Seasonal variability in the diet of juvenile European catfish, Silurus glanis, in the Arno River (Italy) in Florence

While approaches for assessing the impact of invasive species are advancing, fundamental autoecological information such as feeding preferences is often unavailable for many invasive species. The European catfish, Silurus glanis, is a well-known, high-impact invasive predator; however, there remain considerable gaps in knowledge about its feeding preferences and biotic interactions that limit ecological impact predictions for this predator. This study is an investigation of seasonal variability in this predator’s diet during the juvenile life stage using the Arno River, Italy as a model system. This study identified significant differences across seasons, provided further information on the dietary spectrum of this species, and raised important questions for further study.

DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction

Motivation Accurately predicting the quantitative impact of a substitution on a protein’s molecular function would be a great aid in understanding the effects of observed genetic variants across populations. While this remains a challenging task, new approaches can leverage data from the increasing numbers of comprehensive deep mutational scanning (DMS) studies that systematically mutate proteins and measure fitness. Results We introduce DeMaSk, an intuitive and interpretable method based only upon DMS datasets and sequence homologs that predicts the impact of missense mutations within any protein. DeMaSk first infers a directional amino acid substitution matrix from DMS datasets and then fits a linear model that combines these substitution scores with measures of per-position evolutionary conservation and variant frequency across homologs. Despite its simplicity, DeMaSk has state-of-the-art performance in predicting the impact of amino acid substitutions, and can easily and rapidly be applied to any protein sequence. Availability and implementation https://demask.princeton.edu generates fitness impact predictions and visualizations for any user-submitted protein sequence. Supplementary information Supplementary data are available at Bioinformatics online.

Factors Influencing Ensemble Sensitivity–Based Targeted Observing Predictions at Convection-Allowing Resolutions

Ensemble sensitivity analysis (ESA) is applied to select types of observations, in various locations and in advance of forecast convection, to systematically evaluate the effectiveness of ESA-based observation targeting for 10 convection forecasts. To facilitate the analysis, observing system simulation experiments and perfect models are utilized to generate synthetic targeted observations of temperature and pressure for future assimilation with an ensemble prediction system. Various observation assimilation experiments are carried out to assess the impacts of nonlinearity, covariance localization, and numerical noise on ESA-based observation-impact predictions. It is discovered that localization applied during data assimilation restricts targeted-observation increments onto the forecast responses of composite reflectivity and 3-hourly accumulated precipitation, making impact predictions poor. In addition, numerical noise introduced by nonlinear perturbation evolution tends to reduce the correlations between observed and predicted impacts; small, random-perturbation experiments often yielded similar impacts on forecasts as targeted observations. Nonlinearity also manifests in the observation impacts when comparing targeted observations with nontargeted, randomly chosen observations; random observations have seemingly the same impact on forecasts as targeted observations. The results, under idealized conditions and simplified ensemble configurations, demonstrate that ESA-based targeting for nonlinear convection forecasts may be most applicable at short time scales. Important implications for ESA-based targeting methods employed with real-time ensemble systems are also discussed.

Projection of Future Heat Waves in the United States. Part I: Selecting a Climate Model Subset

The widespread increase in global temperature is driving more frequent and more severe local heatwaves within the contiguous United States (CONUS). General circulation models (GCMs) show increasing, but spatially uneven trends in heatwave properties. However, the wide range of model outputs raises the question of the suitability of this method for indicating the future impacts of heatwaves on human health and well-being. This work examines the fitness of 32 models from CMIP5 and their ensemble median to predict a set of heatwave descriptors across the CONUS, by analyzing their capabilities in the simulation of historical heatwaves during 1950–2005. Then, we use a multi-criteria decision-making tool and rank the overall performance of each model for 10 locations with different climates. We found GCMs have different capabilities in the simulation of historical heatwave characteristics. In addition, we observed similar performances for GCMs over the areas with a partially similar climate. The ensemble model showed better performance in simulation of historical heatwave intensity in some locations, while other individual GCMs represented heatwave time-related components more similar to observations. These results are a step towards the use of contemporary weather models to guide heatwave impact predictions.

Relating Bat Passage Rates to Wind Turbine Fatalities

Wind energy siting to minimize impacts to bats would benefit from impact predictions following pre-construction surveys, but whether pre- or even post-construction activity patterns can predict fatalities remains unknown. We tested whether bat passage rates through rotor-swept airspace differ between groups of wind turbines where bat fatalities were found and not found during next-morning dog searches for fatalities. Passage rates differed significantly and averaged four times higher where freshly killed bats were found in next-morning fatality searches. Rates of near misses and risky flight behaviors also differed significantly between groups of turbines where bats were found and not found, and rate of near misses averaged eight times higher where bat fatalities were found in next-morning searches. Hours of turbine operation averaged significantly higher, winds averaged more westerly, and the moon averaged more visible among turbines where and when bat fatalities were found. Although dogs found only one of four bats seen colliding with turbine blades, they found many more bat fatalities than did human-only searchers at the same wind projects, and our fatality estimates were considerably higher. Our rates of observed bat collisions, adjusted for the rates of unseen collisions, would predict four to seven times the fresh fatalities we found using dogs between two wind projects. Despite markedly improved carcass detection through use of dogs, best estimates of bat fatalities might still be biased low due to crippling bias and search radius bias.

Influence of neighboring small sequence variants on functional impact prediction

Once a suitable reference sequence is generated, genomic differences within a species are often assessed by re-sequencing. Variant calling processes can reveal all differences between two strains, accessions, genotypes, or individuals. These variants can be enriched with predictions about their functional implications based on available structural annotations i.e. gene models. Although these functional impact predictions on a per variant basis are often accurate, some challenging cases require the simultaneous incorporation of multiple adjacent variants into this prediction process. Examples are neighboring variants which modify each others’ functional impact. Neighborhood-Aware Variant Impact Predictor (NAVIP) considers all variants within a given protein coding sequence when predicting the functional consequences. As a proof of concept, variants between the Arabidopsis thaliana accessions Columbia-0 and Niederzenz-1 were annotated. NAVIP is freely available on github: https://github.com/bpucker/NAVIP.