Forest species control the quantity and chemistry of organic matter input, which in interaction with the soil physicochemical properties, environmental conditions and microbial community associated ...with a given ecosystem may result in specific patterns of soil organic carbon (SOC) stabilization and chemistry. The objectives of this study were: (a) to characterize the chemistry of soil organic matter and SOC fractions across the gradient from pure aspen (Populus tremuloides Michx.) to pure conifer (Abies lasiocarpa (Hook.) Nutt. and Pseudotsuga menziesii (Mirbel) Franco) stands in semi‐arid montane forests, and (b) to determine whether the effect of overstory composition on SOC chemistry was patent beyond the influence of site conditions and microbial decomposer community. We used Fourier transform infrared spectroscopy to analyse the chemistry of bulk soil (BS), light fraction (LF) and mineral‐associated SOC (MoM) from mineral soils (0–15 cm) sampled across the natural gradient of aspen and mixed conifer stands from northern and southern Utah. Vegetation overstory had a subtle effect on the MoM fraction, indicating higher proportion of aliphatic C with aspen dominance, whereas there were no differences in LF chemistry between vegetation types. Independently of the vegetation cover type, the MoM fraction was enriched in aliphatic C compared to the LF, although the proportion of polysaccharides and C‐O groups increased in the MoM fraction for plot samples. Differentiation between spectra from soils developed on sedimentary rock and soils developed on basalt, quartzite and limestone, highlighted the influence of parent material and mineralogy on MoM chemistry. The patterns in SOC fractions' chemistry do not allow an affirmation that greater SOC storage under aspen is due to the accumulation of recalcitrant compounds (i.e., aliphatic C) and controlled by litter chemistry. Rather, they suggest that the ensemble of litter chemistry, microbial community and soil properties in aspen stands enhances SOC storage.
Highlights
Vegetation overstory and site characteristics (e.g., parent material) influence SOC chemistry and stabilization patterns.
Light fraction SOC spectra did not differ between forest species in the aspen‐conifer ecotone.
The proportion of aliphatic C in mineral‐associated organic carbon (MoM) increased with aspen dominance.
The effect of overstory composition on MoM chemistry was patent beyond the influence of site conditions and microbial decomposer community.
Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuous set of compounds with different chemical compositions, origins, and susceptibilities to decomposition ...that are commonly separated into pools characterised by different responses to
anthropogenic and environmental disturbance. Here we map the contribution of
three SOC fractions to the total SOC content of Australia's soils. The three
SOC fractions, mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for
MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral
models calibrated with measured SOC fractions. We transformed the data using
an isometric-log-ratio (ilr) transformation to account for the closed compositional nature of SOC fractions. The resulting back-transformed ilr components were mapped across Australia. SOC fraction stocks for 0–30 cm were derived with maps of total organic carbon concentration, bulk density,
coarse fragments, and soil thickness. Mapping was done by a quantile regression forest fitted with the ilr-transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified
uncertainty show the unique spatial pattern of SOC fractions in Australia.
MAOC dominated the total SOC with an average of 59 % ± 17 %,
whereas 28 % ± 17 % was PyOC and 13 % ± 11 % was POC.
The allocation of total organic carbon (TOC) to the MAOC fractions increased with depth. SOC vulnerability (i.e. POC/MAOC+PyOC) was greater in areas with Mediterranean and temperate climates. TOC and the distribution among
fractions were the most influential variables in SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that
different mechanisms will control SOC stabilisation and dynamics across the
continent, as shown by the model covariates' importance metric. We estimated the total SOC stocks (0–30 cm) to be 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and
their stocks can be used for modelling SOC dynamics and forecasting changes
in SOC stocks as a response to land use change, management, and climate change.
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.
This study found that soil properties and climate determine soil bacteria and fungi in Australian topsoils. We generated high‐resolution soil bacteria and fungi maps across Australia, revealing microbial hotspots. Human effects on the underground community varied for different microbial phyla.
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.
To assess the potential impact of conifer encroachment on soil organic carbon (SOC) dynamics and storage in montane aspen-conifer forests from the interior western US, we sampled mineral soils (0–15 ...cm) across the aspen-conifer ecotones in southern and northern Utah and quantified total SOC stocks, stable SOC (i.e., mineral-associated SOC (MoM)), labile SOC (i.e., light fraction (LF), decomposable (CO2 release during long-term aerobic incubations) and soluble SOC (hot water extractable organic carbon (HWEOC)). Total SOC storage (47.0 ± 16.5 Mg C ha−1) and labile SOC as LF (14.0 ± 7.10 Mg C ha−1), SOC decomposability (cumulative released CO2-C of 5.6 ± 3.8 g C g−1 soil) or HWEOC (0.6 ± 0.6 mg C g−1 soil) did not differ substantially with vegetation type, although a slight increase in HWEOC was observed with increasing conifer in the overstory. There were statistically significant differences (p = 0.035) in stable MoM storage, which was higher under aspen (31.2 ± 15.1 Mg C ha−1) than under conifer (22.8 ± 9.0 Mg C ha−1), with intermediate values under mixed (25.7 ± 8.8 Mg C ha−1). Texture had the greatest impact on SOC distribution among labile and stable fractions, with increasing stabilization in MoM and decreasing bio-availability of SOC with increasing silt + clay content. Only at lower silt + clay contents (40%–70%) could we discern the influence of vegetation on MoM content. This highlights the importance of chemical protection mechanisms for long-term C sequestration.
Near‐infrared reflectance spectroscopy (NIRS) and partial least squares regression were used to develop prediction models for identifying the species of origin of soil organic C (SOC) in semiarid ...montane forests of quaking aspen (Populus tremuloides Michx.) and mixed conifers in Utah. Artificial mixtures of mineral soils (0–15 cm) sampled under pure aspen and pure conifer cover (n = 415) at four locations were divided into a calibration–validation set (n = 265) for model development and an independent validation set (n = 150) to test model robustness. Models in the 10,000 to 4000 cm−1 spectral region were developed separately with original soil spectra (OS) and organic matter spectra (OM) using the full and truncated (10th–90th percentile) sample sets. The OS models performed better than OM models, and the best OS models showed good prediction ability at the validation step, with R2 = 76%, ratio of standard deviation of reference value to standard error of prediction (RPD) = 2.1 for aspen SOC, and R2 = 74%, RPD = 2.0 for conifer SOC. Model performance decreased at independent validation (R2 = 33– 49%, RPD = 1.2–1.6), probably due to unaccounted variability of site‐specific factors in SOC chemical composition within and among aspen and conifer soils. Current models are still somewhat limited for accurately predicting contributions of aspen vs. conifers in independent samples. More detailed site information, such as texture, mineralogy, geology, and land use history is needed to improve models so that they can be used to provide insight into SOC properties changes along a continuum of aspen to conifer forests in the western United States.
The French soil-test database (Base de Données d'Analyses de Terre: BDAT) is populated with analytical results of agricultural topsoil samples requested by farmers for fertilization planning. The ...coordinates of the farms are unknown due to data confidentiality policies, and the best available georeference is at level of municipality. We compared four approaches for mapping soil texture of agricultural land in Region Centre (France) using BDAT data: 1) a reference approach of mapping the mean of the aggregated data by municipality, 2) a boosted regression tree (BRT) model fitted with the municipality-averaged data, 3) area-to-point cokriging (AToP CK), and 4) a regression kriging version of this (AToP RCK, for which the BRT predictions were used to give the trend). Specifically, parameters for these last two approaches were fitted through the summary statistics approach to AToP kriging, which accounts for the full set of municipality summary statistics data (i.e. the mean, variance and number of measurements from each municipality). We could thus determine whether more complex and statistically-challenging approaches improve our knowledge on the spatial distribution of soil texture compared with maps of data aggregated by municipality. Texture data from 105 sites form the French soil monitoring network (Réseau de Mesures de la Qualité des Sols: RMQS) were used for independent validation. In general, the R2 was greater for sand (average R2=0.69) and silt (average R2=0.72) than for clay (average R2=0.40). The three methods for disaggregating the summary statistics data (BRT, AToP CK, and AToP RCK) showed similar prediction accuracies—although BRT predictions showed the greatest bias—and were better than the BDAT reference approach. AToP RCK was able to give similar prediction accuracy to BRT modelling alone, reduced the bias considerably, and gave a reasonable (although slightly conservative) assessment of prediction uncertainty. The results indicate that geostatistical methods for change of support expand the utility of aggregated data from soil-test databases.
•We compared three methods for disaggregating areal topsoil texture data.•Predictions were better for sand and silt (~R2=0.7) than for clay (~R2=0.4).•Boosted regression tree models showed the greatest bias (−) clay, sand, (+) silt.•Area-to-point (AToP) cokriging and regression cokriging assessed uncertainty well.•AToP regression cokriging was the best disaggregation method in terms of accuracy.
The intensification of human pressures on soil can reduce pedodiversity and decrease soil multifunctionality impacting soil security. Mapping genosoils (least modified soils within a soil class or ...soil map unit by contemporary drivers of soil change) and phenosoils (variants resulting from land use history and management) can be a preliminary step for quantifying soil security dimensions and prioritising areas for soil preservation and regeneration. Genosoil properties can be used as a baseline for assessing the effects of management on soil condition for a particular pedological, climatic and landscape context. In this study, we stratified Australia into 1370 pedogenons (i.e., groups with relatively homogeneous environmental covariates, proxies of soil-forming factors) that represent soil classes prior to the European settlement from 1788 onwards. We overlayed the maps of global Human Modification and the Habitat Condition Assessment System for Australia for identifying areas with minimum human influence on terrestrial ecosystems and soils. Areas with very low human influence were defined as genosoils at the continental level. The percentage of land mapped as genosoils accounted for 56% of the continent and had a median area of 2550 km2. There were 32 pedogenon classes that did not have any remaining genosoils while 218 pedogenon classes had less than 5% of their area as genosoils. The proportion of genosoils protected in conservation areas or managed resource protection varied widely, although almost 25% of the genosoils had at least half of their area under conservation. In addition to soil multifunctionality, the criteria for prioritising soil conservation areas could consider: 1) endangered genosoils and 2) genosoils closest (in the scorpan feature space) to the phenosoils without an existing reference soil.
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•We developed a top-down framework for detecting soil change that can be applied to large areas.•This framework gives a new and valuable stratification of the landscape.•It integrates a theoretical ...model of soil change with a digital soil mapping approach.•Detailed land use history is an essential factor for detecting soil change.
The assessment of changes in soil condition and capability requires the identification of a reference state specific to each soil class. This study develops a framework for mapping soil classes that can be used as a reference state. It identifies soil classes that should have undergone similar historic anthropedogenesis, and differentiate, within each class, zones that have been less affected by human activities. This approach could be used as a baseline for assessing contemporary soil change, as demonstrated in the state of New South Wales in Australia. First, we established soil classes with similar multimillennial natural pedogenesis and historic anthropedogenesis, called pedogenons. This was achieved by applying unsupervised classification (k-means) to a set of quantitative state variables, proxies of the soil-forming factors at the time of the European settlement in New South Wales (climate, relief, parent material, and estimated pre-1750s vegetation). Pedogenon classes were then stratified into subclasses (ranging from remnant pedogenons to different pedophenons) by combining information on native vegetation extent, status (remnant or cleared) and current land use (i.e., land use history). The stratification of 1000 pedogenon classes resulted in 5448 subclasses, ranging from remnant pedogenons (located in protected areas of intact native vegetation), quasi-remnant pedogenons (production with low intervention on remnant native vegetation), cleared, grazing, and cropping pedophenons. The median of the area proportion of the pedogenon that was still preserved as remnant vegetation was 5.3%. This quasi-remnant pedogenon or the less affected pedophenon could be used as reference state. Pedophenon grazing and cropping occupied larger areas, with mean values of 73 km2 and 153 km2, respectively. The application of this framework for assessing soil change is illustrated using legacy data of topsoil pH (5 – 15 cm) as one indicator of soil condition. The ability of the pedogenon and pedophenon subclasses for explaining the variation of three stable (total Si, total Al, clay) and three dynamic (bulk density, particulate organic carbon, pH) soil properties from agricultural soils. A generalised least squares model indicated that the effects of pedogenon, land use history and their interaction on topsoil pH were statistically significant (p < 0.001). Paired comparisons between pedogenon/pedophenon subclasses by pedogenon class were not statistically significant, although we observed the general trend: remnant pedogenon ≈ quasi-remnant pedogenon < pedophenon cleared ≈ pedophenon grazing < pedophenon cropping. Redundancy discriminant analysis indicated that pedogenons explained 40% of the variation of stable and dynamic soil properties, pedogenon/pedophenon subclasses explained 0.1% and the shared effect explained 18%, leaving 42% of unexplained variance. The effects of pedogenon/pedophenon subclasses on the location of group centroids were statistically significant only when dynamic soil properties were considered, but not for stable and dynamic soil properties. This framework can be integrated into a soil security assessment once the indicators of soil condition and capability are translated into soil functions and ecosystem services. Other potential applications include the design of soil monitoring sampling schemes and identifying thresholds of soil degradation.
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