MBN in the soils of central Himalayan forests in India. According to the findings in Babur et al. (2022), carbon stocks demonstrated positive correlations with pH (r = 0.61), total nitrogen (r = 0.6), and ash content (r = 0.41). Conversely, there were negative correlations observed with MBC (r = − 0.46), MBN (r = − 0.61), and BR (r = − 0.48).

Digital elevation model

According to the DEM map of the region (Fig. 3), the elevation gradually increases from west to east. A comparison between the output map of indices and the DEM map highlighted that higher elevations are associated with increased levels of MBC, MBN, BR, and MPN (Figs. 3 and 4). Notably, no substantial changes in q_mic and qCO₂ were observed with increasing elevation. Altitude, a topographic indicator influenced by precipitation and solar radiation, significantly affects soil temperature, moisture, and evaporation, impacting the microbial environment (Carletti et al., 2009; Dearborn & Danby, 2017). Increasing altitude results in climate- and vegetation-induced changes in soil physicochemical properties, ultimately affecting soil microorganisms (Hu et al., 2016; Lu et al., 2019). Soltani Toularoud and Asghari (2021) found that soil MBC, MPN, and BR were significantly higher at higher elevation shoulder slopes compared to the foot and toe of the slope. Dearborn and Danby (2017) observed an elevation-related increase in biological indices on north-facing slopes, while south-facing slopes showed no distinct trend with elevation changes. Zhang et al. (2022) noted significant physicochemical differences in soil samples from three elevations in a dry valley, with the highest values of organic carbon biomass, MBC, total nitrogen, and available nitrogen at higher elevations and the lowest at intermediate elevations. Microbial communities exhibited the lowest abundance and diversity at intermediate elevations. Müller et al. (2017) found that soil MBC varied between elevations, measuring 204 mg C_mic-CO₂ kg⁻¹ at higher elevations and 171 mg C_mic-CO₂ kg⁻¹ at lower elevations. The study emphasized the substantial difference in MBC, indicative of microbial community abundance, particularly at higher elevations. This association with lower temperatures, reduced evaporation, and decreased soil respiration suggests that higher organic carbon provides a more substantial energy source for soil microbial communities in elevated conditions.

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Fig. 3

Digital elevation model (DEM) map of the studied area

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Fig. 4

The spatial variability map of soil biological properties of the studied area

Spatial distribution map of biological indices

To analyze the spatial distribution of soil biological indices in the studied region, a map was generated using kriging. Table 3 provides the specifications of the best-fitted model based on the semivariogram of the examined indices. The chosen variogram model exhibited variations across all three indices. Considering the highest R², range of influence, and the lowest RSS and nugget effect, the optimal model for estimating MBC, MPN, and qCO₂ was determined to be linear. A spherical model was selected for MBN, while an exponential model was preferred for BR and q_mic (Table 2). Maps of soil microbial indices were created using selected variogram models and the kriging interpolation method. Subsequently, soil biological features in the region were classified into four classes, as illustrated in Fig. 4. Kriging-generated maps highlight elevated MBC and MBN levels in the northern and northeastern regions, especially within the forest land use zone. This trend may be attributed to proximity to the forest and the rapid decomposition of plant residues (Chander et al., 1995). In the east (forest land use) and west (grazing land use) regions, q_mic respectively exhibited the highest and lowest values. Increased decomposable organic matter in the soil correlates with heightened microbial and enzymatic activities, resulting in an increase in microbial biomass and q_mic (Chander et al., 1995). In the central and southwest agricultural zones, carbon and nitrogen microbial biomass were notably lower. This decline in microbial indicators can be attributed to the intensive use of pesticides and chemical fertilizers and the loss of decomposable organic matter in the soil due to improper land management and farming practices (Zeng et al., 2009; Dixon and Massey, 1983). Over 55% of the surveyed lands exhibited an organic carbon content exceeding 1% (Fig. 4). The study revealed an ascending trend in SMB, BR, MPN, and q_mic with increasing elevation from west to east (Fig. 3). This pattern may be attributed to the relatively higher precipitation and soil moisture associated with elevated terrain. Furthermore, areas with higher SMB and q_mic showed a decrease in qCO₂, indicating a potential correlation between lower qCO₂ and improved soil biological conditions (Wardle & Ghani, 1995). Ghorbanzadeh et al. (2019) studied the spatial variations and frequency distribution of soil microbial indices and other soil properties in the Shafaroud Forest, northern Iran. They found that the spatial patterns of soil microbial indices were influenced by both non-biotic factors and forest management practices. The generated maps illustrated spatial diversity in soil microbial indices and properties, highlighting a correlation between microbial indices and organic carbon and nitrogen.

Table 3. Models fitted on single semivariogram and summary of geostatistical data of some soil biological indicators

Properties	Model	C0	C+C0	$\frac{C}{C_0+C}$	A0	R2	RSS
MBC	Linear	18561.55	18561.0	0.5	2091	0	2.521E+08
MBN	Spherical	35.5	71.1	0.5	0.5	0.04	6584
BR	Exponential	0.057	0.114	0.5	5110	0.496	0.496
MPN	Linear	125664.9	125664.9	0	2091	0.022	7.8E+08
qmic	Exponential	5.3	10.6	0.5	5110	0.74	0.820
qCO2	Linear	0.019	0.02	0.062	2091	0.02	8.63E-05

To evaluate the interpolation method for estimating soil biological properties, kriging-derived values for these properties were extracted at the sampling locations. The correlation between the measured soil index values in the soil samples and the kriging-predicted values was calculated, as summarized in Table 4. The study revealed significant correlations between measured and kriging-interpolated values for all biological indices, excluding qCO₂. This underscores the effectiveness of kriging in estimating soil biological properties. Bakhshandeh et al. (2019) emphasized kriging’s excellence not only as an interpolation tool but also for providing an unbiased overview of the studied region.

In a related study, Babur et al. (2022) investigated spatial changes in soil organic carbon, SMB, and soil quality indicators after a forest fire in red pine forests. They employed kriging and inverse distance weighting (IDW) interpolation methods. Results indicated that IDW was more effective in predicting apparent density, pH, and MBC and MBN, while kriging performed better for other soil properties.

Table 4. The Pearson correlation coefficient between measured and estimated values of biological properties

Properties	Kriging
Microbial biomass carbon	0.292*
Microbial biomass nitrogen	0.265*
Basal respiration	− 0.234*
Microbial population	− 0.223*
Microbial quotient	0.264*
Metabolic quotient	0.013ns

ns, *, indicate non-significant (p>0.05) and significant at a probability level of 0.05 (p<0.05), respectively.

Conclusion

In this study, the indices for microbial biomass carbon, microbial biomass nitrogen, microbial population, and basal respiration exhibited higher values in forested areas compared to rangeland and agricultural lands. This disparity is attributed to reduced microbial activity in agricultural and rangeland settings, stemming from diminished organic matter (carbon and energy sources), extensive application of agrochemicals (fertilizers and pesticides), increased soil compaction due to machinery use, and intensive grazing by livestock. Consequently, the elevated qCO₂ levels observed in these land uses indicate stress on microorganisms, confirming the aforementioned conditions. This underscores that transitioning from pristine areas to rangelands and agricultural lands could lead to a significant decline in soil microbial diversity and activity over time, posing a substantial threat to ecosystem integrity. All microbial indices analyzed in this study exhibited dependence on topography, suggesting that changes in elevation—affected by climate and vegetation cover—profoundly influence the activity of soil microbial communities and their associated characteristics. This underscores the critical impact of elevation on soil microbial dynamics as essential components of the soil’s living ecosystem. The study findings underscore the efficacy of kriging for estimating microbial indices within the study area, with linear models proving suitable for most microbial indices. Kriging maps depict a consistent increase in microbial indices from the western to the eastern and northern regions, mirroring the elevation gradient. These outcomes reaffirm the positive correlation between elevation and microbial indices, highlighting elevation’s beneficial influence on microbial activity and diversity. Future research should focus on the long-term effects of different land use types on soil microbial biodiversity, enzyme activities, and nutrient cycles to better understand their influence on soil health and ecosystem function. Additionally, exploring the interaction between elevation, microclimatic conditions, and microbial populations, alongside advanced geospatial methods like machine learning, could enhance the accuracy of microbial distribution predictions, supporting more informed land management strategies.

Acknowledgements

We wish to express our gratitude to the University of Mohaghegh Ardabili for offering the resources and supportive environment essential for this research.

Author contribution

Zahra Karimzadeh and Hossein Shahab Arkhazloo: project administration, investigation, conceptualization, formal analysis, writing—original draft, supervision. Ali Ashraf Soltani Toularoud: validation, editing—original draft, supervision, visualization. Tohid Rouhi-Kelarlou: writing—review, editing, and original draft. All authors read and approved the final manuscript.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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