ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning

Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species

Open-source workflow approaches to passive acoustic monitoring of bats

The work was funded by grants to PTM from Carlsberg Semper Ardens Research Projects and the Independent Research Fund Denmark.The affordability, storage and power capacity of compact modern recording hardware have evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non-invasive, cost-effective tool for research and ecological management particularly useful for bats and toothed whales that orient and forage using ultrasonic echolocation. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user-friendly and open access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats. We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open-source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was evaluated against the performance of an expert manual analyst. Workflow performance depended strongly on the signal-to-noise ratio and detection algorithm used: the full deep learning workflow had the best classification accuracy (≤67%) but was computationally too slow for practical large-scale bat PAM. Workflows using PAMGuard's detection module or triggers onboard an SM4BAT or AudioMoth accurately classified up to 47%, 59% and 34%, respectively, of calls to species. Not all workflows included noise sampling critical to estimating changes in detection probability over time, a vital parameter for abundance estimation. The workflow using PAMGuard's detection module was 40 times faster than the full deep learning workflow and missed as few calls (recall for both ~0.6), thus balancing computational speed and performance. We show that complete acoustic detection and classification workflows for bat PAM data can be efficiently automated using open-source software such as PAMGuard and exemplify how detection choices, whether pre- or post-deployment, hardware or software-driven, affect the performance of deep learning classification and the downstream ecological information that can be extracted from acoustic recordings. In particular, understanding and quantifying detection/classification accuracy and the probability of detection are key to avoid introducing biases that may ultimately affect the quality of data for ecological management.Publisher PDFPeer reviewe

Scene analysis in the natural environment

The problem of scene analysis has been studied in a number of different fields over the past decades. These studies have led to a number of important insights into problems of scene analysis, but not all of these insights are widely appreciated. Despite this progress, there are also critical shortcomings in current approaches that hinder further progress. Here we take the view that scene analysis is a universal problem solved by all animals, and that we can gain new insight by studying the problems that animals face in complex natural environments. In particular, the jumping spider, songbird, echolocating bat, and electric fish, all exhibit behaviors that require robust solutions to scene analysis problems encountered in the natural environment. By examining the behaviors of these seemingly disparate animals, we emerge with a framework for studying analysis comprising four essential properties: 1) the ability to solve ill-posed problems, 2) the ability to integrate and store information across time and modality, 3) efficient recovery and representation of 3D scene structure, and 4) the use of optimal motor actions for acquiring information to progress towards behavioral goals

Hunting bats adjust their echolocation to receive weak prey echoes for clutter reduction

This study was funded by the Carlsberg Semper Ardens grant to P.T.M. and by the Emmy Noether program of the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation, grant no. 241711556) to H.R.G. All experiments were carried out under the following licenses: 721/12.06.2017, 180/07.08.2018, and 795/17.05.2019.How animals extract information from their surroundings to guide motor patterns is central to their survival. Here, we use echo-recording tags to show how wild hunting bats adjust their sensory strategies to their prey and natural environment. When searching, bats maximize the chances of detecting small prey by using large sensory volumes. During prey pursuit, they trade spatial for temporal information by reducing sensory volumes while increasing update rate and redundancy of their sensory scenes. These adjustments lead to very weak prey echoes that bats protect from interference by segregating prey sensory streams from the background using a combination of fast-acting sensory and motor strategies. Counterintuitively, these weak sensory scenes allow bats to be efficient hunters close to background clutter broadening the niches available to hunt for insects.Publisher PDFPeer reviewe

Study of Interaction Between Mexican Free-tailed Bats (Tadarida Brasiliensis) and Moths and Counting Moths in a Real Time Video

Brazilian free tailed bats (Tadarida brasiliensis) are among the most abundant and widely distributed species in the southwestern United States in the summer. Because of their high metabolic needs and diverse diets, bats can impact the communities in which they live in a variety of important ways. The role of bats in pollination, seed dispersal and insect control has been proven to be extremely significant. Due to human ignorance, habitat destruction, fear and low reproductive rates of bats, there is a decline in bat populations. T.brasiliensis eats large quantities of insects but is not always successful in prey capture. In the face of unfavorable foraging condition bats reduce energy expenditure by roosting. By studying the interaction between bats and adults insects along with the associated energetics, we estimate the pest control provided by bats in agro-ecosystems to help understand their ecological importance. To visualize the interaction between bats and adult insects, a simulator has been designed. This simulator is based upon an individual based modeling approach. Using the simulator, we investigated the effect of insect densities and their escape response on the foraging pattern of bats. Traditionally synthetic pesticides were used to control pest population. But recently the use of transgenic crops has become widespread because of the benefits such as fewer pesticide applications and increased yield for growers. To study the effect of these transgenic crops on moth densities and subsequently on bats foraging activity, videos were recorded in the fields at Texas. To count the moths in the videos, we utilized image segmentation techniques such as thresholding and connected component labeling. Accuracy up to 90% has been achieved using these techniques

Evidence from Strandings for Geomagnetic Sensitivity in Cetaceans

We tested the hypothesis that cetaceans use weak anomalies in the geomagnetic field as cues for orientation, navigation and/or piloting. Using the positions of 212 stranding events of live animals in the Smith sonian compilation which fall within the boundaries of the USGS East-Coast Aeromagnetic Survey, we found that there are highly significant tendencies for cetaceans to beach themselves near coastal locations with local magnetic minima. Monte-Carlo simulations confirm the significance of these effects. These results suggest that cetaceans have a magnetic sensory systemcomparable to that in other migratory and homing animals, and predict that the magnetic topography and in particular the marine magnetic lineations may play an important role in guiding long-distance migration. The ‘map’ sense of migratoryanimals may therefore be largely based on a simple strategy of following paths of local magnetic minima and avoiding magnetic gradients