Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners.

Soil organic carbon (SOC) regulates terrestrial ecosystem functioning, provides diverse energy sources for soil microorganisms, governs soil structure, and regulates the availability of organically bound nutrients. Investigators in increasingly diverse disciplines recognize how quantifying SOC attributes can provide insight about ecological states and processes. Today, multiple research networks collect and provide SOC data, and robust, new technologies are available for managing, sharing, and analyzing large data sets. We advocate that the scientific community capitalize on these developments to augment SOC data sets via standardized protocols. We describe why such efforts are important and the breadth of disciplines for which it will be helpful, and outline a tiered approach for standardized sampling of SOC and ancillary variables that ranges from simple to more complex. We target scientists ranging from those with little to no background in soil science to those with more soil-related expertise, and offer examples of the ways in which the resulting data can be organized, shared, and discoverable.

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer's Disease.

Since age is the most significant risk factor for the development of Alzheimer's disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This article proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification accuracy, barring the group of participants below the age of 70, for resting state EEG recorded during eyes open. The developed approach is generic and can be used as a powerful tool to examine brain network characteristics and disruption in a user friendly and systematic way.

Comparative analysis of NOAA REFM and SNB3GEO tools for the forecast of the fluxes of high-energy electrons at GEO.

Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field Bz observations at L1.The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast.

Tree-ring δ13C and δ18O, leaf δ13C and wood and leaf N status demonstrate tree growth strategies and predict susceptibility to disturbance.

Understanding how tree growth strategies may influence tree susceptibility to disturbance is an important goal, especially given projected increases in diverse ecological disturbances this century. We use growth responses of tree rings to climate, relationships between tree-ring stable isotopic signatures of carbon (δ(13)C) and oxygen (δ(18)O), wood nitrogen concentration [N], and contemporary leaf [N] and δ(13)C values to assess potential historic drivers of tree photosynthesis in dying and apparently healthy co-occurring northern red oak (Quercus rubra L. (Fagaceae)) during a region-wide oak decline event in Arkansas, USA. Bole growth of both healthy and dying trees responded negatively to drought severity (Palmer Drought Severity Index) and temperature; healthy trees exhibited a positive, but small, response to growing season precipitation. Contrary to expectations, tree-ring δ(13)C did not increase with drought severity. A significantly positive relationship between tree-ring δ(13)C and δ(18)O was evident in dying trees (P < 0.05) but not in healthy trees. Healthy trees' wood exhibited lower [N] than that of dying trees throughout most of their lives (P < 0.05), and we observed a significant, positive relationship (P < 0.05) in healthy trees between contemporary leaf δ(13)C and leaf N (by mass), but not in dying trees. Our work provides evidence that for plants in which strong relationships between δ(13)C and δ(18)O are not evident, δ(13)C may be governed by plant N status. The data further imply that historic photosynthesis in healthy trees was linked to N status and, perhaps, C sink strength to a greater extent than in dying trees, in which tree-ring stable isotopes suggest that historic photosynthesis was governed primarily by stomatal regulation. This, in turn, suggests that assessing the relative dominance of photosynthetic capacity vs stomatal regulation as drivers of trees' C accrual may be a feasible means of predicting tree responses to some disturbance events. Our work demonstrates that a dual isotope, tree-ring approach can be integrated with tree N status to begin to unravel a fundamental question in forest ecology: why do some trees die during a disturbance, while other conspecifics with apparently similar access to resources remain healthy?

Nonlinear interactions in the thalamocortical loop in essential tremor: A model-based frequency domain analysis.

There is increasing evidence to suggest that essential tremor has a central origin. Different structures appear to be part of the central tremorogenic network, including the motor cortex, the thalamus and the cerebellum. Some studies using electroencephalogram (EEG) and magnetoencephalography (MEG) show linear association in the tremor frequency between the motor cortex and the contralateral tremor electromyography (EMG). Additionally, high thalamomuscular coherence is found with the use of thalamic local field potential (LFP) recordings and tremulous EMG in patients undergoing surgery for deep brain stimulation (DBS). Despite a well-established reciprocal anatomical connection between the thalamus and cortex, the functional association between the two structures during "tremor-on" periods remains elusive. Thalamic (Vim) LFPs, ipsilateral scalp EEG from the sensorimotor cortex and contralateral tremor arm EMG recordings were obtained from two patients with essential tremor who had undergone successful surgery for DBS. Coherence analysis shows a strong linear association between thalamic LFPs and contralateral tremor EMG, but the relationship between the EEG and the thalamus is much less clear. These measurements were then analyzed by constructing a novel parametric nonlinear autoregressive with exogenous input (NARX) model. This new approach uncovered two distinct and not overlapping frequency "channels" of communication between Vim thalamus and the ipsilateral motor cortex, defining robustly "tremor-on" versus "tremor-off" states. The associated estimated nonlinear time lags also showed non-overlapping values between the two states, with longer corticothalamic lags (exceeding 50ms) in the tremor active state, suggesting involvement of an indirect multisynaptic loop. The results reveal the importance of the nonlinear interactions between cortical and subcortical areas in the central motor network of essential tremor. This work is important because it demonstrates for the first time that in essential tremor the functional interrelationships between the cortex and thalamus should not be sought exclusively within individual frequencies but more importantly between cross-frequency nonlinear interactions. Should our results be successfully reproduced on a bigger cohort of patients with essential tremor, our approach could be used to create an on-demand closed-loop DBS device, able to automatically activate when the tremor is on.

A century of variation in the dependence of Greenland iceberg calving on ice sheet surface mass balance and regional climate change.

Iceberg calving is a major component of the total mass balance of the Greenland ice sheet (GrIS). A century-long record of Greenland icebergs comes from the International Ice Patrol's record of icebergs (I48N) passing latitude 48° N, off Newfoundland. I48N exhibits strong interannual variability, with a significant increase in amplitude over recent decades. In this study, we show, through a combination of nonlinear system identification and coupled ocean-iceberg modelling, that I48N's variability is predominantly caused by fluctuation in GrIS calving discharge rather than open ocean iceberg melting. We also demonstrate that the episodic variation in iceberg discharge is strongly linked to a nonlinear combination of recent changes in the surface mass balance (SMB) of the GrIS and regional atmospheric and oceanic climate variability, on the scale of the previous 1-3 years, with the dominant causal mechanism shifting between glaciological (SMB) and climatic (ocean temperature) over time. We suggest that this is a change in whether glacial run-off or under-ice melting is dominant, respectively. We also suggest that GrIS calving discharge is episodic on at least a regional scale and has recently been increasing significantly, largely as a result of west Greenland sources.

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

An important step in the identification of cellular automata (CA) is to detect the correct neighborhood before parameter estimation. Many authors have suggested procedures based on the removal of redundant neighbors from a very large initial neighborhood one by one to find the real model, but this often induces ill conditioning and overfitting. This is true particularly for a large initial neighborhood where there are few significant terms, and this will be demonstrated by an example in this paper. By introducing a new criteria and three new techniques, this paper proposes a new adaptive fast CA orthogonal-least-square (Adaptive-FCA-OLS) algorithm, which cannot only adaptively search for the correct neighborhood without any preset tolerance but can also considerably reduce the computational complexity and memory usage. Several numerical examples demonstrate that the Adaptive-FCA-OLS algorithm has better robustness to noise and to the size of the initial neighborhood than other recently developed neighborhood detection methods in the identification of binary CA.

An adaptive wavelet neural network for spatio-temporal system identification.

Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks (AWNN) is introduced for spatio-temporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework.

Neighborhood detection for the identification of spatiotemporal systems.

Neighborhood detection and local state vector construction for the identification of spatiotemporal systems is considered in this paper. Determining the neighborhood size both in the space and time domain can considerably reduce the complexity of the set of candidate model terms for the identification of coupled map lattice models. The computation requirements of the model identification algorithm can also be greatly reduced instead of the more direct identification approach of searching over the entire spatiotemporal neighborhood in the original space. In this paper, a new neighborhood detection method is introduced based on embedding theory for nonlinear dynamical systems to produce an initial spatiotemporal neighborhood for the identification of spatiotemporal systems. Numerical examples are provided to demonstrate the feasibility and applicability of the new neighborhood detection method.

Neighborhood detection using mutual information for the identification of cellular automata.

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually requires a priori information about the observed system, but in many applications little information will be known about the pattern. This paper introduces a new neighborhood detection algorithm which can determine the range of the neighborhood without any knowledge of the system by introducing a criterion based on mutual information (and an indication of over-estimation). A coarse-to-fine identification routine is then proposed to determine the CA rule from the observed pattern. Examples, including data from a real experiment, are employed to evaluate the new algorithm.

Changes in stable isotopic signatures of soil nitrogen and carbon during 40 years of forest development.

Understanding what governs patterns of soil delta15N and delta13C is limited by the absence of these data assembled throughout the development of individual ecosystems. These patterns are important because stable isotopes of soil organic N and C are integrative indicators of biogeochemical processing of soil organic matter. We examined delta15N of soil organic matter (delta15NSOM) and delta13CSOM of archived soil samples across four decades from four depths of an aggrading forest in southeastern USA. The site supports an old-field pine forest in which the N cycle is affected by former agricultural fertilization, massive accumulation of soil N by aggrading trees over four decades, and small to insignificant fluxes of N via NH3 volatilization, nitrification, and denitrification. We examine isotopic data and the N and C dynamics of this ecosystem to evaluate mechanisms driving isotopic shifts over time. With forest development, delta13CSOM became depth-dependent. This trend resulted from a decline of approximately 2 per thousand in the surficial 15 cm of mineral soil to -26.0 per thousand, due to organic matter inputs from forest vegetation. Deeper layers exhibited relatively little trend in delta13CSOM with time. In contrast, delta15NSOM was most dynamic in deeper layers. During the four decades of forest development, the deepest layer (35-60 cm) reached a maximum delta15N value of 9.1 per thousand, increasing by 7.6 per thousand. The transfer of > 800 kg ha(-1) of soil organic N into aggrading vegetation and the forest floor and the apparent large proportion of ectomycorrhizal (ECM) fungi in these soils suggest that fractionation via microbial transformations must be the major process changing delta15N in these soils. Accretion of isotopically enriched compounds derived from microbial cells (i.e., ECM fungi) likely promote isotopic enrichment of soils over time. The work indicates the rapid rate at which ecosystem development can impart delta15NSOM and delta13CSOM signatures associated with undisturbed soil profiles.

Metabolic flux distribution analysis by 13C-tracer experiments using the Markov chain-Monte Carlo method.

Metabolic flux analysis using 13C-tracer experiments is an important tool in metabolic engineering since intracellular fluxes are non-measurable quantities in vivo. Current metabolic flux analysis approaches are fully based on stoichiometric constraints and carbon atom balances, where the over-determined system is iteratively solved by a parameter estimation approach. However, the unavoidable measurement noises involved in the fractional enrichment data obtained by 13C-enrichment experiment and the possible existence of unknown pathways prevent a simple parameter estimation method for intracellular flux quantification. The MCMC (Markov chain-Monte Carlo) method, which obtains intracellular flux distributions through delicately constructed Markov chains, is shown to be an effective approach for deep understanding of the intracellular metabolic network. Its application is illustrated through the simulation of an example metabolic network.

Responses of soil nitrogen dynamics in a Mojave Desert ecosystem to manipulations in soil carbon and nitrogen availability.

We investigated the effects of changes in soil C and N availability on N mineralization, nitrification, denitrification, NH(3) volatilization, and soil respiration in the Mojave Desert. Results indicate a C limitation to microbial N cycling. Soils from underneath the canopies of Larrea tridentata (DC.) Cov., Pleuraphis rigida Thurber, and Lycium spp. exhibited higher rates of CO(2 ) flux, lower rates of NH(3) volatilization, and a decrease in inorganic N (NH(4)(+)-N and NO(3)(-)-N) with C addition. In addition to C limitation, soils from plant interspaces also exhibited a N limitation. Soils from all locations had net immobilization of N over the course of a 15-day laboratory incubation. However, soils from interspaces had lower rates of net nitrification and potential denitrification compared to soils from under plant canopies. The response to changes in C availability appears to be a short-term increase in microbial immobilization of inorganic N. Under controlled conditions, and over a longer time period, the effects of C and N availability appear to give way to larger differences due to spatial location. These findings have implications for ecosystems undergoing changes in soil C and N availability due to such processes as desertification, exotic species invasions, or elevated atmospheric CO(2) concentration.

Identification of probabilistic cellular automata.

The identification of probabilistic cellular automata (PCA) is studied using a new two stage neighborhood detection algorithm. It is shown that a binary probabilistic cellular automaton (BPCA) can be described by an integer-parameterized polynomial corrupted by noise. Searching for the correct neighborhood of a BPCA is then equivalent to selecting the correct terms which constitute the polynomial model of the BPCA, from a large initial term set. It is proved that the contribution values for the correct terms can be calculated independently of the contribution values for the noise terms. This allows the neighborhood detection technique developed for deterministic rules in to be applied with a larger cutoff value to discard the majority of spurious terms and to produce an initial presearch for the BPCA neighborhood. A multiobjective genetic algorithm (GA) search with integer constraints is then evolved to refine the reduced neighborhood and to identify the polynomial rule which is equivalent to the probabilistic rule with the largest probability. A probability table representing the BPCA can then be determined based on the identified neighborhood and the deterministic rule. The new algorithm is tested over a large set of one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) BPCA rules. Simulation results demonstrate the efficiency of the new method.

Identification of the neighborhood and CA rules from spatio-temporal CA patterns.

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually produces a CA rule table without providing a clear understanding of the structure of the neighborhood or the CA rule. In this paper, a new identification method based on using a modified orthogonal least squares or CA-OLS algorithm to detect the neighborhood structure and the underlying polynomial form of the CA rules is proposed. The Quine-McCluskey method is then applied to extract minimum Boolean expressions from the polynomials. Spatio-temporal patterns produced by the evolution of 1D, 2D, and higher dimensional binary CAs are used to illustrate the new algorithm, and simulation results show that the CA-OLS algorithm can quickly select both the correct neighborhood structure and the corresponding rule.

A note on discretization of nonlinear differential equations.

An important issue when integrating nonlinear differential equations on a digital computer is the choice of the time increment or step size. For example, it is known that if this quantity is not sufficiently short, spurious chaotic motions may be induced when integrating a system using several of the well-known methods available in the literature. In this paper, a new approach to discretize differential equations is analyzed in light of computational chaos. It will be shown that the fixed points of the continuous system are preserved under the new discretization approach and that the spurious fixed points generated by higher order approximations depend upon the increment parameter. (c) 2002 American Institute of Physics.

Extracting Boolean rules from CA patterns.

A multiobjective genetic algorithm (GA) is introduced to identify both the neighborhood and the rule set in the form of a parsimonious Boolean expression for both one- and two-dimensional cellular automata (CA). Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic noise.