Coexistence of a generalist owl with its intraguild predator: distance-sensitive or habitat-mediated avoidance?

Intraguild predation is increasingly reported as a population-limiting factor for vertebrate predators. However, long-term coexistence of the intraguild prey with its predator is a common occurrence usually maintained by some form of predator avoidance, which may be achieved through distance-sensitive avoidance (selection of sites as far as possible from the intraguild predator), and/or habitat-mediated avoidance (avoidance of habitats associated with high predation risk). The former is expected when the distribution of the predator is heterogeneous, leaving gaps which can be exploited by the prey, while the latter is expected at high predator densities, when few predation refugia are available. To date, few studies have focused on such switch in predator avoidance under changing scenarios of intraguild predator density. To test this hypothesis, we censused tawny owls (Strix aluco, body mass ∼0.4-0.7 kg) and their intraguild predator, the eagle owl (Bubo bubo, ∼1.5-4 kg), in 12 areas of the Alps. As predicted, tawny owls were indifferent to predator distance in an area of low predation risk, they switched to distance-sensitive avoidance in an area of medium predator density and to habitat-mediated avoidance in an area of high predator density with few available refugia. Actual predation rates were low, but increased with proximity to the intraguild predator nest. Similarly, tawny owl breeding output declined with closeness to an eagle owl nest. Habitat loss associated with predator avoidance translated into population effects, leading to a negative relationship between the densities of the two owl species. The spatial gaps in tawny owl distribution caused by eagle owls indirectly favoured other owl species, resulting in higher diversity of the overall owl community and suggesting that eagle owls acted as keystone predators. Our results suggest that intraguild predation may alter habitat choices and affect density, productivity and guild structure of vertebrate mesopredators. Such effects are probably more common than previously thought. © 2007 The Association for the Study of Animal Behaviour.Peer Reviewe

Survey on deep learning based computer vision for sonar imagery

Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based, approaches for a long time. Over the past 15 years, however, the application of deep learning in this research field has constantly grown. This paper gives a broad overview of past and current research involving deep learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up to four layers outperform conventional methods. The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods.Article number 10515711

Waves generated by rock falls into reservoirs

Since the Vaiont Dam disaster in 1963, there has been an awareness among design engineers of the rockfall problem, where a large landslide into shallow water can generate water waves of appreciable size. The prediction of landslide properties, like size and speed, is qualitative. However, for a given landslide into shallow water, this study aims to provide a quantitative prediction of some important wave properties. Rectangular blocks were dropped vertically into a long horizontal channel of constant width. The leading or first waves generated were mostly unbroken and, although assymmetrical, were similar to solitary waves. Only large, heavy blocks dropped from well above the water surface caused broken waves, these converting into solitary waves at substantial distances downstream. A long oscillatory wave train followed the leading wave, but because its height was usually smaller and subsided at a greater rate than the leading wave, it was not considered to be important. The first wave height is related to the block dimensions and density and its fall height. Subsidence is also studied

Generating Synthetic Sidescan Sonar Snippets Using Transfer-Learning in Generative Adversarial Networks

The training of a deep learning model requires a large amount of data. In case of sidescan sonar images, the number of snippets from objects of interest is limited. Generative adversarial networks (GAN) have shown to be able to generate photo-realistic images. Hence, we use a GAN to augment a baseline sidescan image dataset with synthetic snippets. Although the training of a GAN with few data samples is likely to cause mode collapse, a combination of pre-training using simple simulated images and fine-tuning with real data reduces this problem. However, for sonar data, we show that this approach of transfer-learning a GAN is sensitive to the pre-training step, meaning that the vanishing of the gradients of the GAN's discriminator becomes a critical problem. Here, we demonstrate how to overcome this problem, and thus how to apply transfer-learning to GANs for generating synthetic sidescan snippets in a more robust way. Additionally, in order to further investigate the GAN's ability to augment a sidescan image dataset, the generated images are analyzed in the image and the frequency domain. The work helps other researchers in the field of sonar image processing to augment their dataset with additional synthetic samples