Habitat ecology and food and feeding of the herring bow crab Varuna litterata (Fabricius, 1798) of Cochin backwaters, Kerala, India

Habitat ecology and food and feeding of the herring bow crab, Varuna litterata of Cochin Backwaters, Kerala, India were investigated for a period of one year (April 2011-March 2012). Among the 15 stations surveyed, the crabs were found to occur only in 4 stations, which had a close proximity to the sea. Sediment analysis of the stations revealed that the substratum of these stations is sandy in nature and is rich in organic carbon content (0.79% to 1.07%). These estuarine crabs is euryhaline and are found to be distributed in areas with a sandy substratum, higher organic carbon content and more tidal influx. The stomach contents analysis of crabs examined showed that their diet included crustacean remains, plants, sand and debris, fishes, miscellaneous group and unidentified matter. In adults and sub-adults, crustaceans formed the dominant food group, while in juveniles, sand and debris formed the dominant group. From the present study, V. litterata was found to be a predatory omnivore capable of ingesting both animal and plant tissues

Brachyuran Crabs of Cochin Backwaters- Diversity, Habitat Ecology and Systematics, with special emphasis to Genus Scylla

Among the decapod crustaceans, brachyuran crabs or the true crabs occupy a very significant position due to their ecological and economic value. Crabs support a sustenance fishery in India, even though their present status is not comparable to that of shrimps and lobsters. They are of great demand in the domestic market as well as in the foreign markets. In addition to this, brachyuran crabs are of great ecological importance. They form the conspicuous members of the mangrove ecosystems and play a significant role in detritus formation, nutrient recycling and dynamics of the ecosystem. Considering all these factors, crabs are often considered to be the keystone species of the mangrove ecosystem. Though several works have been undertaken on brachyuran crabs world –wide as well as within the country, reports on the brachyuran crabs of Kerala waters are very scanty. Most of the studies done on brachyuran fauna were from the east coast of India and a very few works from the west coast. Among the edible crabs, mud crabs belonging to genus Scylla forms the most important due to their large size and taste. They are being exported on a large scale to the foreign markets like Singapore, Malaysia and Hong Kong. Kerala is the biggest supplier of live mud crabs and Chennai is the major centre of live mud crab export. However, there exists considerable confusion regarding the identification of mud crabs because of the subtle morphological differences between the species.In this context, an extensive study was undertaken on the brachyuran fauna of Cochin Backwaters, Kerala, India, to have a basic knowledge on their diversity, habitat preference and systematics. The study provides an attempt to resolve the confusion pertaining in the species identification of mud crabs belonging to Genus Scylla. Diversity study revealed the occurrence of 23 species of brachyuran crabs belonging to 16 genera and 8 families in the study area Cochin Backwaters. Among the families, the highest number of species was recorded from Family Portunidae .Among the 23 crab species enlisted from the Cochin backwaters, 5 species are of commercial importance and contribute a major share to the crustacean fishery of the Cochin region. It was observed that, the Cochin backwaters are invaded by certain marine migrant species during the Post monsoon and Pre monsoon periods and they are found to disappear with the onset of monsoon. The study reports the occurrence of the ‘herring bow crab’ Varuna litterata in the Cochin backwaters for the first time. Ecological studies showed that the substratum characteristics influence the occurrence, distribution and abundance of crabs in the sampling stations rather than water quality parameters. The variables which affected the crab distribution the most were Salinity, moisture content in the sediment, organic carbon and the sediment texture. Besides the water and sediment quality parameters, the most important factor influencing the distribution of crabs is the presence of mangroves. The study also revealed that most of the crabs encountered from the study area preferred a muddy substratum, with high organic carbon content and high moisture content. In the present study, an identification key is presented for the brachyuran crabs occurring along the study area the Cochin backwaters and the associated mangrove patches, taking into account the morphological characters coupled with the structure of third maxillipeds, first pleopods of males and the shape of male abdomen. Morphological examination indicated the existence of a morphotype which is comparable with the morphological features of S. tranquebarica, the morphometric study and the molecular analyses confirmed the non existence of S. tranquebarica in the Cochin backwaters.

Bacterial ecology of ancient Saharan salt-enrichment ponds at Teguidda-n-Tessoumt

Little is known about the bacterial ecology of evaporative salt-mining sites (salterns) of which Teguidda-n-Tessoumt at the fringe of the West-African Saharan desert in Niger is a spectacular example with its many-centuries-old and very colorful evaporation ponds. During the different enrichment steps of the salt produced as a widely traded feed supplement for cattle, animal manure is added to the crude brine, which is then desiccated and repeatedly crystallized. This study describes the dominant Bacteria and Archaea communites in the brine from the evaporation ponds and the soil from the mine, which were determined by PCR-DGGE of 16S rDNA. Correspondence analysis of the DGGE-community fingerprints revealed a change in community structure of the brine samples during the sequential evaporation steps which was, however, unaffected by the brine's pH and electric conductivity (EC). The Archaea community was dominated by a phylogenetically diverse group of methanogens, while the Bacteria community was dominated by gamma proteobacteria. Microorganisms contained in the purified salt product have the potential to be broadly disseminated and are fed to livestock across the region. In this manner, the salt mines represent an intriguing example of long-term human activity that has contributed to the continual selection, cultivation, and dissemination of cosmopolitan microorganisms.

Methodische Grundlagen zur Bewahrung und Wiederherstellung großräumig funktionsfähiger ökologischer Beziehungen in der räumlichen Umweltplanung

Mit der Verwirklichung ,Ökologischer Netzwerke‘ werden Hoffnungen zum Stopp des Verlustes der biologischen Vielfalt verknüpft. Sowohl auf gesamteuropäischer Ebene (Pan-European Ecological Network - PEEN) als auch in den einzelnen Staaten entstehen Pläne zum Aufbau von Verbundsystemen. Im föderalen Deutschland werden kleinmaßstäbliche Biotopverbundplanungen auf Landesebene aufgestellt; zum nationalen Biotopverbund bestehen erste Konzepte. Die vorliegende Arbeit ist auf diese überörtlichen, strategisch vorbereitenden Planungsebenen ausgerichtet. Ziele des Verbunds sind der Erhalt von Populationen insbesondere der gefährdeten Arten sowie die Ermöglichung von Ausbreitung und Wanderung. Aufgrund fehlender Datengrundlagen zu den Arten und Populationen ist es nicht ohne weiteres möglich, die Konzepte und Modelle der Populationsökologie in die überörtlichen Planungsebenen zu übertragen. Gemäß der o.g. Zielstellungen sollte sich aber die Planung von Verbundsystemen an den Ansprüchen der auf Verbund angewiesenen Arten orientieren. Ziel der Arbeit war die Entwicklung einer praktikablen GIS-gestützten Planungshilfe zur größtmöglichen Integration ökologischen Wissens unter der Bedingung eingeschränkter Informationsverfügbarkeit. Als Grundlagen dazu werden in Übersichtsform zunächst die globalen, europäisch-internationalen und nationalen Rahmenbedingungen und Anforderungen bezüglich des Aufbaus von Verbundsystemen zusammengestellt. Hier sind die Strategien zum PEEN hervorzuheben, die eine Integration ökologischer Inhalte insbesondere durch die Berücksichtigung räumlich-funktionaler Beziehungen fordern. Eine umfassende Analyse der landesweiten Biotopverbundplanungen der BRD zeigte die teilweise erheblichen Unterschiede zwischen den Länderplanungen auf, die es aktuell nicht ermöglichen, ein schlüssiges nationales Konzept zusammenzufügen. Nicht alle Länder haben landesweite Biotopverbundplanungen und Landeskonzepte, bei denen dem geplanten Verbund die Ansprüche von Arten zugrunde gelegt werden, gibt es nur ansatzweise. Weiterhin wurde eine zielgerichtete Eignungsprüfung bestehender GIS-basierter Modelle und Konzepte zum Verbund unter Berücksichtigung der regelmäßig in Deutschland verfügbaren Datengrundlagen durchgeführt. Da keine integrativen regelorientierten Ansätze vorhanden waren, wurde der vektorbasierte Algorithmus HABITAT-NET entwickelt. Er arbeitet mit ,Anspruchstypen‘ hinsichtlich des Habitatverbunds, die stellvertretend für unterschiedliche ökologische Gruppen von (Ziel-) Arten mit terrestrischer Ausbreitung stehen. Kombiniert wird die Fähigkeit zur Ausbreitung mit einer Grobtypisierung der Biotopbindung. Die wichtigsten Grundlagendaten bilden die jeweiligen (potenziellen) Habitate von Arten eines Anspruchstyps sowie die umgebende Landnutzung. Bei der Bildung von ,Lebensraumnetzwerken‘ (Teil I) werden gestufte ,Funktions- und Verbindungsräume‘ generiert, die zu einem räumlichen System verknüpft sind. Anschließend kann die aktuelle Zerschneidung der Netzwerke durch Verkehrstrassen aufgezeigt werden, um darauf aufbauend prioritäre Abschnitte zur Wiedervernetzung zu ermitteln (Teil II). Begleitend wird das Konzept der unzerschnittenen Funktionsräume (UFR) entworfen, mit dem die Indikation von Habitatzerschneidung auf Landschaftsebene möglich ist. Diskutiert werden schließlich die Eignung der Ergebnisse als kleinmaßstäblicher Zielrahmen, Tests zur Validierung, Vergleiche mit Verbundplanungen und verschiedene Setzungen im GIS-Algorithmus. Erläuterungen zu den Einsatzmöglichkeiten erfolgen beispielsweise für die Bereiche Biotopverbund- und Landschaftsplanung, Raumordnung, Strategische Umweltprüfung, Verkehrswegeplanung, Unterstützung des Konzeptes der Lebensraumkorridore, Kohärenz im Schutzgebietssystem NATURA 2000 und Aufbau von Umweltinformationssystemen. Schließlich wird ein Rück- und Ausblick mit der Formulierung des weiteren Forschungsbedarfs verknüpft.

The Political Ecology of Salmon Aquaculture in Chile

Every German consumes per year, 15% is salmon, which is the third most popular fish in Germany after Alaska-Seelachs and Hering (Keller/Kress 2013: 9). But where does the salmon that ends up on our plates every 6th time we eat fish come from? There's no obligation for producers to declare the origin of their fish products, but if they do so, the latin name of the fish, catching method and catch area should be declared. Salmon, of which about 40% are captured in the wild and the rest brought up in aquacultures, could then be declared as follows: Salmon (salmo salar), aquaculture from Chile. Without any doubt, this makes consumption more transparent, but the standards of production – both, social and ecological ones – and the ecological impacts are still kept in the dark.

A Vector Signal Processing Approach to Color

Surface (Lambertain) color is a useful visual cue for analyzing material composition of scenes. This thesis adopts a signal processing approach to color vision. It represents color images as fields of 3D vectors, from which we extract region and boundary information. The first problem we face is one of secondary imaging effects that makes image color different from surface color. We demonstrate a simple but effective polarization based technique that corrects for these effects. We then propose a systematic approach of scalarizing color, that allows us to augment classical image processing tools and concepts for multi-dimensional color signals.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

Vector-Based Integration of Local and Long-Range Information in Visual Cortex

Integration of inputs by cortical neurons provides the basis for the complex information processing performed in the cerebral cortex. Here, we propose a new analytic framework for understanding integration within cortical neuronal receptive fields. Based on the synaptic organization of cortex, we argue that neuronal integration is a systems--level process better studied in terms of local cortical circuitry than at the level of single neurons, and we present a method for constructing self-contained modules which capture (nonlinear) local circuit interactions. In this framework, receptive field elements naturally have dual (rather than the traditional unitary influence since they drive both excitatory and inhibitory cortical neurons. This vector-based analysis, in contrast to scalarsapproaches, greatly simplifies integration by permitting linear summation of inputs from both "classical" and "extraclassical" receptive field regions. We illustrate this by explaining two complex visual cortical phenomena, which are incompatible with scalar notions of neuronal integration.

Improving Multiclass Text Classification with the Support Vector Machine

We compare Naive Bayes and Support Vector Machines on the task of multiclass text classification. Using a variety of approaches to combine the underlying binary classifiers, we find that SVMs substantially outperform Naive Bayes. We present full multiclass results on two well-known text data sets, including the lowest error to date on both data sets. We develop a new indicator of binary performance to show that the SVM's lower multiclass error is a result of its improved binary performance. Furthermore, we demonstrate and explore the surprising result that one-vs-all classification performs favorably compared to other approaches even though it has no error-correcting properties.

Properties of Support Vector Machines

Support Vector Machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed Support Vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of quadratic programming that depends on a regularization parameter. In this paper we study some mathematical properties of support vectors and show that the decision surface can be written as the sum of two orthogonal terms, the first depending only on the margin vectors (which are SVs lying on the margin), the second proportional to the regularization parameter. For almost all values of the parameter, this enables us to predict how the decision surface varies for small parameter changes. In the special but important case of feature space of finite dimension m, we also show that there are at most m+1 margin vectors and observe that m+1 SVs are usually sufficient to fully determine the decision surface. For relatively small m this latter result leads to a consistent reduction of the SV number.

Notes on PCA, Regularization, Sparsity and Support Vector Machines

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We first review previous results for the approximation of a function from discrete data (Girosi, 1998) in the context of Vapnik"s feature space and dual representation (Vapnik, 1995). We apply them to show 1) that a standard regularization functional with a stabilizer defined in terms of the correlation function induces a regression function in the span of the feature space of classical Principal Components and 2) that there exist a dual representations of the regression function in terms of a regularization network with a kernel equal to a generalized correlation function. We then describe the main observation of the paper: the dual representation in terms of the correlation function can be sparsified using the Support Vector Machines (Vapnik, 1982) technique and this operation is equivalent to sparsify a large dictionary of basis functions adapted to the task, using a variation of Basis Pursuit De-Noising (Chen, Donoho and Saunders, 1995; see also related work by Donahue and Geiger, 1994; Olshausen and Field, 1995; Lewicki and Sejnowski, 1998). In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). We discuss the relation between our results, which are about regression, and the different problem of pattern classification.

From Regression to Classification in Support Vector Machines

We study the relation between support vector machines (SVMs) for regression (SVMR) and SVM for classification (SVMC). We show that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain choice of the parameters. In particular our result is that for $epsilon$ sufficiently close to one, the optimal hyperplane and threshold for the SVMC problem with regularization parameter C_c are equal to (1-epsilon)^{- 1} times the optimal hyperplane and threshold for SVMR with regularization parameter C_r = (1-epsilon)C_c. A direct consequence of this result is that SVMC can be seen as a special case of SVMR.

On the Noise Model of Support Vector Machine Regression

Support Vector Machines Regression (SVMR) is a regression technique which has been recently introduced by V. Vapnik and his collaborators (Vapnik, 1995; Vapnik, Golowich and Smola, 1996). In SVMR the goodness of fit is measured not by the usual quadratic loss function (the mean square error), but by a different loss function called Vapnik"s $epsilon$- insensitive loss function, which is similar to the "robust" loss functions introduced by Huber (Huber, 1981). The quadratic loss function is well justified under the assumption of Gaussian additive noise. However, the noise model underlying the choice of Vapnik's loss function is less clear. In this paper the use of Vapnik's loss function is shown to be equivalent to a model of additive and Gaussian noise, where the variance and mean of the Gaussian are random variables. The probability distributions for the variance and mean will be stated explicitly. While this work is presented in the framework of SVMR, it can be extended to justify non-quadratic loss functions in any Maximum Likelihood or Maximum A Posteriori approach. It applies not only to Vapnik's loss function, but to a much broader class of loss functions.

A Unified Framework for Regularization Networks and Support Vector Machines

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.

An Equivalence Between Sparse Approximation and Support Vector Machines

In the first part of this paper we show a similarity between the principle of Structural Risk Minimization Principle (SRM) (Vapnik, 1982) and the idea of Sparse Approximation, as defined in (Chen, Donoho and Saunders, 1995) and Olshausen and Field (1996). Then we focus on two specific (approximate) implementations of SRM and Sparse Approximation, which have been used to solve the problem of function approximation. For SRM we consider the Support Vector Machine technique proposed by V. Vapnik and his team at AT&T Bell Labs, and for Sparse Approximation we consider a modification of the Basis Pursuit De-Noising algorithm proposed by Chen, Donoho and Saunders (1995). We show that, under certain conditions, these two techniques are equivalent: they give the same solution and they require the solution of the same quadratic programming problem.