Multiscale responses and recovery of soils to wildfire in a sagebrush steppe ecosystem.

Ecological theory predicts a pulse disturbance results in loss of soil organic carbon and short-term respiration losses that exceed recovery of productivity in many ecosystems. However, fundamental uncertainties remain in our understanding of ecosystem recovery where spatiotemporal variation in structure and function are not adequately represented in conceptual models. Here we show that wildfire in sagebrush shrublands results in multiscale responses that vary with ecosystem properties, landscape position, and their interactions. Consistent with ecological theory, soil pH increased and soil organic carbon (SOC) decreased following fire. In contrast, SOC responses were slope aspect and shrub-microsite dependent, with a larger proportional decrease under previous shrubs on north-facing aspects compared to south-facing ones. In addition, respiratory losses from burned aspects were not significantly different than losses from unburned aspects. We also documented the novel formation of soil inorganic carbon (SIC) with wildfire that differed significantly with aspect and microsite scale. Whereas pH and SIC recovered within 37 months post-fire, SOC stocks remained reduced, especially on north-facing aspects. Spatially, SIC formation was paired with reduced respiration losses, presumably lower partial pressure of carbon dioxide (pCO2), and increased calcium availability, consistent with geochemical models of carbonate formation. Our findings highlight the formation of SIC after fire as a novel short-term sink of carbon in non-forested shrubland ecosystems. Resiliency in sagebrush shrublands may be more complex and integrated across ecosystem to landscape scales than predicted based on current theory.

Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution.

From hillslope to small catchment scales (< 50 km2), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m2) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a - 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics.

Invasive wild pigs (Sus scrofa) as a human-mediated source of soil carbon emissions: Uncertainties and future directions.

Invasive wild pigs (Sus scrofa) have been spread by humans outside of their native range and are now established on every continent except Antarctica. Through their uprooting of soil, they affect societal and environmental values. Our recent article explored another threat from their soil disturbance: greenhouse gas emissions (O'Bryan et al., Global Change Biology, 2021). In response to our paper, Don (Global Change Biology, 2021) claims there is no threat to global soil carbon stocks by wild pigs. While we did not investigate soil carbon stocks, we examine uncertainties regarding soil carbon emissions from wild pig uprooting and their implications for management and future research.

Unrecognized threat to global soil carbon by a widespread invasive species.

Most of Earth's terrestrial carbon is stored in the soil and can be released as carbon dioxide (CO2 ) when disturbed. Although humans are known to exacerbate soil CO2 emissions through land-use change, we know little about the global carbon footprint of invasive species. We predict the soil area disturbed and resulting CO2 emissions from wild pigs (Sus scrofa), a pervasive human-spread vertebrate that uproots soil. We do this using models of wild pig population density, soil damage, and their effect on soil carbon emissions. Our models suggest that wild pigs are uprooting a median area of 36,214 km2 (mean of 123,517 km2 ) in their non-native range, with a 95% prediction interval (PI) of 14,208 km2 -634,238 km2 . This soil disturbance results in median emissions of 4.9 million metric tonnes (MMT) CO2 per year (equivalent to 1.1 million passenger vehicles or 0.4% of annual emissions from land use, land-use change, and forestry; mean of 16.7 MMT) but that it is highly uncertain (95% PI, 0.3-94 MMT CO2 ) due to variability in wild pig density and soil dynamics. This uncertainty points to an urgent need for more research on the contribution of wild pigs to soil damage, not only for the reduction of anthropogenically related carbon emissions, but also for co-benefits to biodiversity and food security that are crucial for sustainable development.

Pandanus nutshell generates a palaeoprecipitation record for human occupation at Madjedbebe, northern Australia.

Little is known about the Pleistocene climatic context of northern Australia at the time of early human settlement. Here we generate a palaeoprecipitation proxy using stable carbon isotope analysis of modern and archaeological pandanus nutshell from Madjedbebe, Australia's oldest known archaeological site. We document fluctuations in precipitation over the last 65,000 years and identify periods of lower precipitation during the penultimate and last glacial stages, Marine Isotope Stages 4 and 2. However, the lowest effective annual precipitation is recorded at the present time. Periods of lower precipitation, including the earliest phase of occupation, correspond with peaks in exotic stone raw materials and artefact discard at the site. This pattern is interpreted as suggesting increased group mobility and intensified use of the region during drier periods.

Topographic controls of soil organic carbon on soil-mantled landscapes.

Large uncertainties in global carbon (C) budgets stem from soil carbon estimates and associated challenges in distributing soil organic carbon (SOC) at local to landscape scales owing to lack of information on soil thickness and controls on SOC storage. Here we show that 94% of the fine-scale variation in total profile SOC within a 1.8 km2 semi-arid catchment in Idaho, U.S.A. can be explained as a function of aspect and hillslope curvature when the entire vertical dimension of SOC is measured and fine-resolution (3 m) digital elevation models are utilized. Catchment SOC stocks below 0.3 m depth based on our SOC-curvature model account for >50% of the total SOC indicating substantial underestimation of stocks if sampled at shallower depths. A rapid assessment method introduced here also allows for accurate catchment-wide total SOC inventory estimation with a minimum of one soil pit and topographic data if spatial distribution of total profile SOC is not required. Comparison of multiple datasets shows generality in linear SOC-curvature and -soil thickness relationships at multiple scales. We conclude that mechanisms driving variations in carbon storage in hillslope catchment soils vary spatially at relatively small scales and can be described in a deterministic fashion given adequate topographic data.

Predicting soil thickness on soil mantled hillslopes.

Soil thickness is a fundamental variable in many earth science disciplines due to its critical role in many hydrological and ecological processes, but it is difficult to predict. Here we show a strong linear relationship (r2 = 0.87, RMSE = 0.19 m) between soil thickness and hillslope curvature across both convergent and divergent parts of the landscape at a field site in Idaho. We find similar linear relationships across diverse landscapes (n = 6) with the slopes of these relationships varying as a function of the standard deviation in catchment curvatures. This soil thickness-curvature approach is significantly more efficient and just as accurate as kriging-based methods, but requires only high-resolution elevation data and as few as one soil profile. Efficiently attained, spatially continuous soil thickness datasets enable improved models for soil carbon, hydrology, weathering, and landscape evolution.