Optimized R functions for analysis of ecological community data using the R virtual laboratory (RvLab).

Background: Parallel data manipulation using R has previously been addressed by members of the R community, however most of these studies produce solutions that are not readily available to the average R user. Our targeted users, ranging from the expert ecologist/microbiologists to computational biologists, often experience difficulties in finding optimal ways to exploit the full capacity of their computational resources. In addition, improving performance of commonly used R scripts becomes increasingly difficult especially with large datasets.

Biodiversity's big wet secret: the global distribution of marine biological records reveals chronic under-exploration of the deep pelagic ocean.

Background: Understanding the distribution of marine biodiversity is a crucial first step towards the effective and sustainable management of marine ecosystems. Recent efforts to collate location records from marine surveys enable us to assemble a global picture of recorded marine biodiversity. They also effectively highlight gaps in our knowledge of particular marine regions.

Global patterns and predictors of marine biodiversity across taxa.

Global patterns of species richness and their structuring forces have fascinated biologists since Darwin and provide critical context for contemporary studies in ecology, evolution and conservation. Anthropogenic impacts and the need for systematic conservation planning have further motivated the analysis of diversity patterns and processes at regional to global scales. Whereas land diversity patterns and their predictors are known for numerous taxa, our understanding of global marine diversity has been more limited, with recent findings revealing some striking contrasts to widely held terrestrial paradigms.

Performance comparison of two biotic indices measuring the ecological status of water bodies in the Southern Baltic and Gulf of Lions.

Two biotic indices, ATZI Marine Biotic Index (AMBI) and Benthic Quality Index (BQI) have been recently introduced within the EC Water Framework Directive to assess the quality of marine habitats: both are based on sensitivity/tolerance classification and quantitative information on the composition of soft-bottom macrofauna. Their performance, especially with regard to sampling effort was assessed based on two data sets collected in Southern Baltic and one from the Gulf of Lions Mediterranean. AMBI was not affected by sampling effort but BQI was.