The effects of trawling and primary production on size-structured food webs in seabed ecosystems

Understanding how different drivers shape relationships between abundance and body mass (size spectra) is important for understanding trophic and competitive interactions in food webs and for predicting the effects of human pressures. Here, we sample seabed communities from small polychaetes (1 kg) in the Celtic Sea and the western English Channel to examine how bottom trawling and primary production affect their size spectra and to compare these with predictions from a model that couples predator and detritivore communities. Size spectra were not well approximated by linear fits because of truncation of the size spectra of detritivores. Low primary production resulted in lower abundance of benthic fauna. Bottom trawling reduced the abundance of predators and large detritivores but allowed small detritivores to increase in abundance. These empirical size spectra were partly consistent with predictions from the size spectra model, showing that understanding the structuring of benthic communities requires a consideration of both size and functional group. The findings highlight the need for an ecosystem approach to understanding the effects of exploitation and climate change on marine ecosystems

Complementary Metagenomic Approaches Improve Reconstruction of Microbial Diversity in a Forest Soil.

Soil ecosystems harbor diverse microorganisms and yet remain only partially characterized as neither single-cell sequencing nor whole-community sequencing offers a complete picture of these complex communities. Thus, the genetic and metabolic potential of this "uncultivated majority" remains underexplored. To address these challenges, we applied a pooled-cell-sorting-based mini-metagenomics approach and compared the results to bulk metagenomics. Informatic binning of these data produced 200 mini-metagenome assembled genomes (sorted-MAGs) and 29 bulk metagenome assembled genomes (MAGs). The sorted and bulk MAGs increased the known phylogenetic diversity of soil taxa by 7.2% with respect to the Joint Genome Institute IMG/M database and showed clade-specific sequence recruitment patterns across diverse terrestrial soil metagenomes. Additionally, sorted-MAGs expanded the rare biosphere not captured through MAGs from bulk sequences, exemplified through phylogenetic and functional analyses of members of the phylum Bacteroidetes Analysis of 67 Bacteroidetes sorted-MAGs showed conserved patterns of carbon metabolism across four clades. These results indicate that mini-metagenomics enables genome-resolved investigation of predicted metabolism and demonstrates the utility of combining metagenomics methods to tap into the diversity of heterogeneous microbial assemblages.IMPORTANCE Microbial ecologists have historically used cultivation-based approaches as well as amplicon sequencing and shotgun metagenomics to characterize microbial diversity in soil. However, challenges persist in the study of microbial diversity, including the recalcitrance of the majority of microorganisms to laboratory cultivation and limited sequence assembly from highly complex samples. The uncultivated majority thus remains a reservoir of untapped genetic diversity. To address some of the challenges associated with bulk metagenomics as well as low throughput of single-cell genomics, we applied flow cytometry-enabled mini-metagenomics to capture expanded microbial diversity from forest soil and compare it to soil bulk metagenomics. Our resulting data from this pooled-cell sorting approach combined with bulk metagenomics revealed increased phylogenetic diversity through novel soil taxa and rare biosphere members. In-depth analysis of genomes within the highly represented Bacteroidetes phylum provided insights into conserved and clade-specific patterns of carbon metabolism

Is the Screening Test of the French Version of the Dementia Quality of Life Questionnaire Indispensable?

The aim of this study was to evaluate the usefulness of the screening questions in the French version of the Dementia Quality of Life (DQoL) questionnaire. To assess the psychometric properties of the French DQoL, 155 patients with mild-to-moderate dementia were recruited. Here, we compared the psychometric properties of the instrument between patients who passed the screening test (n = 109) and the whole study population (n = 155). The French DQoL version showed a good test-retest reliability at a 2-week interval (0.95 ≤ intraclass correlation coefficients ≤ 1.0), and an average internal consistency (0.58 ≤ Cronbach's α ≤ 0.87) for the 2 study groups. Significant differences were observed in the 2 groups for 4 dimensions of the DQoL regarding dementia severity (Cornell scale), and for 3 dimensions evaluating depression (MMSE). Convergent validity with the Duke Health Profile revealed many significant correlations between dimensions not only in the 109 patients, but also in the whole study population. Our study demonstrated that patients who failed the screening procedure nonetheless seemed to be able to answer the DQoL questionnaire, the whole study group showing acceptable psychometric properties

To achieve a sustainable blue future, progress assessments must include interdependencies between the sustainable development goals

The Sustainable Development Goals (SDGs) focus on providing society with a sustainable future. Progress toward the goals is being tracked by a series of indicators. These indicators show progress toward individual goals and targets but do not show how success or failure in relation to one goal might affect success or failure in another area. We show how interactions between the oceans and human poverty, hunger, and gender equity are hidden by indicator assessments and how this undermines the capacity of governments and organizations to maximize long-term moves toward sustainability. These findings are important for decision makers who work in the public and private sectors and wish to avoid unforeseen outcomes when implementing sustainability initiatives. Here, we suggest extensions to the current assessment framework to help counteract the identified issues, providing a research agenda for scientists working in all fields of sustainability science

Classes of Multiple Decision Functions Strongly Controlling FWER and FDR

This paper provides two general classes of multiple decision functions where each member of the first class strongly controls the family-wise error rate (FWER), while each member of the second class strongly controls the false discovery rate (FDR). These classes offer the possibility that an optimal multiple decision function with respect to a pre-specified criterion, such as the missed discovery rate (MDR), could be found within these classes. Such multiple decision functions can be utilized in multiple testing, specifically, but not limited to, the analysis of high-dimensional microarray data sets.Comment: 19 page