Integrating Microbiological Quality Indicators and Soil Properties Through Score Functions to Assessment Land Uses Changes in Colombian Andisols

Integrating Microbiological Quality Indicators and Soil Properties Through Score Functions to Assessment Land Uses Changes in Colombian Andisols | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating Microbiological Quality Indicators and Soil Properties Through Score Functions to Assessment Land Uses Changes in Colombian Andisols Raul Aranguren, Julio Cañón, Maria Teresa Florez, Erica Lumini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8544511/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reliable assessment of soil functionality requires integrative indicators that capture biological, physicochemical, and environmental responses to land-use disturbances. This study evaluated whether a reduced suite of low-cost microbial, chemical, and microclimatic indicators is sufficient to discriminate soil functional states across agricultural fields, mining sites, and non-disturbed areas in Andisols of northeastern Antioquia, Colombia. Soil samples were analyzed for microbial abundance and respiration, organic carbon, moisture, solute concentration, and microclimatic variables. Dimensionality reduction and variable integration were performed using geometric means, factor analysis, and scoring functions. Three indicators: microbial respiration rate (MRRat/C), total dissolved solids (TDS), and relative air humidity (RAH), were identified as the most informative and least redundant within the multivariate domain. Factor analysis showed that microbial indicators contributed 34.77% of total variance, demonstrating their central role in distinguishing functional soil states. Soil Quality Indices (SQI) derived from nonlinear scoring functions with importance-coefficient weighting yielded the highest sensitivity in differentiating land uses, showing clear declines from non-perturbed to agricultural to mining areas. Results highlight the strong responsiveness of microbially mediated processes to disturbance and support the adoption of non-linear, weighted indices as early-warning tools for vulnerable tropical Andisols. Compound indicators Geometric mean Nonlinear functions Soil quality Index Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction, scope and main objectives Soil functionality is a central attribute of terrestrial ecosystems, reflecting the capacity of soils to sustain biological productivity, regulate biogeochemical cycles, and maintain environmental quality (Lehmann et al., 2020). The assessment of soil functional status across contrasting land use regimes has gained importance alongside intensifying global anthropogenic pressures. Agricultural intensification, for example, can alter nutrient dynamics, soil organic matter turnover, and microbial activity (Breitkreuz et al., 2021; Emde et al., 2021), while mining activities often impose more severe disturbances, including shifts in soil physicochemical properties, microclimatic conditions, and degradation of microbial communities (Ramos et al., 2022; Yuan et al., 2023). These processes compromise essential soil functions and, consequently, the ecosystem services they support. Reliable diagnostic tools are therefore needed to evaluate soil quality in landscapes where both agriculture and mining coexist and exert cumulative impacts on soil systems. For instance, in tropical regions, rapid land use transitions, primarily driven by agricultural expansion and mining, have exerted tremendous pressure on soil systems, often impairing their functional integrity (Agbeshie et al., 2025; Wang et al., 2025). Advances in soil quality assessment have produced numerous indices that integrate biological, chemical, and physical indicators. However, many soil quality indices require extensive variable sets, resulting in high costs, operational complexity at scale, and interpretive challenges within routine monitoring frameworks (Thakur et al., 2022). Consequently, a critical knowledge gap persists regarding the capacity of a minimum indicator set to adequately represent soil functionality across contrasting land uses without sacrificing critical information on soil system state. While multivariate analyses facilitate the streamlining of indicator selection, the subsequent strategy for their integration remains unresolved. Methodological ambiguities persist concerning optimal combination rules, specifically the selection of weighting schemes (linear versus non-linear) (De Paul Obade & Lal, 2016; Thakur et al., 2022). Likewise, a corresponding gap exists in selecting aggregation algorithms that can synthesize compound variables serving as high-fidelity metrics for complex, multivariate soil components considering that reducing the number of variables is useful only if key functional information is not lost (P. Li et al., 2023; Qian et al., 2023). Previous research has used composite indices, multivariate reduction techniques, and threshold-based scoring systems to evaluate soil functionality. These approaches highlight the value of biological variables, particularly microbial activity metrics, as sensitive early-warning indicators of disturbance (Alori et al., 2024; Yang et al., 2025). Additionally, Scoring Functions (SF) have been useful to standardize raw measurements into unitless performance values, enabling the representation of both linear and non-linear ecological responses within soils. This transformation is critical for capturing threshold behaviors, diminishing returns, and saturation dynamics inherent to microbial processes and soil biogeochemistry (Amgain et al., 2022; Chahal et al., 2023). Likewise, studies have explored the integration of related variables into compound indicators based on Geometric Means (GM), which reduce dimensionality while preserving proportional relationships among variables. For example, many soil quality studies use the geometric mean diameter (GMD) of soil aggregates as a key indicator because it provides a robust, process-consistent measure of aggregate size distribution and stability (Gondim et al., 2023; Y. Li et al., 2023). Besides, numerous soil quality studies employ the GM of microbial enzymatic activities as an integrative functional indicator (Martín-Sanz et al., 2022; Paz-Ferreiro et al., 2012; Paz‐Ferreiro & Fu, 2016) given that GM in multi-enzyme index decreases sharply when one or more enzyme activities decline, making the metric sensitive to early functional impairments that might be masked by arithmetic averaging (Nardo et al., 2005). The GM is suited to for variables that differ in units, dynamic ranges, and typically exhibit log-normal distributions, as it is based on multiplicative rather than additive relationships, reduces sensitivity to extreme values, and preserves meaningful proportional differences following appropriate data normalization (Petrović et al., 2023). However, the efficacy of these methods for summarizing other functionally relevant variables such as environmental temperatures and culturable microbial populations in soil systems remains shortly assessed, especially, within vulnerable contexts like tropical Andisols under agricultural and extractive pressures. In this context, the present study asks whether soil functionality in natural forest, agricultural areas, and mining sites can be reliably represented using a minimum set of single and compound indicators, with the natural forest serving as a non-perturbed reference condition. The simple indicators include direct measures such as microbial respiration per unit carbon, soil moisture, total organic carbon, electrical conductivity, total dissolved solids, and pH. The compound indicators are derived via GM from related variables, including environmental temperature descriptors, fungal and mesophilic bacterial abundance, and microbial colony-type diversity. This study delivers a scalable framework for early soil health monitoring in resource-limited tropical regions, using accessible indicators to benchmark disturbed soils against undisturbed Andisols. The central question is whether this integrated set provides a consistent and interpretable response across a disturbance gradient from non-disturbed forest to agricultural and mining land uses. The objective of this study is therefore to identify and test a set of single and GM-based compound indicators capable of representing by a scoring function soil functionality in a natural forest reference system and discriminating soils from agricultural areas and mining sites. We hypothesize that the minimum indicators set obtained from a combination of single and compound variables will able to differentiate non-perturbed, agricultural, and mining soils, reflecting the expected gradient of functional degradation. S tate of the art Soils constitute dynamic, living systems that sustain plant productivity, regulate biogeochemical fluxes, and maintain ecosystem stability. Their functional capacity emerges from interactions among biological processes, physicochemical properties, and external environmental drivers (Nunes et al., 2020). Rapid land use transitions worldwide have intensified the need for robust, diagnostically sensitive indicators capable of detecting functional alterations. Current researchers underscores that soil health evaluations require integrative approaches that acknowledge the coupled nature of soil processes and the pronounced sensitivity of biological components to disturbance. Studies by Fierer et al., 2021, Smith et al., 2021 and Li et al., 2024 demonstrate that biological indicators frequently exhibit earlier responses to land use change than conventional physical or chemical metrics. For instance, soil microorganisms are primary regulators of terrestrial biogeochemical functioning. Their metabolic activities govern the rates and pathways of carbon and nitrogen transformations and mediate the solubilization, mineralization, and mobilization of phosphorus (Z. Li et al., 2021; Philippot et al., 2024). Likewise, during periods of elevated resource availability, microorganisms immobilize inorganic nitrogen and other limiting elements within cellular biomass (Čapek et al., 2021). This immobilization provides a short-term nutrient sink that reduces leaching losses and conserves nutrients within the soil system. Additionally, microbial communities actively modify soil physical architecture. They synthesize extracellular polymeric substances (including glomalin, polysaccharides, and related biopolymers) that function as binding agents to facilitate the formation and stabilization of soil aggregates (Olagoke et al., 2022). Aggregate development, in turn, enhances the formation of a porous and aerated soil matrix, thereby improving hydraulic conductivity, water infiltration, and water holding capacity (Ferreira et al., 2023). Owing to their pivotal role in soil processes and their acute sensitivity to physicochemical and environmental change, microbiological parameters are indispensable indicators of soil functional integrity. Recent investigations indicate that microbial respiration, microbial biomass, and metrics of microbial metabolic activity are among the most sensitive indicators of disturbance in agricultural and mining landscapes. Microbial processes can function as early warning signals of degradation or recovery even when bulk physicochemical properties remain comparatively stable (Joos et al., 2023; Xia et al., 2024). Nevertheless, the interpretation of microbiological metrics is often challenging because microbial responses are strongly modulated by moisture dynamics, temperature fluctuations, and changes in soil chemical conditions (Fierer et al., 2021). Consequently, research on soil quality assessment increasingly advocates for integrative analytical frameworks that combine biological indicators with environmental and physicochemical variables. Andisols are soils that typically contain high concentrations of organic carbon and exhibit high porosity, properties that confer exceptional water-retention capacity, cation-exchange potential, and structural stability. Under intensified land use, however, the earliest signs of degradation usually appear in microclimatic regulation, microbial activity, and aggregate dynamics rather than in bulk chemical properties such as total carbon, total nitrogen, or pH. When monitoring focuses exclusively on classical fertility indicators, early functional decline can remain undetected even while critical biological processes and structural attributes are already deteriorating (Bhaduri et al., 2022; Ghimire et al., 2023; Kong et al., 2025). Detecting functional degradation at an early stage allows managers and researchers to identify subtle shifts in microbial efficiency, mycorrhizal connectivity, enzyme activity, and soil infiltration capacity before the system crosses thresholds that lead to accelerated carbon losses, compaction, and erosion. Methodology Study Area and Sampling Design The study was conducted in Andisols of northeastern Antioquia, Colombia, a region with pronounced gradients in vegetation cover, microclimatic conditions, and land-use intensity. Soils in the area are characterized by high organic matter content and strong surface reactivity, which makes them particularly sensitive to disturbances associated with agricultural practices and mining activities. Soil samples were collected in the municipalities of La Ceja (6°01′14″ N, 75°25′39″ W), El Retiro (6°03′36″ N, 75°24′19″ W), and Envigado (6°10′19″ N, 75°35′09″ W) during September and October 2018. In each municipality, three land-use types were sampled: agricultural land uses (Agr), mining projects (Mp), and non-disturbed areas (Np). For every land-use type within each municipality, 10 composite soil samples were collected from the upper horizon (0–20 cm), following a simple random selection to spatially distribute sampling locations. Environmental and Soil Physicochemical Analyses Microclimate at each sampling point was characterized with a Kestrel 5500 Weather Meter, recording air temperature, relative air humidity (RAH), heat index, dew-point temperature, wind-chill temperature, and atmospheric pressure. In the GAIA research group laboratories, soil pH in water, total dissolved solids (TDS), and electrical conductivity were measured with a LaMotte Salt/pH/TDS/Temp TRACER 1766, while total organic carbon and gravimetric moisture content were determined using the Walkley & Black method (Babej et al., 2024) and gravimetric method (Tan et al., 2019) respectively. Microbiological Indicators Microbiological indicators of diversity and abundance were CFU types and mesophilic bacteria and fungi (CFU g⁻¹ soil). Mesophilic bacterial and fungal abundance was quantified as colony-forming units using plate-count of serial dilution technique (Werheni Ammeri et al., 2023), and microbial respiration rate (MRRat/C) was estimated by the chloroform fumigation–incubation method (Alessi et al., 2011). Multivariate Integration and Index Construction Dimensionality reduction used geometric means for similar variables following the procedure developed by Raiesi & Kabiri (2016). Factor analysis identified the Minimal Data Set (MDS) based on importance coefficients and redundancy checks. Linear and nonlinear scoring functions were applied to normalize variables, and Soil Quality Indices (SQI) were computed both with and without importance-coefficient weighting. The Minimal Data Set (MDS) was constructed to select representative soil variables as follows: initially, variables of the same type were aggregated using the geometric mean (GM) to reduce dimensionality while preserving joint behavior, yielding three composite variables: GM TCol (fungal to bacterial CFU ratio), GM MicAbn (combined bacterial and fungal CFU abundance), and GM Temp (temperature-related microclimatic parameters). Next, one-way ANOVA identified variables significantly affected by land use, which were then subjected to factor analysis to assess their contributions to the overall multivariate structure. Importance coefficients (IC) for each variable were calculated by multiplying the absolute factor loadings by each factor’s variance-explained weight and summing these products across retained factors, identifying variables strongly associated with main variation gradients and thus informative for land-use discrimination. To reduce redundancy, variables highly correlated within the same factor were screened by Pearson correlation coefficients, removing those with lower IC values to maximize unique information while minimizing collinearity. The selected MDS variables were transformed into dimensionless scores using the linear (A) and non-linear (B) scoring functions proposed by Raiesi & Kabiri (2016), which allowed integration of indicators measured on different scales into a unified soil-quality index. In the SQI, w was the IC of each variable and s is the value of the variable. We also calculated SQI without including w considering that all MDS factors had the same relative importance. Results The analysis revealed differences in soil conditions among the three land-use types. With the exception of pH and atmospheric pressure (PA), all environmental variables, soil physicochemical properties, and microbial indicators responded significantly to land use (ANOVA, p < 0.05), which confirms the high sensitivity of the selected indicators to disturbance regimes. The variables attempted by geometric means showed consistent shifts in microclimatic, microbial, and chemical attributes across Agr, Mp, and Np areas (Figure 1). Factor analysis identified a well-defined multivariate structure comprising three factors with eigenvalues greater than 1, jointly explaining 71.87% of the total variance (Figure 2). Factor 1 (34.77%) was dominated by variables related to biological activity and organic matter dynamics, including GM Abundance Fung./Bact., MRRat/C, %C, and gravimetric moisture, underscoring the central role of microbial processes and carbon availability in regulating soil functional states. Factor 2 (20.49%) showed very high loadings for electrical conductivity and total dissolved solids (TDS), capturing variability associated with solute concentrations and ionic strength, particularly in soils influenced by fertilization and mining-derived inputs. Factor 3 (16.60%) grouped humidity and temperature descriptors, with RAH and GM Temperatures contributing strongly but with opposite signs, highlighting the importance of microclimatic regulation in volcanic soils with high surface reactivity (Table 1). Table 1. Factor Analysis of a set of variables to measure soil functionality in Colombian Andisols Variable Factor 1 Factor 2 Factor 3 Communality Specific Variance RAH 0.05 -0.10 0.88 0.79 0.20 GM Temp. -0.05 0.04 -0.84 0.71 0.28 GM TCol. -0.44 0.03 0.15 0.22 0.77 GM MicAbn. 0.73 -0.19 -0.14 0.59 0.40 MRRat/C -0.86 0.10 -0.21 0.80 0.19 Cond. -0.08 0.95 -0.08 0.93 0.06 TDS -0.11 0.96 -0.08 0.96 0.03 Moist 0.70 0.27 0.30 0.66 0.33 TOC 0.80 -0.16 0.28 0.76 0.23 Eigenvalue 3.12 1.84 1.49 Variance (%) 34.77 20.49 16.64 Cumulative variance (%) 34.77 55.27 71.87 Note: Relative Air Humidity (RAH); Geometric Mean Temperatures (GM Temp); Geometric Mean Type of Colonies (GM TCol); Geometric Mean Microbial Abundances (GM MicAbn); Microbial Respiration Rate (MRRat/C); Conductivity (Cond); Total Dissolved Solids (TDS); Moisture (Moist); Total Organic Carbon (TOC). p-value matrix for pairwise correlations among variables in a factor analysis showed a large number of significant correlations (p-value < 0.05) between the variables of each factor (Figure 3). To MDS were selected as the variables more informative the MRRat/C, TDS, and RAH as the most informative and least redundant indicators across the multivariate domain. These three variables formed the final MDS, capturing the dominant biological, chemical, and microclimatic gradients associated with land-use effects. SQI derived from the MDS showed marked contrasts among land-use types (Figure 4). Across all index formulations, Mp consistently exhibited the lowest SQI values, Np the highest, and Agr occupied an intermediate position. Indices based on nonlinear scoring functions produced the greatest separation among land uses, indicating that nonlinear transformations captured threshold responses and diminishing marginal changes typical of microbial and physicochemical processes. Incorporation of IC-based weighting further increased index sensitivity, sharpening contrasts among land uses and improving the capacity to detect functional degradation associated with vegetation removal, altered nutrient inputs, and microclimatic disruption. Discussion The results reveal that a small set of microbially and environmentally sensitive indicators provides a clear functional distinction among natural forest, agricultural soils, and Andisols affected by mining activities. Microbial respiration, solute concentration, and relative air humidity were consistently the most responsive and least redundant indicators, capturing major shifts in soil quality features along a disturbance gradient. These findings confirm that soil functionality in volcanic systems can be characterized using a minimal indicator set, with microbial metabolic responses and microclimate variables acting as sensitive early-warning signals of soil quality disturbance. The strong contribution of microbial respiration and metrics derived from culturable microbial aligns with evidence indicating that microbial metabolism responds more rapidly to land-use disturbance than bulk physicochemical attributes (Semenov et al., 2025 ). Higher values in respiration rates in non-perturbed areas indicate efficient use of carbon substrates and balanced respiratory activity, whereas depressed values in agricultural areas and mining sites suggest metabolic constraints (Bröring et al., 2023 ; Ferreira De Lima et al., 2023). The close alignment between respiration rates and CFU-based metrics highlights the interconnected nature of microbial abundance, metabolic capacity, and colony structure when soils experience increasing levels of disturbance. Previous studies in another type of soil have shown pronounced reductions in microbial biomass and respiration following agricultural intensification or mining activities, driven by declines in organic matter inputs (De Oliveira Santos et al., 2022 ), structural disruption (Lima et al., 2022 ), and altered moisture regimes (Siebielec et al., 2020 ). The present pattern, where microbial respiration sharply decreases from forest to agriculture and mining sites, corroborates these observations and reinforces the role of microbial metabolism as a core integrator of soil functional state. Given that Andisols rely heavily on organic matter to maintain structural integrity, concomitant reductions in organic carbon and soil moisture intensify the effects of disturbance on microbial communities and contribute to the broader functional deterioration observed in disturbed sites. The selection of relative air humidity as a key indicator further underscores the importance of microclimatic regulation in these high-organic-matter soils. Similar observations have been reported in Andisols and other volcanic-derived soils, where microclimate strongly constrains microbial enzymatic activity and decomposition processes (Lyu et al., 2021 , 2024 ). Mining and intensive agriculture often modify canopy cover, surface roughness, and moisture buffering capacity, generating microclimatic shifts that propagate rapidly to microbial processes (Diba et al., 2025 ; Peng et al., 2021 ). The strong performance of relative air humidity in discriminating land uses is consistent with this mechanistic understanding. Complementarily, the identification of total dissolved solids as a chemical indicator highly sensible to soil matrix perturbations aligns with established evidence. Specifically, the studies have demonstrated that ionic enrichment from sources such as fertilizers or mining residues induces osmotic stress, suppresses microbial growth (Luo et al., 2025 ), and accelerates the disruption of soil chemical equilibria across land-use gradients (Haj-Amor et al., 2022 ). Evidence from tropical mining landscapes has demonstrated that ionic toxicity and salinization often precede declines in nutrient status, making TDS a useful proxy for early disturbance (Corwin & Yemoto, 2020 ; Karikari et al., 2021 ; Stavi et al., 2021 ). The discriminative strength of TDS in this study reinforces the view that simple, inexpensive indicators can reliably capture chemically driven degradation in disturbed Andisols. Respect to the scoring functions, the performance of the non-linear, importance-weighted soil quality index reflects widely recognized non-linearities in soil ecological responses. Microbiological, environmental, and physicochemical attributes interact through feedbacks that generate threshold behavior, amplification effects, and non-proportional changes in soil function (Noronha et al., 2025 ; Wubs, 2025 ). Recent studies have shown that non-linear scoring functions and weighted indices outperform linear approaches when variables differ in sensitivity and ecological relevance to soil quality assessments across different land uses (Amgain et al., 2022 ; Iheshiulo et al., 2024 ; Lenka et al., 2022 ). In this study, the stronger discrimination among land uses achieved through the non-linear weighted index aligns also with the fact that the non-linear functions in soil quality index lies in its ability to separate intermediate disturbance levels (Uthappa et al., 2024; L. Wang et al., 2023). The use of compound indicators computed through geometric means contributed meaningfully to the integration of microbial and microclimatic variables related with the temperature. The ability of geometric means to preserve proportional relationships and minimize noise in ecologically variable indicators has been emphasized in this work. Although the final minimal set consisted exclusively of single indicators, compound metrics played an essential role in the dimensionality reduction process. This finding confirms that composite variables derived from culturable populations of fungi and mesophilic bacteria can exhibit strong correlations with soil changes driven by land-use alterations. The study benefits from a cross-land-use design that captures realistic gradients of disturbance. Nevertheless, several limitations should be acknowledged. The evaluation was conducted at a single time point, preventing assessment of seasonal stability in indicator behavior. Additionally, reliance on culturable microbial indicators necessarily captures only a subset of microbial diversity. Therefore, molecular and enzymatic indicators could provide complementary, mechanistic insights. The results verify the viability of a short set of indicators to discriminate soil changes and confirm that information derived from compound indicators enhances the analytical process, even when not retained in the final MDS. The sensitivity of microbial and microclimatic indicators positions them as promising candidates for early-warning systems in rapidly changing landscapes, including those facing mining expansion, agricultural intensification, or climate-driven stress. Future research should assess temporal dynamics of the minimal indicator set, test its robustness across multiple volcanic and non-volcanic soil types, and incorporate microbial functional genes or enzymatic profiles to refine ecological interpretation. Development of standardized thresholds and cross-regional scoring calibrations could further advance the implementation of minimalistic yet powerful soil-functionality diagnostics in monitoring at larger scales. Conclusions This study demonstrates that soil functionality in Andisols subjected to contrasting land uses can be effectively diagnosed using a minimal suite of biologically and environmentally sensitive indicators. Microbial respiration, total dissolved solids, and relative air humidity emerged as a robust Minimum Data Set capable of distinguishing natural forest soils from those affected by agriculture and mining. These indicators captured the primary axes of functional variation (biological activity, ionic environment, and microclimatic regulation) highlighting their value as early-warning signals of soil disturbances. The integration of microbial indicators with physicochemical and environmental variables demonstrates that biological metrics as CFU-based indicators and microbial respiration rates are essential for detecting the earliest stages of soil functional decline. This confirms that microbial responses operate as leading indicators of degradation, preceding measurable changes in most soil chemical attributes. Likewise, the strong differentiation among land uses achieved by the non-linear, IC-weighted SQI highlights the importance of adopting multivariate, dynamic assessment frameworks in volcanic soils. Traditional linear indices may underestimate both the magnitude and timing of functional losses, whereas the non-linear SQI can capture subtle shifts in moderately disturbed systems and provides a clearer trajectory of degradation across Np, Agr, and Mp areas. Overall, this work contributes both conceptual and methodological advances to the assessment of soil functionality. 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05:24:31","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165275,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8544511/v1/2625bad21d6e002c6a134250.html"},{"id":100003998,"identity":"bcde31c3-315e-4d18-9c74-0095e288a872","added_by":"auto","created_at":"2026-01-12 05:24:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBehavior of environmental variables, soil properties and microbial indicators integrated by geometric means in Colombian Andisols.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Relative Air Humidity (RAH); Geometric Mean Temperatures (GM Temp); Geometric Mean Type of Colonies (GM TCol); Geometric Mean Microbial Abundances (GM MicAbn); Microbial Respiration Rate (MRRat/C); Conductivity (Cond); Total Dissolved Solids (TDS); Moisture (Moist); Total Organic Carbon (TOC); Non-disturbed areas (Np); Agricultural areas (Agr); Mining projects (Mp).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8544511/v1/b69b502784b39222b1abccb3.png"},{"id":100003993,"identity":"e579aeb1-915d-45ab-add4-771bf9c44d54","added_by":"auto","created_at":"2026-01-12 05:24:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25769,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariables in three factors calculated by FA in Colombian Andisols.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8544511/v1/758767862556db0f2769b38a.png"},{"id":100003990,"identity":"da786fa9-04f9-4853-9f4e-c10be2c50ea0","added_by":"auto","created_at":"2026-01-12 05:24:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ep-value matrix for pairwise correlations among variables in a factor analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Relative Air Humidity (RAH); Geometric Mean Temperatures (GM Temp); Geometric Mean Type of Colonies (GM TCol); Geometric Mean Microbial Abundances (GM MicAbn); Microbial Respiration Rate (MRRat/C); Conductivity (Cond); Total Dissolved Solids (TDS); Moisture (Moist); Total Organic Carbon (TOC).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8544511/v1/fa0aebee5cf3f025353b2534.png"},{"id":100003991,"identity":"ed707716-5955-4030-a9e3-41a6d77b7dfa","added_by":"auto","created_at":"2026-01-12 05:24:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSoil Quality Index computed to different land uses in Andisols of Antioquia, Colombia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Non-disturbed areas (Np); Agricultural areas (Agr); Mining projects (Mp); Soil quality index computed by linear function and importance coefficients (SQI L-CI); Soil quality index computed by linear function without importance coefficients (SQI L); Soil quality index computed by non-linear function and importance coefficients (SQI NL-CI); Soil quality index computed by non-linear function without importance coefficients (SQI NL).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8544511/v1/9e92d9e045d67b30b6757423.png"},{"id":100004051,"identity":"015ce7c5-b3f2-4405-8cb6-2e9d594555bd","added_by":"auto","created_at":"2026-01-12 05:24:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1089356,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8544511/v1/c98e852f-69ac-40be-8c61-ad3917f59eca.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegrating Microbiological Quality Indicators and Soil Properties Through Score Functions to Assessment Land Uses Changes in Colombian Andisols\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction, scope and main objectives","content":"\u003cp\u003eSoil functionality is a central attribute of terrestrial ecosystems, reflecting the capacity of soils to sustain biological productivity, regulate biogeochemical cycles, and maintain environmental quality\u0026nbsp;(Lehmann et\u0026nbsp;al., 2020). The assessment of soil functional status across contrasting land use regimes has gained importance alongside intensifying global anthropogenic pressures. Agricultural intensification, for example, can alter nutrient dynamics, soil organic matter turnover, and microbial activity\u0026nbsp;(Breitkreuz et\u0026nbsp;al., 2021; Emde et\u0026nbsp;al., 2021), while mining activities often impose more severe disturbances, including shifts in soil physicochemical properties, microclimatic conditions, and degradation of microbial communities\u0026nbsp;(Ramos et\u0026nbsp;al., 2022; Yuan et\u0026nbsp;al., 2023). These processes compromise essential soil functions and, consequently, the ecosystem services they support. Reliable diagnostic tools are therefore needed to evaluate soil quality in landscapes where both agriculture and mining coexist and exert cumulative impacts on soil systems. For instance, in tropical regions, rapid land use transitions, primarily driven by agricultural expansion and mining, have exerted tremendous pressure on soil systems, often impairing their functional integrity\u0026nbsp;(Agbeshie et\u0026nbsp;al., 2025; Wang et\u0026nbsp;al., 2025).\u003c/p\u003e\n\u003cp\u003eAdvances in soil quality assessment have produced numerous indices that integrate biological, chemical, and physical indicators. However, many soil quality indices require extensive variable sets, resulting in high costs, operational complexity at scale, and interpretive challenges within routine monitoring frameworks\u0026nbsp;(Thakur et\u0026nbsp;al., 2022). Consequently, a critical knowledge gap persists regarding the capacity of a minimum indicator set to adequately represent soil functionality across contrasting land uses without sacrificing critical information on soil system state. While multivariate analyses facilitate the streamlining of indicator selection, the subsequent strategy for their integration remains unresolved. Methodological ambiguities persist concerning optimal combination rules, specifically the selection of weighting schemes (linear versus non-linear)\u0026nbsp;(De Paul Obade \u0026amp; Lal, 2016; Thakur et\u0026nbsp;al., 2022). Likewise, a corresponding gap exists in selecting aggregation algorithms that can synthesize compound variables serving as high-fidelity metrics for complex, multivariate soil components considering that reducing the number of variables is useful only if key functional information is not lost\u0026nbsp;(P. Li et\u0026nbsp;al., 2023; Qian et\u0026nbsp;al., 2023).\u003c/p\u003e\n\u003cp\u003ePrevious research has used composite indices, multivariate reduction techniques, and threshold-based scoring systems to evaluate soil functionality. These approaches highlight the value of biological variables, particularly microbial activity metrics, as sensitive early-warning indicators of disturbance\u0026nbsp;(Alori et\u0026nbsp;al., 2024; Yang et\u0026nbsp;al., 2025). Additionally, Scoring Functions (SF) have been useful to standardize raw measurements into unitless performance values, enabling the representation of both linear and non-linear ecological responses within soils. This transformation is critical for capturing threshold behaviors, diminishing returns, and saturation dynamics inherent to microbial processes and soil biogeochemistry (Amgain et al., 2022; Chahal et al., 2023).\u003c/p\u003e\n\u003cp\u003eLikewise, studies have explored the integration of related variables into compound indicators based on Geometric Means (GM), which reduce dimensionality while preserving proportional relationships among variables. For example, many soil quality studies use the geometric mean diameter (GMD) of soil aggregates as a key indicator because it provides a robust, process-consistent measure of aggregate size distribution and stability\u0026nbsp;(Gondim et\u0026nbsp;al., 2023; Y. Li et\u0026nbsp;al., 2023). Besides, numerous soil quality studies employ the GM of microbial enzymatic activities as an integrative functional indicator\u0026nbsp;(Martín-Sanz et\u0026nbsp;al., 2022; Paz-Ferreiro et\u0026nbsp;al., 2012; Paz‐Ferreiro \u0026amp; Fu, 2016)\u0026nbsp;given that GM in multi-enzyme index decreases sharply when one or more enzyme activities decline, making the metric sensitive to early functional impairments that might be masked by arithmetic averaging\u0026nbsp;(Nardo et\u0026nbsp;al., 2005).\u003c/p\u003e\n\u003cp\u003eThe GM is suited to for variables that differ in units, dynamic ranges, and typically exhibit log-normal distributions, as it is based on multiplicative rather than additive relationships, reduces sensitivity to extreme values, and preserves meaningful proportional differences following appropriate data normalization\u0026nbsp;(Petrović et\u0026nbsp;al., 2023). However, the efficacy of these methods for summarizing other functionally relevant variables such as environmental temperatures and culturable microbial populations in soil systems remains shortly assessed, especially, within vulnerable contexts like tropical Andisols under agricultural and extractive pressures.\u003c/p\u003e\n\u003cp\u003eIn this context, the present study asks whether soil functionality in natural forest, agricultural areas, and mining sites can be reliably represented using a minimum set of single and compound indicators, with the natural forest serving as a non-perturbed reference condition. The simple indicators include direct measures such as microbial respiration per unit carbon, soil moisture, total organic carbon, electrical conductivity, total dissolved solids, and pH. The compound indicators are derived via GM from related variables, including environmental temperature descriptors, fungal and mesophilic bacterial abundance, and microbial colony-type diversity. This study delivers a scalable framework for early soil health monitoring in resource-limited tropical regions, using accessible indicators to benchmark disturbed soils against undisturbed Andisols.\u003c/p\u003e\n\u003cp\u003eThe central question is whether this integrated set provides a consistent and interpretable response across a disturbance gradient from non-disturbed forest to agricultural and mining land uses. The objective of this study is therefore to identify and test a set of single and GM-based compound indicators capable of representing by a scoring function soil functionality in a natural forest reference system and discriminating soils from agricultural areas and mining sites. We hypothesize that the minimum indicators set obtained from a combination of single and compound variables will able to differentiate non-perturbed, agricultural, and mining soils, reflecting the expected gradient of functional degradation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003cstrong\u003etate of the art\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoils constitute dynamic, living systems that sustain plant productivity, regulate biogeochemical fluxes, and maintain ecosystem stability. Their functional capacity emerges from interactions among biological processes, physicochemical properties, and external environmental drivers\u0026nbsp;(Nunes et\u0026nbsp;al., 2020). Rapid land use transitions worldwide have intensified the need for robust, diagnostically sensitive indicators capable of detecting functional alterations. Current researchers underscores that soil health evaluations require integrative approaches that acknowledge the coupled nature of soil processes and the pronounced sensitivity of biological components to disturbance. Studies by \u0026nbsp;Fierer et\u0026nbsp;al., 2021,\u0026nbsp;Smith et\u0026nbsp;al., 2021\u0026nbsp;and\u0026nbsp;Li et\u0026nbsp;al., 2024\u0026nbsp;demonstrate that biological indicators frequently exhibit earlier responses to land use change than conventional physical or chemical metrics.\u003c/p\u003e\n\u003cp\u003eFor instance, soil microorganisms are primary regulators of terrestrial biogeochemical functioning. Their metabolic activities govern the rates and pathways of carbon and nitrogen transformations and mediate the solubilization, mineralization, and mobilization of phosphorus\u0026nbsp;(Z. Li et\u0026nbsp;al., 2021; Philippot et\u0026nbsp;al., 2024). Likewise, during periods of elevated resource availability, microorganisms immobilize inorganic nitrogen and other limiting elements within cellular biomass\u0026nbsp;(Čapek et\u0026nbsp;al., 2021). This immobilization provides a short-term nutrient sink that reduces leaching losses and conserves nutrients within the soil system. Additionally, microbial communities actively modify soil physical architecture. They synthesize extracellular polymeric substances (including glomalin, polysaccharides, and related biopolymers) that function as binding agents to facilitate the formation and stabilization of soil aggregates\u0026nbsp;(Olagoke et\u0026nbsp;al., 2022). Aggregate development, in turn, enhances the formation of a porous and aerated soil matrix, thereby improving hydraulic conductivity, water infiltration, and water holding capacity\u0026nbsp;(Ferreira et\u0026nbsp;al., 2023). Owing to their pivotal role in soil processes and their acute sensitivity to physicochemical and environmental change, microbiological parameters are indispensable indicators of soil functional integrity.\u003c/p\u003e\n\u003cp\u003eRecent investigations indicate that microbial respiration, microbial biomass, and metrics of microbial metabolic activity are among the most sensitive indicators of disturbance in agricultural and mining landscapes. Microbial processes can function as early warning signals of degradation or recovery even when bulk physicochemical properties remain comparatively stable\u0026nbsp;(Joos et\u0026nbsp;al., 2023; Xia et\u0026nbsp;al., 2024). Nevertheless, the interpretation of microbiological metrics is often challenging because microbial responses are strongly modulated by moisture dynamics, temperature fluctuations, and changes in soil chemical conditions\u0026nbsp;(Fierer et\u0026nbsp;al., 2021). Consequently, research on soil quality assessment increasingly advocates for integrative analytical frameworks that combine biological indicators with environmental and physicochemical variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAndisols are soils that typically contain high concentrations of organic carbon and exhibit high porosity, properties that confer exceptional water-retention capacity, cation-exchange potential, and structural stability. Under intensified land use, however, the earliest signs of degradation usually appear in microclimatic regulation, microbial activity, and aggregate dynamics rather than in bulk chemical properties such as total carbon, total nitrogen, or pH. When monitoring focuses exclusively on classical fertility indicators, early functional decline can remain undetected even while critical biological processes and structural attributes are already deteriorating\u0026nbsp;(Bhaduri et\u0026nbsp;al., 2022; Ghimire et\u0026nbsp;al., 2023; Kong et\u0026nbsp;al., 2025). Detecting functional degradation at an early stage allows managers and researchers to identify subtle shifts in microbial efficiency, mycorrhizal connectivity, enzyme activity, and soil infiltration capacity before the system crosses thresholds that lead to accelerated carbon losses, compaction, and erosion.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cem\u003eStudy Area and Sampling Design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in Andisols of northeastern Antioquia, Colombia, a region with pronounced gradients in vegetation cover, microclimatic conditions, and land-use intensity. Soils in the area are characterized by high organic matter content and strong surface reactivity, which makes them particularly sensitive to disturbances associated with agricultural practices and mining activities. Soil samples were collected in the municipalities of La Ceja (6\u0026deg;01\u0026prime;14\u0026Prime; N, 75\u0026deg;25\u0026prime;39\u0026Prime; W), El Retiro (6\u0026deg;03\u0026prime;36\u0026Prime; N, 75\u0026deg;24\u0026prime;19\u0026Prime; W), and Envigado (6\u0026deg;10\u0026prime;19\u0026Prime; N, 75\u0026deg;35\u0026prime;09\u0026Prime; W) during September and October 2018. In each municipality, three land-use types were sampled: agricultural land uses (Agr), mining projects (Mp), and non-disturbed areas (Np). For every land-use type within each municipality, 10 composite soil samples were collected from the upper horizon (0\u0026ndash;20 cm), following a simple random selection to spatially distribute sampling locations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEnvironmental and Soil Physicochemical Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMicroclimate at each sampling point was characterized with a Kestrel 5500 Weather Meter, recording air temperature, relative air humidity (RAH), heat index, dew-point temperature, wind-chill temperature, and atmospheric pressure. In the GAIA research group laboratories, soil pH in water, total dissolved solids (TDS), and electrical conductivity were measured with a LaMotte Salt/pH/TDS/Temp TRACER 1766, while total organic carbon and gravimetric moisture content were determined using the Walkley \u0026amp; Black method\u0026nbsp;(Babej et\u0026nbsp;al., 2024)\u0026nbsp;and gravimetric method\u0026nbsp;(Tan et\u0026nbsp;al., 2019)\u0026nbsp;respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMicrobiological Indicators\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMicrobiological indicators of diversity and abundance were CFU types and mesophilic bacteria and fungi (CFU g⁻\u0026sup1; soil). Mesophilic bacterial and fungal abundance was quantified as colony-forming units using plate-count of serial dilution technique\u0026nbsp;(Werheni Ammeri et\u0026nbsp;al., 2023), and microbial respiration rate (MRRat/C) was estimated by the chloroform fumigation\u0026ndash;incubation method\u0026nbsp;(Alessi et\u0026nbsp;al., 2011).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMultivariate Integration and Index Construction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDimensionality reduction used geometric means for similar variables following the procedure developed by\u0026nbsp;Raiesi \u0026amp; Kabiri (2016). \u0026nbsp; Factor analysis identified the Minimal Data Set (MDS) based on importance coefficients and redundancy checks. Linear and nonlinear scoring functions were applied to normalize variables, and Soil Quality Indices (SQI) were computed both with and without importance-coefficient weighting.\u003c/p\u003e\n\u003cp\u003eThe Minimal Data Set (MDS) was constructed to select representative soil variables as follows: initially, variables of the same type were aggregated using the geometric mean (GM) to reduce dimensionality while preserving joint behavior, yielding three composite variables: GM TCol (fungal to bacterial CFU ratio), GM MicAbn (combined bacterial and fungal CFU abundance), and GM Temp (temperature-related microclimatic parameters). Next, one-way ANOVA identified variables significantly affected by land use, which were then subjected to factor analysis to assess their contributions to the overall multivariate structure. Importance coefficients (IC) for each variable were calculated by multiplying the absolute factor loadings by each factor\u0026rsquo;s variance-explained weight and summing these products across retained factors, identifying variables strongly associated with main variation gradients and thus informative for land-use discrimination. To reduce redundancy, variables highly correlated within the same factor were screened by Pearson correlation coefficients, removing those with lower IC values to maximize unique information while minimizing collinearity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe selected MDS variables were transformed into dimensionless scores using the linear (A) and non-linear (B) scoring functions proposed by\u0026nbsp;Raiesi \u0026amp; Kabiri (2016), which allowed integration of indicators measured on different scales into a unified soil-quality index.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eIn the SQI, \u003cem\u003ew\u003c/em\u003e was the IC of each variable and \u003cem\u003es\u003c/em\u003e is the value of the variable. We also calculated SQI without including w considering that all MDS factors had the same relative importance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis revealed differences in soil conditions among the three land-use types. With the exception of pH and atmospheric pressure (PA), all environmental variables, soil physicochemical properties, and microbial indicators responded significantly to land use (ANOVA, p \u0026lt; 0.05), which confirms the high sensitivity of the selected indicators to disturbance regimes. The variables attempted by geometric means showed consistent shifts in microclimatic, microbial, and chemical attributes across Agr, Mp, and Np areas (Figure 1).\u003c/p\u003e\n\u003cp\u003eFactor analysis identified a well-defined multivariate structure comprising three factors with eigenvalues greater than 1, jointly explaining 71.87% of the total variance (Figure 2). Factor 1 (34.77%) was dominated by variables related to biological activity and organic matter dynamics, including GM Abundance Fung./Bact., MRRat/C, %C, and gravimetric moisture, underscoring the central role of microbial processes and carbon availability in regulating soil functional states. Factor 2 (20.49%) showed very high loadings for electrical conductivity and total dissolved solids (TDS), capturing variability associated with solute concentrations and ionic strength, particularly in soils influenced by fertilization and mining-derived inputs. Factor 3 (16.60%) grouped humidity and temperature descriptors, with RAH and GM Temperatures contributing strongly but with opposite signs, highlighting the importance of microclimatic regulation in volcanic soils with high surface reactivity (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Factor Analysis of a set of variables to measure soil functionality in Colombian Andisols\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecific Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003eRAH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003eGM Temp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003eGM TCol.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003eGM MicAbn.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003eMRRat/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e-0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003eCond.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003eMoist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003eTOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigenvalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariance (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.9294%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative variance (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.878%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e55.27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4831%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.3355%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Relative Air Humidity (RAH); Geometric Mean Temperatures (GM Temp); Geometric Mean Type of Colonies (GM TCol); Geometric Mean Microbial Abundances (GM MicAbn); Microbial Respiration Rate (MRRat/C); Conductivity (Cond); Total Dissolved Solids (TDS); Moisture (Moist); Total Organic Carbon (TOC).\u003c/p\u003e\n\u003cp\u003ep-value matrix for pairwise correlations among variables in a factor analysis showed a large number of significant correlations (p-value \u0026lt; 0.05) between the variables of each factor (Figure 3). To MDS were selected as the variables more informative the MRRat/C, TDS, and RAH as the most informative and least redundant indicators across the multivariate domain. These three variables formed the final MDS, capturing the dominant biological, chemical, and microclimatic gradients associated with land-use effects.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SQI derived from the MDS showed marked contrasts among land-use types (Figure 4). Across all index formulations, Mp consistently exhibited the lowest SQI values, Np the highest, and Agr occupied an intermediate position. Indices based on nonlinear scoring functions produced the greatest separation among land uses, indicating that nonlinear transformations captured threshold responses and diminishing marginal changes typical of microbial and physicochemical processes. Incorporation of IC-based weighting further increased index sensitivity, sharpening contrasts among land uses and improving the capacity to detect functional degradation associated with vegetation removal, altered nutrient inputs, and microclimatic disruption.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results reveal that a small set of microbially and environmentally sensitive indicators provides a clear functional distinction among natural forest, agricultural soils, and Andisols affected by mining activities. Microbial respiration, solute concentration, and relative air humidity were consistently the most responsive and least redundant indicators, capturing major shifts in soil quality features along a disturbance gradient. These findings confirm that soil functionality in volcanic systems can be characterized using a minimal indicator set, with microbial metabolic responses and microclimate variables acting as sensitive early-warning signals of soil quality disturbance.\u003c/p\u003e \u003cp\u003eThe strong contribution of microbial respiration and metrics derived from culturable microbial aligns with evidence indicating that microbial metabolism responds more rapidly to land-use disturbance than bulk physicochemical attributes (Semenov et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Higher values in respiration rates in non-perturbed areas indicate efficient use of carbon substrates and balanced respiratory activity, whereas depressed values in agricultural areas and mining sites suggest metabolic constraints (Br\u0026ouml;ring et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ferreira De Lima et al., 2023). The close alignment between respiration rates and CFU-based metrics highlights the interconnected nature of microbial abundance, metabolic capacity, and colony structure when soils experience increasing levels of disturbance.\u003c/p\u003e \u003cp\u003ePrevious studies in another type of soil have shown pronounced reductions in microbial biomass and respiration following agricultural intensification or mining activities, driven by declines in organic matter inputs (De Oliveira Santos et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), structural disruption (Lima et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and altered moisture regimes (Siebielec et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The present pattern, where microbial respiration sharply decreases from forest to agriculture and mining sites, corroborates these observations and reinforces the role of microbial metabolism as a core integrator of soil functional state. Given that Andisols rely heavily on organic matter to maintain structural integrity, concomitant reductions in organic carbon and soil moisture intensify the effects of disturbance on microbial communities and contribute to the broader functional deterioration observed in disturbed sites.\u003c/p\u003e \u003cp\u003eThe selection of relative air humidity as a key indicator further underscores the importance of microclimatic regulation in these high-organic-matter soils. Similar observations have been reported in Andisols and other volcanic-derived soils, where microclimate strongly constrains microbial enzymatic activity and decomposition processes (Lyu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Mining and intensive agriculture often modify canopy cover, surface roughness, and moisture buffering capacity, generating microclimatic shifts that propagate rapidly to microbial processes (Diba et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The strong performance of relative air humidity in discriminating land uses is consistent with this mechanistic understanding.\u003c/p\u003e \u003cp\u003eComplementarily, the identification of total dissolved solids as a chemical indicator highly sensible to soil matrix perturbations aligns with established evidence. Specifically, the studies have demonstrated that ionic enrichment from sources such as fertilizers or mining residues induces osmotic stress, suppresses microbial growth (Luo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and accelerates the disruption of soil chemical equilibria across land-use gradients (Haj-Amor et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Evidence from tropical mining landscapes has demonstrated that ionic toxicity and salinization often precede declines in nutrient status, making TDS a useful proxy for early disturbance (Corwin \u0026amp; Yemoto, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Karikari et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stavi et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The discriminative strength of TDS in this study reinforces the view that simple, inexpensive indicators can reliably capture chemically driven degradation in disturbed Andisols.\u003c/p\u003e \u003cp\u003eRespect to the scoring functions, the performance of the non-linear, importance-weighted soil quality index reflects widely recognized non-linearities in soil ecological responses. Microbiological, environmental, and physicochemical attributes interact through feedbacks that generate threshold behavior, amplification effects, and non-proportional changes in soil function (Noronha et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wubs, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent studies have shown that non-linear scoring functions and weighted indices outperform linear approaches when variables differ in sensitivity and ecological relevance to soil quality assessments across different land uses (Amgain et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Iheshiulo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lenka et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, the stronger discrimination among land uses achieved through the non-linear weighted index aligns also with the fact that the non-linear functions in soil quality index lies in its ability to separate intermediate disturbance levels (Uthappa et al., 2024; L. Wang et al., 2023).\u003c/p\u003e \u003cp\u003eThe use of compound indicators computed through geometric means contributed meaningfully to the integration of microbial and microclimatic variables related with the temperature. The ability of geometric means to preserve proportional relationships and minimize noise in ecologically variable indicators has been emphasized in this work. Although the final minimal set consisted exclusively of single indicators, compound metrics played an essential role in the dimensionality reduction process. This finding confirms that composite variables derived from culturable populations of fungi and mesophilic bacteria can exhibit strong correlations with soil changes driven by land-use alterations.\u003c/p\u003e \u003cp\u003eThe study benefits from a cross-land-use design that captures realistic gradients of disturbance. Nevertheless, several limitations should be acknowledged. The evaluation was conducted at a single time point, preventing assessment of seasonal stability in indicator behavior. Additionally, reliance on culturable microbial indicators necessarily captures only a subset of microbial diversity. Therefore, molecular and enzymatic indicators could provide complementary, mechanistic insights.\u003c/p\u003e \u003cp\u003eThe results verify the viability of a short set of indicators to discriminate soil changes and confirm that information derived from compound indicators enhances the analytical process, even when not retained in the final MDS. The sensitivity of microbial and microclimatic indicators positions them as promising candidates for early-warning systems in rapidly changing landscapes, including those facing mining expansion, agricultural intensification, or climate-driven stress. Future research should assess temporal dynamics of the minimal indicator set, test its robustness across multiple volcanic and non-volcanic soil types, and incorporate microbial functional genes or enzymatic profiles to refine ecological interpretation. Development of standardized thresholds and cross-regional scoring calibrations could further advance the implementation of minimalistic yet powerful soil-functionality diagnostics in monitoring at larger scales.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that soil functionality in Andisols subjected to contrasting land uses can be effectively diagnosed using a minimal suite of biologically and environmentally sensitive indicators. Microbial respiration, total dissolved solids, and relative air humidity emerged as a robust Minimum Data Set capable of distinguishing natural forest soils from those affected by agriculture and mining. These indicators captured the primary axes of functional variation (biological activity, ionic environment, and microclimatic regulation) highlighting their value as early-warning signals of soil disturbances.\u003c/p\u003e \u003cp\u003eThe integration of microbial indicators with physicochemical and environmental variables demonstrates that biological metrics as CFU-based indicators and microbial respiration rates are essential for detecting the earliest stages of soil functional decline. This confirms that microbial responses operate as leading indicators of degradation, preceding measurable changes in most soil chemical attributes. Likewise, the strong differentiation among land uses achieved by the non-linear, IC-weighted SQI highlights the importance of adopting multivariate, dynamic assessment frameworks in volcanic soils. Traditional linear indices may underestimate both the magnitude and timing of functional losses, whereas the non-linear SQI can capture subtle shifts in moderately disturbed systems and provides a clearer trajectory of degradation across Np, Agr, and Mp areas.\u003c/p\u003e \u003cp\u003eOverall, this work contributes both conceptual and methodological advances to the assessment of soil functionality. It clarifies how minimal indicator sets can retain diagnostic power, shows how compound indicators enhance information integration, and establishes a pathway for developing soil quality indices that are operationally feasible, ecologically meaningful, and adaptable to diverse environmental contexts. The marked sensitivity of Andisols to land use pressures emphasizes the need for continuous monitoring programs that prioritize microbial and carbon related indicators. Because these soils depend heavily on organic matter dynamics and microbially mediated processes, disturbance can rapidly compromise essential ecosystem functions. Implementing biologically informed monitoring can support more effective land management decisions and help prevent irreversible soil degradation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgbeshie, A. A., Awuah, R., Amoako, V., Akurugu, R. A., Ofori-Adjei, N. B., Abugre, S., \u0026amp; Sarfo, D. A. (2025). Soil quality response to land use change in a tropical semi-deciduous forest zone of Ghana. \u003cem\u003eSustainable Environment\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 2464389. https://doi.org/10.1080/27658511.2025.2464389\u003c/li\u003e\n \u003cli\u003eAlessi, D. S., Walsh, D. M., \u0026amp; Fein, J. 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Vegetation restoration effects on soil carbon and nutrient concentrations and enzymatic activities in post-mining lands are mediated by mine type, climate, and former soil properties. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e879\u003c/em\u003e, 163059. https://doi.org/10.1016/j.scitotenv.2023.163059\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Research Group in Modeling and Environmental Management (GAIA), Medellín, Colombia.","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Compound indicators, Geometric mean, Nonlinear functions, Soil quality Index","lastPublishedDoi":"10.21203/rs.3.rs-8544511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8544511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReliable assessment of soil functionality requires integrative indicators that capture biological, physicochemical, and environmental responses to land-use disturbances. This study evaluated whether a reduced suite of low-cost microbial, chemical, and microclimatic indicators is sufficient to discriminate soil functional states across agricultural fields, mining sites, and non-disturbed areas in Andisols of northeastern Antioquia, Colombia. Soil samples were analyzed for microbial abundance and respiration, organic carbon, moisture, solute concentration, and microclimatic variables. Dimensionality reduction and variable integration were performed using geometric means, factor analysis, and scoring functions. Three indicators: microbial respiration rate (MRRat/C), total dissolved solids (TDS), and relative air humidity (RAH), were identified as the most informative and least redundant within the multivariate domain. Factor analysis showed that microbial indicators contributed 34.77% of total variance, demonstrating their central role in distinguishing functional soil states. Soil Quality Indices (SQI) derived from nonlinear scoring functions with importance-coefficient weighting yielded the highest sensitivity in differentiating land uses, showing clear declines from non-perturbed to agricultural to mining areas. Results highlight the strong responsiveness of microbially mediated processes to disturbance and support the adoption of non-linear, weighted indices as early-warning tools for vulnerable tropical Andisols.\u003c/p\u003e","manuscriptTitle":"Integrating Microbiological Quality Indicators and Soil Properties Through Score Functions to Assessment Land Uses Changes in Colombian Andisols","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:24:06","doi":"10.21203/rs.3.rs-8544511/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e724d7c-47ca-412c-a164-6a7ed1795fb2","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T05:24:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:24:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8544511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8544511","identity":"rs-8544511","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}