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Replacing long-lived, rarely disturbed vegetation with short-lived, frequently disturbed vegetation is a widespread phenomenon in the Anthropocene that can influence ecosystem functioning and soil development by reducing the abundance of deep roots. We explore how sources and fate of soil CO2 vary with organic substrate source, abundance of respiring biota (i.e., roots and soil microbes), season, and soil depth. We quantified multiple isotopic signatures of CO2 (δ13C, Δ14C, δ18O) as well as concentrations and δ18O of free O2 in the upper 5 m of soil at sites where root abundances and soil organic C have been previously quantified: in late-successional forests, cultivated fields, and ~ 80 y old regenerating pine forests growing on previously cultivated land. We hypothesized that soil CO2sources would vary across soil depth and land cover, reflecting varying abundances of organic substrates, and seasonally as the dominance of root vs. microbial CO2 production changes through the year. δ13C–CO2 revealed respiration of C4-derived substrates in cultivated fields particularly during the growing season. This effect was not evident in soils of regenerating pine or older hardwood forests, suggesting that ~ 80 y of pine inputs to reforested soils have been sufficient to dominate microbial substrate selection over any remnant, historic agricultural C4 inputs. Δ14C–CO2 diverged by land use at 3 and 5 m, indicating that more recently-produced photosynthate is available for mineralization in forests compared to cultivated plots, and in late-successional forests compared to regenerating pine forests. At 1.5, 3, and 5 m in forested plots we observed evidence of respiratory demands on soil pore space O2. In these soils, we observed declines in [O2] compared to other depths and to the agricultural plots and concurrent increases in δ18O of free O2, consistent with the idea that roots and heterotrophic soil microbes are more active where photosynthate is more available. The δ18O–CO2 values, a likely proxy for δ18O of soil porewater, exhibited 18O enrichment during the winter, when many sampling wells were flooded, compared to growing season values. These data suggest an isotopically-distinct and laterally-flowing source of CO2-laden porewater during winter months. Combined, these datasets document how ~ 80 y of forest regeneration can provide sufficient C inputs to mask any microbial mineralization of decades-old organic inputs, but belowground C inputs still lag those of late successional forests. We also infer that lateral and vertical flows of water can serve as a sink for biotically-generated CO2, and that where deep soil [CO2] is lower due to lower root and microbial activities, production of carbonic acid is also diminished. Where reaction rates are weathering limited, a paucity of deep roots imposed by anthropogenic land cover change thus may limit the production of this agent of soil development and the C sink represented by the silicate weathering it can promote. The data suggest deep and persistent effects of the loss of deeply rooted long-lived vegetation on deep soil C storage and transformations that promote acid-dissolution weathering reactions that help form soil itself. [ABSTRACT FROM AUTHOR]

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 (δ13C) and oxygen (δ18O), wood nitrogen concentration [N], and contemporary leaf [N] and δ13C 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 δ13C did not increase with drought severity. A significantly positive relationship between tree-ring δ13C and δ18O 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 δ13C and leaf N (by mass), but not in dying trees. Our work provides evidence that for plants in which strong relationships between δ13C and δ18O are not evident, δ13C 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 treering 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? [ABSTRACT FROM AUTHOR]

In this paper, we study the electromagnetic scattering of a conducting and permeable sphere buried in sea sediments. Instead of taking a uniform conducting medium in the previous work, we model marine environments as a layered medium that consists of the air, the sea, and the sediment. We adopt an integral equation technique to compute time-harmonic solutions for background and scattered fields under fundamental source excitations, i.e., vertical and horizontal magnetic dipoles. The corresponding transient scattering responses to causal step waveform are computed through the digital sine transform. The derived fundamental solutions provide convenient formulas tailored for three-layer medium modeling. The numerical experiments demonstrate that the scattered responses computed in the different backgrounds are approaching almost to the same decays at late times. However, the background fields can significantly mask the scattered responses. Subtracting assumed uniform background responses from “measured” total fields in the three-layered medium cannot provide a correct scattering response in the interested time range, i.e., 0.1–25 ms. To remove the background fields, we propose a conceptual gradiometer system that has receiver cubes installed radially symmetric with respect to a transmitting antenna. The results demonstrate that the suitable differential combinations are able to yield the scattering responses that well agree with those of a free space as the layered background fields in these combined receivers are equal and their influence are automatically canceled out. [ABSTRACT FROM PUBLISHER]

Temperature responses of denitrifying microbes likely play a governing role in the production and consumption of NO. We investigated temperature effects on denitrifier communities and their potential to produce NO and N by incubating grassland soils collected in multiple seasons at four temperatures with N-enriched NO for ~24 h. We quantified [NO] concentration across time, estimated its production and reduction to N, and quantified relative abundance of genes responsible for NO production ( cnorB) and reduction ( nosZ). In all seasons, net NO production was positively linked to incubation temperature, with highest estimates of net and gross NO production in late spring soils. NO dynamics were tightly coupled to changes in denitrifier community structure, which occurred on both seasonal and incubation time scales. We observed increases in nosZ abundance with increasing incubation temperature after 24 h, and relatively larger increases in cnorB abundance from winter to late June. The difference between incubation and in situ temperature was a robust predictor of cnorB:nosZ. These data provide convincing evidence that short-term increases in temperature can induce remarkably rapid changes in community structure that increase the potential for reduction of NO to N, and that seasonal adaptation of denitrifying communities is linked to seasonal changes in potential NO production, with warmer seasons linked to large increases in NO production potential. This work helps explain observations of high spatial and temporal variation in NO effluxes, and highlights the importance of temperature as an influence on denitrification enzyme kinetics, denitrifier physiology and community adaptations, and associated NO efflux and reduction. [ABSTRACT FROM AUTHOR]

The relationship between the solar wind and the proton flux at geosynchronous Earth orbit (GEO) is investigated using the error reduction ratio (ERR) analysis. The ERR analysis is able to search for the most appropriate inputs that control the evolution of the system. This approach is a black box method and is able to derive a mathematical model of a system from input-output data. This method is used to analyse eight energy ranges of the proton flux at GEO from 80 keV to 14.5MeV. The inputs to the algorithm were solar wind velocity, density and pressure; the Dst index; the solar energetic proton (SEP) flux; and a function of the interplanetary magnetic field (IMF) tangential magnitude and clock angle. The results show that for lowest five energy channels (80 to 800 keV) the GEO proton fluxes are controlled by the solar wind velocity with a lag of two to three days. However, above 350 keV, the SEP fluxes, accounts for a significant portion of the GEO proton flux variance. For the highest three energy channels (0.74 to 14.5 MeV), the SEPs account for the majority of the ERR. The results also show an anisotropy of protons with gyrocenters inside GEO and outside GEO, where the protons inside GEO are controlled partly by the Dst index and also an IMF-clock angle function. [ABSTRACT FROM AUTHOR]

This paper describes the identification of a temperature dependent FitzHugh-Nagumo model directly from experimental observations with controlled inputs. By studying the steady states and the trajectory of the phase of the variables, the stability of the model is analyzed and a rule to generate oscillation waves is proposed. The dependence of the oscillation frequency and propagation speed on the model parameters is then investigated to seek the appropriate control variables, which then become functions of temperature in the identified model. The results show that the proposed approach can provide a good representation of the dynamics of the oscillatory behavior of a Belousov-Zhabotinskii reaction. [ABSTRACT FROM AUTHOR]

A Bayesian system identification approach to modelling stochastic linear dynamical systems involving multiple scales is proposed, where multiscale means that the output of the system is observed at one (coarse) resolution whilst the input of the system can only be observed at another (fine) resolution. The proposed method identifies linear models at different levels of resolution, where the link between the two resolutions is realised via a non-overlapping averaging process. The averaged data at the coarse level of resolution is assumed to be a set of observations from an implied process so that the implied process and the output of the system result in an errors-in-variables model at the coarse level of resolution. By using a Bayesian inference and Markov chain Monte Carlo method, such a modelling framework results in different dynamical models at different levels of resolution at the same time. The new method is also shown to have the ability to combine information across different levels of resolution. Simulation examples are provided to show the efficiency of the new method. Furthermore, an application to the analysis of the relativistic electron intensity at the geosynchronous orbit is also included. [ABSTRACT FROM AUTHOR]

The wavefront profile and the propagation velocity of waves in an experimentally observed Belousov-Zhabotinskii reaction are analyzed and a revised FitzHumgh-Nagumo (FHN) model of these systems is identified. The ratio between the excitation period and the recovery period, for a solitary wave is studied, and included within the model. Averaged traveling velocities at different spatial positions are shown to be consistent under the same experimental conditions. The relationship between the propagation velocity and the curvature of the wavefront are also studied to deduce the diffusion coefficient in the model, which is a function of the curvature of the wavefront and not a constant. The application of the identified model is demonstrated on real experimental data and validated using multistep ahead predictions. [ABSTRACT FROM AUTHOR]

The forecast of high energy electron fluxes in the radiation belts is important because the exposure of modern spacecraft to high energy particles can result in significant damage to onboard systems. A comprehensive physical model of processes related to electron energisation that can be used for such a forecast has not yet been developed. In the present paper a systems identification approach is exploited to deduce a dynamic multiple regression model that can be used to predict the daily maximum of high energy electron fluxes at geosynchronous orbit from data. It is shown that the model developed provides reliable predictions. [ABSTRACT FROM AUTHOR]

In this article, the identification of a class of multiscale spatio-temporal dynamical systems, which incorporate multiple spatial scales, from observations is studied. The proposed approach is a combination of Adams integration and an orthogonal least squares algorithm, in which the multiscale operators are expanded, using polynomials as basis functions, and the spatial derivatives are estimated by finite difference methods. The coefficients of the polynomials can vary with respect to the space domain to represent the feature of multiple scales involved in the system dynamics and are approximated using a B-spline wavelet multi-resolution analysis. The resulting identified models of the spatio-temporal evolution form a system of partial differential equations with different spatial scales. Examples are provided to demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

It is known that the least-squares (LS) class of algorithms produce unbiased estimates providing certain assumptions are met. There are many practical problems, however, where the required assumptions are violated. Typical examples include non-linear dynamical system identification problems, where the input and output observations are affected by measurement uncertainty and possibly correlated noise. This will result in biased LS estimates and the identified model will exhibit poor generalisation properties. Model estimation for this type of error-in-variables problem is investigated in this study, and a new identification scheme based on a bootstrap algorithm is proposed to improve the model estimates for non-linear dynamical system identification. [ABSTRACT FROM AUTHOR]

In this article, a new algorithm for the multiscale identification of spatio-temporal dynamical systems is derived. It is shown that the input and output observations can be represented in a multiscale manner based on a wavelet multiresolution analysis. The system dynamics at some specific scale of interest can then be identified using an orthogonal forward least-squares algorithm. This model can then be converted between different scales to produce predictions of the system outputs at different scales. The method can be applied to both multiscale and conventional spatio-temporal dynamical systems. For multiscale systems, the method can generate a parsimonious and effective model at a coarser scale while considering the effects from finer scales. Additionally, the proposed method can be used to improve the performance of the identification when the measurements are noisy. Numerical examples are provided to demonstrate the application of the proposed new approach. [ABSTRACT FROM AUTHOR]

This article introduces a new residual-based recursive parameter estimation algorithm for linear partial differential equations. The main idea is to replace the unmeasurable noise variables by estimates of the noise and to compute recursively both the model parameters and the noise estimates. It is proven that under some mild assumptions the estimated parameters converge to the true values with probability one. Numerical examples that demonstrate the effectiveness of the proposed approach are also provided. [ABSTRACT FROM AUTHOR]

A method of identifying the transition rule, encapsulated in a modified cellular automata (CA) model, is demonstrated using experimentally observed data of the evolution of dendritic crystal growth patterns in NH4Br crystals. The influence of the factors, such as experimental set-up and image preprocessing, color and size calibrations, on the method of identification are discussed in detail. A noise reduction parameter and the diffusion velocity of the crystal boundary are also considered. The results show that the proposed method can in principle provide a good representation of the dendritic growth anisotropy. [ABSTRACT FROM AUTHOR]

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is introduced to implement a new constructive learning algorithm for training generalized cellular neural networks (GCNNs) for the identification of spatio-temporal evolutionary (STE) systems. The basic idea of the new PSO-based learning algorithm is to successively approximate the desired signal by progressively pursuing relevant orthogonal projections. This new algorithm will thus be referred to as the orthogonal projection pursuit (OPP) algorithm, which is in mechanism similar to the conventional projection pursuit approach. A novel two-stage hybrid training scheme is proposed for constructing a parsimonious GCNN model. In the first stage, the orthogonal projection pursuit algorithm is applied to adaptively and successively augment the network, where adjustable parameters of the associated units are optimized using a particle swarm optimizer. The resultant network model produced at the first stage may be redundant. In the second stage, a forward orthogonal regression (FOR) algorithm, aided by mutual information estimation, is applied to refine and improve the initially trained network. The effectiveness and performance of the proposed method is validated by applying the new modeling framework to a spatio-temporal evolutionary system identification problem. [ABSTRACT FROM AUTHOR]

New results for model order reduction, for weakly non-linear systems in the frequency domain, are derived based on a parametric modelling approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A new adaptive orthogonal search (AOS) algorithm is proposed for model subset selection and non-linear system identification. Model structure detection is a key step in any system identification problem. This consists of selecting significant model terms from a redundant dictionary of candidate model terms, and determining the model complexity (model length or model size). The final objective is to produce a parsimonious model that can well capture the inherent dynamics of the underlying system. In the new AOS algorithm, a modified generalized cross-validation criterion, called the adjustable prediction error sum of squares (APRESS), is introduced and incorporated into a forward orthogonal search procedure. The main advantage of the new AOS algorithm is that the mechanism is simple and the implementation is direct and easy, and more importantly it can produce efficient model subsets for most non-linear identification problems. [ABSTRACT FROM AUTHOR]

In this paper, a fast identification algorithm for non-linear dynamic stochastic system identification is presented. The algorithm extends the classical orthogonal forward regression (OFR) algorithm so that instead of using the error reduction ratio (ERR) for term selection, a new optimality criterion, Shannon's entropy power reduction ratio (EPRR), is introduced to deal with both Gaussian and non-Gaussian signals. It is shown that the new algorithm is both fast and reliable and examples are provided to illustrate the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

The identification problem for spatiotemporal patterns which are generated by autonomous Cellular Neural Networks (CNN) is investigated in this paper. The application of traditional identification algorithms to these special spatiotemporal systems can produce poor models due to the inherent piecewise nonlinear structure of CNN. To solve this problem, a new type of Coupled Map Lattice model with output constraints and corresponding identification algorithms are proposed in the present study. Numerical examples show that the identified CML models have good prediction capabilities even over the long term, and the main dynamics of the original patterns appears to be well represented. [ABSTRACT FROM AUTHOR]

Many complex and interesting spatiotemporal patterns have been observed in a wide range of scientific areas. In this paper, two kinds of spatiotemporal patterns including spot replication and Turing systems are investigated and new identification methods are proposed to obtain Coupled Map Lattice (CML) models for this class of systems. Initially, a new correlation analysis method is introduced to determine an appropriate temporal and spatial data sampling procedure for the identification of spatiotemporal systems. A new combined Orthogonal Forward Regression and Bayesian Learning algorithm with Laplace priors is introduced to identify sparse and robust CML models for complex spatiotemporal patterns. The final identified CML models are validated using correlation-based model validation tests for spatiotemporal systems. Numerical results illustrate the identification procedure and demonstrate the validity of the identified models. [ABSTRACT FROM AUTHOR]

In this article, the identification and analysis of spatio-temporal dynamical systems is presented. An approximated B-spline wavelet representation of spatio-temporal dynamical systems is identified using an orthogonal least-squares algorithm from measured data. Control variables are incorporated to represent controlled external inputs and/or some system parameters for the purpose of analysis. The identified system models can be used to evaluate how the external inputs or system parameters affect the evolution of the spatio-temporal dynamics and pattern formation. Two examples are used to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

The purpose of model validation is to test the goodness of fit of the identified model and to check whether the model is an appropriate representation of the underlying system. Model validation is a final and essential stage in most system identification procedures. Although model validation for nonlinear temporal systems has been extensively studied, model validation for spatiotemporal systems is still an open question. In this paper, correlation based methods, which have been successfully applied in nonlinear temporal systems are extended and enhanced to validate models of spatiotemporal systems. New nonlinear correlation functions are constructed based on the inputs, one-step-ahead predicted outputs and the residuals to test the dependency between the residuals and inputs/outputs. Examples are included to demonstrate the application of the tests. [ABSTRACT FROM AUTHOR]

The identification of binary cellular automata from spatio-temporal binary patterns is investigated in this paper. Instead of using the usual Boolean or multilinear polynomial representation, the Fourier transform representation of Boolean functions is employed in terms of a Fourier basis. In this way, the orthogonal forward regression least-squares algorithm can be applied directly to detect the significant terms and to estimate the associated parameters. Compared with conventional methods, the new approach is much more robust to noise. Examples are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

New methods of identifying the transition rule of a Belousov–Zhabotinskii (BZ) reaction directly from experimental data using cellular automata (CA) models are investigated. The experimental set-up and new techniques for image pre-processing to ensure the identification of representative models are discussed including noise reduction, pixel and color calibration. Two kinds of models, the Greenberg–Hasting model (GHM) and the polynomial CA model are studied in detail. It is shown that the results of identifying a real BZ reacting system are very encouraging and the predicted patterns compare well with the imaged patterns both visually and quantitatively. [ABSTRACT FROM AUTHOR]

In this paper, a modified orthogonal forward regression (OFR) least-squares algorithm is presented for system identification and modelling from noisy regressors. Under the assumption that the energy and signal-to-noise ratio (SNR) of the signals are known or can be estimated, it is shown that unbiased estimates of the Error reduction ratios (ERRs) and the parameters can be obtained in each forward regression step. Examples are provided to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

Although cellular automata have been widely studied as a class of spatio temporal systems, very few investigators have studied how to identify the CA rules given observations of the patterns. A solution using a polynomial realization to describe the CA rule is reviewed in the present study based on the application of an orthogonal least squares algorithm. Three new neighbourhood detection methods are then reviewed as important preliminary analysis procedures to reduce the complexity of the estimation. The identification of excitable media is discussed using simulation examples and real data sets and a new method for the identification of hybrid CA is introduced. [ABSTRACT FROM AUTHOR]

Excitable media represent an important class of spatio-temporal systems which can be studied using cellular automata models. In the present study examples of excitable media behavior generated using simple cellular automata models are introduced. A mutual information algorithm is then derived to determine the neighborhood, the excitation threshold, and the number of excitation states. Based on this information two methods of identifying the rule which describes the excitable media pattern using a multimodel and a polynomial model are introduced. The results are illustrated using simulated examples and real data from a Belousov–Zhabotinskii experiment. [ABSTRACT FROM AUTHOR]

Wavelet based non-parametric additive NARX models are proposed for nonlinear input-output system identification. By expanding each functional component of the non-parametric NARX model into wavelet multiresolution expansions, the non-parametric estimation problem becomes a linear-in-the-parameters problem, and least-squares-based methods such as the orthogonal forward regression (OFR) approach, coupled with model size determination criteria, can be used to select the model terms and estimate the parameters. Wavelet based additive models, combined with model order determination and variable selection approaches, are capable of handling problems of high dimensionality. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach for the identification of coupled map lattice models of complex spatio-temporal patterns from measured data. The nonlinear functionals describing the evolution of the spatio-temporal patterns are constructed using B-spline wavelet and scaling functions. This provides a multiresolution approximation for the underlying spatio-temporal dynamics. An orthogonal least squares algorithm is used to determine the suitable terms from wavelet functions to form an accurate representation of the nonlinear spatio-temporal dynamics. Three examples are used to demonstrate the application of the proposed new approach. [ABSTRACT FROM AUTHOR]

A new approach for the estimation of spatial derivatives and the identification of a class of continuous spatio-temporal dynamical systems from experimental data is presented in this study. The proposed identification approach is a combination of implicit Adams integration and an orthogonal forward regression algorithm (OFR), in which the operators are expanded using polynomials as basis functions. The noisy experimental data are de-noised by using biorthogonal spline wavelet filters and the spatial derivatives are estimated using a multiresolution analysis method. Finally, a bootstrap method is applied to refine the identified parameters from the OFR algorithm. The resulting identified models of the spatio-temporal evolution form a system of partial differential equations. Examples are provided to demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Disturbed grassland soils are often cited as having the potential to store large amounts of carbon (C). Fertilization of grasslands can promote soil C storage, but little is known about the generation of recalcitrant pools of soil organic matter (SOM) with management treatments, which is critical for long-term soil C storage. We used a combination of soil incubations, size fractionation and acid hydrolysis of SOM, [C], [N], and stable isotopic analyses, and biomass quality indices to examine how fertilization and haying can impact SOM dynamics in Kansan grassland soils. Fertilized soils possessed 113% of the C possessed by soils subjected to other treatments, an increase predominantly harbored in the largest size fraction (212–2,000 μm). This fraction is frequently associated with more labile material. Haying and fertilization/haying, treatments that more accurately mimic true management techniques, did not induce any increase in soil C. The difference in 15N-enrichment between size fractions was consistent with a decoupling of SOM processing between pools with fertilization, congruent with gains of SOM in the largest size fraction promoted by fertilization not moving readily into smaller fractions that frequently harbor more recalcitrant material. Litterfall and root biomass C inputs increased 104% with fertilization over control plots, and this material possessed lower C:N ratios. Models of incubation mineralization kinetics indicate that fertilized soils have larger pools of labile organic C. Model estimates of turnover rates of the labile and recalcitrant C pools did not differ between treatments (65.5 ± 7.2 and 2.9 ± 0.3 μg C d−1, respectively). Although fertilization may promote greater organic inputs into these soils, much of that material is transformed into relatively labile forms of soil C; these data highlight the challenges of managing grasslands for long-term soil C sequestration. [ABSTRACT FROM AUTHOR]

The long term prediction of non-linear dynamical time series, based on identified multiresolution wavelet models, from historically observed data sets is investigated and a new direct prediction approach is introduced. Prediction results based on the new direct scheme are compared with those from iterative methods and it is shown that improved predictions can be obtained using the new approach. [ABSTRACT FROM AUTHOR]

Understanding what governs patterns of soil δ15N and δ13C 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 δ15N of soil organic matter (δ15NSOM) and δ13CSOM 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, δ13CSOM became depth-dependent. This trend resulted from a decline of ~2‰ in the surficial 15 cm of mineral soil to −26.0‰, due to organic matter inputs from forest vegetation. Deeper layers exhibited relatively little trend in δ13CSOM with time. In contrast, δ15NSOM was most dynamic in deeper layers. During the four decades of forest development, the deepest layer (35–60 cm) reached a maximum δ15N value of 9.1‰, increasing by 7.6‰. 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 δ15N 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 δ15NSOM and δ13CSOM signatures associated with undisturbed soil profiles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The generation of generalized frequency response functions for multi-imput–multi-output non-linear system models is investigated with particular emphasis on the cross-kernel transforms. A unified framework for the output response of non-linear systems in the frequency domain is also introduced. [ABSTRACT FROM AUTHOR]

Traditionally the Volterra time and frequency domain analysis tools cannot be applied to severely non-linear systems. In this paper, a new method of building a time-domain NARX MISO model for a class of severely SISO non-linear systems that exhibit subharmonics is introduced and it is shown how this allows the Volterra time and frequency domain analysis to be extended to this class of non-linear systems. The new approach is based on decomposing the original single input based on a Fourier analysis to provide a set of modified input signals which have the same period as the output signal. A MISO NARX model can then be constructed from the decomposed multiple inputs and the single output signal. The resulting MISO model is shown to meet the basic requirement for the existence of a Volterra series representation from which important frequency domain properties can be derived, explained and discussed. This is done by first introducing the derivation of generalized frequency response functions (GFRFs) from time domain MISO NARX models. The steady state response synthesis problem using the input spectrum and the MISO GFRFs is then investigated in order to verify the effectiveness and accuracy of the MISO modelling approach for severely non-linear systems. Finally a new frequency domain analysis method is introduced for systems that exhibit subharmonic oscillations. [ABSTRACT FROM AUTHOR]

In Part 1 of this paper the concepts of input and output frequency subdomains were introduced to give insight into the relation between one dimensional and multi-dimensional frequency spaces. The visualisation of both magnitude and phase responses of third order generalized frequency response functions was also presented. In this, the second part, symbolic expansion techniques are introduced and together with the results achieved in Part 1, yield new methods for analysing the properties of generalized frequency response functions. Case studies are included to illustrate the application of the new methods. [ABSTRACT FROM AUTHOR]

The characteristics of generalized frequency response functions (GFRFs) of non-linear systems in higher dimensional space are investigated using a combination of graphical and symbolic decomposition techniques. It is shown how a systematic analysis can be achieved for a wide class of non-linear systems in the frequency domain using the proposed methods. The paper is divided into two parts. In Part 1, the concepts of input and output frequency subdomains are introduced to give insight into the relationship between one dimensional and multi-dimensional frequency spaces. The visualization of both magnitude and phase responses of third order generalized frequency response functions is presented for the first time. In Part 2 symbolic expansion techniques are introduced and new methods are developed to analyse the properties of generalized frequency response functions of non-linear systems described by the NARMAX class of models. Case studies are included in Part 2 to illustrate the application of the new methods. [ABSTRACT FROM AUTHOR]

A comparison between polynomial and wavelet expansions for the identification of coupled map lattice (CML) models for deterministic spatiotemporal dynamical systems is presented in this paper. The pattern dynamics generated by smooth and nonsmooth nonlinear maps in a well-known two-dimensional CML structure are analyzed. By using an orthogonal feedforward regression algorithm (OFR), polynomial and wavelet models are identified for the CML's in chaotic regimes. The quantitative dynamical invariants such as the largest Lyapunov exponents and correlation dimensions are estimated and used to evaluate the performance of the identified models. [ABSTRACT FROM AUTHOR]

A novel fast cellular automata orthogonal least squares (FCA-OLS) identification method is introduced by extending and developing the CA-OLS identification method presented in a previous study by Billings and Yang. Both one-dimensional CA and two-dimensional CA were identified with FCA-OLS as the simulation examples. The simulation results show that the new method significantly reduces the computational time compared with existing methods. [ABSTRACT FROM AUTHOR]

In this paper, an analysis of the energy transfer properties of non-linear systems in the frequency domain is studied based on a new concept known as non-linear output frequency response functions (NOFRFs). The new concept allows the analysis to be implemented in a manner similar to the analysis of linear systems in the frequency domain, and provides great insight into the mechanisms which dominate the non-linear behaviour. The new analysis is also helpful for the design of non-linear systems in the frequency domain. [ABSTRACT FROM AUTHOR]

A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilize the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least-squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model. [ABSTRACT FROM AUTHOR]

A new neighborhood selection method is presented for both deterministic and probabilistic cellular automata models. The detection criteria are built explicitly on the corresponding contribution which is made to the value of each updated cell from each detected cell in the evolution. Theoretical analysis and numerical simulations demonstrate the effectiveness of this new method. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) dynamics ultimately govern the ability of soil to provide long-term C sequestration and the nutrients required for ecosystem productivity. Predicting belowground responses to elevated CO2 requires an integrated understanding of SOM transformations and the microbial activity that governs them. It remains unclear how the microorganisms upon which these transformations depend will function in an elevated CO2 world. This study examines SOM transformations and microbial metabolism in soils from the Duke Free Air Carbon Enrichment site in North Carolina, USA. We assessed microbial respiration and net nitrogen (N) mineralization in soils with and without elevated CO2 exposure during a 100-day incubation. We also traced the depleted C isotopic signature of the supplemental CO2 into SOM and the soils' phospholipid fatty acids (PLFA), which serve as biomarkers for living cells. Cumulative net N mineralization in elevated CO2 soils was 50% that in control soils after a 100-day incubation. Respiration was not altered with elevated CO2. C : N ratios of bulk SOM did not change with elevated CO2, but incubation data suggest that the C : N ratios of mineralized organic matter increased with elevated CO2. Values of SOMδ13C were depleted with elevated CO2 (−26.7±0.2 vs.−30.2±0.3‰), reflecting the depleted signature of the supplemental CO2. We comparedδ13C of individual PLFA with theδ13C of SOM to discern incorporation of the depleted C isotopic signature into soil microbial groups in elevated CO2 plots. PLFA i15:0, a15:0, and 10Met18:0 reflected significant incorporation of recently produced photosynthate, suggesting that the bacterial groups defined by these biomarkers are active metabolizers in elevated CO2 soils. At least one of these groups (actinomycetes, 10Met18:0) specializes in metabolizing less labile substrates. Because control plots did not receive an equivalent13C tracer, we cannot determine from these data whether this group of organisms was stimulated by elevated CO2 compared with these organisms in control soils. Stimulation of this group, if it occurred in the elevated CO2 plot, would be consistent with a decline in the availability of mineralizable organic matter with elevated CO2, which incubation data suggest may be the case in these soils. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach for the local reconstruction of coupled map lattice (CML) models of stochastic spatio-temporal dynamics from measured data. The nonlinear functionals describing the evolution of the spatio-temporal patterns are constructed using B-spline wavelet and scaling functions. This provides a multiresolution approximation for the underlying spatio-temporal dynamics. An orthogonal least squares algorithm is used to determine significant terms from wavelet functions to form an accurate representation of the nonlinear spatio-temporal dynamics. Two examples are used to demonstrate the application of the proposed new approach. [ABSTRACT FROM AUTHOR]

A new modelling framework for identifying and reconstructing chaotic systems is developed based on multir esolution wavelet decompositions. Qualitative model validation is used to compare the multir esolution wavelet models and it is shown that the dynamical features of chaotic systems can be captured by the identified models providing the wavelet basis functions are properly selected. Two basis selection algorithms, orthogonal least squares and a new matching pursuit orthogonal least squares, are considered and compared. Several examples are included to illustrate the results. [ABSTRACT FROM AUTHOR]

A new NARMA based smoothing algorithm is introduced for chaotic and nonchaotic time series. The new algorithm employs a cross-validation method to determine the smoother structure, requires very little user interaction, and can be combined with wavelet thresholding to further enhance the noise reduction. Numerical examples are included to illustrate the application of the new algorithm. [ABSTRACT FROM AUTHOR]

The purpose of variable selection is to pre-select a subset consisting of the significant variables or to eliminate the redundant variables from all the candidate variables of a system under study prior to model term detection. It is required that the selected significant variables alone should sufficiently represent the system. Generally, not all the model terms, which are produced by combining different variables, make an equal contribution to the system output and terms, which make little contribution, can be omitted. A parsimonious representation, which contains only the significant terms, can often be obtained without the loss of representational accuracy by eliminating the redundant terms. Based on these observations, a new variable and term selection algorithm is proposed in this paper. The term detection algorithm can be applied to the general class of non-linear modelling problems which can be expressed as a linear-in-the-parameters form. The variable selection procedure is based on locally linear and cross-bilinear models, which are used together with the forward orthogonal least squares (OLS) and error reduction ratio (ERR) approach to determine the significant terms and to pre-select the important variables for both time series and input-output systems. Several numerical examples are provided to illustrate the applicability and effectiveness of the new approach. [ABSTRACT FROM AUTHOR]