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 ad hoc 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. Furthermore, the implementations described here can be of significant interest to expert bioinformaticians or R developers. Therefore, our goals can be summarized as: (i) description of a complete methodology for the analysis of large datasets by combining capabilities of diverse R packages, (ii) presentation of their application through a virtual R laboratory (RvLab) that makes execution of complex functions and visualization of results easy and readily available to the end-user.

New information

In this paper, the novelty stems from implementations of parallel methodologies which rely on the processing of data on different levels of abstraction and the availability of these processes through an integrated portal. Parallel implementation R packages, such as the pbdMPI (Programming with Big Data – Interface to MPI) package, are used to implement Single Program Multiple Data (SPMD) parallelization on primitive mathematical operations, allowing for interplay with functions of the vegan package. The dplyr and RPostgreSQL R packages are further integrated offering connections to dataframe like objects (databases) as secondary storage solutions whenever memory demands exceed available RAM resources.

The RvLab is running on a PC cluster, using version 3.1.2 (2014-10-31) on a x86_64-pc-linux-gnu (64-bit) platform, and offers an intuitive virtual environmet interface enabling users to perform analysis of ecological and microbial communities based on optimized vegan functions.

A beta version of the RvLab is available after registration at: https://portal.lifewatchgreece.eu/

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. In particular, the deep pelagic ocean--the largest biome on Earth--is chronically under-represented in global databases of marine biodiversity.

METHODOLOGY/PRINCIPAL FINDINGS

We use data from the Ocean Biogeographic Information System to plot the position in the water column of ca 7 million records of marine species occurrences. Records from relatively shallow waters dominate this global picture of recorded marine biodiversity. In addition, standardising the number of records from regions of the ocean differing in depth reveals that regardless of ocean depth, most records come either from surface waters or the sea bed. Midwater biodiversity is drastically under-represented.

CONCLUSIONS/SIGNIFICANCE

The deep pelagic ocean is the largest habitat by volume on Earth, yet it remains biodiversity's big wet secret, as it is hugely under-represented in global databases of marine biological records. Given both its value in the provision of a range of ecosystem services, and its vulnerability to threats including overfishing and climate change, there is a pressing need to increase our knowledge of Earth's largest ecosystem.

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. Two modifications were proposed for BQI (i.e., BQI) (1) the removal of the scaling term (i.e., BQI(W)), and (2) the replacement of the scaling term by different scaling term (i.e., BQI(ES)). Both modified BQIs were largely independent of sampling effort. Variability was slightly lower for BQI(W) than for BQI(ES). BQI was highly correlated with BQI(W) and with BQI(ES) both in the Southern Baltic and in the Gulf of Lions. However, the proportions of stations, which were not attributed the same ecological quality status (EcoQ) when using BQI and its two modified forms were always high. Differences in ecological classification were mostly due to the scales used to infer EcoQ. Based on this study we recommend to use BQI(ES) in future studies because it apparently constitutes the best compromise in (1) being independent of sampling effort, (2) limiting the variability in computation in relation with sampling effort, (3) being correlated with BQI and corresponding EcoQ.

Ammonia toxicity as a criterion for the evaluation of larval quality in the prawn Macrobrachium rosenbergii

The feasibility of a short-term ammonia toxicity test as an evaluation criterion for larval quality was assessed in three trials. In each one, Macrobrachium rosenbergii larvae originating from the same spawn were nutritionally differentiated in two groups by feeding them either a nutrient-rich (Artemia nauplii enriched for 24 h with n-3 highly unsaturated fatty acids (HUFA) and ascorbic acid (AA)) or a nutrient-poor diet (Artemia nauplii starved for 24 h). Throughout their development, larvae from both treatments were exposed during 24 h to six concentrations of total ammonia (NH(4)(+)+NH(3)) and a control (no ammonia added). Based on mortality rates, the median lethal concentration for 50% of the population (LC(50)) was estimated. As expected from earlier work, larvae fed the optimal diet presented higher n-3 HUFA and AA contents as well as higher growth and metamorphosis rates. From the moment the effect of diet quality was analytically detectable in the tissues of the larvae, the ammonia test was able to distinguish both groups of larvae. Differences in ammonia tolerance were observed as early as larval stage 4 and remained evident throughout larval development. The short-term ammonia toxicity test proved to be a valuable, sensitive and reproducible criterion for the establishment of larval quality.