Coupled Effects of Soil Texture and Hydrothermal Regimes on Soil Nutrient Spatial Patterns: Superimposed Impact of Photovoltaic Installations in Desert Ecosystems

Coupled Effects of Soil Texture and Hydrothermal Regimes on Soil Nutrient Spatial Patterns: Superimposed Impact of Photovoltaic Installations in Desert Ecosystems | 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 Coupled Effects of Soil Texture and Hydrothermal Regimes on Soil Nutrient Spatial Patterns: Superimposed Impact of Photovoltaic Installations in Desert Ecosystems Li Yan, Jinrong Hu, Guangchao Cao, Yujian Zhong, Yan Wang, Deli Ye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7072101/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Energy, Ecology and Environment → Version 1 posted 10 You are reading this latest preprint version Abstract The rapid development of photovoltaic technology is transforming China’s energy structure and reducing carbon emissions, but large-scale photovoltaic facility deployment in arid regions affects soil development and nutrient dynamics. The effects of different photovoltaic support types on soil nutrients and ecological stoichiometry, however, are not fully understood. In this study, three configurations—UFPV (under-module of fixed-axis), IFPV (inter-module of fixed-axis), and ITPV (inter-module of single-axis tracking)—and CG (control group) were compared in the Talatan Beach photovoltaic park in Qinghai. Soil carbon, nitrogen, and phosphorus stoichiometry were analyzed along with environmental drivers. Results showed soil nutrient levels were significantly lower UFPV compared to the control, while the ITPV better maintained soil nutrient levels. The relative contributions of major environmental factors to the spatial variability of soil nutrient stoichiometry were as follows: soil water content (18.49%), temperature (12.14%), belowground biomass (10.34%), clay content (9.90%), precipitation (9.86%), sand content (9.74%), silt content (9.56%), and bulk density (6.29%). Photovoltaic deployment affects soil nutrients in desert areas not only through physical shading effects, but also by reshaping the local microenvironment and creating complex cascading responses among the "photovoltaic–vegetation–soil" system, thereby indirectly influencing soil properties. These findings provide insights for ecological risk management and sustainable low-carbon development in arid regions. PV deployment C:N:P stoichiometry Desert ecosystems Environmental drivers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction With increasing pressure from global climate change, the transformation towards a low-carbon energy structure has become a shared objective of the international community (IEA 2022 ). As a core renewable energy technology, photovoltaic (PV) solar power is widely recognized as one of the most promising options for achieving this transition. By the end of 2023, the cumulative global installed capacity of PV exceeded 1.6 TW, with an annual increase of 420 GW. Among these, China held the leading position globally, contributing 216.88 GW of new installations in 2023 and reaching a cumulative capacity of 609.49 GW (Jäger-Waldau 2024 ). Currently, large-scale PV installations are mainly concentrated in arid and semi-arid regions, particularly at the desert–grassland ecotone, where optimal solar radiation conditions and relatively low land acquisition costs provide favorable conditions for PV projects (Hernandez et al. 2015 ). However, extensive PV development often leads to marked changes in land use patterns, with natural ecosystems being transformed into land uses dominated by anthropogenic facilities. Such changes directly alter regional land cover types and ecological processes, which may result in a range of ecological and environmental issues, including shifts in vegetation patterns, soil structure degradation, higher erosion risk, and habitat fragmentation (Capellán-Pérez et al. 2017 ; Choi et al. 2021 ; Li and Gao 2021 ). Soil plays a critical role in nutrient cycling and material storage within terrestrial ecosystems, fundamentally regulating plant growth and microbial activity through the ecological stoichiometry of organic matter and key elements such as carbon (C), nitrogen (N), and phosphorus (P). Maintaining stoichiometric balance is essential for ecosystem stability (Cleveland and Liptzin 2007 ; Xu et al. 2013 ; Gao et al. 2024 ). Ecological stoichiometry, which clarifies elemental balances and the regulatory mechanisms in ecological processes, provides a theoretical basis for understanding the impacts of human activities such as energy development on ecosystem function (Sterner and Elser 2003 ). Previous studies have shown that, in arid and semi-arid regions, soil C, N, and P cycles are jointly regulated by climate, geography, and soil factors. These constraints often create complex spatial patterns and may result in “decoupling” among elemental relationships, leading to C:N:P stoichiometric imbalances and affecting ecosystem structure, function, and resilience (Sterner and Elser 2003 ; Yu et al. 2011 ; Delgado-Baquerizo et al. 2013 ; Yang et al. 2014 ; Jiao et al. 2016 ; Tan and Wang 2016 ; Luo et al. 2020 ). Soil C, N, and P cycling in arid and semi-arid environments is highly sensitive and complex, with anthropogenic interventions capable of significantly altering stoichiometric patterns. Against this background, the large-scale deployment of PV facilities has increasingly become a dominant mode of land use in these areas. Studies indicate that PV arrays can improve regional ecological conditions by regulating microclimates, optimizing soil moisture distribution, and promoting nutrient cycling processes, thereby facilitating soil and vegetation restoration and enhancing ecosystem functions such as wind erosion control (Liu et al. 2023 ; Wu et al. 2024 ; Yue et al. 2025 ). These beneficial effects are mainly attributed to microclimate regulation, the redistribution of water, and enhanced nutrient cycling driven by the presence of PV panels (Yue et al. 2025 ). It is important to note that different PV panel structures and installation modes (e.g., fixed-axis, single-axis tracking, dual-axis tracking) impact solar radiation and surface thermal environment in different ways, leading to variations in soil physicochemical properties and nutrient cycling, and consequently differing ecological outcomes (Yue et al. 2021 ). Quantitative analysis of the mechanisms by which different PV facility types affect the near-surface environment and soil C:N:P stoichiometry is therefore vitally important for guiding ecosystem management and the sustainable development of renewable energy in arid and semi-arid regions. The Talatan Photovoltaic Industrial Park, located in the Gonghe Basin of Qinghai Province, represents a typical alpine, arid–semi-arid transition zone, characterized by low soil organic matter and a fragile ecological environment (Lu et al. 2023 ). Previous research has found that PV facilities can enhance local soil carbon sequestration and increase certain soil nutrient contents (Wu et al. 2024 ). Additionally, the accumulation capacities of soil carbon and nitrogen differ among different facility types (Marrou et al. 2013 ). Thus, the mechanisms underlying shifts in soil C:N:P stoichiometry due to PV deployment in arid climates are the result of interactions among regional climate, soil physicochemical properties, PV facility type, and management practices at multiple scales. Nevertheless, systematic research on how broad PV deployment affects the stoichiometric patterns and driving mechanisms of soil C:N:P in desert ecosystems remains scarce, especially regarding the differences caused by varying facility types, which are yet to be fully clarified (Yue et al. 2025 ). In this context, the present study systematically compares the responses of soil C:N:P stoichiometric patterns to three PV array types—UFPV, IFPV, and ITPV—at the Talatan PV industrial park. Using multi-layer soil and vegetation sampling, combined with multivariate statistical analysis and structural equation modeling, we further probe the coupled effects of environmental factors (climate, vegetation, soil texture) on soil nutrient cycling. We evaluate the implications of these changes for ecosystem function and sustainability in the region. The main research objectives are as follows: (1) to quantify the characteristics and differences in total and available soil nutrients and their stoichiometric ratios across different PV facility types; (2) to disentangle the primary and secondary contributions and impact pathways of key environmental drivers on spatial patterns and stoichiometric responses of soil nutrients; and (3) to provide scientific support and management recommendations for the coordinated development of large-scale PV deployment and ecological conservation in arid regions. Overall, this research offers theoretical and policy guidance for understanding the ecological effects of PV development, enhancing soil carbon sequestration in arid lands, and promoting a synergistic integration of ecological conservation and economic growth. The work not only enriches the theoretical framework of C–N–P stoichiometric ecology in arid regions under human perturbation, but also contributes technical support for ecological risk management, green low-carbon development, and the sustainable management of PV energy landscapes. 2. Data and Methods 2.1 Study Area Overview The Talatan Beach area is situated on the left bank of the Yellow River in Gonghe County, Hainan Tibetan Autonomous Prefecture, Qinghai Province, in the central-western region of the Gonghe Basin (Fig. 1 ). The study region covers approximately 1,958 km² and is located at an elevation of 2,900–3,100 m. It experiences an arid climate, with average annual temperature, precipitation, evaporation, sunshine hours, total solar radiation, and wind speed of 4.1°C, 246.3 mm, 1,716.7 mm, 2,300–3,500 h, 6,564.26 MJ/m², and 1.8 m/s, respectively. The prevailing winds are westerly and northwesterly (Kang et al. 2020 ). The parent soil materials are primarily loess or gravel, resulting in dominant sandy loam and light loam soils, with gravel and sandy layers at depth (Wu et al. 2024 ). Due to strong northwesterly winds, wind-blown sand activity is considerable, causing significant wind erosion and land desertification, with an average annual wind-eroded soil thickness of 1–1.5 cm. The principal soil type is kastanozem with a thickness of 50–70 cm, and surface vegetation is mainly desert steppe (Zhao et al. 1996; Guo et al. 2008 ). Owing to the region’s abundant solar radiation and land resources, Talatan Beach has become a key area for clean energy development in Qinghai Province. As the largest centralized photovoltaic (PV) power generation site in China, the Talatan Beach PV Industrial Park has served as a pillar of Hainan Prefecture’s multi-GW renewable energy base since its establishment in 2013. Adjacent to the Longyangxia Reservoir, the park covers a planned area of about 609 km², with an installed capacity of 850 MW and annual utilization of 1,508 hours, Which makes it one of the largest grid-connected PV stations in Northwest China (Yan 2024 ). 2.2 Experimental Design and Sample Collection In late July 2023, during the peak vegetation growth season, sampling plots were established across different PV configurations: UFPV, IFPV, and ITPV. Areas naturally exposed, without PV panels or fencing and located 1 km outside the PV park, served as CG. Soil and biomass samples were collected within 50 × 50 cm standard quadrats. Within each quadrat, three soil samples were collected using a soil auger following a diagonal pattern. Samples from each layer were pooled, stratified into 0–10 cm, 10–20 cm, and 20–30 cm depths. After homogenization, plant roots, stones, and other debris were removed by hand, and samples were quartered, bagged, and stored for further processing. All samples were air-dried and sieved prior to analysis. For biomass sampling, aboveground biomass (AGB) was collected by clipping all vegetation at the soil surface within the quadrat. Belowground biomass (BGB) was collected using a 7 cm diameter soil corer at depths of 0–10 cm, 10–20 cm, and 20–30 cm, with three replicates per quadrat. Both AGB and BGB samples were oven-dried at 65°C to constant weight before weighing. In total, 40 sampling sites and 46 quadrats were surveyed: 8 UFPV sites, 8 IFPV sites, 14 ITPV sites, and 14 control sites. After data consolidation and quality control, 135 soil samples, 46 AGB samples, and 46 BGB samples were retained for analysis. 2.3 Measurement Items and Methods Soil moisture content (SMC) was determined gravimetrically by oven-drying samples at 105°C until constant weight. Bulk density (BD) was measured using the ring knife method. Soil pH was measured by potentiometry in a 1:2.5 soil:water mixture. Particle size distribution was measured with a laser diffraction analyzer (Mastersizer 3000, Malvern Panalytical, UK). Soil organic carbon (SOC) was quantified using a Vario TOC cube analyzer (Elementar, Germany), total nitrogen (TN) with a Vario MACRO cube elemental analyzer, and total phosphorus (TP) using inductively coupled plasma optical emission spectrometry (ICP-OES) after strong acid digestion. Available potassium (AK) was determined by extraction with 1 mol/L NH₄OAc followed by flame photometry; alkaline hydrolyzable nitrogen (AN) by alkaline hydrolysis diffusion; and available phosphorus (AP) by 0.5 mol/L NaHCO₃ extraction and molybdenum-antimony colorimetry (Bao 2000 ). 2.4 Extraction of Environmental Factors Meteorological variables, including temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), were extracted from the National Tibetan Plateau Data Center ( https://data.tpdc.ac.cn ), utilizing 1-km resolution monthly mean datasets for the period 1901–2023 (Peng 2019 , 2020, 2022). The normalized difference vegetation index (NDVI) for the 2023 growing season (June–August) was synthesized from 30 m-resolution Landsat imagery processed on the Google Earth Engine (GEE) platform. Clouds and outliers, as well as the effects of PV panel shading, were minimized by median compositing. All remote sensing data were projected to WGS 1984. 2.5 Statistical Methods Normality of all data was evaluated using the Shapiro–Wilk test, and homogeneity of variance was checked with Levene’s test (car package, R). The Kruskal–Wallis test and Dunn’s post hoc test (FSA package) were used as non-parametric alternatives to one-way ANOVA to assess the effects of PV configuration on SOC, TN, TP, AN, AK, and AP. The significance threshold was α = 0.05, and significant differences were visualized using the ggpubr package. Associations between environmental factors and soil nutrients or stoichiometry were analyzed using the Mantel test (linkET, ggplot2, and dplyr packages; Mantel 1967 ). Redundancy analysis (RDA) was performed with the vegan package to elucidate relationships between soil nutrients/stoichiometry and environmental variables. To quantify the relative contributions of environmental factors, hierarchical partitioning and Monte Carlo permutation tests (rdacca.hp package) were conducted. Partial least squares path modeling (PLS-PM; plspm package, Russolillo 2012 ) was applied to investigate the pathways by which PV infrastructure influences soil nutrient cycling and stoichiometry through key drivers. All statistical analyses and visualizations were performed using RStudio (version 3.3.9). 3. Results 3.1 Total Soil nutrients and Stoichiometric Characteristics under PV Deployment in Desert Ecosystems Table S1 presents the physical and chemical properties of soils at a depth of 0–30 cm in the study area. Figure 2 illustrates the variations in soil nutrients and their stoichiometric ratios among four treatments: UFPV, IFPV, ITPV, and CG. Results indicate that the impact of PV deployment on soil nutrient content varies by facility type. Notably, the UFPV type exhibited a significant decline in SOC, TN, and TP compared to other groups. In contrast, IFPV and ITPV plots maintained higher nutrient levels, which were close to or slightly below those of the control. In terms of soil stoichiometric ratios, no significant differences were observed between PV types and the control based on multiple group comparisons. According to Kruskal-Wallis tests, significant differences ( p < 0.05) were present among the four groups for SOC, TN, and TP. Specifically, mean SOC contents were 7.94 g/kg (UFPV), 9.74 g/kg (IFPV), 9.49 g/kg (ITPV), and 10.51 g/kg (CG). Post hoc Dunn’s tests indicated that SOC in UFPV was significantly lower than CG (a decrease of 24.5%), but not significantly different from IFPV or ITPV. For TN, mean values were 1.48 g/kg (UFPV), 1.96 g/kg (IFPV), 1.88 g/kg (ITPV), and 2.16 g/kg (CG). UFPV showed significantly lower TN than all other groups (by 31.5%, 24.5%, and 21.3%, respectively), as further supported by Dunn’s tests. For TP, means were 0.45 g/kg (UFPV), 0.49 g/kg (IFPV), 0.53 g/kg (ITPV), and 0.55 g/kg (CG); UFPV was significantly lower than ITPV and CG, but not significantly different from IFPV. Regarding C:N, C:P, and N:P ratios, neither Kruskal-Wallis tests nor Dunn’s post hoc tests identified significant differences among groups, and all group means were comparable. This pattern was also reflected in the strong overlap observed in violin and box plots of these ratios (Fig. 2 ). 3.2 Available Soil Nutrients and Stoichiometric Characteristics under PV Deployment in Desert Ecosystems Figure 3 presents the changes in available soil nutrients and their stoichiometric ratios among the four treatments. Kruskal-Wallis analysis revealed significant differences ( p < 0.05) among the groups for AN, AK, AN:AK, and AP:AK ratios. Specifically, mean AN contents were 47.92 mg/kg (UFPV), 70.27 mg/kg (IFPV), 71.98 mg/kg (ITPV), and 75.36 mg/kg (CG), with UFPV significantly lower than the other three groups as confirmed by Dunn’s post hoc tests. For available phosphorus (AP), group means were 1.38 mg/kg (UFPV), 1.74 mg/kg (IFPV), 1.54 mg/kg (ITPV), and 1.68 mg/kg (CG). Overall, there was no significant main effect of PV deployment on AP, though UFPV differed from CG in pairwise comparison, but AP differences among the PV types remained small. For AK, mean values were 116.73 mg/kg (UFPV), 153.21 mg/kg (IFPV), 110.40 mg/kg (ITPV), and 169.74 mg/kg (CG). UFPV showed significantly lower AK than CG and IFPV, while the differences among IFPV, ITPV, and CG were not significant, indicating that UFPV represented the lowest AK level. The AN:AP ratios were 39.49 (UFPV), 44.99 (IFPV), 52.23 (ITPV), and 48.57 (CG), with Kruskal-Wallis test showing a marginal effect (P = 0.066), suggesting a potential trend; Dunn’s test identified significant differences between UFPV and both ITPV and CG. As for the AN:AK ratios (0.53, 0.52, 0.74, and 0.51 for UFPV, IFPV, ITPV, and CG, UFPV was significantly lower than ITPV, while all other pairwise differences were non-significant. For AP:AK, values were 0.01, 0.01, 0.02, and 0.01, with ITPV significantly higher than the other groups. 3.3 Relationships between PV Deployment Types, Environmental Factors, and Nutrient Variables To investigate the relationships among soil nutrient traits, stoichiometric ratios, and environmental factors—including soil properties (clay, silt, sand, SMC, BD, pH), climatic factors (TMP, PET, PRE), vegetation factors (AGB, BGB, NDVI), and PV deployment types UFPV, IFPV, ITPV, CG—we used Spearman correlation and Mantel tests (Table S2) to analyze total soil nutrients ( SOC, TN, TP, TK), available soil nutrients ( AN, AP, AK), total soil nutrient ratios (C:N, C:P, N:P), and available soil nutrient ratios (AN:AP, AN:AK, AP:AK) (Fig. 4 ). Results showed that total soil nutrients were strongly and positively correlated (r = 0.2–0.3, p < 0.001) with clay content, SMC, and TMP, and were significantly affected by the UFPV treatment (r = 0.21, p < 0.01). Available soil nutrients also showed significant correlations (r < 0.2, p < 0.05) with clay, silt, sand, SMC, BGB, and UFPV. Both total soil nutrient ratios and available soil nutrient ratios were most strongly associated with SMC (r = 0.16, p < 0.001). Redundancy analysis (RDA) was used to further clarify the main determinants of soil nutrient variation under PV deployment (detailed results are provided in Table S4.). RDA results revealed that the first two axes (RDA1: 53.33%, RDA2: 19.43%) together explained 72.76% of the total constrained variance in soil nutrients and stoichiometry, indicating that the selected soil, climate, vegetation, and PV-type factors accounted for a large share of spatial variation. Soil physical properties (clay, silt, sand, SMC, BD) and climate factors (TMP, PRE) were identified as primary drivers, with sand and BD contributing most to positive RDA1 variation, and clay, silt, and SMC dominating the negative axis. Total soil nutrients (SOC, TN, TP, TK) correlated most strongly and positively with fine soil particles (clay, silt) and SMC, but negatively with sand content and BD. AN showed a similar pattern as total soil nutrients, primarily driven by fine texture and moisture. In the RDA biplot, control, ITPV, and IFPV were associated with relatively moist, fine-textured, nutrient-rich habitats, whereas UFPV corresponded to sandy soils with greater BD. Hierarchical partitioning analysis demonstrated the independent explanatory contributions as follows: SMC (18.49%) > TMP (12.14%) > BGB (10.34%) > clay (9.90%) > PRE (9.86%) > sand (9.74%) > silt (9.56%) > BD (6.29%), underscoring the dominance of SMC, TMP, texture, and BD in structuring nutrient spatial patterns—consistent with Mantel and RDA results. 3.4 Mechanisms of Photovoltaic Regulation on Soil Nutrients and Their Stoichiometry To further elucidate the mechanisms by which PV deployment regulates soil nutrient dynamics and stoichiometry in arid regions, partial least squares structural equation modeling (PLS-SEM) was applied to construct a path model integrating PV facility types, climatic factors, soil and vegetation properties, and their interactions. This approach enabled effective quantification and hypothesis testing of the direct and indirect (total) effects exerted on soil nutrient stocks, availability, and stoichiometric ratios. The PLS-SEM results (Fig. 6 , Table S5) indicated a high model fit, with a Goodness of Fit (GOF) statistic of 0.50. The model explained 69% of the variance in soil total nutrient ratio ( R² = 0.69), which was primarily determined by total soil nutrients. In comparison, 45% of the variance in soil available nutrients ( R² = 0.45) was jointly explained by vegetation, soil physical properties, and total soil nutrients. The model also explained 35% of the variance in soil available nutrient ratios ( R² = 0.35). Path coefficients showed that most environmental variables had significant positive or negative direct and/or total effects on soil nutrients and stoichiometry. Specifically, PV facilities exerted significant direct positive effects on vegetation and soil, thereby indirectly promoting nutrient stocks and stoichiometric balance. In contrast, climatic factors demonstrated significant negative direct impacts—either directly on vegetation or indirectly via total soil nutrients—on soil nutrient status and stoichiometry. Figure 7 quantitatively decomposes the direct, indirect, and total effects of environmental drivers. Overall, soil nutrient stocks, available soil nutrients, and their stoichiometric ratios were positively related to PV deployment type, vegetation, and soil physical attributes, and negatively related to climate factors. 4. Discussion 4.1 Heterogeneous Effects of PV Deployment Types on Soil Nutrients in Desert Ecosystems Soil nutrient content and its stoichiometric ratios are key indicators of soil organic matter composition and quality, and they effectively reflect the impact of different management interventions on soil health (Ma 2004; Zhang 2019). In this study, we found that UFPV had significantly negative effects on both total and available soil nutrients; notably, the concentrations of SOC, TN, and available soil nutrients (AN, AK) were significantly lower than those in CG. IFPV also showed a certain decline compared to CG. These patterns are consistent with domestic and international studies reporting negative impacts of fixed-axis PV arrays on soil fertility and C stocks, indicating the widespread and long-term nature of nutrient loss and ecosystem function degradation associated with fixed-axis PV installations (Moscatelli et al. 2020 ; Zhang et al. 2025 ). At a local scale, vegetation can intercept and redistribute precipitation, creating pronounced spatial heterogeneity (Yuan et al. 2017 ). In fixed-axis PV areas, persistent shading produces a “rain shadow effect,” reducing direct rainfall inputs (Elamri et al. 2018 ) and further magnifying local hydrological changes (Thorne et al. 2015 ). PV panels can also increase local air temperature through panel-induced warming, offsetting the cooling effect of shading, thus raising air temperature and lowering relative humidity beneath panels, as well as amplifying the diurnal range of surface temperatures (Zhao et al. 2016; Gao et al. 2016 ; Armstrong et al. 2016 ; Wu 2021). These altered soil-hydrothermal conditions can suppress the establishment and growth of pioneer desert species such as Haloxylon and Artemisia (Song et al. 2023 ), reduce litter input and organic matter accumulation, and negatively affect the abundance and activity of microbial groups, including N-fixing and ammonia-oxidizing bacteria, further impairing soil nutrient accumulation (Zhang et al. 2024 ). In addition, total phosphorus (TP) in desert soils is primarily supplied through parent material weathering (Jobbágy and Jackson 2001 ; He et al. 2014; Chen et al. 2025 ), and the observed declines in TP under UFPV may be linked to disrupted vegetation-litter cycling and reduced mineral weathering rates under low Soil moisture content, a trend observed widely in desert PV zones (Shang et al. 2020). In contrast, single-axis tracking PV systems show clear advantages in maintaining soil nutrients, largely due to their ability to optimize the microenvironment through dynamic tilt adjustments. Previous research has shown that tracking systems reduce environmental impacts by an average of 17% compared with fixed-axis PV types, cutting greenhouse gas emissions, land occupation, and water use while promoting aboveground biomass accumulation and improving topsoil nutrient availability (Lassio et al. 2022 ; Zhang et al. 2024 ). Our results confirm that nutrient levels in ITPV areas were statistically indistinguishable from the control, highlighting this ecological coordination effect. The periodic rotation of tracking panels reduces the area of persistent shading, promotes a more even distribution of light and precipitation beneath panels, alleviates ecological stress, and helps maintain plant diversity (Zhou et al. 2019; Liu 2021; Yue 2022 ). From an engineering perspective, fixed-axis and tracking PV systems differ in panel density, tilt control, and operation mode—characteristics that fundamentally shape their disturbance patterns and microenvironmental effects at the ground surface (Koussa et al. 2011 ). Thus, optimizing structural parameters and enhancing compatibility with ecosystem processes will be essential for green solar transitions in desert regions. 4.2 Heterogeneity of Soil Nutrients under Environmental Controls and Superimposed Photovoltaic Effects Water scarcity and nutrient deficiency are key constraints on ecological processes in desert ecosystems. Identifying the dominant drivers of soil nutrient status is critical for evaluating ecosystem functions under large-scale centralized PV deployment. Our results verify a typical mechanism under arid conditions: soil nutrients are co-regulated by regional climate and local soil physicochemical attributes such as texture, BD, and SMC (Lu et al. 2023 ; Wang et al. 2024 ). Specifically, both total soil nutrients (total soil nutrients) and available soil nutrients (available soil nutrients) were strongly positively correlated with fine fractions (clay, silt) and SMC, and negatively with BD and sand content. These findings suggest that fine-textured soils, by enhancing moisture retention, nutrient adsorption, and root penetration, play a crucial role in nutrient accumulation and utilization in arid lands (Li et al. 2009 ; Huntley et al. 2023). RDA further identified soil physical properties (clay, silt, sand, SMC, BD) and climatic factors (TMP, PRE) as the main vectors driving spatial nutrient variation. The control and ITPV groups were associated with relatively favorable environmental conditions and higher nutrient levels, underscoring the fundamental roles of suitable soil structure and hydrothermal conditions in sustaining nutrient cycling (Delgado et al. 2017; Bauke et al. 2022). PLS-SEM results provided quantitative evidence for the dominant roles of soil physical environment and climate in nutrient distribution. Path analysis indicated that soil physical attributes had significant positive direct effects on total soil nutrients (+ 0.35***) and available soil nutrients (+ 0.31***), pinpointing their primary contribution to soil nutrient stocks and availability. Meanwhile, arid climate exerted a notable negative effect on vegetation growth (-0.47***), indirectly limiting organic matter input and nutrient cycling—a suppression likely related to the high evaporation–low precipitation regime, which constrains biomass production, litter decomposition, and nutrient recirculation (Cui et al. 2019 ; Tariq et al. 2024). As a key biotic mediator, vegetation facilitates nutrient cycling via biomass accumulation, litterfall, and rhizosphere activity, supporting increased nutrient availability in arid environments (Zhao et al. 2018 ; Tariq et al. 2024). This study delineated an indirect “climate–vegetation–nutrient” pathway, underscoring the coupling of biotic and abiotic processes in shaping soil nutrient patterns. Within this primary control structure, PV deployments do not directly determine soil nutrient content. PLS-SEM effect decomposition indicated that PV type promoted soil nutrient enhancement mainly by indirectly improving soil properties and stimulating vegetation growth. Kruskal-Wallis tests likewise revealed lower baseline soil nutrient content under native arid conditions CG, and the nutrient differences under PV deployment reflected positive additive effects atop this baseline (Li et al. 2022). Thus, in desert ecosystems, soil physical and hydrothermal properties provide the core framework for nutrient spatial variation, with biological processes acting in synergy (Lu et al. 2023 ; Zhou et al. 2023 ; Wang et al. 2024 ). PV deployment, as an anthropogenic disturbance, exerts its positive effects on nutrients mainly via localized modification of these core drivers. This mechanistic understanding, from a biogeochemical perspective, is valuable both for decoding the ecological roles of solar infrastructure and for informing sustainable PV management in arid regions. 4.3 Cascading Effects and Sustainable Management Implications of PV-Induced Regulation In arid areas, PV system deployment offers a double opportunity: the availability of vast, low-productivity land enables large-scale solar installation, thus promoting green energy and reducing carbon emissions (Wang et al. 2024 ), while PV systems can also enhance local environmental conditions and contribute to ecological restoration of degraded land (Wu et al. 2014 ). However, desert ecosystems are inherently fragile, vulnerable to land degradation and desertification (Abdi et al. 2014). PV installations exert complex effects on soil, microclimate, and vegetation; technical choices and land management practices create microenvironmental heterogeneity and divergent biological responses, generating marked spatial heterogeneity in soil nutrients—a fact that underscores the challenge of aligning PV expansion with ecological integrity (Zhang et al. 2025 ). Balancing energy production and ecological services is thus paramount for sustainable solar development. This study found that the effects of PV deployment on soil nutrients extend beyond mere shading; by reshaping local microenvironments, PV systems induce intricate cascade responses among PV–vegetation–soil elements, leading to spatially heterogeneous nutrient patterns. Such heterogeneity reveals the directional regulatory potential of PV design on ecological processes. Pathway analysis demonstrated that PV deployment initially manifests as significant positive effects on the vegetation growth environment and soil physical properties. These effects are then transmitted through both biotic (vegetation) and abiotic (soil) mediators, indirectly enhancing overall soil nutrient availability and bioavailability. This is consistent with previous findings (Li et al. 2016; Li et al. 2020 ; Wu et al. 2021 ; Li et al. 2021), highlighting the centrality of PV–vegetation coupling in shaping desert soil nutrient dynamics. Furthermore, our path models confirm that PV deployment, depending on type, significantly boosts vegetation growth, suggesting that improvements in ecological conditions are key to driving belowground biogeochemical benefits (Liu et al. 2019 ; Zhang et al. 2025 ). Enhanced vegetation, in turn, increases soil nutrient effectiveness through litterfall, root exudation, and microbial rhizosphere effects. Therefore, the differential impact of PV design types on vegetation growth is a critical factor explaining the heterogeneity in soil nutrient responses. For instance, lower nutrient levels under UFPV likely reflect both unfavorable physical soil conditions and weaker stimulation of plant growth, with inadequate biotically driven nutrient cycling, highlighting an energy–ecosystem services trade-off intrinsic to PV infrastructure that must be addressed via technology choice and ecological management. From a nutrient optimization perspective, available soil nutrients and their stoichiometric ratios (such as AN:AP, AN:AK, AP:AK) were more sensitive to microenvironmental changes and vegetation feedbacks than total stocks. Our findings indicate these ratios respond more strongly to the integrated effects of PV structures and ecological restoration (see Fig. 6 ), emphasizing the importance of targeted management, particularly vegetation rehabilitation, in supporting both PV ecosystem co-benefits and soil functions. Based on these insights, systematic ecological management is recommended for PV deployment in deserts. Priority should be given to single-axis tracking or smart adaptive PV systems, which dynamically adjust panel angles to balance shade and light, optimizing conditions for vegetation. Increasing mounting height can improve ventilation, water infiltration, and grazing compatibility, facilitating vegetation recovery. Combining local ecosystem engineering and native plant restoration—such as topsoil application or microhabitat amelioration—strengthens positive PV–vegetation–soil feedbacks. Robust long-term monitoring systems, focusing on available soil nutrients and stoichiometric ratios, should be established to support adaptive management and scientific decision-making. These strategies can help clarify the ecological efficacy of technical and management interventions, including restoration, on soil fertility and biogeochemical functions in desert PV systems from short- to mid-term timeframes, with special attention to synergistic effects between vegetation recovery and nutrient cycling. Through systematic optimization of design, integrated ecological interventions, and evidence-based monitoring, the ecological integrity of these fragile systems can be sustained alongside green energy development. It should be noted that this research was based on short-term soil and vegetation data from the large Talatan Beach PV base. Thus, it may not fully capture longer-term ecological effects. Moreover, our surveys were limited to fixed-axis and single-axis tracking PV, as these predominate locally, and did not include all emerging PV configurations, which restricts the generalizability of our findings. Finally, our ecological assessments focused primarily on soil nutrients and biomass, with limited attention to microbial function. Future studies should incorporate long-term, multi-scale monitoring, expand the diversity of PV technologies studied, integrate multidimensional ecosystem indicators, and intensify management evaluation in order to provide more comprehensive scientific guidance for the sustainable development of arid-zone PV systems. 5. Conclusions This study demonstrates that fixed-axis PV systems (IFPV, UFPV) significantly reduce soil nutrient content in desert ecosystems, while single-axis tracking systems (ITPV) maintain nutrient levels comparable to natural controls, supporting ecosystem stability. The spatial heterogeneity of soil carbon, nitrogen, and phosphorus is predominantly governed by abiotic factors such as soil hydrothermal conditions, texture, and bulk density, while PV deployment primarily exerts superimposed environmental effects. Furthermore, regional TMP and PRE indirectly regulate the availability of soil nutrients by constraining vegetation growth. Importantly, available nutrient pools and their stoichiometric ratios demonstrated higher sensitivity to ecological changes than total nutrient stocks, underscoring their value as robust indicators for assessing and monitoring soil ecological dynamics in photovoltaic-impacted ecosystems. To promote sustainable and synergistic development of renewable energy and ecosystem health in arid regions, we recommend giving priority to single-axis tracking PV configurations, integrating long-term, dynamic monitoring of available nutrients and stoichiometric ratios into PV project planning, and tailoring ecological restoration measures—such as native vegetation rehabilitation and microhabitat improvement—to local conditions. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by 2022 Kunlun Talents Program for High-End Innovation and Entrepreneurship of Qinghai Province Qing Talent No. 1 (2023) and State Power Investment Corporation Huanghe Hydropower Development Co., Ltd. Science and Technology Projects (KY-C-2024-GF04, KY-C-2025-HB05). Author Contribution L.Y. wrote the original draft, prepared visualizations, developed the methodology, conducted the investigation and formal analysis, and contributed to the conceptualization. J.H. contributed to writing—review and editing, and writing the original draft. G.C. contributed to writing—review and editing, provided supervision and resources, and acquired funding. Y.Z. and Y.W. conducted the investigation. D.Y. and H.M. provided resources and acquired funding. All authors reviewed the manuscript. Acknowledgement This work was supported by 2022 Kunlun Talents Program for High-End Innovation and Entrepreneurship of Qinghai Province Qing Talent No. 1 (2023) and State Power Investment Corporation Huanghe Hydropower Development Co., Ltd. Science and Technology Projects (KY-C-2024-GF04, KY-C-2025-HB05). 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J. Environ. Manag. 343(1), 118186. https://doi.org/10.1016/j.jenvman.2023.118186. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.doc Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Energy, Ecology and Environment → Version 1 posted Editorial decision: Revision requested 09 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers invited by journal 11 Jul, 2025 Editor assigned by journal 10 Jul, 2025 Submission checks completed at journal 10 Jul, 2025 First submitted to journal 08 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7072101","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484669130,"identity":"82eb5029-6f88-4ab7-9d35-79bcafdda615","order_by":0,"name":"Li Yan","email":"","orcid":"","institution":"Qinghai Normal University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yan","suffix":""},{"id":484669131,"identity":"07ddd2bc-5821-489a-9b56-60db88f7b65a","order_by":1,"name":"Jinrong Hu","email":"","orcid":"","institution":"Qinghai Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jinrong","middleName":"","lastName":"Hu","suffix":""},{"id":484669132,"identity":"b5b3c5df-afdf-4535-ab30-ddf383dc3ffc","order_by":2,"name":"Guangchao Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYFADCSD+wHAAziZOC+MMkrUw8xCjhb/98OHXPDV37PpnNz98bFN2J8/gAPPB2zwMdnk4zT6TlmbNc+xZ8ow7x4yNc849KzY4wJZszcOQXIxLiwFDjpkxD9vhZAOJBDPp3LbDiRsO8JhJA12Y2IBLC/8boJZ/IC3p36QtwVr4v+HXIpFj/Ji37bAdkGEmzQixhQ2vFokbz9IY5/YdTpC4kVNs2HPucOLMw2zGlnMMknFq4e9PPvzhzbfD9vwz0jc++FF2OLHvePPDG28q7HBqAQI2UCxAFbABMTMkWPAB5g9Awp4BrmUUjIJRMApGARoAAAk8W2yBrqUkAAAAAElFTkSuQmCC","orcid":"","institution":"Qinghai Normal University","correspondingAuthor":true,"prefix":"","firstName":"Guangchao","middleName":"","lastName":"Cao","suffix":""},{"id":484669133,"identity":"280ab067-e8ca-4fd4-92cc-2f8b853be7e1","order_by":3,"name":"Yujian Zhong","email":"","orcid":"","institution":"Qinghai Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yujian","middleName":"","lastName":"Zhong","suffix":""},{"id":484669134,"identity":"d0f5dffd-a269-467f-8bca-a7336527fa12","order_by":4,"name":"Yan Wang","email":"","orcid":"","institution":"Qinghai Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wang","suffix":""},{"id":484669135,"identity":"642505af-278c-42a4-a81d-adeb5a18b35d","order_by":5,"name":"Deli Ye","email":"","orcid":"","institution":"The Plateau Ecology Research Center of Qinghai Huanghe Hydropower Development Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Deli","middleName":"","lastName":"Ye","suffix":""},{"id":484669136,"identity":"b2a1a4af-3144-4dd0-bbbf-22322c030a5b","order_by":6,"name":"Hongyuan Ma","email":"","orcid":"","institution":"The Plateau Ecology Research Center of Qinghai Huanghe Hydropower Development Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Hongyuan","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-07-08 07:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7072101/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7072101/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40974-025-00400-9","type":"published","date":"2026-01-07T15:58:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86739324,"identity":"62b0f400-2587-4c9b-804f-90dc3a8780ae","added_by":"auto","created_at":"2025-07-15 06:26:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15573408,"visible":true,"origin":"","legend":"\u003cp\u003eLocation and overview of the study area: (a) The location of Gonghe Basin on the Qinghai-Tibet Plateau; (b) Geomorphology of Talatan Beach and surroundings (QHN.Mot.: Qinghai Nanshan; EL Mot.: Ela Mountain); (c) Distribution of sampling sites on satellite imagery, with dashed lines denoting buffer zones.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7072101/v1/b55a91ff59a942a90ac3d9a8.png"},{"id":86739786,"identity":"b2b4a560-874e-401d-ade4-c8748957f97b","added_by":"auto","created_at":"2025-07-15 06:34:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2196258,"visible":true,"origin":"","legend":"\u003cp\u003eTotal soil nutrient contents and stoichiometric characteristics among PV deployment types in desert ecosystems.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7072101/v1/cf1e6fddfd721ed03d781b90.png"},{"id":86739318,"identity":"ac3cd522-c529-428c-bff4-a567727b049b","added_by":"auto","created_at":"2025-07-15 06:26:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2328874,"visible":true,"origin":"","legend":"\u003cp\u003eAvailable soil nutrient contents and stoichiometric ratios among PV deployment types in desert ecosystems.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7072101/v1/c76e4276af6b09c82f576c5d.png"},{"id":86739337,"identity":"17c391f0-8126-44d2-8864-ed76da9fbff6","added_by":"auto","created_at":"2025-07-15 06:26:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169257,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation network between PV deployment types, environmental factors, and soil nutrient variables (The results of the correlation analysis are presented in Table S3).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7072101/v1/a3f6d4d8d29963da27a75565.png"},{"id":86739790,"identity":"9069e8ee-d43b-49bb-b61f-b553dfc37321","added_by":"auto","created_at":"2025-07-15 06:34:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1655152,"visible":true,"origin":"","legend":"\u003cp\u003e(a) RDA of soil nutrients and stoichiometry; (b) Hierarchical partitioning analysis of environmental drivers.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7072101/v1/defad5351e42ae0f3a3e3f9c.png"},{"id":86739331,"identity":"0ca0e0d0-6caa-4fbc-9ef2-0d995e0b657e","added_by":"auto","created_at":"2025-07-15 06:26:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1927858,"visible":true,"origin":"","legend":"\u003cp\u003ePathways by which environmental factors affect soil nutrient dynamics (PLS-SEM).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7072101/v1/dddf1e638da549fe35a1f8cc.png"},{"id":86739787,"identity":"f021aba8-9609-4220-ae48-73e9c1d64c0f","added_by":"auto","created_at":"2025-07-15 06:34:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135032,"visible":true,"origin":"","legend":"\u003cp\u003eDirect, indirect, and total effects of environmental drivers on soil nutrients and stoichiometry.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7072101/v1/e23b13d172c92760ba1e7e8d.png"},{"id":100069469,"identity":"8122694f-e29e-4c50-86bc-607d12d02150","added_by":"auto","created_at":"2026-01-12 16:14:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23320768,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7072101/v1/5d111c01-9b55-47d5-9d69-d7ad340b9eed.pdf"},{"id":86739320,"identity":"aa81b8ff-9740-4bd5-8783-24c66a1c8502","added_by":"auto","created_at":"2025-07-15 06:26:47","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":238592,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-7072101/v1/6699f0f2ecb71a88091ebc27.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coupled Effects of Soil Texture and Hydrothermal Regimes on Soil Nutrient Spatial Patterns: Superimposed Impact of Photovoltaic Installations in Desert Ecosystems","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith increasing pressure from global climate change, the transformation towards a low-carbon energy structure has become a shared objective of the international community (IEA \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a core renewable energy technology, photovoltaic (PV) solar power is widely recognized as one of the most promising options for achieving this transition. By the end of 2023, the cumulative global installed capacity of PV exceeded 1.6 TW, with an annual increase of 420 GW. Among these, China held the leading position globally, contributing 216.88 GW of new installations in 2023 and reaching a cumulative capacity of 609.49 GW (J\u0026auml;ger-Waldau \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Currently, large-scale PV installations are mainly concentrated in arid and semi-arid regions, particularly at the desert\u0026ndash;grassland ecotone, where optimal solar radiation conditions and relatively low land acquisition costs provide favorable conditions for PV projects (Hernandez et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, extensive PV development often leads to marked changes in land use patterns, with natural ecosystems being transformed into land uses dominated by anthropogenic facilities. Such changes directly alter regional land cover types and ecological processes, which may result in a range of ecological and environmental issues, including shifts in vegetation patterns, soil structure degradation, higher erosion risk, and habitat fragmentation (Capell\u0026aacute;n-P\u0026eacute;rez et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Choi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li and Gao \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSoil plays a critical role in nutrient cycling and material storage within terrestrial ecosystems, fundamentally regulating plant growth and microbial activity through the ecological stoichiometry of organic matter and key elements such as carbon (C), nitrogen (N), and phosphorus (P). Maintaining stoichiometric balance is essential for ecosystem stability (Cleveland and Liptzin \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Ecological stoichiometry, which clarifies elemental balances and the regulatory mechanisms in ecological processes, provides a theoretical basis for understanding the impacts of human activities such as energy development on ecosystem function (Sterner and Elser \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Previous studies have shown that, in arid and semi-arid regions, soil C, N, and P cycles are jointly regulated by climate, geography, and soil factors. These constraints often create complex spatial patterns and may result in \u0026ldquo;decoupling\u0026rdquo; among elemental relationships, leading to C:N:P stoichiometric imbalances and affecting ecosystem structure, function, and resilience (Sterner and Elser \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Delgado-Baquerizo et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jiao et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tan and Wang \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSoil C, N, and P cycling in arid and semi-arid environments is highly sensitive and complex, with anthropogenic interventions capable of significantly altering stoichiometric patterns. Against this background, the large-scale deployment of PV facilities has increasingly become a dominant mode of land use in these areas. Studies indicate that PV arrays can improve regional ecological conditions by regulating microclimates, optimizing soil moisture distribution, and promoting nutrient cycling processes, thereby facilitating soil and vegetation restoration and enhancing ecosystem functions such as wind erosion control (Liu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yue et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These beneficial effects are mainly attributed to microclimate regulation, the redistribution of water, and enhanced nutrient cycling driven by the presence of PV panels (Yue et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It is important to note that different PV panel structures and installation modes (e.g., fixed-axis, single-axis tracking, dual-axis tracking) impact solar radiation and surface thermal environment in different ways, leading to variations in soil physicochemical properties and nutrient cycling, and consequently differing ecological outcomes (Yue et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Quantitative analysis of the mechanisms by which different PV facility types affect the near-surface environment and soil C:N:P stoichiometry is therefore vitally important for guiding ecosystem management and the sustainable development of renewable energy in arid and semi-arid regions.\u003c/p\u003e\u003cp\u003eThe Talatan Photovoltaic Industrial Park, located in the Gonghe Basin of Qinghai Province, represents a typical alpine, arid\u0026ndash;semi-arid transition zone, characterized by low soil organic matter and a fragile ecological environment (Lu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous research has found that PV facilities can enhance local soil carbon sequestration and increase certain soil nutrient contents (Wu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the accumulation capacities of soil carbon and nitrogen differ among different facility types (Marrou et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thus, the mechanisms underlying shifts in soil C:N:P stoichiometry due to PV deployment in arid climates are the result of interactions among regional climate, soil physicochemical properties, PV facility type, and management practices at multiple scales. Nevertheless, systematic research on how broad PV deployment affects the stoichiometric patterns and driving mechanisms of soil C:N:P in desert ecosystems remains scarce, especially regarding the differences caused by varying facility types, which are yet to be fully clarified (Yue et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this context, the present study systematically compares the responses of soil C:N:P stoichiometric patterns to three PV array types\u0026mdash;UFPV, IFPV, and ITPV\u0026mdash;at the Talatan PV industrial park. Using multi-layer soil and vegetation sampling, combined with multivariate statistical analysis and structural equation modeling, we further probe the coupled effects of environmental factors (climate, vegetation, soil texture) on soil nutrient cycling. We evaluate the implications of these changes for ecosystem function and sustainability in the region. The main research objectives are as follows: (1) to quantify the characteristics and differences in total and available soil nutrients and their stoichiometric ratios across different PV facility types; (2) to disentangle the primary and secondary contributions and impact pathways of key environmental drivers on spatial patterns and stoichiometric responses of soil nutrients; and (3) to provide scientific support and management recommendations for the coordinated development of large-scale PV deployment and ecological conservation in arid regions.\u003c/p\u003e\u003cp\u003eOverall, this research offers theoretical and policy guidance for understanding the ecological effects of PV development, enhancing soil carbon sequestration in arid lands, and promoting a synergistic integration of ecological conservation and economic growth. The work not only enriches the theoretical framework of C\u0026ndash;N\u0026ndash;P stoichiometric ecology in arid regions under human perturbation, but also contributes technical support for ecological risk management, green low-carbon development, and the sustainable management of PV energy landscapes.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area Overview\u003c/h2\u003e\u003cp\u003eThe Talatan Beach area is situated on the left bank of the Yellow River in Gonghe County, Hainan Tibetan Autonomous Prefecture, Qinghai Province, in the central-western region of the Gonghe Basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study region covers approximately 1,958 km\u0026sup2; and is located at an elevation of 2,900\u0026ndash;3,100 m. It experiences an arid climate, with average annual temperature, precipitation, evaporation, sunshine hours, total solar radiation, and wind speed of 4.1\u0026deg;C, 246.3 mm, 1,716.7 mm, 2,300\u0026ndash;3,500 h, 6,564.26 MJ/m\u0026sup2;, and 1.8 m/s, respectively. The prevailing winds are westerly and northwesterly (Kang et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe parent soil materials are primarily loess or gravel, resulting in dominant sandy loam and light loam soils, with gravel and sandy layers at depth (Wu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to strong northwesterly winds, wind-blown sand activity is considerable, causing significant wind erosion and land desertification, with an average annual wind-eroded soil thickness of 1\u0026ndash;1.5 cm. The principal soil type is kastanozem with a thickness of 50\u0026ndash;70 cm, and surface vegetation is mainly desert steppe (Zhao et al. 1996; Guo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Owing to the region\u0026rsquo;s abundant solar radiation and land resources, Talatan Beach has become a key area for clean energy development in Qinghai Province. As the largest centralized photovoltaic (PV) power generation site in China, the Talatan Beach PV Industrial Park has served as a pillar of Hainan Prefecture\u0026rsquo;s multi-GW renewable energy base since its establishment in 2013. Adjacent to the Longyangxia Reservoir, the park covers a planned area of about 609 km\u0026sup2;, with an installed capacity of 850 MW and annual utilization of 1,508 hours, Which makes it one of the largest grid-connected PV stations in Northwest China (Yan \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Experimental Design and Sample Collection\u003c/h2\u003e\u003cp\u003eIn late July 2023, during the peak vegetation growth season, sampling plots were established across different PV configurations: UFPV, IFPV, and ITPV. Areas naturally exposed, without PV panels or fencing and located 1 km outside the PV park, served as CG. Soil and biomass samples were collected within 50 \u0026times; 50 cm standard quadrats.\u003c/p\u003e\u003cp\u003eWithin each quadrat, three soil samples were collected using a soil auger following a diagonal pattern. Samples from each layer were pooled, stratified into 0\u0026ndash;10 cm, 10\u0026ndash;20 cm, and 20\u0026ndash;30 cm depths. After homogenization, plant roots, stones, and other debris were removed by hand, and samples were quartered, bagged, and stored for further processing. All samples were air-dried and sieved prior to analysis. For biomass sampling, aboveground biomass (AGB) was collected by clipping all vegetation at the soil surface within the quadrat. Belowground biomass (BGB) was collected using a 7 cm diameter soil corer at depths of 0\u0026ndash;10 cm, 10\u0026ndash;20 cm, and 20\u0026ndash;30 cm, with three replicates per quadrat. Both AGB and BGB samples were oven-dried at 65\u0026deg;C to constant weight before weighing.\u003c/p\u003e\u003cp\u003eIn total, 40 sampling sites and 46 quadrats were surveyed: 8 UFPV sites, 8 IFPV sites, 14 ITPV sites, and 14 control sites. After data consolidation and quality control, 135 soil samples, 46 AGB samples, and 46 BGB samples were retained for analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Measurement Items and Methods\u003c/h2\u003e\u003cp\u003eSoil moisture content (SMC) was determined gravimetrically by oven-drying samples at 105\u0026deg;C until constant weight. Bulk density (BD) was measured using the ring knife method. Soil pH was measured by potentiometry in a 1:2.5 soil:water mixture. Particle size distribution was measured with a laser diffraction analyzer (Mastersizer 3000, Malvern Panalytical, UK). Soil organic carbon (SOC) was quantified using a Vario TOC cube analyzer (Elementar, Germany), total nitrogen (TN) with a Vario MACRO cube elemental analyzer, and total phosphorus (TP) using inductively coupled plasma optical emission spectrometry (ICP-OES) after strong acid digestion. Available potassium (AK) was determined by extraction with 1 mol/L NH₄OAc followed by flame photometry; alkaline hydrolyzable nitrogen (AN) by alkaline hydrolysis diffusion; and available phosphorus (AP) by 0.5 mol/L NaHCO₃ extraction and molybdenum-antimony colorimetry (Bao \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Extraction of Environmental Factors\u003c/h2\u003e\u003cp\u003eMeteorological variables, including temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), were extracted from the National Tibetan Plateau Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.tpdc.ac.cn\u003c/span\u003e\u003cspan address=\"https://data.tpdc.ac.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ), utilizing 1-km resolution monthly mean datasets for the period 1901\u0026ndash;2023 (Peng \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, 2020, 2022). The normalized difference vegetation index (NDVI) for the 2023 growing season (June\u0026ndash;August) was synthesized from 30 m-resolution Landsat imagery processed on the Google Earth Engine (GEE) platform. Clouds and outliers, as well as the effects of PV panel shading, were minimized by median compositing. All remote sensing data were projected to WGS 1984.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Methods\u003c/h2\u003e\u003cp\u003eNormality of all data was evaluated using the Shapiro\u0026ndash;Wilk test, and homogeneity of variance was checked with Levene\u0026rsquo;s test (car package, R). The Kruskal\u0026ndash;Wallis test and Dunn\u0026rsquo;s post hoc test (FSA package) were used as non-parametric alternatives to one-way ANOVA to assess the effects of PV configuration on SOC, TN, TP, AN, AK, and AP. The significance threshold was α\u0026thinsp;=\u0026thinsp;0.05, and significant differences were visualized using the ggpubr package.\u003c/p\u003e\u003cp\u003eAssociations between environmental factors and soil nutrients or stoichiometry were analyzed using the Mantel test (linkET, ggplot2, and dplyr packages; Mantel \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). Redundancy analysis (RDA) was performed with the vegan package to elucidate relationships between soil nutrients/stoichiometry and environmental variables. To quantify the relative contributions of environmental factors, hierarchical partitioning and Monte Carlo permutation tests (rdacca.hp package) were conducted. Partial least squares path modeling (PLS-PM; plspm package, Russolillo \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) was applied to investigate the pathways by which PV infrastructure influences soil nutrient cycling and stoichiometry through key drivers.\u003c/p\u003e\u003cp\u003eAll statistical analyses and visualizations were performed using RStudio (version 3.3.9).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Total Soil nutrients and Stoichiometric Characteristics under PV Deployment in Desert Ecosystems\u003c/h2\u003e\u003cp\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e presents the physical and chemical properties of soils at a depth of 0\u0026ndash;30 cm in the study area. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the variations in soil nutrients and their stoichiometric ratios among four treatments: UFPV, IFPV, ITPV, and CG. Results indicate that the impact of PV deployment on soil nutrient content varies by facility type. Notably, the UFPV type exhibited a significant decline in SOC, TN, and TP compared to other groups. In contrast, IFPV and ITPV plots maintained higher nutrient levels, which were close to or slightly below those of the control.\u003c/p\u003e\u003cp\u003eIn terms of soil stoichiometric ratios, no significant differences were observed between PV types and the control based on multiple group comparisons. According to Kruskal-Wallis tests, significant differences (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) were present among the four groups for SOC, TN, and TP. Specifically, mean SOC contents were 7.94 g/kg (UFPV), 9.74 g/kg (IFPV), 9.49 g/kg (ITPV), and 10.51 g/kg (CG). Post hoc Dunn\u0026rsquo;s tests indicated that SOC in UFPV was significantly lower than CG (a decrease of 24.5%), but not significantly different from IFPV or ITPV. For TN, mean values were 1.48 g/kg (UFPV), 1.96 g/kg (IFPV), 1.88 g/kg (ITPV), and 2.16 g/kg (CG). UFPV showed significantly lower TN than all other groups (by 31.5%, 24.5%, and 21.3%, respectively), as further supported by Dunn\u0026rsquo;s tests. For TP, means were 0.45 g/kg (UFPV), 0.49 g/kg (IFPV), 0.53 g/kg (ITPV), and 0.55 g/kg (CG); UFPV was significantly lower than ITPV and CG, but not significantly different from IFPV.\u003c/p\u003e\u003cp\u003eRegarding C:N, C:P, and N:P ratios, neither Kruskal-Wallis tests nor Dunn\u0026rsquo;s post hoc tests identified significant differences among groups, and all group means were comparable. This pattern was also reflected in the strong overlap observed in violin and box plots of these ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Available Soil Nutrients and Stoichiometric Characteristics under PV Deployment in Desert Ecosystems\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the changes in available soil nutrients and their stoichiometric ratios among the four treatments. Kruskal-Wallis analysis revealed significant differences (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) among the groups for AN, AK, AN:AK, and AP:AK ratios. Specifically, mean AN contents were 47.92 mg/kg (UFPV), 70.27 mg/kg (IFPV), 71.98 mg/kg (ITPV), and 75.36 mg/kg (CG), with UFPV significantly lower than the other three groups as confirmed by Dunn\u0026rsquo;s post hoc tests. For available phosphorus (AP), group means were 1.38 mg/kg (UFPV), 1.74 mg/kg (IFPV), 1.54 mg/kg (ITPV), and 1.68 mg/kg (CG). Overall, there was no significant main effect of PV deployment on AP, though UFPV differed from CG in pairwise comparison, but AP differences among the PV types remained small.\u003c/p\u003e\u003cp\u003eFor AK, mean values were 116.73 mg/kg (UFPV), 153.21 mg/kg (IFPV), 110.40 mg/kg (ITPV), and 169.74 mg/kg (CG). UFPV showed significantly lower AK than CG and IFPV, while the differences among IFPV, ITPV, and CG were not significant, indicating that UFPV represented the lowest AK level. The AN:AP ratios were 39.49 (UFPV), 44.99 (IFPV), 52.23 (ITPV), and 48.57 (CG), with Kruskal-Wallis test showing a marginal effect (P\u0026thinsp;=\u0026thinsp;0.066), suggesting a potential trend; Dunn\u0026rsquo;s test identified significant differences between UFPV and both ITPV and CG. As for the AN:AK ratios (0.53, 0.52, 0.74, and 0.51 for UFPV, IFPV, ITPV, and CG, UFPV was significantly lower than ITPV, while all other pairwise differences were non-significant. For AP:AK, values were 0.01, 0.01, 0.02, and 0.01, with ITPV significantly higher than the other groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Relationships between PV Deployment Types, Environmental Factors, and Nutrient Variables\u003c/h2\u003e\u003cp\u003eTo investigate the relationships among soil nutrient traits, stoichiometric ratios, and environmental factors\u0026mdash;including soil properties (clay, silt, sand, SMC, BD, pH), climatic factors (TMP, PET, PRE), vegetation factors (AGB, BGB, NDVI), and PV deployment types UFPV, IFPV, ITPV, CG\u0026mdash;we used Spearman correlation and Mantel tests (Table S2) to analyze total soil nutrients ( SOC, TN, TP, TK), available soil nutrients ( AN, AP, AK), total soil nutrient ratios (C:N, C:P, N:P), and available soil nutrient ratios (AN:AP, AN:AK, AP:AK) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResults showed that total soil nutrients were strongly and positively correlated (r\u0026thinsp;=\u0026thinsp;0.2\u0026ndash;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with clay content, SMC, and TMP, and were significantly affected by the UFPV treatment (r\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Available soil nutrients also showed significant correlations (r\u0026thinsp;\u0026lt;\u0026thinsp;0.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with clay, silt, sand, SMC, BGB, and UFPV. Both total soil nutrient ratios and available soil nutrient ratios were most strongly associated with SMC (r\u0026thinsp;=\u0026thinsp;0.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRedundancy analysis (RDA) was used to further clarify the main determinants of soil nutrient variation under PV deployment (detailed results are provided in Table S4.). RDA results revealed that the first two axes (RDA1: 53.33%, RDA2: 19.43%) together explained 72.76% of the total constrained variance in soil nutrients and stoichiometry, indicating that the selected soil, climate, vegetation, and PV-type factors accounted for a large share of spatial variation. Soil physical properties (clay, silt, sand, SMC, BD) and climate factors (TMP, PRE) were identified as primary drivers, with sand and BD contributing most to positive RDA1 variation, and clay, silt, and SMC dominating the negative axis. Total soil nutrients (SOC, TN, TP, TK) correlated most strongly and positively with fine soil particles (clay, silt) and SMC, but negatively with sand content and BD. AN showed a similar pattern as total soil nutrients, primarily driven by fine texture and moisture. In the RDA biplot, control, ITPV, and IFPV were associated with relatively moist, fine-textured, nutrient-rich habitats, whereas UFPV corresponded to sandy soils with greater BD. Hierarchical partitioning analysis demonstrated the independent explanatory contributions as follows: SMC (18.49%)\u0026thinsp;\u0026gt;\u0026thinsp;TMP (12.14%)\u0026thinsp;\u0026gt;\u0026thinsp;BGB (10.34%)\u0026thinsp;\u0026gt;\u0026thinsp;clay (9.90%)\u0026thinsp;\u0026gt;\u0026thinsp;PRE (9.86%)\u0026thinsp;\u0026gt;\u0026thinsp;sand (9.74%)\u0026thinsp;\u0026gt;\u0026thinsp;silt (9.56%)\u0026thinsp;\u0026gt;\u0026thinsp;BD (6.29%), underscoring the dominance of SMC, TMP, texture, and BD in structuring nutrient spatial patterns\u0026mdash;consistent with Mantel and RDA results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Mechanisms of Photovoltaic Regulation on Soil Nutrients and Their Stoichiometry\u003c/h2\u003e\u003cp\u003eTo further elucidate the mechanisms by which PV deployment regulates soil nutrient dynamics and stoichiometry in arid regions, partial least squares structural equation modeling (PLS-SEM) was applied to construct a path model integrating PV facility types, climatic factors, soil and vegetation properties, and their interactions. This approach enabled effective quantification and hypothesis testing of the direct and indirect (total) effects exerted on soil nutrient stocks, availability, and stoichiometric ratios.\u003c/p\u003e\u003cp\u003eThe PLS-SEM results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S5) indicated a high model fit, with a Goodness of Fit (GOF) statistic of 0.50. The model explained 69% of the variance in soil total nutrient ratio (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.69), which was primarily determined by total soil nutrients. In comparison, 45% of the variance in soil available nutrients (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.45) was jointly explained by vegetation, soil physical properties, and total soil nutrients. The model also explained 35% of the variance in soil available nutrient ratios (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.35).\u003c/p\u003e\u003cp\u003ePath coefficients showed that most environmental variables had significant positive or negative direct and/or total effects on soil nutrients and stoichiometry. Specifically, PV facilities exerted significant direct positive effects on vegetation and soil, thereby indirectly promoting nutrient stocks and stoichiometric balance. In contrast, climatic factors demonstrated significant negative direct impacts\u0026mdash;either directly on vegetation or indirectly via total soil nutrients\u0026mdash;on soil nutrient status and stoichiometry. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e quantitatively decomposes the direct, indirect, and total effects of environmental drivers. Overall, soil nutrient stocks, available soil nutrients, and their stoichiometric ratios were positively related to PV deployment type, vegetation, and soil physical attributes, and negatively related to climate factors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Heterogeneous Effects of PV Deployment Types on Soil Nutrients in Desert Ecosystems\u003c/h2\u003e\u003cp\u003eSoil nutrient content and its stoichiometric ratios are key indicators of soil organic matter composition and quality, and they effectively reflect the impact of different management interventions on soil health (Ma 2004; Zhang 2019). In this study, we found that UFPV had significantly negative effects on both total and available soil nutrients; notably, the concentrations of SOC, TN, and available soil nutrients (AN, AK) were significantly lower than those in CG. IFPV also showed a certain decline compared to CG. These patterns are consistent with domestic and international studies reporting negative impacts of fixed-axis PV arrays on soil fertility and C stocks, indicating the widespread and long-term nature of nutrient loss and ecosystem function degradation associated with fixed-axis PV installations (Moscatelli et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt a local scale, vegetation can intercept and redistribute precipitation, creating pronounced spatial heterogeneity (Yuan et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In fixed-axis PV areas, persistent shading produces a \u0026ldquo;rain shadow effect,\u0026rdquo; reducing direct rainfall inputs (Elamri et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and further magnifying local hydrological changes (Thorne et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). PV panels can also increase local air temperature through panel-induced warming, offsetting the cooling effect of shading, thus raising air temperature and lowering relative humidity beneath panels, as well as amplifying the diurnal range of surface temperatures (Zhao et al. 2016; Gao et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Armstrong et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wu 2021). These altered soil-hydrothermal conditions can suppress the establishment and growth of pioneer desert species such as Haloxylon and Artemisia (Song et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), reduce litter input and organic matter accumulation, and negatively affect the abundance and activity of microbial groups, including N-fixing and ammonia-oxidizing bacteria, further impairing soil nutrient accumulation (Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, total phosphorus (TP) in desert soils is primarily supplied through parent material weathering (Jobb\u0026aacute;gy and Jackson \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; He et al. 2014; Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and the observed declines in TP under UFPV may be linked to disrupted vegetation-litter cycling and reduced mineral weathering rates under low Soil moisture content, a trend observed widely in desert PV zones (Shang et al. 2020).\u003c/p\u003e\u003cp\u003eIn contrast, single-axis tracking PV systems show clear advantages in maintaining soil nutrients, largely due to their ability to optimize the microenvironment through dynamic tilt adjustments. Previous research has shown that tracking systems reduce environmental impacts by an average of 17% compared with fixed-axis PV types, cutting greenhouse gas emissions, land occupation, and water use while promoting aboveground biomass accumulation and improving topsoil nutrient availability (Lassio et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our results confirm that nutrient levels in ITPV areas were statistically indistinguishable from the control, highlighting this ecological coordination effect. The periodic rotation of tracking panels reduces the area of persistent shading, promotes a more even distribution of light and precipitation beneath panels, alleviates ecological stress, and helps maintain plant diversity (Zhou et al. 2019; Liu 2021; Yue \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From an engineering perspective, fixed-axis and tracking PV systems differ in panel density, tilt control, and operation mode\u0026mdash;characteristics that fundamentally shape their disturbance patterns and microenvironmental effects at the ground surface (Koussa et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, optimizing structural parameters and enhancing compatibility with ecosystem processes will be essential for green solar transitions in desert regions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Heterogeneity of Soil Nutrients under Environmental Controls and Superimposed Photovoltaic Effects\u003c/h2\u003e\u003cp\u003eWater scarcity and nutrient deficiency are key constraints on ecological processes in desert ecosystems. Identifying the dominant drivers of soil nutrient status is critical for evaluating ecosystem functions under large-scale centralized PV deployment. Our results verify a typical mechanism under arid conditions: soil nutrients are co-regulated by regional climate and local soil physicochemical attributes such as texture, BD, and SMC (Lu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specifically, both total soil nutrients (total soil nutrients) and available soil nutrients (available soil nutrients) were strongly positively correlated with fine fractions (clay, silt) and SMC, and negatively with BD and sand content. These findings suggest that fine-textured soils, by enhancing moisture retention, nutrient adsorption, and root penetration, play a crucial role in nutrient accumulation and utilization in arid lands (Li et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Huntley et al. 2023). RDA further identified soil physical properties (clay, silt, sand, SMC, BD) and climatic factors (TMP, PRE) as the main vectors driving spatial nutrient variation. The control and ITPV groups were associated with relatively favorable environmental conditions and higher nutrient levels, underscoring the fundamental roles of suitable soil structure and hydrothermal conditions in sustaining nutrient cycling (Delgado et al. 2017; Bauke et al. 2022).\u003c/p\u003e\u003cp\u003ePLS-SEM results provided quantitative evidence for the dominant roles of soil physical environment and climate in nutrient distribution. Path analysis indicated that soil physical attributes had significant positive direct effects on total soil nutrients (+\u0026thinsp;0.35***) and available soil nutrients (+\u0026thinsp;0.31***), pinpointing their primary contribution to soil nutrient stocks and availability. Meanwhile, arid climate exerted a notable negative effect on vegetation growth (-0.47***), indirectly limiting organic matter input and nutrient cycling\u0026mdash;a suppression likely related to the high evaporation\u0026ndash;low precipitation regime, which constrains biomass production, litter decomposition, and nutrient recirculation (Cui et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tariq et al. 2024). As a key biotic mediator, vegetation facilitates nutrient cycling via biomass accumulation, litterfall, and rhizosphere activity, supporting increased nutrient availability in arid environments (Zhao et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tariq et al. 2024). This study delineated an indirect \u0026ldquo;climate\u0026ndash;vegetation\u0026ndash;nutrient\u0026rdquo; pathway, underscoring the coupling of biotic and abiotic processes in shaping soil nutrient patterns.\u003c/p\u003e\u003cp\u003eWithin this primary control structure, PV deployments do not directly determine soil nutrient content. PLS-SEM effect decomposition indicated that PV type promoted soil nutrient enhancement mainly by indirectly improving soil properties and stimulating vegetation growth. Kruskal-Wallis tests likewise revealed lower baseline soil nutrient content under native arid conditions CG, and the nutrient differences under PV deployment reflected positive additive effects atop this baseline (Li et al. 2022). Thus, in desert ecosystems, soil physical and hydrothermal properties provide the core framework for nutrient spatial variation, with biological processes acting in synergy (Lu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). PV deployment, as an anthropogenic disturbance, exerts its positive effects on nutrients mainly via localized modification of these core drivers. This mechanistic understanding, from a biogeochemical perspective, is valuable both for decoding the ecological roles of solar infrastructure and for informing sustainable PV management in arid regions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Cascading Effects and Sustainable Management Implications of PV-Induced Regulation\u003c/h2\u003e\u003cp\u003eIn arid areas, PV system deployment offers a double opportunity: the availability of vast, low-productivity land enables large-scale solar installation, thus promoting green energy and reducing carbon emissions (Wang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while PV systems can also enhance local environmental conditions and contribute to ecological restoration of degraded land (Wu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, desert ecosystems are inherently fragile, vulnerable to land degradation and desertification (Abdi et al. 2014). PV installations exert complex effects on soil, microclimate, and vegetation; technical choices and land management practices create microenvironmental heterogeneity and divergent biological responses, generating marked spatial heterogeneity in soil nutrients\u0026mdash;a fact that underscores the challenge of aligning PV expansion with ecological integrity (Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Balancing energy production and ecological services is thus paramount for sustainable solar development.\u003c/p\u003e\u003cp\u003eThis study found that the effects of PV deployment on soil nutrients extend beyond mere shading; by reshaping local microenvironments, PV systems induce intricate cascade responses among PV\u0026ndash;vegetation\u0026ndash;soil elements, leading to spatially heterogeneous nutrient patterns. Such heterogeneity reveals the directional regulatory potential of PV design on ecological processes. Pathway analysis demonstrated that PV deployment initially manifests as significant positive effects on the vegetation growth environment and soil physical properties. These effects are then transmitted through both biotic (vegetation) and abiotic (soil) mediators, indirectly enhancing overall soil nutrient availability and bioavailability. This is consistent with previous findings (Li et al. 2016; Li et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al. 2021), highlighting the centrality of PV\u0026ndash;vegetation coupling in shaping desert soil nutrient dynamics. Furthermore, our path models confirm that PV deployment, depending on type, significantly boosts vegetation growth, suggesting that improvements in ecological conditions are key to driving belowground biogeochemical benefits (Liu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Enhanced vegetation, in turn, increases soil nutrient effectiveness through litterfall, root exudation, and microbial rhizosphere effects. Therefore, the differential impact of PV design types on vegetation growth is a critical factor explaining the heterogeneity in soil nutrient responses. For instance, lower nutrient levels under UFPV likely reflect both unfavorable physical soil conditions and weaker stimulation of plant growth, with inadequate biotically driven nutrient cycling, highlighting an energy\u0026ndash;ecosystem services trade-off intrinsic to PV infrastructure that must be addressed via technology choice and ecological management.\u003c/p\u003e\u003cp\u003eFrom a nutrient optimization perspective, available soil nutrients and their stoichiometric ratios (such as AN:AP, AN:AK, AP:AK) were more sensitive to microenvironmental changes and vegetation feedbacks than total stocks. Our findings indicate these ratios respond more strongly to the integrated effects of PV structures and ecological restoration (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), emphasizing the importance of targeted management, particularly vegetation rehabilitation, in supporting both PV ecosystem co-benefits and soil functions.\u003c/p\u003e\u003cp\u003eBased on these insights, systematic ecological management is recommended for PV deployment in deserts. Priority should be given to single-axis tracking or smart adaptive PV systems, which dynamically adjust panel angles to balance shade and light, optimizing conditions for vegetation. Increasing mounting height can improve ventilation, water infiltration, and grazing compatibility, facilitating vegetation recovery. Combining local ecosystem engineering and native plant restoration\u0026mdash;such as topsoil application or microhabitat amelioration\u0026mdash;strengthens positive PV\u0026ndash;vegetation\u0026ndash;soil feedbacks. Robust long-term monitoring systems, focusing on available soil nutrients and stoichiometric ratios, should be established to support adaptive management and scientific decision-making. These strategies can help clarify the ecological efficacy of technical and management interventions, including restoration, on soil fertility and biogeochemical functions in desert PV systems from short- to mid-term timeframes, with special attention to synergistic effects between vegetation recovery and nutrient cycling. Through systematic optimization of design, integrated ecological interventions, and evidence-based monitoring, the ecological integrity of these fragile systems can be sustained alongside green energy development.\u003c/p\u003e\u003cp\u003eIt should be noted that this research was based on short-term soil and vegetation data from the large Talatan Beach PV base. Thus, it may not fully capture longer-term ecological effects. Moreover, our surveys were limited to fixed-axis and single-axis tracking PV, as these predominate locally, and did not include all emerging PV configurations, which restricts the generalizability of our findings. Finally, our ecological assessments focused primarily on soil nutrients and biomass, with limited attention to microbial function. Future studies should incorporate long-term, multi-scale monitoring, expand the diversity of PV technologies studied, integrate multidimensional ecosystem indicators, and intensify management evaluation in order to provide more comprehensive scientific guidance for the sustainable development of arid-zone PV systems.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study demonstrates that fixed-axis PV systems (IFPV, UFPV) significantly reduce soil nutrient content in desert ecosystems, while single-axis tracking systems (ITPV) maintain nutrient levels comparable to natural controls, supporting ecosystem stability.\u003c/p\u003e\u003cp\u003eThe spatial heterogeneity of soil carbon, nitrogen, and phosphorus is predominantly governed by abiotic factors such as soil hydrothermal conditions, texture, and bulk density, while PV deployment primarily exerts superimposed environmental effects. Furthermore, regional TMP and PRE indirectly regulate the availability of soil nutrients by constraining vegetation growth. Importantly, available nutrient pools and their stoichiometric ratios demonstrated higher sensitivity to ecological changes than total nutrient stocks, underscoring their value as robust indicators for assessing and monitoring soil ecological dynamics in photovoltaic-impacted ecosystems. To promote sustainable and synergistic development of renewable energy and ecosystem health in arid regions, we recommend giving priority to single-axis tracking PV configurations, integrating long-term, dynamic monitoring of available nutrients and stoichiometric ratios into PV project planning, and tailoring ecological restoration measures\u0026mdash;such as native vegetation rehabilitation and microhabitat improvement\u0026mdash;to local conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by 2022 Kunlun Talents Program for High-End Innovation and Entrepreneurship of Qinghai Province Qing Talent No. 1 (2023) and State Power Investment Corporation Huanghe Hydropower Development Co., Ltd. Science and Technology Projects (KY-C-2024-GF04, KY-C-2025-HB05).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.Y. wrote the original draft, prepared visualizations, developed the methodology, conducted the investigation and formal analysis, and contributed to the conceptualization. J.H. contributed to writing\u0026mdash;review and editing, and writing the original draft. G.C. contributed to writing\u0026mdash;review and editing, provided supervision and resources, and acquired funding. Y.Z. and Y.W. conducted the investigation. D.Y. and H.M. provided resources and acquired funding. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by 2022 Kunlun Talents Program for High-End Innovation and Entrepreneurship of Qinghai Province Qing Talent No. 1 (2023) and State Power Investment Corporation Huanghe Hydropower Development Co., Ltd. Science and Technology Projects (KY-C-2024-GF04, KY-C-2025-HB05). We gratefully acknowledge all colleagues from the Testing Center of our laboratory and the staff at the Talatan Photovoltaic Power Station for their valuable assistance in fieldwork and experimental procedures.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdi, O.A., Glover, E.K., Luukkanen, O., 2013. Causes and impacts of land degradation and desertification: Case study of the Sudan. Int. J. Agric. For. 3(2), 40\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eArmstrong, A., Ostle, N.J., Whitaker, J., 2016. Solar park microclimate and vegetation management effects on grassland carbon cycling. Environ. Res. Lett. 11(7), 074016. https://doi.org/10.1088/1748-9326/11/7/074016.\u003c/li\u003e\n\u003cli\u003eBao, S.D., 2000. 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Effects of vegetation restoration on soil properties and vegetation attributes in the arid and semi-arid regions of China. J. Environ. Manag. 343(1), 118186. https://doi.org/10.1016/j.jenvman.2023.118186.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"energy-ecology-and-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eeae","sideBox":"Learn more about [Energy, Ecology and Environment](http://link.springer.com/journal/40974)","snPcode":"40974","submissionUrl":"https://submission.nature.com/new-submission/40974/3","title":"Energy, Ecology and Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"PV deployment, C:N:P stoichiometry, Desert ecosystems, Environmental drivers","lastPublishedDoi":"10.21203/rs.3.rs-7072101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7072101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid development of photovoltaic technology is transforming China\u0026rsquo;s energy structure and reducing carbon emissions, but large-scale photovoltaic facility deployment in arid regions affects soil development and nutrient dynamics. The effects of different photovoltaic support types on soil nutrients and ecological stoichiometry, however, are not fully understood. In this study, three configurations\u0026mdash;UFPV (under-module of fixed-axis), IFPV (inter-module of fixed-axis), and ITPV (inter-module of single-axis tracking)\u0026mdash;and CG (control group) were compared in the Talatan Beach photovoltaic park in Qinghai. Soil carbon, nitrogen, and phosphorus stoichiometry were analyzed along with environmental drivers. Results showed soil nutrient levels were significantly lower UFPV compared to the control, while the ITPV better maintained soil nutrient levels. The relative contributions of major environmental factors to the spatial variability of soil nutrient stoichiometry were as follows: soil water content (18.49%), temperature (12.14%), belowground biomass (10.34%), clay content (9.90%), precipitation (9.86%), sand content (9.74%), silt content (9.56%), and bulk density (6.29%). Photovoltaic deployment affects soil nutrients in desert areas not only through physical shading effects, but also by reshaping the local microenvironment and creating complex cascading responses among the \"photovoltaic\u0026ndash;vegetation\u0026ndash;soil\" system, thereby indirectly influencing soil properties. These findings provide insights for ecological risk management and sustainable low-carbon development in arid regions.\u003c/p\u003e","manuscriptTitle":"Coupled Effects of Soil Texture and Hydrothermal Regimes on Soil Nutrient Spatial Patterns: Superimposed Impact of Photovoltaic Installations in Desert Ecosystems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 06:26:42","doi":"10.21203/rs.3.rs-7072101/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-09T08:07:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T07:27:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T18:46:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319272016821901679385618539882874614264","date":"2025-09-24T07:04:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249466619578534897763968346889975655300","date":"2025-07-13T13:21:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143405538875397287526328215111234219555","date":"2025-07-11T09:11:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-11T09:00:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-10T14:12:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-10T14:09:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Energy, Ecology and Environment","date":"2025-07-08T07:47:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"energy-ecology-and-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eeae","sideBox":"Learn more about [Energy, Ecology and Environment](http://link.springer.com/journal/40974)","snPcode":"40974","submissionUrl":"https://submission.nature.com/new-submission/40974/3","title":"Energy, Ecology and Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"694aad78-2277-4c95-b13c-d90ac3b2c4ad","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:05:53+00:00","versionOfRecord":{"articleIdentity":"rs-7072101","link":"https://doi.org/10.1007/s40974-025-00400-9","journal":{"identity":"energy-ecology-and-environment","isVorOnly":false,"title":"Energy, Ecology and Environment"},"publishedOn":"2026-01-07 15:58:56","publishedOnDateReadable":"January 7th, 2026"},"versionCreatedAt":"2025-07-15 06:26:42","video":"","vorDoi":"10.1007/s40974-025-00400-9","vorDoiUrl":"https://doi.org/10.1007/s40974-025-00400-9","workflowStages":[]},"version":"v1","identity":"rs-7072101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7072101","identity":"rs-7072101","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}