49 research outputs found

    Spatial and Temporal Changes in Ecosystem Carbon Pools Following Juniper Encroachment and Removal

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    Proliferation of woody plants is a predominant global land cover change of the past century, particularly in dryland ecosystems. Woody encroachment and its potential impacts (e.g., livestock forage, wildlife habitat, hydrological cycling) have led to widespread brush management. Although woody plants may have substantial impacts on soils, uncertainty remains regarding woody encroachment and brush management influences on carbon (C) pools. Surface C pools (shallow soils and litter) may be particularly dynamic in response to encroachment and brush management. However, we have limited understanding of spatiotemporal patterns of surface C responses or how surface pools respond relative to aboveground C, litter, roots, and deep soil organic C. Spatial variability and lack of basic ecological data in woody-encroached dryland ecosystems present challenges to filling this data gap. We assessed the impact of western juniper (Juniperus occidentalis) encroachment and removal on C pools in a semi-arid sagebrush ecosystem. We used spatially-intensive sampling to create sub-canopy estimates of surface soil C (0–10 cm depth) and litter C pools that consider variation in tree size/age and sub-canopy location for live juniper and around stumps that were cut 7 years prior to sampling. We coupled the present size distribution of junipers with extensive existing allometric information about juniper in this region to estimate how landscape-level C pools would change through time under future management and land cover scenarios. Juniper encroachment and removal leads to substantial changes in C pools. Best-fit models for surface soil and litter C included positive responses to shrub basal diameter and negative responses to increasing relative distance from the bole to dripline. Juniper removal led to a net loss of surface C as a function of large decreases in litter C and small increases in surface soil C. At the landscape scale, deep soil C was the largest C pool (77 Mg C ha−1), with an apparent lack of sensitivity to management. Overall, encroachment led to substantial increases in C storage over time as juniper size increased (excluding deep soil C, ecosystem C pools increased from 13.5 to 30.2 Mg C ha−1 with transition from sagebrush-dominated to present encroachment levels). The largest pool of accumulation was juniper aboveground C, with important other pools including juniper roots, litter, and surface soil C. Woody encroachment and subsequent brush management can have substantive impacts on ecosystem C pools, although our data suggest the spatiotemporal patterns of surface C pools need to be properly accounted for to capture C pool responses. Our approach of coupling spatially-intensive surface C information with shrub distribution and allometric data is an effective method for characterizing ecosystem C pools, offering an opportunity for filling in knowledge gaps regarding woody encroachment and brush management impacts on local-to-regional ecosystem C pools

    Effect of litter fall on soil nutrient content and pH, and its consequences in view of climate change (Síkfőkút DIRT Project)

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    In the DIRT ( Detritus Input and Removal Treatment ) field experiments established at the Síkf ő kút Site (North Hungary) in October 2000, an experi ment was initiated to study the long-term effects of litter quality and quantity on pH and nu trient content (organic carbon, N forms, PO 4 3– , K + , Mg 2+ , Ca 2+ ) of soil in a Quercetum petraeae-cerris forest. An eight-year litter manipulation demonstrated a close connection between the changes in pH and Mg 2+ and Ca 2+ concentration. The decline of litter production, the decrease of the s oil pH due to lower Mg 2+ and Ca 2+ input lead to consequent reduction of soil buffering capacity. Th e acidification interferes with the decomposition process of litter and humus compounds. Our results suggest decreases in organic matter content, total N, Ca 2+ and Mg 2+ concentrations in the soil as a consequence of dec line in forest litter production induced by climate change and a resulting degradati on of the soil over a longer perio

    Divergent controls of soil organic carbon between observations and process-based models

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    The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions
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