Drivers and human impacts on topsoil bacterial and fungal community biogeography across Australia.

Soil microbial diversity mediates a wide range of key processes and ecosystem services influencing planetary health. Our knowledge of microbial biogeography patterns, spatial drivers and human impacts at the continental scale remains limited. Here, we reveal the drivers of bacterial and fungal community distribution in Australian topsoils using 1384 soil samples from diverse bioregions. Our findings highlight that climate factors, particularly precipitation and temperature, along with soil properties, are the primary drivers of topsoil microbial biogeography. Using random forest machine-learning models, we generated high-resolution maps of soil bacteria and fungi across continental Australia. The maps revealed microbial hotspots, for example, the eastern coast, southeastern coast, and west coast were dominated by Proteobacteria and Acidobacteria. Fungal distribution is strongly influenced by precipitation, with Ascomycota dominating the central region. This study also demonstrated the impact of human modification on the underground microbial community at the continental scale, which significantly increased the relative abundance of Proteobacteria and Ascomycota, but decreased Chloroflexi and Basidiomycota. The variations in microbial phyla could be attributed to distinct responses to altered environmental factors after human modifications. This study provides insights into the biogeography of soil microbiota, valuable for regional soil biodiversity assessments and monitoring microbial responses to global changes.

Australian terrestrial environments harbour extensive RNA virus diversity.

Australia is home to a diverse range of unique native fauna and flora. To address whether Australian ecosystems also harbour unique viruses, we performed meta-transcriptomic sequencing of 16 farmland and sediment samples taken from the east and west coasts of Australia. We identified 2460 putatively novel RNA viruses across 18 orders, the vast majority of which belonged to the microbe-associated phylum Lenarviricota. In many orders, such as the Nodamuvirales and Ghabrivirales, the novel viruses identified here comprised entirely new clades. Novel viruses also fell between established genera or families, such as in the Cystoviridae and Picornavirales, while highly divergent lineages were identified in the Sobelivirales and Ghabrivirales. Viral read abundance and alpha diversity were influenced by sampling site, soil type and land use, but not by depth from the surface. In sum, Australian soils and sediments are home to remarkable viral diversity, reflecting the biodiversity of local fauna and flora.

Australian soil microbiome: A first sightseeing regional prediction driven by cycles of soil temperature and pedogenic variations.

Declines in soil multifunctionality (e.gsoil capacity to provide food and energy) are closely related to changes in the soil microbiome (e.g., diversity) Determining ecological drivers promoting such microbiome changes is critical knowledge for protecting soil functions. However, soil-microbe interactions are highly variable within environmental gradients and may not be consistent across studies. Here we propose that analysis of community dissimilarity (ß-diversity) is a valuable tool for overviewing soil microbiome spatiotemporal changes. Indeed, ß-diversity studies at larger scales (modelling and mapping) simplify complex multivariate interactions and refine our understanding of ecological drivers by also giving the possibility of expanding the environmental scenarios. This study represents the first spatial investigation of ß-diversity in the soil microbiome of New South Wales (800,642 km2 ), Australia. We used metabarcoding soil data (16S rRNA and ITS genes) as exact sequence variants (ASVs) and UMAP (Uniform Manifold Approximation and Projection) as the distance metric. ß-Diversity maps (1000-m resolution)-concordance correlations of 0.91-0.96 and 0.91-0.95 for bacteria and fungi, respectively-showed soil biome dissimilarities driven primarily by soil chemistry-pH and effective cation exchange capacity (ECEC)-and cycles of soil temperature-land surface temperature (LST-phase and LST-amplitude). Regionally, the spatial patterns of microbes parallel the distribution of soil classes (e.g., Vertosols) beyond spatial distances and rainfall, for example. Soil classes can be valuable discriminants for monitoring approaches, for example pedogenons and pedophenons. Ultimately, cultivated soils exhibited lower richness due to declines in rare microbes which might compromise soil functions over time.

Baseline high-resolution maps of organic carbon content in Australian soils.

We introduce a new dataset of high-resolution gridded total soil organic carbon content data produced at 30 m × 30 m and 90 m × 90 m resolutions across Australia. For each product resolution, the dataset consists of six maps of soil organic carbon content along with an estimate of the uncertainty represented by the 90% prediction interval. Soil organic carbon maps were produced up to a depth of 200 cm, for six intervals: 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm and 100-200 cm. The maps were obtained through interpolation of 90,025 depth-harmonized organic carbon measurements using quantile regression forest and a large set of environmental covariates. Validation with 10-fold cross-validation showed that all six maps had relatively small errors and that prediction uncertainty was adequately estimated. The soil carbon maps provide a new baseline from which change in future carbon stocks can be monitored and the influence of climate change, land management, and greenhouse gas offset can be assessed.

Soil carbon sequestration potential in global croplands.

Improving the amount of organic carbon in soils is an attractive alternative to partially mitigate climate change. However, the amount of carbon that can be potentially added to the soil is still being debated, and there is a lack of information on additional storage potential on global cropland. Soil organic carbon (SOC) sequestration potential is region-specific and conditioned by climate and management but most global estimates use fixed accumulation rates or time frames. In this study, we model SOC storage potential as a function of climate, land cover and soil. We used 83,416 SOC observations from global databases and developed a quantile regression neural network to quantify the SOC variation within soils with similar environmental characteristics. This allows us to identify similar areas that present higher SOC with the difference representing an additional storage potential. We estimated that the topsoils (0-30 cm) of global croplands (1,410 million hectares) hold 83 Pg C. The additional SOC storage potential in the topsoil of global croplands ranges from 29 to 65 Pg C. These values only equate to three to seven years of global emissions, potentially offsetting 35% of agriculture's 85 Pg historical carbon debt estimate due to conversion from natural ecosystems. As SOC store is temperature-dependent, this potential is likely to reduce by 14% by 2040 due to climate change in a "business as usual" scenario. The results of this article can provide a guide to areas of focus for SOC sequestration, and highlight the environmental cost of agriculture.

Convolutional neural network for soil microplastic contamination screening using infrared spectroscopy.

Microplastics are emerging pollutants that exist in our environment. Microplastics are synthetic polymers that have particles size smaller than 5 mm. Rapid screening of microplastics contamination in the soil could assist in identifying anomalous concentrations of microplastics in the terrestrial environment. Because there is no rule on the maximum concentration limit on how much microplastics can exist within the soil, the concentration of microplastics collected from industrial areas around metropolitan Sydney was used as a baseline. Spectra obtained from the visible-near-infrared (vis-NIR) spectra has been shown to be feasible in predicting microplastics in the soil. Instead of creating a regression model predicting the concentration of microplastic, a classification model for screening was proposed. A convolutional neural network (CNN) model was trained to classify the soil sample into various degrees of contamination based on concentration. We also delved into the CNN model to understand how the CNN model classifies the spectral data input. The model performance was first tested on two levels of classification (contaminated vs. non-contaminated). The model was able to classify the uncontaminated samples into the appropriate class more accurately than the contaminated samples. When the number of classes were gradually increased, the classification accuracy for the higher level of contaminated samples improved. Transfer learning CNN model further improved the classification prediction only on the extremes, but not the intermediate classes.

Soil Properties Drive Microbial Community Structure in a Large Scale Transect in South Eastern Australia.

Soil microbial communities directly affect soil functionality through their roles in the cycling of soil nutrients and carbon storage. Microbial communities vary substantially in space and time, between soil types and under different land management. The mechanisms that control the spatial distributions of soil microbes are largely unknown as we have not been able to adequately upscale a detailed analysis of the microbiome in a few grams of soil to that of a catchment, region or continent. Here we reveal that soil microbes along a 1000 km transect have unique spatial structures that are governed mainly by soil properties. The soil microbial community assessed using Phospholipid Fatty Acids showed a strong gradient along the latitude gradient across New South Wales, Australia. We found that soil properties contributed the most to the microbial distribution, while other environmental factors (e.g., temperature, elevation) showed lesser impact. Agricultural activities reduced the variation of the microbial communities, however, its influence was local and much less than the overall influence of soil properties. The ability to predict the soil and environmental factors that control microbial distribution will allow us to predict how future soil and environmental change will affect the spatial distribution of microbes.

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia.

Soil colour is often used as a general purpose indicator of internal soil drainage. In this study we developed a necessarily simple model of soil drainage which combines the tacit knowledge of the soil surveyor with observed matrix soil colour descriptions. From built up knowledge of the soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining soils generally follows the colour sequence of red → brown → yellow → grey → black soil matrix colours. For each soil profile, soil drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference soil colours of the soil drainage colour sequence. The estimation of drainage index at each profile incorporates the whole-profile descriptions of soil colour where necessary, and is weighted such that observation of soil colour at depth and/or dominantly observed horizons are given more preference than observations near the soil surface. The soil drainage index, by definition disregards surficial soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of soil drainage we digitally mapped the index across our study area. Spatial inference of the drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson's correlation coefficients indicated the variables most strongly correlated with soil drainage were topographic wetness index (-0.34), mid-slope position (-0.29), multi-resolution valley bottom flatness index (-0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of soil drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 soil drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of soil colour description, these results are acceptable. Furthermore, the spatial distribution of soil drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique soil colour and soil drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged.

Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions.

Spatial modelling of environmental data commonly only considers spatial variability as the single source of uncertainty. In reality however, the measurement errors should also be accounted for. In recent years, infrared spectroscopy has been shown to offer low cost, yet invaluable information needed for digital soil mapping at meaningful spatial scales for land management. However, spectrally inferred soil carbon data are known to be less accurate compared to laboratory analysed measurements. This study establishes a methodology to filter out the measurement error variability by incorporating the measurement error variance in the spatial covariance structure of the model. The study was carried out in the Lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred topsoil and subsoil soil carbon data are available. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The results revealed that the measurement error can be effectively filtered-out through the proposed technique. When the measurement error was filtered from the data, the prediction variance almost halved, which ultimately yielded a greater certainty in spatial predictions of soil carbon. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important outcome, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified. Although this study dealt with soil carbon data, this method is amenable for filtering the measurement error of any kind of continuous spatial environmental data.

The location- and scale- specific correlation between temperature and soil carbon sequestration across the globe.

Much research has been conducted to understand the spatial distribution of soil carbon stock and its temporal dynamics. However, an agreement has not been reached on whether increasing global temperature has a positive or negative feedback on soil carbon stocks. By analysing global maps of soil organic carbon (SOC) using a spherical wavelet analysis, it was found that the correlation between SOC and soil temperature at the regional scale was negative between 52° N and 40° S parallels and positive beyond this region. This was consistent with a few previous studies and it was assumed that the effect was most likely due to the temperature-dependent SOC formation (photosynthesis) and decomposition (microbial activities and substrate decomposability) processes. The results also suggested that the large SOC stocks distributed in the low-temperature areas might increase under global warming while the small SOC stocks found in the high-temperature areas might decrease accordingly. Although it remains unknown whether the potential increasing soil carbon stocks in the low-temperature areas can offset the loss of carbon stocks in the high-temperature areas, the location- and scale- specific correlations between SOC and temperature should be taken into account for modeling SOC dynamics and SOC sequestration management.

Evaluating a Bayesian modelling approach (INLA-SPDE) for environmental mapping.

Understanding the uncertainty in spatial modelling of environmental variables is important because it provides the end-users with the reliability of the maps. Over the past decades, Bayesian statistics has been successfully used. However, the conventional simulation-based Markov Chain Monte Carlo (MCMC) approaches are often computationally intensive. In this study, the performance of a novel Bayesian inference approach called Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation (INLA-SPDE) was evaluated using independent calibration and validation datasets of various skewed and non-skewed soil properties and was compared with a linear mixed model estimated by residual maximum likelihood (REML-LMM). It was found that INLA-SPDE was equivalent to REML-LMM in terms of the model performance and was similarly robust with sparse datasets (i.e. 40-60 samples). In comparison, INLA-SPDE was able to estimate the posterior marginal distributions of the model parameters without extensive simulations. It was concluded that INLA-SPDE had the potential to map the spatial distribution of environmental variables along with their posterior marginal distributions for environmental management. Some drawbacks were identified with INLA-SPDE, including artefacts of model response due to the use of triangle meshes and a longer computational time when dealing with non-Gaussian likelihood families.

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives.

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1km in 2014, followed by an update at a resolution of 250m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

Taking account of uncertainties in digital land suitability assessment.

Simulations are used to generate plausible realisations of soil and climatic variables for input into an enterprise land suitability assessment (LSA). Subsequently we present a case study demonstrating a LSA (for hazelnuts) which takes into account the quantified uncertainties of the biophysical model input variables. This study is carried out in the Meander Valley Irrigation District, Tasmania, Australia. It is found that when comparing to a LSA that assumes inputs to be error free, there is a significant difference in the assessment of suitability. Using an approach that assumes inputs to be error free, 56% of the study area was predicted to be suitable for hazelnuts. Using the simulation approach it is revealed that there is considerable uncertainty about the 'error free' assessment, where a prediction of 'unsuitable' was made 66% of the time (on average) at each grid cell of the study area. The cause of this difference is that digital soil mapping of both soil pH and conductivity have a high quantified uncertainty in this study area. Despite differences between the comparative methods, taking account of the prediction uncertainties provide a realistic appraisal of enterprise suitability. It is advantageous also because suitability assessments are provided as continuous variables as opposed to discrete classifications. We would recommend for other studies that consider similar FAO (Food and Agriculture Organisation of the United Nations) land evaluation framework type suitability assessments, that parameter membership functions (as opposed to discrete threshold cutoffs) together with the simulation approach are used in concert.

Soil organic carbon across scales.

Mechanistic understanding of scale effects is important for interpreting the processes that control the global carbon cycle. Greater attention should be given to scale in soil organic carbon (SOC) science so that we can devise better policy to protect/enhance existing SOC stocks and ensure sustainable use of soils. Global issues such as climate change require consideration of SOC stock changes at the global and biosphere scale, but human interaction occurs at the landscape scale, with consequences at the pedon, aggregate and particle scales. This review evaluates our understanding of SOC across all these scales in the context of the processes involved in SOC cycling at each scale and with emphasis on stabilizing SOC. Current synergy between science and policy is explored at each scale to determine how well each is represented in the management of SOC. An outline of how SOC might be integrated into a framework of soil security is examined. We conclude that SOC processes at the biosphere to biome scales are not well understood. Instead, SOC has come to be viewed as a large-scale pool subjects to carbon flux. Better understanding exists for SOC processes operating at the scales of the pedon, aggregate and particle. At the landscape scale, the influence of large- and small-scale processes has the greatest interaction and is exposed to the greatest modification through agricultural management. Policy implemented at regional or national scale tends to focus at the landscape scale without due consideration of the larger scale factors controlling SOC or the impacts of policy for SOC at the smaller SOC scales. What is required is a framework that can be integrated across a continuum of scales to optimize SOC management.

Citations and the h index of soil researchers and journals in the Web of Science, Scopus, and Google Scholar.

Citation metrics and h indices differ using different bibliometric databases. We compiled the number of publications, number of citations, h index and year since the first publication from 340 soil researchers from all over the world. On average, Google Scholar has the highest h index, number of publications and citations per researcher, and the Web of Science the lowest. The number of papers in Google Scholar is on average 2.3 times higher and the number of citations is 1.9 times higher compared to the data in the Web of Science. Scopus metrics are slightly higher than that of the Web of Science. The h index in Google Scholar is on average 1.4 times larger than Web of Science, and the h index in Scopus is on average 1.1 times larger than Web of Science. Over time, the metrics increase in all three databases but fastest in Google Scholar. The h index of an individual soil scientist is about 0.7 times the number of years since his/her first publication. There is a large difference between the number of citations, number of publications and the h index using the three databases. From this analysis it can be concluded that the choice of the database affects widely-used citation and evaluation metrics but that bibliometric transfer functions exist to relate the metrics from these three databases. We also investigated the relationship between journal's impact factor and Google Scholar's h5-index. The h5-index is a better measure of a journal's citation than the 2 or 5 year window impact factor.

Soil carbon determination by thermogravimetrics.

Determination of soil constituents and structure has a vital role in agriculture generally. Methods for the determination of soil carbon have in particular gained greater currency in recent times because of the potential that soils offer in providing offsets for greenhouse gas (CO2-equivalent) emissions. Ideally, soil carbon which can also be quite diverse in its makeup and origin, should be measureable by readily accessible, affordable and reliable means. Loss-on-ignition is still a widely used method being suitably simple and available but may have limitations for soil C monitoring. How can these limitations be better defined and understood where such a method is required to detect relatively small changes during soil-C building? Thermogravimetric (TGA) instrumentation to measure carbonaceous components has become more interesting because of its potential to separate carbon and other components using very precise and variable heating programs. TGA related studies were undertaken to assist our understanding in the quantification of soil carbon when using methods such as loss-on-ignition. Combining instrumentation so that mass changes can be monitored by mass spectrometer ion currents has elucidated otherwise hidden features of thermal methods enabling the interpretation and evaluation of mass-loss patterns. Soil thermogravimetric work has indicated that loss-on-ignition methods are best constrained to temperatures from 200 to 430 °C for reliable determination for soil organic carbon especially where clay content is higher. In the absence of C-specific detection where mass only changes are relied upon, exceeding this temperature incurs increasing contributions from inorganic sources adding to mass losses with diminishing contributions related to organic matter. The smaller amounts of probably more recalcitrant organic matter released at the higher temperatures may represent mineral associated material and/or simply more refractory forms.

Kriging method evaluation for assessing the spatial distribution of urban soil lead contamination.

Describing contaminant spatial distribution is an integral component of risk assessment. Application of geostatistical techniques for this purpose has been demonstrated previously. These techniques may provide both an estimate of the concentration at a given unsampled location, as well as the probability that the concentration at that location will exceed a critical threshold concentration. This research is a comparative study between multiple indicator kriging and kriging with the cumulative distribution function of order statistics, with both local and global variograms. The aim was to determine which of the four methods is best able to delineate between "contaminated" and "clean" soil. The four methods were validated with a subset of data values that were not used in the prediction. Method performance was assessed by calculating the root mean square error (RMSE), analysis of variance, the proportion of sites misclassified by each method as either "clean" when they were actually "contaminated" or vice versa, and the expected loss for each misclassification type. The data used for the comparison were 807 topsoil Pb concentrations from the inner-Sydney suburbs of Glebe and Camperdown, Australia. While there was very little difference between the four methods, multiple indicator kriging was found to produce the most accurate predictions for delineating "clean" from "contaminated" soil.