Integration of passive acoustic monitoring data into OBIS-SEAMAP, a global biogeographic database, to advance spatially-explicit ecological assessments

AimSpecies distribution models (SDMs) are essential tools in ecology and conservation. However, the scarcity of visual sightings of marine mammals in remote polar areas hinders the effective application of SDMs there. Passive acoustic monitoring (PAM) data provide year‐round information and overcome foul weather limitations faced by visual surveys. However, the use of PAM data in SDMs has been sparse so far. Here, we use PAM‐based SDMs to investigate the spatiotemporal distribution of the critically endangered Antarctic blue whale in the Weddell Sea.LocationThe Weddell Sea.MethodsWe used presence‐only dynamic SDMs employing visual sightings and PAM detections in independent models. We compared the two independent models with a third combined model that integrated both visual and PAM data, aiming at leveraging the advantages of each data type: the extensive spatial extent of visual data and the broader temporal/environmental range of PAM data.ResultsVisual and PAM data prove complementary, as indicated by a low spatial overlap between daily predictions and the low predictability of each model at detections of other data types. Combined data models reproduced suitable habitats as given by both independent models. Visual data models indicate areas close to the sea ice edge (SIE) and with low‐to‐moderate sea ice concentrations (SIC) as suitable, while PAM data models identified suitable habitats at a broader range of distances to SIE and relatively higher SIC.Main ConclusionsThe results demonstrate the potential of PAM data to predict year‐round marine mammal habitat suitability at large spatial scales. We provide reasons for discrepancies between SDMs based on either data type and give methodological recommendations on using PAM data in SDMs. Combining visual and PAM data in future SDMs is promising for studying vocalized animals, particularly when using recent advances in integrated distribution modelling methods.

Navy training events involving the use of explosives pose a potential threat to marine mammals. This study used passive acoustic and visual monitoring data to evaluate marine mammals’ behavioral responses to noise from explosive events. Monitoring was conducted during five training events in the Virginia Capes (VACAPES) Range Complex during August/September of 2009–2012. Passive acoustic monitoring methods ranged from a single hydrophone to an array of sonobuoys monitored in real time. Visual monitoring effort over the five events totaled approximately 34 h (day before events: 10.1 h; days of events: 22.3 h; day after events: 1.5 h), yielding a total of 27 marine mammal sightings. Approximately 54 h of acoustic data were collected before, during, and after the five events. Behavioral changes were evaluated based on analysis of vocalizations detected before, during, and after explosions and concurrent data from visual sightings. For time periods with both visual and acoustic monitoring data, detection methods were compared to evaluate effectiveness. Continuing use and evaluation of both visual and passive acoustic methods for monitoring of explosive training events will improve our knowledge of potential impact resulting from explosive events and help improve management and conservation of marine mammals.

ABSTRACTAutomated detection of acoustic signals is crucial for effective monitoring of sound‐producing animals and their habitats across ecologically relevant spatial and temporal scales. Recent advances in deep learning have made these approaches more accessible. However, few deep learning approaches can be implemented natively in the R programming environment; approaches that run natively in R may be more accessible for ecologists. The “torch for R” ecosystem has made deep learning with convolutional neural networks (CNNs) accessible for R users. Here, we evaluate a workflow for the automated detection and classification of acoustic signals from passive acoustic monitoring (PAM) data. Our specific goals include (1) present a method for automated detection of gibbon calls from PAM data using the “torch for R” ecosystem, (2) conduct a series of benchmarking experiments and compare the results of six CNN architectures; and (3) investigate how well the different architectures perform on data sets of the female calls from two different gibbon species: the northern gray gibbon (Hylobates funereus) and the southern yellow‐cheeked crested gibbon (Nomascus gabriellae). We found that the highest‐performing architecture depended on the species and test data set. We successfully deployed the top‐performing model for each gibbon species to investigate spatial variation in gibbon calling behavior across two grids of autonomous recording units in Danum Valley Conservation Area, Malaysia and Keo Seima Wildlife Sanctuary, Cambodia. The fields of deep learning and automated detection are rapidly evolving, and we provide the methods and data sets as benchmarks for future work.

Passive acoustic monitoring (PAM) data collection has been growing exponentially, resulting in petabytes of data that document ocean soundscapes, how they change over time, and what animals use these ecosystems at varying timescales. Efficiently extracting this critical information and comparing it to other datasets in the context of ecosystem-based management is a Big Data challenge that traditional desktop processing methods cannot address. The curation, management, and dissemination of PAM datasets is another challenge in need of collaborative progress. To meet these exigencies, a multi-agency funded Sound Cooperative (SoundCoop) project is building community-focused, national cyberinfrastructure capability for PAM data to promote improved, scalable and sustainable accessibility and applications for management and science. Driven by partnerships and framed by four case studies, the SoundCoop has established guidance on the standardized processing of sound level metrics using free software toolkits and begun developing core cyberinfrastructure components that future PAM projects can leverage. U.S. and international scientists contributed PAM data collected across 10 long-term monitoring projects to operationalize the production of hybrid-millidecade spectra across a diversity of labs/instruments. Collectively, the contributed data demonstrate the value of standardized processing that enables the creation of comparable results from disparate monitoring efforts.

Various methods for estimating animal density from visual data, including distance sampling (DS) and spatially explicit capture-recapture (SECR), have recently been adapted for estimating call density using passive acoustic monitoring (PAM) data, e.g., recordings of animal calls. Here we summarize three methods available for passive acoustic density estimation: plot sampling, DS, and SECR. The first two require distances from the sensors to calling animals (which are obtained by triangulating calls matched among sensors), but SECR only requires matching (not localizing) calls among sensors. We compare via simulation what biases can arise when assumptions underlying these methods are violated. We use insights gleaned from the simulation to compare the performance of the methods when applied to a case study: bowhead whale call data collected from arrays of directional acoustic sensors at five sites in the Beaufort Sea during the fall migration 2007–2014. Call detections were manually extracted from the recordings by human observers simultaneously scanning spectrograms of recordings from a given site. The large discrepancies between estimates derived using SECR and the other two methods were likely caused primarily by the manual detection procedure leading to non-independent detections among sensors, while errors in estimated distances between detected calls and sensors also contributed to the observed patterns. Our study is among the first to provide a direct comparison of the three methods applied to PAM data and highlights the importance that all assumptions of an analysis method need to be met for correct inference.

Passive acoustic monitoring is increasingly being used as a cost‐effective way to study wildlife populations, especially those that are difficult to census using conventional methods. Burrow‐nesting seabirds are among the most threatened birds globally, but they are also one of the most challenging taxa to census, making them prime candidates for research into such automated monitoring platforms. Passive acoustic monitoring has the potential to determine presence/absence or quantify burrow‐nesting populations, but its effectiveness remains unclear. We compared passive acoustic monitoring, tape‐playbacks andGPStracking data to investigate the ability of passive acoustic monitoring to capture unbiased estimates of within‐colony variation in nest density for the Manx ShearwaterPuffinus puffinus. Variation in acoustic activity across 12 study plots on an island colony was examined in relation to burrow density and environmental factors across 2 years. As predicted fewer calls were recorded when wind speed was high, and on moon‐lit nights, but there was no correlation between acoustic activity and the density of breeding birds within the plots as determined by tape‐playback surveys. Instead, acoustic indices correlated positively with spatial variation in the in‐colony flight activity of breeding individuals detected byGPS. Although passive acoustic monitoring has enormous potential in avian conservation, our results highlight the importance of understanding behaviour when using passive acoustic monitoring to estimate density and distribution.

Increased knowledge about marine mammal seasonal distribution and species assemblage from the South Orkney Islands waters is needed for the development of management regulations of the commercial fishery for Antarctic krill (Euphausia superba) in this region. Passive acoustic monitoring (PAM) data were collected during the autumn and winter seasons in two consecutive years (2016, 2017), which represented highly contrasting environmental conditions due to the 2016 El Niño event. We explored differences in seasonal patterns in marine mammal acoustic presence between the two years in context of environmental cues and climate variability. Acoustic signals from five baleen whale species, two pinniped species and odontocete species were detected and separated into guilds. Although species diversity remained stable over time, the ice-avoiding and ice-affiliated species dominated before and after the onset of winter, respectively, and thus demonstrating a shift in guild composition related to season. Herein, we provide novel information about local marine mammal species diversity, community structure and residency times in a krill hotspot. Our study also demonstrates the utility of PAM data and its usefulness in providing new insights into the marine mammal habitat use and responses to environmental conditions, which are essential knowledge for the future development of a sustainable fishery management in a changing ecosystem.

Many organizations collect large passive acoustic monitoring (PAM) data sets that need to be efficiently and reliably analyzed. To determine appropriate methods for effective analysis of big PAM data sets, we undertook a literature review of baleen whale PAM analysis methods. Methodologies from 166 studies (published between 2000–2019) were summarized, and a detailed review was performed on the 94 studies that recorded more than 1,000 hr of acoustic data (“big data”). Analysis techniques for extracting baleen whale information from PAM data sets varied depending on the research observed. A spectrum of methodologies was used and ranged from manual analysis of all acoustic data by human experts to completely automated techniques with no manual validation. Based on this assessment, recommendations are provided to encourage robust research methods that are comparable across studies and sectors, achievable across research groups, and consistent with previous work. These include using automated techniques when possible to increase efficiency and repeatability, supplementing automation with manual review to calculate automated detector performance, and increasing consistency in terminology and presentation of results. This work can be used to facilitate discussion for minimum standards and best practices to be implemented in the field of marine mammal PAM.

North Atlantic right whales (NARW) are one of the most critically endangered marine mammals with abundance estimates for the North Atlantic population at about 438 individuals cataloged in 2008. Data presented here were part of a larger, long-term study to assess presence of marine mammals, sea turtles, and birds along the New Jersey coast in advance of wind farm development. Seasonal presence of NARW off New Jersey was characterized using static passive acoustic monitoring (PAM) and line-transect shipboard surveys. NARW upcalls were detected on 115 days over 21 months of deployment (March 2008 to December 2009) with a significant difference in number of upcalls detected between PAM stations by month (F-ratio = 3.1292, df = 22, p = 0.000). NARW upcalls were detected from March to June and September to December 2008 and from January to March and in June 2009, with the greatest number of calls detected during spring months annually. Presence of NARW was confirmed by sighting data, with sightings all seasons except summer. Four sightings were recorded: three during November, December, and January when right whales are on calving grounds farther south or in the Gulf of Maine and the fourth sighting was a cow-calf pair in May.

Hydrophones are deployed throughout the ocean to perform passive acoustic monitoring. This technique is a powerful tool for marine mammal sound detection due to its advantage of being able to collect data overnight, year-round, and in inclement weather. However, hundreds of terabytes of data produced each year pose a significant challenge for data analysis. The aim of this study was to investigate the use of entropy-based techniques to achieve automatic detection of marine mammal tonal calls in passive acoustic monitoring data. A weighted spectral entropy technique was developed to alleviate the impact of underwater noise along with a novel algorithmic detector. The detector includes an adaptive bandpass filter, a time–frequency domain transform, and a likelihood ratio test for calculating the optimal detection threshold in addition to the Weighted Spectral Entropy Technique. The proposed entropy-based technique and the automatic detector were assessed with synthetic and real-world data and the performance was compared to other state-of-the-art techniques. The results indicate that the proposed method outperforms the other techniques when evaluated using various types of low signal-to-noise ratio tonal signals.

ABSTRACTIn this paper, we present an automatic method, without human supervision, for the detection and classification of blue whale vocalizations from passive acoustic monitoring (PAM) data using Hidden Markov Model technology implemented with a state-of-the-art machine learning platform, the Kaldi speech processing toolkit. 157.5 hours of PAM data were annotated for model training and testing, selected from a dataset collected from the Corcovado Gulf, Chilean Patagonia in 2016. The system obtained produced 85.3% accuracy for detection and classification of a range of different blue whale vocalizations. This system was then validated by comparing its unsupervised detection and classification results with the published results of southeast Pacific blue whale song phrase (‘SEP2’) via spectrogram cross-correlation, involving a dataset collected with a different hydrophone instrument. The proposed system led to a reduction in the root mean square error relative to published results as high as 80% when compared with comparable methods employed elsewhere. This is a significant step in advancing the monitoring of endangered whale populations in this region, which remains poorly covered in terms of PAM and general ocean observation. With further training, testing and validation, this system can be applied to other target signals and regions of the world ocean.

This paper will present the results of processing long-term passive acoustic monitoring (PAM) data collected July through October 2015 in the Northern Gulf of Mexico in the vicinity of the Deep Water Horizon oil spill site to aid in understanding factors driving the distribution of sperm and beaked whales in the Gulf of Mexico. The Littoral Acoustic Demonstration Center -Gulf Ecological Monitoring and Modeling Consortium (LADC-GEMM) deployed five bottom-anchored acoustic moorings (LADC EARS buoys) at 10, 25, and 50 nmi distance from the 2010 oil spill location. Autonomous surface vehicles and a glider were simultaneously operated in the area for the additional collection of PAM data. The daily and monthly activity of three species of beaked whales exhibits spatial and seasonal variability, which appear to be correlated with levels of anthropogenic noise at the monitoring sites. Acoustic detection data are used to estimate abundances at three sites and compare them to the estimates obtained from baseline data collected before (2007) and right after (2010) the oil spill. Long-term abundance trends for both beaked and sperm whales are discussed. [Research supported by GOMRI.]

Successful conservation and management of marine top predators rely on detailed documentation of spatiotemporal behavior. For cetacean species, this information is key to defining stocks, habitat use, and mitigating harmful interactions. Research focused on this goal is employing methodologies such as visual observations, tag data, and passive acoustic monitoring (PAM) data. However, many studies are temporally limited or focus on only one or few species. In this study, we make use of an existing long‐term (2009–2019), labeled PAM data set to examine spatiotemporal patterning of at least 10 odontocete (toothed whale) species in the Hawaiian Islands using compositional analyses and modeling techniques. Species composition differs among considered sites, and this difference is robust to seasonal movement patterns. Temporally, hour of day was the most significant predictor of detection across species and sites, followed by season, though patterns differed among species. We describe long‐term trends in species detection at one site and note that they are markedly similar for many species. These trends may be related to long‐term, underlying oceanographic cycles that will be the focus of future study. We demonstrate the variability of temporal patterns even at relatively close sites, which may imply that wide‐ranging models of species presence are missing key fine‐scale movement patterns. Documented seasonal differences in detection also highlights the importance of considering season in survey design both regionally and elsewhere. We emphasize the utility of long‐term, continuous monitoring in highlighting temporal patterns that may relate to underlying climatic states and help us predict responses to climate change. We conclude that long‐term PAM records are a valuable resource for documenting spatiotemporal patterns and can contribute many insights into the lives of top predators, even in highly studied regions such as the Hawaiian Islands.

Northward range shifts are increasingly being identified in mobile animals that are responding to climate change. Range shifts are consequential to animal ecology, ecosystem function, and conservation goals, yet for many species these cannot be characterised without means of synoptically measuring their distribution. The distribution of critically endangered North Atlantic right whale (Eubalaena glacialis;NARW) north of 45°N has been largely unknown due to a lack of systematic monitoring. The objectives of this study were to characterize the spatial and temporal variation in NARW acoustic occurrence in the northern portion of their foraging range. In addition, we sought to identify relevant NARW migratory corridors and explore potential previously unidentified high-use habitats beyond the highly surveyed Gulf of St. Lawrence (GSL). To achieve this, passive acoustic monitoring data were collected and analyzed from 67 moorings and 13 gliders deployed (across 38 recording stations) throughout the Atlantic Canadian continental shelf, between 42°N and 58°N during 2015 through 2017. The results support that while a portion of the population has moved northward into the GSL, this shift was constrained to temperate latitudinal ranges < 52°N during the study period. NARWs were not detected in the Labrador Sea and Newfoundland Shelf, despite their preferred prey occurring in those areas. NARWs were present on the Scotian Shelf (45°N) nearly year-round, but only from May through December in the Cabot Strait (50°N). These results indicate that the northern range of the population is probably influenced by energetic requirements to minimize the distance between suitable foraging habitat and low latitude calving grounds, rather than an absence of suitable foraging conditions in high latitude waters, or other environmental or physiological factors. This work provides critical information to conserve the species and mitigate human-induced risks.

The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine mammals, birds, and bats, its application to primates is limited. Black-and-white ruffed lemurs (Varecia variegata) are a promising species to test PAM with due to their distinctive and loud roar-shrieks. Furthermore, these lemurs are challenging to monitor via traditional methods due to their fragmented and often unpredictable distribution in Madagascar's dense eastern rainforests. Our goal in this study was to develop a machine learning pipeline for automated call detection from PAM data, compare the effectiveness of PAM versus in-person observations, and investigate diel patterns in lemur vocal behavior. We did this study at Mangevo, Ranomafana National Park by concurrently conducting focal follows and deploying autonomous recorders in May-July 2019. We used transfer learning to build a convolutional neural network (optimized for recall) that automated the detection of lemur calls (57-h runtime; recall = 0.94, F1 = 0.70). We found that PAM outperformed in-person observations, saving time, money, and labor while also providing re-analyzable data. Using PAM yielded novel insights into V. variegata diel vocal patterns; we present the first published evidence of nocturnal calling. We developed a graphic user interface and open-sourced data and code, to serve as a resource for primatologists interested in implementing PAM and machine learning. By leveraging the potential of this pipeline, we can address the urgent need for effective primate population surveys to inform conservation strategies.