Reconstructing Velocities of Migrating Birds from Weather Radar – A Case Study in Computational Sustainability

Bird migration occurs at the largest of global scales, but monitoring such movements can be challenging. In the US there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR-88D network of US weather radars hold valuable and detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data

Reconstructing Velocities of Migrating Birds from Weather Radar – A Case Study in Computational Sustainability

Bird migration occurs at the largest of global scales, but monitoring such movements can be challenging. In the US there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR-88D network of US weather radars hold valuable and detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data

Morning flight behavior of nocturnally migrating birds along the western basin of Lake Erie

Many species of birds that normally migrate during the night have been observed engaging in so‐called morning flights during the early morning. The results of previous studies have supported the hypothesis that one function of morning flights is to compensate for wind drift that birds experienced during the night. Our objective was to further explore this hypothesis in a unique geographic context. We determined the orientation of morning flights along the southern shore of Lake Erie\u27s western basin during the spring migrations of 2016 and 2017. This orientation was then compared to the observed orientation of nocturnal migration. Additionally, the orientation of the birds engaged in morning flights following nights with drifting winds was compared to that of birds following nights with non‐drifting winds. The morning flights of most birds at our observation site were oriented to the west‐northwest, following the southern coast of Lake Erie. Given that nocturnal migration was oriented generally east of north, the orientation of morning flight necessarily reflected compensation for accumulated, seasonal wind drift resulting from prevailingly westerly winds. However, the orientation of morning flights was similar following nights with drifting and non‐drifting winds, suggesting that birds on any given morning were not necessarily re‐orienting as an immediate response to drift that occurred the previous night. Given the topographical characteristics of our observation area, the west‐northwest movement of birds in our study is likely best explained as a more complex interaction that could include some combination of compensation for wind drift, a search for suitable stopover habitat, flying in a direction that minimizes any loss in progressing northward toward the migratory goal, and avoidance of a lake crossing

Robust sound event detection in bioacoustic sensor networks

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 201

On computational models of animal movement behaviour

Finding structures and patterns in animal movement data is essential towards understanding a variety of behavioural phenomena, as well as shedding light into the relationships between animals among conspecifics and across different taxa with respect to their environments. The recent advances in the field of computational intelligence coupled with the proliferation of low-cost telemetry devices have made the gathering and analyses of behavioural data of animals in their natural habitat and in a wide range of context possible with aid of devices such as Global Positioning System (GPS). The sensory input that animals receive from their environment, and the corresponding motor output, as well as the neural basis of this relationship most especially as it affects movement, encode a lot of information regarding the welfare and survival of these animals and other organisms in nature's ecosystem. This has huge implications in the area of biodiversity monitoring, global health and understanding disease progression. Encoding, decoding and quantifying these functional relationships however can be challenging, boring and labour intensive. Artificial intelligence holds promise in solving some of these problems and even stand to benefit as understanding natural intelligence for instance can aid in the advancement of artificial intelligence. In this thesis, I investigate and propose several computational methods leveraging information theoretic metrics and also modern machine learning methods including supervised, unsupervised and a novel combination of both towards understanding, predicting, forecasting and quantifying a variety of animal movement phenomena at different time scales across different taxa and species. Most importantly the models proposed in this thesis tackle important problems bordering on human and animal welfare as well as their intersection. Crucially, I investigate several information theoretic metrics towards mining animal movement data, after which I propose machine learning and statistical techniques for automatically quantifying abnormal movement behaviour in sheep with Batten disease using unsupervised methods. In addition, I propose a predictive model capable of forecasting migration patterns in Turkey vulture as well as their stop-over decisions using bidirectional recurrent neural networks. And finally, I propose a model of sheep movement behaviour in a flock leveraging insights in cognitive neuroscience with modern deep learning models. Overall, the models of animal movement behaviour developed in this thesis are useful to a wide range of scientists in the field of neuroscience, ethology, veterinary science, conservation and public health. Although these models have been designed for understanding and predicting animal movement behaviour, in a lot of cases they scale easily into other domains such as human behaviour modelling with little modifications. I highlight the importance of continuous research in developing computational models of animal movement behaviour towards improving our understanding of nature in relation to the interaction between animals and their environments

Glaciers and the Polar Environment

Glaciers and Polar regions provide important clues to understanding the past and present status of the Earth system, as well as to predict future forms of our planet. In particular, Antarctica, composed of an ice-covered continent in its center and the surrounding Sothern Ocean, has been gradually investigated during the last half century by all kinds of scientific branches; bioscience, physical sciences, geoscience, oceanography, environmental studies, together with technological components. This book covers topics on the recent development of all kinds of scientific research on glaciers and Antarctica, in the context of currently on-going processes in the extreme environment in polar regions