Restoration Method Research Articles

Enhanced Soil Fertility and Carbon Sequestration in Urban Green Spaces through the Application of Fe-Modified Biochar Combined with Plant Growth-Promoting Bacteria.

The soil of urban green spaces is severely degraded due to human activities during urbanization, and it is crucial to investigate effective measures that can restore the ecological functions of the soil. This study investigated the effects of plant growth promoting bacteria (Bacillus clausii) and Fe-modified biochar on soil fertility increases and mechanisms of carbon sequestration. Additionally, the effects on C-cycling-related enzyme activity and the bacterial community were also explored. Six treatments included no biochar or Bacillus clausii suspension added (CK), only Bacillus clausii suspension (BC), only biochar (B), only Fe-modified biochar (FeB), biochar combined with Bacillus clausii (BBC), and Fe-modified biochar combined with Bacillus clausii (FeBBC). Compared with other treatments, the FeBBC treatment significantly decreased soil pH, alleviated soil alkalization, and increased the alkali-hydro nitrogen content in the soil. Compared to the individual application of FeB and BC, the FeBBC treatment significantly improved aggregates' stability and positively improved soil fertility and ecological function. Additionally, compared to the individual application of FeB and BC, the soil organic carbon (SOC), particulate organic carbon (POC), and soil inorganic carbon (SIC) contents for the FeBBC-treated soil increased by 28.46~113.52%, 66.99~434.72%, and 7.34~10.04%, respectively. In the FeBBC treatment, FeB can improve soil physicochemical properties and provide bacterial attachment sites, increase the abundance and diversity of bacterial communities, and promote the uniform distribution of carbon-related bacteria in the soil. Compared to a single ecological restoration method, FeBBC treatment can improve soil fertility and carbon sequestration, providing important reference values for urban green space soil ecological restoration.

Evaluation of beach nourishment in Mar del plata, Argentina: An application of the CoastSat toolkit

Sandy coasts play a crucial role in various human activities and support the economies of many coastal states. Due to their importance, coastal erosion mitigation techniques are often implemented. Among these techniques, beach nourishment is considered an environmentally acceptable method for coastal protection and restoration. In this work, a methodology for the evaluation of such projects is proposed and applied to the biggest beach nourishment project in the city of Mar del Plata, Argentina. The nourishment was carried out between 1998 and 1999 in Playa Grande (PG), Varese (V) and Bristol (B) bays. The CoastSat toolkit was implemented to obtain shoreline positions from satellite imagery and to derive a 36-year monthly time series of beach width in the region. For the analysis, three distinctives time lapses are identified: pre-intervention, response, and new equilibrium. An exponential decay function was fitted to determine the response lapse and its characteristics. Metrics such as mean beach width and trends were computed and used to compare the condition of beaches before and after the project. Results show that the region experienced a net growth after nourishment, with beach width doubling in Bristol and Playa Grande, and increasing tenfold in Varase. Nourishment did not alter the erosive trends of Playa Grande and Bristol, nor the growth trend of Varese. Regarding renourishment planning, according to our findings, for Playa Grande and Bristol beaches to achieve a net growth of 1 m, it would require the deposition of 17 m³ of sediment per meter of coastline. This study highlights the suitability of the CoastSat toolkit for assessing the effectiveness of beach regeneration.

Dynamic Evaluation Method of Electric Power New Energy and River Basin Ecological Restoration

The wide application of renewable energy can reduce the carbon emissions to the environment and the consumption of petrochemical resources, improve the improvement rate of the natural environment, and at the same time, ecological restoration can improve the ecology of the river basin and produce sustainable improvement of the environment together with renewable energy. In this paper, renewable energy is taken as the research object, and the ecological restoration method of the river basin is jointly evaluated to comprehensively evaluate the natural environment. First, the carbon neutrality data of ecological restoration and renewable energy in the river basin were collected, classified, collated and integrated, and the analysis indicators included CO2 emissions, plant coverage, soil and water conservation, and the cost of power generation. Then, the spatial regression model was used for data processing and calculation, and the ecological coefficient and carbon emission coefficient were determined in an iterative manner. Finally, the actual case is used for dynamic evaluation and analysis. The results show that the new power and watershed ecological restoration methods can improve the natural environment, improve the plant coverage rate and soil and water conservation rate, with an increase rate of 25~30%, reduce carbon dioxide emissions and power generation costs, and reduce the reduction rate of 10~30%, and at the same time, a good ecological environment will also react to new energy and expand the development space of new energy, with an expansion rate of 10%. Therefore, the method of electric power new energy and river basin ecological restoration can improve the natural environment, promote the development of electric power new energy, and optimize the overall structure of new energy.

Deep denoiser prior driven relaxed iterated Tikhonov method for low-count PET image restoration

Objective. Low-count positron emission tomography (PET) imaging is an efficient way to promote more widespread use of PET because of its short scan time and low injected activity. However, this often leads to low-quality PET images with clinical image reconstruction, due to high noise and blurring effects. Existing PET image restoration (IR) methods hinder their own restoration performance due to the semi-convergence property and the lack of suitable denoiser prior. Approach. To overcome these limitations, we propose a novel deep plug-and-play IR method called Deep denoiser Prior driven Relaxed Iterated Tikhonov method (DP-RI-Tikhonov). Specifically, we train a deep convolutional neural network denoiser to generate a flexible deep denoiser prior to handle high noise. Then, we plug the deep denoiser prior as a modular part into a novel iterative optimization algorithm to handle blurring effects and propose an adaptive parameter selection strategy for the iterative optimization algorithm. Main results. Simulation results show that the deep denoiser prior plays the role of reducing noise intensity, while the novel iterative optimization algorithm and adaptive parameter selection strategy can effectively eliminate the semi-convergence property. They enable DP-RI-Tikhonov to achieve an average quantitative result (normalized root mean square error, structural similarity) of (0.1364, 0.9574) at the stopping iteration, outperforming a conventional PET IR method with an average quantitative result of (0.1533, 0.9523) and a state-of-the-art deep plug-and-play IR method with an average quantitative result of (0.1404, 0.9554). Moreover, the advantage of DP-RI-Tikhonov becomes more obvious at the last iteration. Experiments on six clinical whole-body PET images further indicate that DP-RI-Tikhonov successfully reduces noise intensity and recovers fine details, recovering sharper and more uniform images than the comparison methods. Significance. DP-RI-Tikhonov’s ability to reduce noise intensity and effectively eliminate the semi-convergence property overcomes the limitations of existing methods. This advancement may have substantial implications for other medical IR.

Comparative Evaluation of Fracture Resistance in Different Post and Core Systems with Varying Ferrule Locations: An In Vitro Study.

Endodontically treated teeth often require 360° ferrule to retain post and core restoration. The literature support on scientific evidence of partial ferrule is limited, hence this study was conducted to evaluate the fracture resistance of two post and core systems, i.e., Edelweiss and glass fiber post and core system with varying ferrule locations. About 54 extracted mandibular 1st premolars were divided into Edelweiss post and core (Group A) and glass fiber post and core (Group B). They were further subdivided into 3 groups each (Partial functional, partial non-functional, and circumferential). A standardized endodontic procedure was carried out on all the specimens. For group A, an Edelweiss drill of 1.4 mm was used to prepare the post space. Canals were then etched, a bonding agent was applied and posts were luted with paracore resin. For group B, post space was prepared with a straight fissure bur, followed by the same procedure as that of group A. Core build-up was done with composite resin. Cast crowns were fabricated and cemented with GIC. Fracture resistance was evaluated with UTM with a compressive load capacity of 1000 N until fracture occurred. The results of this study stated that in the Edelweiss group partial functional ferrule showed higher fracture resistance and in the glass fiber group circumferential ferrule showed higher fracture resistance. The study also showed that MOF is favorable in all the groups. The type of ferrule was one of the factors that affected the fracture resistance of Edelweiss and Glass fiber post and core group of specimens. The study also concluded that there was a predominant favorable mode of fracture across all groups, in all different ferrule locations. This study opens multiple avenues and methods in tooth restoration and shows that even a partial functional ferrule is sufficient for restoration, provided the right system is used. How to cite this article: Jadhav PD, Shetty R, Mehta D, et al. Comparative Evaluation of Fracture Resistance in Different Post and Core Systems with Varying Ferrule Locations: An In Vitro Study. J Contemp Dent Pract 2024;25(5):480-485.

Light reduction and watering enhance flora memory awakening after forest topsoil translocation

Plant propagules are crucial flora memory materials in restoration practice. Awakening flora memory from plant propagules (i.e. seeds, root fragments, rhizomes, corms, and tubers) in the translocated topsoil from a donor site is a rapid method for forest restoration on degraded sites globally. However, it remains unclear to what extent manageable measures, such as light reduction and watering, affect flora memory awakening. We employed a quadratic saturation D‐optimal design in a forest topsoil translocation experiment to quantify the effects of light reduction and watering on flora memory awakening. We used the cumulative number and richness of plantlets emerging from plant propagules to represent the extent of flora memory awakening. Our results show that any combinations of light reduction and watering significantly increased both the cumulative number and richness of plantlets across different life forms. Light reduction had a significantly more positive effect on flora memory awakening than watering. Different life forms exhibited different parabolic or positive linear relationships in responses to light reduction and watering. Light reduction of about 60% of the degraded site and watering of about 80% of the donor site resulted in the highest number and richness of plantlets. We concluded that light reduction and watering combinations were effective in kick‐starting the flora memory awakening in a semiarid subtropical forest after topsoil translocation.

Short-term effects of restoration measures on vegetation community and soil characteristics of an Achnatherum inebrians-type degraded alpine grassland in the Eastern Qilian Mountains

The Qilian Mountains constitute an important ecological security barrier in the northwest of China, with a wealth of habitat types and biological species. They have a profound impact on the surrounding area and even the world. However, due to both natural and anthropogenic influences, Achnatherum inebrians is widely distributed in this region, seriously threatening the balance of the grass-soil-livestock ecosystem. To explore appropriate restoration measures of alpine grassland of the Eastern Qilian Mountains degraded by the presence of A. inebrians, we studied the effects of restoration measures on the A. inebrians population, the grassland vegetation community, and soil characteristics following sward ripping (SR) and swarding ripping plus reseeding excellent forage (SREF) in Tianzhu County, Gansu Province; adjacent A. inebrians-type degraded alpine grassland without any measures was the control (CK). The SR and SREF inhibited A. inebrians growth and reproduction, increased the proportion of edible forage and the community species diversity, (especially aboveground biomass and sum of sub-coverage of edible forage by as much as two and three times), and improved soil nutrients and soil quality. The radar analysis showed that SREF had the best effect on the A. inebrians-type degraded alpine grassland. Correlation analysis showed that edible forage community, plant diversity, soil moisture, and available nutrient content were the main factors driving the restoration of this degraded alpine grassland. Implementing measures tailored to local conditions could effectively inhibit the spread of A. inebrians, stimulate edible forage growth, and improve the grassland plant community and soil environment. The results indicated a reasonable method of restoration for this poisonous weed-type degraded grassland and protection of the ecological environment.

Native bee habitat restoration: key ecological considerations from recent North American literature

Habitat loss is a primary driver of global biodiversity decline, negatively impacting many species, including native bees. One approach to counteract the consequences of habitat loss is through restoration, which includes the transformation of degraded or damaged habitats to increase biodiversity. In this review, we survey bee habitat restoration literature over the last 14 years to provide insights into how best to promote bee diversity and abundance through the restoration of natural landscapes in North America. We highlight relevant questions and concepts to consider throughout the various stages of habitat restoration projects, categorizing them into pre-, during-, and post-restoration stages. We emphasize the importance of planning species- and site-specific strategies to support bees, including providing floral and non-floral resources and increasing nest site availability. Lastly, we underscore the significance of conducting evaluations and long-term monitoring following restoration efforts. By identifying effective restoration methods, success indicators, and areas for future research, our review presents a comprehensive framework that can guide land managers during this urgent time for bee habitat restoration.

Reinforcing Visual Content Integrity through Image Restoration and AI Recognition

A number of factors can cause photographs to deteriorate over time, losing their clarity and aesthetic appeal. The goal of this research is to help restore priceless memories because images act as memory triggers and better image quality can increase clarity and visual comfort. This research is important because it may aid in the recovery and preservation of special memories that were captured in damaged photos. Restoring an image's visual quality has the power to arouse feelings and create closer ties to the past.AI has made great progress in several areas in the recent past. However, questions concerning the veracity and authenticity of visual content have been raised due to the possibility of false information and the spread of deepfakes. We set out to create an automated system that could accurately distinguish between photographs taken by hand and images created by artificial intelligence as a solution to this problem. Furthermore, there are some useful advantages to improving image quality, including better vitality, higher clarity, and less eye strain when viewing the restored photos. All things considered, this study shows how deep learning using GANs works well for image restoration, emphasising its usefulness in retrieving priceless memories and improving visual content for wider uses. The findings of this study demonstrate the potential benefits that AI-driven image restoration methods could have on our daily interactions with visual information in the digital age as well as our personal life.

Correcting Optical Aberration via Depth-Aware Point Spread Functions.

Optical aberration is a ubiquitous degeneration in realistic lens-based imaging systems. Optical aberrations are caused by the differences in the optical path length when light travels through different regions of the camera lens with different incident angles. The blur and chromatic aberrations manifest significant discrepancies when the optical system changes. This work designs a transferable and effective image simulation system of simple lenses via multi-wavelength, depth-aware, spatially-variant four-dimensional point spread functions (4D-PSFs) estimation by changing a small amount of lens-dependent parameters. The image simulation system can alleviate the overhead of dataset collecting and exploiting the principle of computational imaging for effective optical aberration correction. With the guidance of domain knowledge about the image formation model provided by the 4D-PSFs, we establish a multi-scale optical aberration correction network for degraded image reconstruction, which consists of a scene depth estimation branch and an image restoration branch. Specifically, we propose to predict adaptive filters with the depth-aware PSFs and carry out dynamic convolutions, which facilitate the model's generalization in various scenes. We also employ convolution and self-attention mechanisms for global and local feature extraction and realize a spatially-variant restoration. The multi-scale feature extraction complements the features across different scales and provides fine details and contextual features. Extensive experiments demonstrate that our proposed algorithm performs favorably against state-of-the-art restoration methods.

Grazing exclusion enriches arbuscular mycorrhizal fungal communities and improves soil organic carbon sequestration in the alpine steppe of northern Tibet

Fencing for grazing exclusion is regarded as a traditional and effective method for the natural restoration of degraded alpine steppe, and it effectively promotes plant growth and enhances soil carbon stocks. Arbuscular mycorrhizal fungi (AMF) are essential microorganisms in grassland that play a major role in plant-derived C translocation into the soil. However, the effects of fencing on AMF communities and their contributions to soil carbon sequestration are still unclear. In this study, alpine steppe areas with three different fencing durations (free grazing, medium-term fencing for 5-6 years and long-term fencing for more than 10 years) in the northern Tibetan Plateau were selected to explore the effects of grazing exclusion on AMF communities and their roles in soil carbon sequestration. The results showed that medium- and long-term fencing significantly increased both plant aboveground biomass and soil organic carbon (SOC) content. The AMF community composition varied significantly during different fencing durations, with a dramatic increase in the relative abundance of Glomus but a significant reduction in the relative abundance of Diversispora with longer fencing time. Medium-term fencing significantly increased AMF richness and the Shannon-Wiener index. Meanwhile, fencing significantly increased hyphal length density (HLD), glomalin-related soil protein (GRSP) and the proportion of macroaggregates (250-2,000 μm), all of which contribute positively to SOC. Structural equation modeling revealed that fencing time positively influenced HLD and the AMF community composition, subsequently affecting T-GRSP, which was tightly correlated with SOC. Our findings suggest the potentially important contribution of AMF to SOC sequestration, so more attention should be paid to AMF during alpine steppe fencing, particularly for enhancing the efficiency of degraded grassland restoration efforts.

Coordinated method for islanding division and component restoration in active distribution networks aimed at enhanced resilience

Coordinated method for islanding division and component restoration in active distribution networks aimed at enhanced resilience

Load restoration of electricity distribution systems using a novel two‐stage method

AbstractThis paper proposes a new comprehensive load restoration (LR) method for electrical distribution networks. Since two main technologies of switching equipment are there in the modern distribution networks, namely manual switches (MSs) and remote‐controlled switches (RCSs), this article has benefited from this concept effectively. A two‐stage algorithm that provides the system operators with the ability to recover part of the loads in the shortest possible time by RCSs is proposed. After this step, the remaining loads will be restored by a combination of MSs and RCSs. The other strength of this algorithm is to provide accurate and practical solutions so that the sequence of switching actions is clearly defined. Also, using an innovative index called expected weighted energy not supplied as the objective function of the main problem will ensure the operators recover the maximum amount of load in the shortest time possible. This novel method was applied on a sample standard IEEE distribution test network. The simulation results proved the effectiveness of this proposed method.

Image restoration model for microscopic defocused images based on blurring kernel guidance

Defocus blurring imaging seriously affects the observation accuracy and application range of optical microscopes, and the blurring kernel function is a key parameter for high-resolution image restoration. However, its solving process is complicated and high in computational cost. Image restoration based on most neural networks has high requirements on data sets and the image resolution after restoration is limited because of the lack of quantitative estimation of blurring kernels. In this study, an image restoration method guided by blurring kernel estimation for microscopic defocused images is proposed. First, to reduce the blurring kernel estimation error caused by the positive and negative difference in microscopic defocused imaging, a defocused image classification network is designed to classify the input defocused images with different defocus distances and directions, and its output images are input into the blurring kernel extraction network composed of the feature extraction, correlation, and blurring kernel reconstruction layers. Second, a non-blind defocused image restoration model to restore the high-resolution images is proposed by introducing the blurring kernel extraction module into the restoration network based on U-Net, and the blurring kernel estimation and image restoration losses are jointly trained to realize image restoration guided by blurring kernel estimation. Finally, the experimental results of our proposed method demonstrate significant improvements in both the peak signal-to-noise ratio and structural similarity index measure when compared to other methods.

Coarse-to-fine mechanisms mitigate diffusion limitations on image restoration

Recent years have witnessed the remarkable performance of diffusion models in various vision tasks. However, for image restoration that aims to recover clear images with sharper details from given degraded observations, diffusion-based methods may fail to recover promising results due to inaccurate noise estimation. Moreover, simple constraining noises cannot effectively learn complex degradation information, which subsequently hinders the model capacity. To solve the above problems, we propose a coarse-to-fine diffusion Transformer (C2F-DFT) to mitigate diffusion limitations mentioned before on image restoration. Specifically, the proposed C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN) within a new coarse-to-fine training mechanism. The DFSA and DFN with embedded diffusion steps respectively capture the long-range diffusion dependencies and learn hierarchy diffusion representation to guide the restoration process in different time steps. In the coarse training stage, our C2F-DFT estimates noises and then generates the final clean image by a sampling algorithm. To further improve the restoration quality, we propose a simple yet effective fine training pipeline. It first exploits the coarse-trained diffusion model with fixed steps to generate restoration results, which then would be constrained with corresponding ground-truth ones to optimize the models to remedy the unsatisfactory results affected by inaccurate noise estimation. Extensive experiments show that C2F-DFT significantly outperforms diffusion-based restoration method IR-SDE and achieves competitive performance compared with Transformer-based state-of-the-art methods on 3 tasks, including image deraining, image deblurring, and real image denoising. The source codes and visual results are available at https://github.com/wlydlut/C2F-DFT.

Comparison of the coexistence pattern of mangrove macrobenthos between natural and artificial reforestation.

The abandoned pond-to-mangrove restoration project provides greater advantages than tidal flats afforestation in restoring mangrove ecosystem services and will be the primary method for mangrove restoration in the future. The existing methods for abandoned pond-to-mangrove restoration include artificial restoration through 'dike-breaking, filling with imported soil and tree planting' and natural restoration through 'dike-breaking and natural succession'. However, little is known about which restoration strategy (natural or artificial restoration) provides more benefits to the biodiversity of mangrove macrobethos. Given a prevailing view suggested that artificial restoration should be the preferred approach for accelerating recovery of biodiversity and vegetation structure in tropical regions, we hypothesised higher macrobenthic biodiversity and more complex community structure in artificial restoration than in natural restoration. To test this hypothesis, macrobenthic biodiversity and ecological processes were monitored in a typical abandoned pond-to-mangrove area of Dongzhaigang Bay, China, where artificial and natural restoration methods were used concurrently. Differences in macrobenthic biodiversity, community structure and ecological processes were compared using diversity indices, complex network analysis and null models. Similar species composition and ecological niche overlap and width among macrobenthos were observed at artificial and natural restoration sites. The biotic heterogeneity and interaction among macrobenthos were higher at the natural restoration sites than at the artificial restoration sites. Macrobenthos community assembly at natural and artificial restoration sites was both determined by deterministic processes, with environmental filtering dominating, which explained 52% and 54% of the variations in macrobenthic community structures respectively. Although our findings did not validate the research hypothesis, higher biotic heterogeneity and species interaction among macrobenthos could support natural restoration as the primary method for abandoned pond-to-mangrove projects, because it is a nature-based solution for mangrove restoration.

TWO-LEVEL CONCEPT FOR SIMULATING UNIFORM INTERFERENCE-RESISTANT DIGITAL DATA TRANSMISSION

The article formalizes, creates and provides for consideration the concept of a single secure interference-resistant data transmission channel. In modern cybersecurity theory and practice, there is the NIST Cybersecurity Framework, which is a set of recommendations for reducing risks for organizations. In order for high-level data to be secure, there are requirements for the SIA triad, namely confidentiality, integrity and availability of information. Therefore, in the future, the expediency of the work and its result will directly depend on the satisfaction of the SIA conditions. As you know, high-level data: such as e-mail, visual data in the GUI of various applications, etc., are transmitted over low-level communication channels: such as cables, wireless radio communication channels, and others. At each of the levels for safe transmission of information, there are specific pests. At high levels, the main threat to information is man and the human factor. The lower the level of information transmission becomes, the more nature, natural obstacles and random short phenomena begin to influence. In order for the user to be able to use various devices without a threat to the confidentiality, integrity and availability of information, it is necessary to actively and continuously develop, improve and improve the existing methods of data protection, restoration, transmission and storage. Each aspect in this struggle for security is both an advantage and a disadvantage: excessive security is not appropriate for mass traffic, complexity does not always provide adequate security, and so on. Therefore, the optimality and expediency of the methods used becomes an important factor. For these reasons, the paper proposes a relatively simple, but no less effective approach to maintaining SIA requirements.

Research on deep learning restoration algorithm of X-ray backscatter imaging based on virtual training dataset

The X-ray lobster eye lens, an innovative technique for focusing high-energy radiation, enables wide-field X-ray imaging. However, its inherent cross point spread function introduces noise and degradation into the resultant images. Conventional image restoration methods are inadequate for suppressing such noise. This paper introduces a backscatter image restoration technique utilizing a virtual training dataset. By convolving the point spread function (PSF) with an object to simulate the image degradation process, the method generates a multitude of convolved images for deep learning training, eliminating the need for manual annotation. Given the high structural similarity between the synthetic convolved images and actual backscatter images, the trained model effectively restores real backscatter images. The restoration process yields a structural similarity index (SSIM) of 0.86 and a mean intersection over union (MIoU) of 0.83 when compared to the reference images. This approach mitigates the limitations of sparse real backscatter datasets, substantially reducing image acquisition time, decreasing radiation flux, and enhancing system safety.

Research on the Purification Effect of Aquatic Plants on Polluted Water

Aquatic plants are an effective ecological restoration method for water purification. This article reviews the importance and role of plants in water purification, and explores their research progress in the purification mechanisms of pollutants such as nitrogen and phosphorus, their impact on water ecosystems, and their applications in water pollution prevention and control. The importance and future research directions of aquatic plants in water pollution control were analyzed based on their adaptability and biodiversity. Research has found that aquatic plants have significant purification effects and a wide range of applications in water purification. Future research can continue to delve into the purification mechanisms and ecological effects of aquatic plants, in order to promote the advancement of water technology. The importance of aquatic plants in water purification cannot be ignored and is expected to become an important means of future water environment restoration.

Practical applications of soil microbiota to improve ecosystem restoration: current knowledge and future directions.

Soil microbiota are important components of healthy ecosystems. Greater consideration of soil microbiota in the restoration of biodiverse, functional, and resilient ecosystems is required to address the twin global crises of biodiversity decline and climate change. In this review, we discuss available and emerging practical applications of soil microbiota into (i) restoration planning, (ii) direct interventions for shaping soil biodiversity, and (iii) strategies for monitoring and predicting restoration trajectories. We show how better planning of restoration activities to account for soil microbiota can help improve progress towards restoration targets. We show how planning to embed soil microbiota experiments into restoration projects will permit a more rigorous assessment of the effectiveness of different restoration methods, especially when complemented by statistical modelling approaches that capitalise on existing data sets to improve causal understandings and prioritise research strategies where appropriate. In addition to recovering belowground microbiota, restoration strategies that include soil microbiota can improve the resilience of whole ecosystems. Fundamentally, restoration planning should identify appropriate reference target ecosystem attributes and - from the perspective of soil microbiota - comprehensibly consider potential physical, chemical and biological influences on recovery. We identify that inoculating ecologically appropriate soil microbiota into degraded environments can support a range of restoration interventions (e.g. targeted, broad-spectrum and cultured inoculations) with promising results. Such inoculations however are currently underutilised and knowledge gaps persist surrounding successful establishment in light of community dynamics, including priority effects and community coalescence. We show how the ecological trajectories of restoration sites can be assessed by characterising microbial diversity, composition, and functions in the soil. Ultimately, we highlight practical ways to apply the soil microbiota toolbox across the planning, intervention, and monitoring stages of ecosystem restoration and address persistent open questions at each stage. With continued collaborations between researchers and practitioners to address knowledge gaps, these approaches can improve current restoration practices and ecological outcomes.