Sensitive aerial hearing within a noisy nesting soundscape in a deep-diving seabird, the common murre Uria aalge

Diving seabirds face a combination of sound exposure in marine and terrestrial environments due to increasing human encroachment on coastal ecosystems. Yet the sound-sensitivity and sensory ecology of this threatened group of animals is largely unknown, complicating effective management and conservation. Here, we characterize aspects of the acoustic ecology of the common murre Uria aalge, one of the deepest diving alcid seabirds. Electrophysiological aerial hearing thresholds were measured for 12 wild, nesting individuals and compared to conspecific vocalizations and short-term aerial soundscape dynamics of their cliff nesting habitat. Auditory responses were measured from 0.5 to 6 kHz, with a lowest mean threshold of 30 dB at 2 kHz and generally sensitive hearing from 1 to 3.5 kHz. The short-term murre nesting soundscape contained biotic sounds from con- and heterospecific avifauna; broadband sounds levels of 56-69 dB re: 20 µPa rms (0.1-10 kHz) were associated with both diel and tidal-cycle factors. Five murre vocalization types showed dominant spectral emphasis at or below the region of best hearing. Common murre hearing appears to be less sensitive than a related alcid, the Atlantic puffin Fratercula arctica, but more sensitive than other non-alcid diving birds described to date, suggesting that adaptations for deep diving have not caused a loss of the species’ hearing ability above water. Overall, frequencies of common murre hearing and vocalization overlap with many anthropogenic noise sources, indicating that the species is susceptible to disturbance from a range of noise types

Identification of candidate pelagic marine protected areas through a seabird seasonal-, multispecific- and extinction risk-based approach

With increasing pressure on the oceans from environmental change, there has been a global call for improved protection of marine ecosystems through the implementation of marine protected areas (MPAs). Here, we used species distribution modelling (SDM) of tracking data from 14 seabird species to identify key marine areas in the southwest Atlantic Ocean, valuing areas based on seabird species occurrence, seasonality and extinction risk. We also compared overlaps between the outputs generated by the SDM and layers representing important human threats (fishing intensity, ship density, plastic and oil pollution, ocean acidification), and calculated loss in conservation value using fishing and ship density as cost layers. The key marine areas were located on the southern Patagonian Shelf, overlapping extensively with areas of high fishing activity, and did not change seasonally, while seasonal areas were located off south and southeast Brazil and overlapped with areas of high plastic pollution and ocean acidification. Non-seasonal key areas were located off northeast Brazil on an area of high biodiversity, and with relatively low human impacts. We found support for the use of seasonal areas depending on the seabird assemblage used, because there was a loss in conservation value for the seasonal compared to the non-seasonal approach when using ‘cost’ layers. Our approach, accounting for seasonal changes in seabird assemblages and their risk of extinction, identified additional candidate areas for incorporation in the network of pelagic MPAs

Six pelagic seabird species of the North Atlantic engage in a fly-and-forage strategy during their migratory movements

Funding Information: We thank all the fieldworkers for their hard work collecting data. Funding for this study was provided by the Norwegian Ministry for Climate and the Environment, the Norwegian Ministry of Foreign Affairs and the Norwegian Oil and Gas Association along with 8 oil companies through the SEATRACK project (www. seapop. no/ en/ seatrack). Fieldwork in Norwegian colonies (incl. Svalbard and Jan Mayen) was supported by the SEAPOP program (www.seapop.no, grant no. 192141). The French Polar Institute (IPEV project 330 to O.C.) supported field operation for Kongsfjord kittiwakes. The work on the Isle of May was also supported by the Natural Environment Research Council (Award NE/R016429/1 as part of the UK-SCaPE programme delivering National Capability). We thank Maria Bogdanova for field support and data processing. Finally, we thank 3 anonymous reviewers for their help improving the first version of the manuscript.Peer reviewedPublisher PD

Identification of candidate pelagic marine protected areas through a seabird seasonal-, multispecific- and extinction risk-based approach

With increasing pressure on the oceans from environmental change, there has been a global call for improved protection of marine ecosystems through the implementation of marine protected areas (MPAs). Here, we used species distribution modelling (SDM) of tracking data from 14 seabird species to identify key marine areas in the southwest Atlantic Ocean, valuing areas based on seabird species occurrence, seasonality and extinction risk. We also compared overlaps between the outputs generated by the SDM and layers representing important human threats (fishing intensity, ship density, plastic and oil pollution, ocean acidification), and calculated loss in conservation value using fishing and ship density as cost layers. The key marine areas were located on the southern Patagonian Shelf, overlapping extensively with areas of high fishing activity, and did not change seasonally, while seasonal areas were located off south and southeast Brazil and overlapped with areas of high plastic pollution and ocean acidification. Non-seasonal key areas were located off northeast Brazil on an area of high biodiversity, and with relatively low human impacts. We found support for the use of seasonal areas depending on the seabird assemblage used, because there was a loss in conservation value for the seasonal compared to the non-seasonal approach when using ‘cost’ layers. Our approach, accounting for seasonal changes in seabird assemblages and their risk of extinction, identified additional candidate areas for incorporation in the network of pelagic MPAs

Global assessment of marine plastic exposure risk for oceanic birds

Plastic pollution is distributed patchily around the world’s oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species

Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders

Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict “healthy” brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk. © 2020, The Author(s)

Tensor dropout for robust learning

CNNs achieve high levels of performance by leveraging deep, over-parametrized neural architectures, trained on large datasets. However, they exhibit limited generalization abilities outside their training domain and lack robustness to corruptions such as noise and adversarial attacks. To improve robustness and obtain more computationally and memory efficient models, better inductive biases are needed. To provide such inductive biases, tensor layers have been successfully proposed to leverage multi-linear structure through higher-order computations. In this paper, we propose tensor dropout, a randomization technique that can be applied to tensor factorizations, such as those parametrizing tensor layers. In particular, we study tensor regression layers, parametrized by low-rank weight tensors and augmented with our proposed tensor dropout. We empirically show that our approach improves generalization for image classification on ImageNet and CIFAR-100. We also establish state-of-the-art accuracy for phenotypic trait prediction on the largest available dataset of brain MRI (U.K. Biobank), where multi-linear structure is paramount. In all cases, we demonstrate superior performance and significantly improved robustness, both to noisy inputs and to adversarial attacks. We establish the theoretical validity of our approach and the regularizing effect of tensor dropout by demonstrating the link between randomized tensor regression with tensor dropout and deterministic regularized tensor regression. © 2007-2012 IEEE

Robust optical autofocus system utilizing neural networks trained for extended range and time-course and automated multiwell plate imaging including single molecule localization microscopy.

We present a robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, e.g. due to changes in temperature or mechanical drift. It is also useful for automated slide scanning or multiwell plate imaging where the sample(s) to be imaged may not be in the same horizontal plane throughout the image data acquisition. To address the impact of (thermal or mechanical) fluctuations over time in the optical autofocus system itself, we utilise a convolutional neural network (CNN) that is trained over multiple days to account for such fluctuations. To address the trade-off between axial precision and range of the autofocus, we implement orthogonal optical readouts with separate CNN training data, thereby achieving an accuracy well within the 600 nm depth of field of our 1.3 numerical aperture objective lens over a defocus range of up to approximately +/-100 μm. We characterise the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localisation microscopy. This article is protected by copyright. All rights reserved