Construction of data-driven models to predict the occurrence of planktonic species in the North-Sea

Marine habitat suitability models typically predict the potential distribution of organisms based on basic abiotic variables such as salinity, oxygen concentrations, temperature fluctuations (Gogina & Zettler, 2010) or sediment class information (Degraer et al., 2008; Willems et al., 2008). Recently, Dachs & Méjanelle (2010) claimed that the modification of biota composition due to marine pollution is a factor to be taken into account in marine habitat suitability models. Although the anthropogenic pressure on the environment has been exponentially increasing during the last six decades (Dachs & Méjanelle, 2010), the global effect of human inputs on oceanic phytoplankton remains unknown (Echeveste et al., 2010). A limited number of studies have assessed the impact of anthropogenic stressors on phytoplankton in marine environments at a global level (Faust et al., 2003; Magnusson et al.,2008). In order to fill this knowledge gap, this research tries to determine to what extent pollution data can be used to predict the occurrence of the phytoplanktonic organisms compared to basic abiotic variables. Here we explored this issue by developing classification trees relating physical-chemical variables with the occurrence of the potential harmful toxic algae Odontella sinensis

Per capita interactions and stress tolerance drive stress-induced changes in biodiversity effects on ecosystem functions

Environmental stress changes the relationship between biodiversity and ecosystem functions, but the underlying mechanisms are poorly understood. Because species interactions shape biodiversity-ecosystem functioning relationships, changes in per capita interactions under stress (as predicted by the stress gradient hypothesis) can be an important driver of stress-induced changes in these relationships. To test this hypothesis, we measure productivity in microalgae communities along a diversity and herbicide gradient. On the basis of additive partitioning and a mechanistic community model, we demonstrate that changes in per capita interactions do not explain effects of herbicide stress on the biodiversity-productivity relationship. Instead, assuming that the per capita interactions remain unaffected by stress, causing species densities to only change through differences in stress tolerance, suffices to predict the stress-induced changes in the biodiversity-productivity relationship and community composition. We discuss how our findings set the stage for developing theory on how environmental stress changes biodiversity effects on ecosystem functions

Self-supervised automated wrapper generation for weblog data extraction

Data extraction from the web is notoriously hard. Of the types of resources available on the web, weblogs are becoming increasingly important due to the continued growth of the blogosphere, but remain poorly explored. Past approaches to data extraction from weblogs have often involved manual intervention and suffer from low scalability. This paper proposes a fully automated information extraction methodology based on the use of web feeds and processing of HTML. The approach includes a model for generating a wrapper that exploits web feeds for deriving a set of extraction rules automatically. Instead of performing a pairwise comparison between posts, the model matches the values of the web feeds against their corresponding HTML elements retrieved from multiple weblog posts. It adopts a probabilistic approach for deriving a set of rules and automating the process of wrapper generation. An evaluation of the model is conducted on a dataset of 2,393 posts and the results (92% accuracy) show that the proposed technique enables robust extraction of weblog properties and can be applied across the blogosphere for applications such as improved information retrieval and more robust web preservation initiatives

Intelligent Self-Repairable Web Wrappers

The amount of information available on the Web grows at an incredible high rate. Systems and procedures devised to extract these data from Web sources already exist, and different approaches and techniques have been investigated during the last years. On the one hand, reliable solutions should provide robust algorithms of Web data mining which could automatically face possible malfunctioning or failures. On the other, in literature there is a lack of solutions about the maintenance of these systems. Procedures that extract Web data may be strictly interconnected with the structure of the data source itself; thus, malfunctioning or acquisition of corrupted data could be caused, for example, by structural modifications of data sources brought by their owners. Nowadays, verification of data integrity and maintenance are mostly manually managed, in order to ensure that these systems work correctly and reliably. In this paper we propose a novel approach to create procedures able to extract data from Web sources -- the so called Web wrappers -- which can face possible malfunctioning caused by modifications of the structure of the data source, and can automatically repair themselves.\u