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Right on track?
(2019)
Satellite telemetry is an increasingly utilized technology in wildlife research, and current devices can track individual animal movements at unprecedented spatial and temporal resolutions. However, as we enter the golden age of satellite telemetry, we need an in-depth understanding of the main technological, species-specific and environmental factors that determine the success and failure of satellite tracking devices across species and habitats. Here, we assess the relative influence of such factors on the ability of satellite telemetry units to provide the expected amount and quality of data by analyzing data from over 3,000 devices deployed on 62 terrestrial species in 167 projects worldwide. We evaluate the success rate in obtaining GPS fixes as well as in transferring these fixes to the user and we evaluate failure rates. Average fix success and data transfer rates were high and were generally better predicted by species and unit characteristics, while environmental characteristics influenced the variability of performance. However, 48% of the unit deployments ended prematurely, half of them due to technical failure. Nonetheless, this study shows that the performance of satellite telemetry applications has shown improvements over time, and based on our findings, we provide further recommendations for both users and manufacturers.
The new generation of solar instruments provides better spectral, spatial, and temporal resolution for a better understanding of the physical processes that take place on the Sun. Multiple-component profiles are more commonly observed with these instruments. Particularly, the He i 10830 triplet presents such peculiar spectral profiles, which give information on the velocity and magnetic fine structure of the upper chromosphere. The purpose of this investigation is to describe a technique to efficiently fit the two blended components of the He i 10830 triplet, which are commonly observed when two atmospheric components are located within the same resolution element. The observations used in this study were taken on 2015 April 17 with the very fast spectroscopic mode of the GREGOR Infrared Spectrograph (GRIS) attached to the 1.5-m GREGOR solar telescope, located at the Observatorio del Teide, Tenerife, Spain. We apply a double-Lorentzian fitting technique using Levenberg-Marquardt least-squares minimization. This technique is very simple and much faster than inversion codes. Line-of-sight Doppler velocities can be inferred for a whole map of pixels within just a few minutes. Our results show sub-and supersonic downflow velocities of up to 32 km s(-1) for the fast component in the vicinity of footpoints of filamentary structures. The slow component presents velocities close to rest. (C) 2016 WILEY-VCH Verlag GmbH& Co. KGaA, Weinheim
Improved measurements of the photospheric and chromospheric three-dimensional magnetic and flow fields are crucial for a precise determination of the origin and evolution of active regions. We present an illustrative sample of multi-instrument data acquired during a two-week coordinated observing campaign in August 2015 involving, among others, the GREGOR solar telescope (imaging and near-infrared spectroscopy) and the space missions Solar Dynamics Observatory (SDO) and Interface Region Imaging Spectrograph (IRIS). The observations focused on the trailing part of active region NOAA 12396 with complex polarity inversion lines and strong intrusions of opposite polarity flux. The GREGOR Infrared Spectrograph (GRIS) provided Stokes IQUV spectral profiles in the photospheric Si i.1082.7 nm line, the chromospheric He I lambda 1083.0 nm triplet, and the photospheric Ca I lambda 1083.9 nm line. Carefully calibrated GRIS scans of the active region provided maps of Doppler velocity and magnetic field at different atmospheric heights. We compare quick-look maps with those obtained with the " Stokes Inversions based on Response functions" (SIR) code, which furnishes deeper insight into the magnetic properties of the region. We find supporting evidence that newly emerging flux and intruding opposite polarity flux are hampering the formation of penumbrae, i.e., a penumbra fully surrounding a sunspot is only expected after cessation of flux emergence in proximity to the sunspots. (C) 2016 WILEY-VCH Verlag GmbH& Co.KGaA, Weinheim
The surface shear viscosity of a myelin mimetic Langmuir monolayer is investigated upon adsorption of myelin basic protein (MBP). We measure an increase of the surface shear viscosity at picomolar concentrations of the protein, suggesting that the globular conformation of MBP changes upon adsorption at the monolayer. The conformational change enables hydrodynamic interactions of the proteins, with a typical separation of hundreds of nanometers. This unfolding is essential for the compactification of the myelin sheath, serving an enhanced saltatory signal transduction in vertebrates. The viscometry used extends the sensitivity of standard surface viscometers toward lower viscosities
Moving in the Anthropocene
(2018)
Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.
Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data
(2018)
Ground based gamma-ray observations with Imaging Atmospheric Cherenkov Telescopes (IACTs) play a significant role in the discovery of very high energy (E > 100 GeV) gamma-ray emitters. The analysis of IACT data demands a highly efficient background rejection technique, as well as methods to accurately determine the position of its source in the sky and the energy of the recorded gamma-ray. We present results for background rejection and signal direction reconstruction from first studies of a novel data analysis scheme for IACT measurements. The new analysis is based on a set of Convolutional Neural Networks (CNNs) applied to images from the four H.E.S.S. phase-I telescopes. As the H.E.S.S. cameras pixels are arranged in a hexagonal array, we demonstrate two ways to use such image data to train CNNs: by resampling the images to a square grid and by applying modified convolution kernels that conserve the hexagonal grid properties. The networks were trained on sets of Monte-Carlo simulated events and tested on both simulations and measured data from the H.E.S.S. array. A comparison between the CNN analysis to current state-of-the-art algorithms reveals a clear improvement in background rejection performance. When applied to H.E.S.S. observation data, the CNN direction reconstruction performs at a similar level as traditional methods. These results serve as a proof-of-concept for the application of CNNs to the analysis of events recorded by IACTs. (C) 2018 Published by Elsevier B.V.
In Germany, active bat rabies surveillance was conducted between 1993 and 2012. A total of 4546 oropharyngeal swab samples from 18 bat species were screened for the presence of EBLV-1- , EBLV-2- and BBLV-specific RNA. Overall, 0 center dot 15% of oropharyngeal swab samples tested EBLV-1 positive, with the majority originating from Eptesicus serotinus. Interestingly, out of seven RT-PCR-positive oropharyngeal swabs subjected to virus isolation, viable virus was isolated from a single serotine bat (E. serotinus). Additionally, about 1226 blood samples were tested serologically, and varying virus neutralizing antibody titres were found in at least eight different bat species. The detection of viral RNA and seroconversion in repeatedly sampled serotine bats indicates long-term circulation of the virus in a particular bat colony. The limitations of random-based active bat rabies surveillance over passive bat rabies surveillance and its possible application of targeted approaches for future research activities on bat lyssavirus dynamics and maintenance are discussed.
In Germany, active bat rabies surveillance was conducted between 1993 and 2012. A total of 4546 oropharyngeal swab samples from 18 bat species were screened for the presence of EBLV-1- , EBLV-2- and BBLV-specific RNA. Overall, 0 center dot 15% of oropharyngeal swab samples tested EBLV-1 positive, with the majority originating from Eptesicus serotinus. Interestingly, out of seven RT-PCR-positive oropharyngeal swabs subjected to virus isolation, viable virus was isolated from a single serotine bat (E. serotinus). Additionally, about 1226 blood samples were tested serologically, and varying virus neutralizing antibody titres were found in at least eight different bat species. The detection of viral RNA and seroconversion in repeatedly sampled serotine bats indicates long-term circulation of the virus in a particular bat colony. The limitations of random-based active bat rabies surveillance over passive bat rabies surveillance and its possible application of targeted approaches for future research activities on bat lyssavirus dynamics and maintenance are discussed.
Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area
) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.