Soil science: Scavenging for scrap metal

Organisms acquire some elements from the environment with ease. Diffusion alone often provides enough carbon dioxide, oxygen and water. But getting other elements requires more effort, spurring unique evolutionary adaptations: instead of taking up nutrients from the soil, some plants in acidic bogs trap insects to obtain nitrogen and phosphorus 1; geophagy — or eating dirt — may sometimes be important for acquiring iron by primates 2; plants and microorganisms secrete compounds that liberate phosphorus from unavailable forms in soil 3; and many bacteria secrete metal-scavenging compounds called siderophores to capture iron and copper 4,5. Evidence has mounted that molybdenum is also specifically targeted 6. On page 243 of this issue, Bellenger and colleagues 7 confirm this, showing that siderophores produced by the nitrogen-fixing bacterium Azotobacter vinelandiibind with molybdenum and vanadium in the laboratory, promoting uptake of these metals.

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Authors:Duval BD Hungate BA

Plant water potential Ψ is regulated by stomatal responses to atmospheric moisture demand D and soil water availability W, but the timescales of influence and interactions between these drivers of plant Ψ are poorly understood. Here, we quantify the effects of antecedent D and W on plant Ψ in the desert shrub Larrea tridentata. Repeated measurements of plant baseline water potential ΨB and diurnal water potential ΨD were analyzed in a Bayesian framework to evaluate the influence of antecedent D and W at daily and subdaily timescales. Both ΨB and ΨD exhibited negative, 2- to 4-d lagged responses to daily-scale D; conversely, plant ΨD responded almost instantaneously to subdaily D, though the direction of this response depended on antecedent moisture conditions. Plant ΨB and ΨD responded positively and immediately (no lag) to shallow W, which contrasts the negative, lagged (6-7 d) response to deep W. The changing sensitivity of ΨD to subdaily D highlights shifting modes of plant Ψ regulation: D effects on ΨD range from negative to neutral to positive depending on past conditions and time of day. Explicit consideration of antecedent conditions across multiple timescales can reveal important complexities in plant responses.

Global change is shifting the seasonality of vegetation in ecosystems around the globe. High-frequency digital camera imagery, and vegetation indices derived from that imagery, is facilitating better tracking of phenological responses to environmental variation. This method, commonly referred to as the “phenocam” approach, is well-suited to several specific applications, including: close-up observation of individual organisms; long-term canopy-level monitoring at individual sites; automated regional-to-continental scale observatory networks; and tracking responses to experimental treatments. Several camera networks are already well-established, and some camera records are a more than a decade long. These data can be used to identify the environmental controls on phenology in different ecosystems, which will contribute to the development of improved prognostic phenology models. This article is protected by copyright. All rights reserved.

Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate for complex global land models. This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of SOC. Experiments on three types of soil carbon cycle models, including the Community Land Model with the Carnegie–Ames–Stanford Approach biogeochemistry submodel (CLM-CASA') and two microbial models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. Compared to predictions using the tuned parameter values through Bayesian MCMC, the root mean squared errors (RMSEs) between the predictions using the calibrated parameter values with surrogate-base optimization and the observations could be reduced by up to 12% for different SOC models. Meanwhile, the corresponding computational cost is lower than other global optimization algorithms.