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The quantity of passive acoustic data collected in marine environments is rapidly expanding; however, the software developments required to meaningfully process large volumes of soundscape data have lagged behind. A significant bottleneck in the analysis of biological patterns in soundscape datasets is the human effort required to identify and annotate individual acoustic events, such as diverse and abundant fish sounds. This paper addresses this problem by training a YOLOv5 convolutional neural network (CNN) to automate the detection of tonal and pulsed fish calls in spectrogram data from five tropical coral reefs in the U.S. Virgin Islands, building from over 22 h of annotated data with 55 015 fish calls. The network identified fish calls with a mean average precision of up to 0.633, while processing data over 25× faster than it is recorded. We compare the CNN to human annotators on five datasets, including three used for training and two untrained reefs. CNN-detected call rates reflected baseline reef fish and coral cover observations; and both expected biological (e.g., crepuscular choruses) and novel call patterns were identified. Given the importance of reef-fish communities, their bioacoustic patterns, and the impending biodiversity crisis, these results provide a vital and scalable means to assess reef community health. 

In this paper, we propose a novel method for autonomously seeking out sparsely distributed targets in an unknown underwater environment. Our Sparse Adaptive Search and Sample (SASS) algorithm mixes low-altitude observations of discrete targets with high-altitude observations of the surrounding substrates. By using prior information about the distribution of targets across substrate types in combination with belief modelling over these substrates in the environment, high-altitude observations provide information that allows SASS to quickly guide the robot to areas with high target densities. A maximally informative path is autonomously constructed online using Monte Carlo Tree Search with a novel acquisition function to guide the search to maximise observations of unique targets. We demonstrate our approach in a set of simulated trials using a novel generative species model. SASS consistently outperforms the canonical boustrophedon planner by up to 36% in seeking out unique targets in the first 75 - 90% of time it takes for a boustrophedon survey. Additionally, we verify the performance of SASS on two real world coral reef datasets. 

In this paper, we present an approach that enables long-term monitoring of biological activity on coral reefs by extending mission time and adaptively focusing sensing resources on high-value periods. Coral reefs are one of the most biodiverse ecosystems on the planet; yet they are also among the most imperiled: facing bleaching, ecological community collapses due to global climate change, and degradation from human activities. Our proposed method improves the ability of scientists to monitor biological activity and abundance using passive acoustic sensors. We accomplish this by extracting periodicities from the observed abundance, and using them to predict future abundance. This predictive model is then used with a Monte Carlo Tree Search planning algorithm to schedule sampling at periods of high biological activity, and power down the sensor during periods of low activity. In simulated experiments using long-term acoustic datasets collected in the US Virgin Islands, our adaptive Online Sensor Scheduling algorithm is able to double the lifetime of a sensor while simultaneously increasing the average observed acoustic activity by 21%. 

Abstract—The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef organisms and their habitat. The CUREE platform presented in this paper provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following. We demonstrate these capabilities by describing two field deployments on coral reefs in the US Virgin Islands. In the first deployment, we show that CUREE can identify the preferred habitat type of snapping shrimp in a reef through a combination of a visual survey, habitat characterization, and a soundscape survey. In the second deployment, we demonstrate CUREE’s ability to follow arbitrary animals by separately following a barracuda and stingray for several minutes each in midwater and benthic environments, respectively.