Independent Spatial Patterns Research Articles

The role of edaphic, vegetational and spatial factors in structuring soil animal communities in a floodplain forest of the Dnipro river

Abstract This paper examines the role of ecological factors, derived from principal component analysis performed on edaphic and vegetational dataset as well as spatial variables, in structuring the soil macrofauna community of the Dnipro floodplain within the ‘Dnipro-Orilsky’ Nature Reserve (Ukraine). The soil macrofauna was defined as invertebrates visible to the naked eye (macroscopic organisms). The test points formed a regular grid with a mesh size of 3 m with 7 × 15 dimensions. Thus, the total test point number was 105. At each point, soil-zoological samples of 0.25 × 0.25 m were taken for quantifying the soil macrofauna. The spatial structure was modeled by a set of independent spatial patterns obtained by means of principal coordinates of neighbor matrices analysis (PCNM-variables). Spatial PCNM-variables explain significantly more variations of the community (19.9%) than edaphic factors (4.1%) and vegetation factors (3.2%). Spatial and combined environmental and spatial effects were divided into three components: broad-scale component was characterized by periodicity of spatial variation with a wavelength of 24.0–44.5 m, medium-scale – 11.1–20 m, fine-scale – 6.6–11.0 m. For a broad-scale component, environmental factors of a vegetational nature are more important, for medium-scale, edaphic factors are more important, for fine-scale, both vegetation and edaphic are important. For litter-dwelling animals, the most characteristic spatial patterns are on the broad and medium-scale levels. For endogeic and anecic animals, the most significant variability is on the fine-scale level.

Specificity of spatial organization of evoked EEG rhythms in patients with paranoid schizophrenia

The study aimed at analyzing the spatial patterns of evoked event-related oscillations in patients with paranoid schizophrenia and their relationship with clinical symptoms of disease. Evoked delta, theta, alpha, beta, and gamma rhythms in response to target auditory stimulus in an oddball paradigm were studied in 21 schizophrenic patients and 22 healthy subjects. The independent spatial patterns were revealed within each of the frequency range using the principal component analysis. Each spatial pattern was characterized by the specificity of intra- and inter-hemispheric relations. The schizophrenic patients were characterized by a decrease in the evoked activity in the theta range with the most pronounced changes in the frontal-central areas of the right hemisphere and parietal-occipital areas bilaterally. Associations of the evoked rhythms with PANSS positive and negative symptoms were identified. The study demonstrated the high functional significance of evoked EEG rhythms changes for neurophysiological characteristics of patients with paranoid schizophrenia.

Spatial Models To Estimate Long-Term Exposure To NO, NO2, And NOx In The Mega-City Of Tehran, Iran

Introduction: Land use regression (LUR) models based on measurement campaigns are frequently used to assess long-term exposure to ambient air pollution. Our aim was to develop LUR models based on fixed-site monitoring station data for estimating annual and seasonal spatial variation in nitrogen oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx) concentrations in the heavily polluted city of Tehran, Iran. Methods: We used hourly 2010 data for NO, NO2 and NOx from 23 fixed sites in the urban monitoring network. A total of 210 geographic variables were generated to potentially explain spatial patterns of air pollution concentrations. A standard approach was developed for LUR modeling given the specific situation of Tehran (Sci. Total Env., 2014, 488:343-353), and LUR models were developed for annual, cool-season, and warm-season NO, NO2, and NOx. Results: The annual NO, NO2, and NOx concentrations were 88.1, 53.1, and 141.8 ppb, respectively. The correlation for the annual, cool-season, and warm-season averages, ranged from 0.96 to 0.99 for NO, 0.74 to 0.95 for NO2, and 0.94 to 0.99 for NOx. The correlation of NO and NO2 ranged from 0.24 to 0.44. However, the correlation of NO and NOx ranged from 0.93 to 0.98. The correlation of NO2 and NOx ranged from 0.35 to 0.58. The adjusted R2 values ranged from 0.60 to 0.71 for NO models, 0.58 to 0.68 for NO2 models, and 0.50 to 0.73 for NOx models. The most predictive variables were distance to traffic sources and high traffic areas, distance to primary schools, density of official areas, slope, and elevation. Conclusions: Our models indicate that spatial patterns did not much vary across the seasons. However, different pollutants, in particular NO and NO2, had independent spatial patterns. Overall, our models performed moderately well. In a next step, we will evaluate whether larger number of sites may lead to better models.

Geographies of Financial Stability: Bank Failures and Rescues in the United States During the Great Recession

The 2008 financial crisis was the second instance since the Great Depression that many hundreds of financial institutions failed across the United States. The rescue staged by the federal government, however, was unprecedented in scale, involving an initial Congressional authorization of $700 billion disbursed to financial companies through Treasury, as opposed to the normal resolution process for distressed institutions by the Federal Deposit Insurance Corporation (FDIC). There is, as yet, no geography of these bank failures and rescues even though locational data for both types are publicly available. This paper is a comparative analysis of the spatial distribution of these two policy programs. We find that the programs observed separate and largely independent spatial patterns at the state-level, and these were associated with changes in market structure: consolidation among insured banks, and growth among bank holding companies (BHCs). Our analysis uses organizational models of decision-making in government to address the causes of the transformations of financial and regulatory spaces.

Spatial patterns in accretion on barrier-island salt marshes

On minerogenic barrier-island salt marshes, sedimentation is spatially heterogeneous. Although the main forcing factors for sedimentation are known, much less is known about the characteristic sizes of this spatial patterning. Such patterning gives information on the spatial component of salt-marsh formation and on the uncertainty in measured accretion rates. We used variograms (geostatistics) to study the size of spatial patterns in the thickness of salt-marsh deposits, based on a database of over 10,000 soil cores. These were taken at various spatial scales ranging from metres to kilometres, along a chronosequence representing 10–150 years of salt-marsh formation at three barrier islands in the Wadden Sea (south-eastern North Sea). The general complexity of salt-marsh accretion was reflected in the observed patterns of the thickness of the marsh deposits. The patterns were nested and ranged in horizontal size from 3 m on sites with micro-topography, to 900 m at the scale of the entire marsh. Their structure and size changed with salt-marsh age and there was no characteristic pattern size. Although the pre-marsh topography is an important large-scale control, during salt-marsh development independent spatial patterns are superimposed. When scaling up data on salt-marsh sedimentation, the presence of spatial patterning adds uncertainty to the prediction. The consequence of the complexity of these patterns is that the spatial uncertainty is a (not necessarily linear) function of the area under consideration, which can only be quantified if it is explicitly measured. Our findings therefore pose a cautionary note to studies of salt-marsh resilience to sea-level rise: reliable estimates can only be derived if they are based on measurements that take into account the entire salt marsh.

Relative role of deterministic and stochastic determinants of soil animal community: a spatially explicit analysis of oribatid mites

1. Ecologists are debating the relative role of deterministic and stochastic determinants of community structure. Although the high diversity and strong spatial structure of soil animal assemblages could provide ecologists with an ideal ecological scenario, surprisingly little information is available on these assemblages. 2. We studied species-rich soil oribatid mite assemblages from a Mediterranean beech forest and a grassland. We applied multivariate regression approaches and analysed spatial autocorrelation at multiple spatial scales using Moran's eigenvectors. Results were used to partition community variance in terms of the amount of variation uniquely accounted for by environmental correlates (e.g. organic matter) and geographical position. Estimated neutral diversity and immigration parameters were also applied to a soil animal group for the first time to simulate patterns of community dissimilarity expected under neutrality, thereby testing neutral predictions. 3. After accounting for spatial autocorrelation, the correlation between community structure and key environmental parameters disappeared: about 40% of community variation consisted of spatial patterns independent of measured environmental variables such as organic matter. Environmentally independent spatial patterns encompassed the entire range of scales accounted for by the sampling design (from tens of cm to 100 m). This spatial variation could be due to either unmeasured but spatially structured variables or stochastic drift mediated by dispersal. Observed levels of community dissimilarity were significantly different from those predicted by neutral models. 4. Oribatid mite assemblages are dominated by processes involving both deterministic and stochastic components and operating at multiple scales. Spatial patterns independent of the measured environmental variables are a prominent feature of the targeted assemblages, but patterns of community dissimilarity do not match neutral predictions. This suggests that either niche-mediated competition or environmental filtering or both are contributing to the core structure of the community. This study indicates new lines of investigation for understanding the mechanisms that determine the signature of the deterministic component of animal community assembly.

Independent spatial patterns: an integration of common spatial patterns with independent component analysis for multi- class discrimination in EEG based brain-computer interfaces

Independent spatial patterns (ISP) aims to integrate common spatial patterns (CSP) and Independent component analysis (ICA) by utilizing the best features of the two methods and at the same time going round some of the caveats associated with them. In the case of ICA importance of components cannot be pre-determined. Principal component analysis (PCA) dimensional reduction does not provide reasonable estimate [1]. A visual selection of ICA components [2] still performed sub-optimally. On the other hand, multi-class implementation of CSP is also plagued with number of issues. It works on heuristics by employing one-vs-rest scheme [3]. The resultant 4 projection matrices (in the case of 4-class problem) creates identification problem: there is no way to determine whether the observed differences in conditions are due to multiple projection matrices or distinct brain activity. No wonder, low detection rate is usually observed for unseen data. Based on the physiological considerations left and right hand movement can be envisioned as (-vely) correlated tasks. That is, a trial associated with left imagination would typically depict de-synchronization over contra-lateral region (around C4) followed by synchronization over ipsi-lateral region (around C3). Almost diametrically reverse phenomenon is observed in the case of right movement imagination. Same line of argument is valid for foot and tongue movement imaginations [4]. Therefore, one pair of correlated signals was separated from the other pair into distinct clusters- each containing only 4 components - by employing CSP. On account of CSP criterion of maximization of kurtosis by Eigen value decomposition [5], these clusters are nothing but orthogonal independent components. However, orthogonality with respect to independence criterion is an unnecessary constraint. Therefore, the basis was further rotated by employing infomax principle [6] to achieve maximal independence. Experimental paradigm and details of feature extraction and classification scheme can be found in [2]. Succinctly, analysis was performed on 4 class motor imagery data sets consisted of 8 subjects and two sessions. Spatial filters and classifiers were built on the first session and employed on second session. For estimating ICA and ISP data sets were pre-filtered with same settings. Evaluation and estimation of CSP is detailed in [2]. It should be noted that ISP has only one projection matrix and utilized 8 components which was significantly less than (full) 22 components of ICA and 24 CSP projections. The performance of ISP was found to be marginally better than ICA and slightly worse in comparison with CSP (see Figure ​Figure1).1). However, ISP has the potential of improving further its overall performance by the optimization of infomax. This can be achieved by incorporating prior information available in the form of distinct clusters as constraint in infomax cost function. Therefore, ICA decomposition can be performed by minimizing the information between clusters while at the same time maximizing information of individual components of each cluster. The work in this direction is in progress. Figure 1

Localization of Latent Epileptic Activities Using Spatio-Temporal Independent Component Analysis of fMRI Data

Localizing interictal epileptic activities is a difficult problem in clinical practice. We report a novel noninvasive technique, resting functional magnetic resonance imaging (fMRI) with spatio-temporal independent component analysis (ICA), for localizing interictal epileptic activities. First, the fMRI data is separated into independent spatial patterns by spatial-ICA, and the patterns with Z-values larger than a threshold are selected as the potential spatial patterns of the epileptic activities. Second, the temporal series of the active points in the selected patterns are separated by temporal-ICA, and the component with the biggest Gaussian deviation (kurtosis) is selected as the representative of the epileptic discharge activity in a sub-region. Finally, those spatial sub-regions, which have distinct epileptic discharge activities confirmed by temporal-ICA are considered as the epileptic foci. This method was applied to fMRI data of six epileptic patients, and the results are consistent with the clinical assessment. Though more studies are required to validate this technique, the above preliminary results demonstrate the potential of using the resting fMRI with spatio-temporal ICA to detect and localize latent epileptic activities.