Passive Acoustic Recordings Research Articles

Advanced montane bird monitoring using self-supervised learning and transformer on passive acoustic data

Passive acoustic monitoring combined with deep learning-based bird sound classifiers is an effective tool, particularly in remote areas. While self-supervised learning has recently excelled in natural language processing and image recognition, its application to bird sound recognition remains limited. This study proposes an innovative self-supervised learning approach, which leverages vast amounts of passive acoustic recordings for pre-training, followed by fine-tuning of target species. Compared to the three state-of-the-art models based on transfer learning from ImageNet, the proposed method demonstrated improvements across all species, with even more significant gains for tail-end species. These results confirm that domain-specific pre-training in self-supervised learning enhances downstream recognition tasks and provides greater robustness, benefiting tail-end species in imbalanced ecological datasets. Our experiments further demonstrate that integrating open-source datasets and data augmentation techniques is the most effective strategy for mitigating data imbalances and cross-domain issues. In addition, introducing a ‘catch-all’ category into training datasets has been shown to improve model robustness in open set recognition scenarios. We also identified the minimum viable sample size requirements for our proposed model and explored the impact of overlapping bird vocalizations during dawn choruses on model performance. Targeting 31 bird species in the montane regions of subtropical Taiwan, the model achieved a class-wise mean average precision of 0.782 and an overall precision of 85.6 % at the F0.5 threshold in dawn chorus soundscape recordings. This study confirms the effectiveness and advantages of self-supervised learning in bird sound recognition, supporting long-term monitoring of bird distribution and vocal activity in remote montane areas.

Distribution and Seasonality of the Omura's Whale (Balaenoptera omurai) in Australia Based on Passive Acoustic Recordings.

The Omura's whale (Balaenoptera omurai) is one of the most recently described species of baleen whale. Initially known only from stranding and whaling specimens, it has now been identified in all ocean basins excluding the central and eastern Pacific. Unlike most baleen whales that migrate between the poles and the equator seasonally, the Omura's whale is known to inhabit tropical to sub-tropical waters year-round. In Australian waters, there remain fewer than 30 confirmed visual sightings over the past decade. However, based on acoustic records, the Omura's whale has been detected off areas of the northwest coast of Australia year-round. This study utilises passive acoustic recordings from 41 locations around Australia from 2005 to 2023 to assess the distribution and seasonality of the Omura's whale. The seasonal presence of Omura's whale vocalisations varied by location, with higher presence at lower latitudes. Vocalisations were detected year-round in the Joseph Bonaparte Gulf in the Timor Sea, and near Browse Island and Scott Reef, in the Kimberley region. In the Pilbara region, acoustic presence mostly peaked from February to April and no acoustic presence was consistently observed from July to September across all sites. The most southerly occurrence of Omura's whale vocalisations was recorded off the North West Cape in the Gascoyne region. Vocalisations similar but not identical to those of the Omura's whale were detected in the Great Barrier Reef. The identified seasonal distribution provides valuable information to assess environmental and anthropogenic pressures on the Omura's whale and to aid in creating management and conservation policies for the species in Australia.

A novel method for estimating avian roost sizes using passive acoustic recordings

Abstract Communal bird roosts serve as information centres and a means of thermoregulation for many species. While some communally roosting species are major pests and cause dis‐amenities, others are of conservation concern. Estimating the population of roosting birds can provide a useful proxy of population size and possibly a more reliable estimate than other sampling techniques. However, estimating these populations is challenging as some roosts are large and often occluded in foliage. Previous acoustic methods such as paired sampling, microphone arrays and use of call rate have been used to estimate bird abundances; however, these are less suited for estimating large roost populations where hundreds of individuals are calling in unison. To address this challenge, we explored using machine learning techniques to estimate a roost population of the Javan myna, Acridotheres javanicus, an invasive species in Singapore. While one may expect to use sound intensity to estimate roost sizes, it is affected by various factors such as distance to the recorder, local propagation conditions (e.g. buildings and trees), weather conditions, and noise from other sources. Here, we used a deep neural network to extract higher order statistics from the sound recordings and use those to help estimate roost sizes. Additionally, we validated our method using automated visual analysis with a dual‐camera setup and manual bird counts. Our estimated bird counts over time using our acoustic model matched the automated visual estimates and manual bird counts at a selected Javan myna roost, thus validating our approach. Our acoustic model estimated close to 400 individual mynas roosting in a single tree. Analyses of additional recordings of Javan myna roosts conducted on two separate occasions and at a different roost location using our acoustic model showed that our roost estimates over time also matched our automated visual estimates well. Practical implication: Our novel approach of estimating communal roost sizes can be achieved robustly using a simple portable acoustic recording system. Our method has multiple applications such as testing the efficacy of avian roost population control measures (e.g. roost tree pruning) and monitoring the populations of threatened bird species that roost communally.

Correction to “A novel method for estimating avian roost sizes using passive acoustic recordings using deep neural network”

Correction to “A novel method for estimating avian roost sizes using passive acoustic recordings using deep neural network”

Temporal patterns in dolphin foraging activity in the Mediterranean Sea: insights from vocalisations recorded during the ACCOBAMS Survey Initiative

Marine organisms continually adapt their physiology and behaviour to temporal variations in their environment, resulting in diurnal rhythmic behaviour, particularly when foraging. In delphinids, these rhythms can be studied by recording echolocation clicks, which can provide indicators of foraging activity. The foraging rhythms of delphinids and their relationship to temporal parameters are poorly documented and most studies so far have used moored passive acoustic systems. The present study provides, for the first time, information on the activity rhythms of delphinids investigated in relation with temporal variables at a basin scale from a moving platform, in the western and central Mediterranean Sea. We used passive acoustic recordings collected by hydrophones towed along transect lines during the ACCOBAMS Survey Initiative in the summer 2018. We extracted variables that may influence daily and monthly rhythms, including time of day, lunar cycle, lunar illumination and sea state and fitted generalised additive models. The nycthemeral and lunar cycles were the two main factors influencing dolphin activity rhythms. Echolocation activity was predominant at night, with a maximum of 0.026 acoustic events per minute at 21:00/22:00 compared to as few as 0.0007 events per minute at 11:00. These events were also more frequent during the third quarter of the moon; 0.033 acoustic events on day 22 of the lunar cycle as opposed to 0.0008 on day 8 of the lunar cycle, corresponding to the first quarter of the moon. Variations in the echolocation activity of delphinids in the Mediterranean Sea could reflect variation in their foraging effort and be related to prey density, composition, accessibility and catchability within dolphin foraging depth range. These results should also improve interpretation of passive acoustic monitoring data.

Performance of unmarked abundance models with data from machine‐learning classification of passive acoustic recordings

AbstractThe ability to conduct cost‐effective wildlife monitoring at scale is rapidly increasing due to the availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human‐based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low‐cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low‐cost ARUs provide a promising way of obtaining abundance estimates for large‐scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in the forests of Pennsylvania during the spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle–Nichols and time‐to‐detection models to estimate the abundance of two species from detection histories generated by applying a machine‐learning classifier to ARU‐gathered data. We compared abundance estimates from these models with estimates from the same models fit using point‐count data and to two additional models appropriate for point counts, the N‐mixture model and distance models. We found that the Royle–Nichols and time‐to‐detection models can be used with ARU data to produce abundance estimates similar to those generated by a point‐count‐based study but with greater precision. ARU‐based models produced confidence or credible intervals that were on average 31.9% (±11.9 SE) smaller than their point‐count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine‐learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support for the use of ARUs in abundance‐based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation recommendations and actions.

Comparing the cost-effectiveness of drones, camera trapping and passive acoustic recorders in detecting changes in koala occupancy.

Quantifying the cost-effectiveness of alternative sampling methods is crucial for efficient biodiversity monitoring and detection of population trends. In this study, we compared the cost-effectiveness of three novel sampling methods for detecting changes in koala (Phascolarctos cinereus) occupancy: thermal drones, passive acoustic recorders and camera trapping. Specifically, we fitted single-season occupancy-detection models to data recorded from 46 sites in eight bioregions of New South Wales, Australia, between 2018 and 2022. We explored the effect of weather variables on daily detection probability for each method and, using these estimates, calculated the statistical power to detect 30%, 50% and 80% declines in koala occupancy. We calculated power for different combinations of sites (1-200) and repeat surveys (2-40) and developed a cost model that found the cheapest survey design that achieved 80% power to detect change. On average, detectability of koalas was highest with one 24-h period of acoustic surveys (0.32, 95% CI's: 0.26, 0.39) compared to a 25-ha flight of drone surveys (0.28, 95% 0.15, 0.48) or a 24-h period of camera trapping consisting of six cameras (0.019, 95% CI's: 0.014, 0.025). We found a negative quadratic relationship between detection probability and air temperature for all three methods. Our power and cost analysis suggested that 148 sites surveyed with acoustic recorders deployed for 14 days would be the cheapest method to sufficiently detect a 30% decline in occupancy with 80% power. We recommend passive acoustic recorders as the most efficient sampling method for monitoring koala occupancy compared to cameras or drones. Further comparative studies are needed to compare the relative effectiveness of these methods and others when the monitoring objective is to detect change in koala abundance over time.

The Brazilian Santos basin underwater soundscape monitoring project (PMPAS-BS)

This paper describes the Santos Basin Underwater Soundscape Monitoring Project (PMPAS-BS), a Brazilian ocean soundscape monitoring initiative. The main objective of the project is to quantify and assess hydroacoustic noise of anthropogenic origin in a large sedimentary basin extending from 23° S to 28° S on the southeastern Brazilian continental margin of the South Atlantic Ocean. Noise associated with oil and gas (O&G) exploration and production activities is the primary target, but this oceanic region also has busy shipping lanes for commercial, military, and fishing vessels. The two main hubs of Brazil’s export and import of goods by sea are located in this region: Santos and Rio de Janeiro ports. The project has three measurement components: mobile monitoring based on gliders and drifting acoustic profilers, fixed shallow-water monitoring based on acoustic measurements at coastal stations near shipping lanes associated with exploration and production activities in the Santos Basin, and fixed oceanic monitoring based on deep-water mooring lines equipped with passive autonomous acoustic recorders near production units, shipping lanes, and areas with lower intensity of O&G activities (pristine or reference sites). Numerical modeling of anthropogenic underwater acoustic noise has also been included as a fourth project component. The PMPAS-BS covers an area of more than 251,000 km2 and uses several instruments with different methods and sensors for acoustic measurements. Its results provide current sound levels over a very large region of the western South Atlantic, both in areas more and less affected by anthropogenic activities.

Where's Whaledo: A software toolkit for array localization of animal vocalizations.

Where's Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of Ziphius cavirostris. We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins.

A decade of change and stability for fin whale song in the North Atlantic

Fin whale song is a ubiquitous low-frequency pulsing that has been recorded in every ocean basin. In recent years, the complexity of fin whale song has been realized. In the North Atlantic, this song is made up of two low-frequency note types (A and B notes) and one higher-frequency note type (upsweeps). The A and B notes are produced with both singlet and doublet inter-note intervals. These song patterns are interwoven throughout an individual’s song and have been observed to shift over time, both gradually and suddenly. Like many baleen whales, the population size of fin whales is poorly defined. Passive acoustic monitoring has been suggested as a way to estimate abundance if a cue rate or calling rate can be determined. In this study, we used recordings from 119 fin whale tracks from 2013–2023 generated from passive acoustic recorders in the North Atlantic to define fin whale song patterns in this ocean basin, identify changes in these song patterns over time, and calculate cue rates. The fin whales recorded sang in 4 distinct patterns. Inter-note intervals were 25 s for A-A singlets, 13 s for B-B singlets, 12/20 s for B-B doublets, and 10/11 s for A-B doublets. B-B doublet inter-note intervals significantly increased from May 2019–April 2023 and A-B inter-note intervals showed an intra-annual increasing trend that reset each summer. Fin whale song in the North Atlantic also often includes higher frequency upsweep notes immediately prior to B notes. While A and B note peak frequency was steady over these years, upsweep peak frequency significantly decreased, continuing a similar trend that has been observed over 30 years. Despite these changes, the dominant song pattern remained consistent and cue rate for actively singing whales was relatively stable, which is in contrast to patterns observed in the North Pacific. Many of these nuances in fin whale singing behavior are newly described herein, and these findings are vital for monitoring baseline behavior, behavioral trends over time, and ultimately estimating abundance of a poorly understood species.

Bottom water hypoxia suppresses fish chorusing in estuariesa).

Hypoxia in coastal ecosystems is increasing as a result of water quality declines from nutrient pollution. Hypoxia negatively affects fish populations and marine life, limiting their spawning habitats, population size, and growth. In this study, two approaches were used to understand the effect of hypoxia on the chorusing and reproductive behavior of fishes in estuaries. One approach used a water quality meter integrated with a prototype passive acoustic recorder, developed to monitor dissolved oxygen and fish chorusing simultaneously and continuously at sites with normoxic and hypoxic conditions. In a second approach, passive acoustic recorders were deployed near ambient water quality monitoring stations, monitored by the North Carolina agencies in estuaries where hypoxia occurs periodically. In both approaches, when hypoxia (dissolved oxygen < 4.0 mg/L) occurred, fish chorusing was diminished or ceased. A strong correlation was observed between bottom water dissolved oxygen and the power spectral density in a 100-200 Hz frequency band associated with red drum (Sciaenops ocellatus, Sciaenidae) calling. Passive acoustic monitoring stations and integrated passive acoustic and water quality meters should be used in estuarine hypoxia monitoring efforts to examine the expanding areas of hypoxia and its impact on fish critical spawning habitats.

Using acoustic monitoring to evaluate the co-benefits of urban restoration

Restoring urban greenspaces is an increasingly common nature-based solution aiming to bolster biodiversity while benefiting human wellbeing. However, there is limited understanding of the co-benefits, including potential trade-offs and synergies between outcomes for nature and people. Although passive acoustic recording may be a valuable outcome monitoring tool, appropriate analytical techniques are unknown. We explore how different acoustic analyses capture patterns in species diversity and harmful noise pollution using 2021– 2023 acoustic data from Detroit, Michigan. Data were collected as part of a longitudinal panel study aimed at quantifying the effects of park restoration on physical activity, stress, and cardio-metabolic health. We used AudioMoth recording devices at 100 sites: 10 in the neighborhood around each of 5 restored parks, where native plants were seeded, and 10 around each of 5 unmaintained control parks, matched to restored parks based on specified conditions. Recordings were analyzed by manually identifying bird species and noise in a subset of spectrograms, using an artificial neural network (BirdNET), and using acoustic indices. We found that using acoustic indices in a model could accurately predict bird species diversity and richness, but not community composition. We compared outputs from each type of analysis with mental health indicators of study participants.

Exploiting passive acoustic recordings of seismic survey datasets

Marine seismic reflection surveys provide a dense and abundant dataset covering a large region of geological interest in the ocean. These data provide an opportunity for acoustic analysis with varying environmental characteristics, both in the water column and the seabed. In a typical survey, an airgun array is fired tens to hundreds of thousands of times, and hundreds of receiver channels record the acoustic signal reflected from the seabed after each shot. Additionally, the high amplitude signal broadcast from airgun arrays used in these surveys may be measured passively by hydrophones located close to the survey. In this work, we consider the data measured by Ocean Observatories Initiative (OOI) hydrophones adjacent to two seismic surveys, MGL1905, and MGL2104. In each case, a 6600 in3 airgun array is fired ∼every 37.5 m along multiple survey lines extending 10s to 100s of kilometers. Each survey generates multiple terabytes of acoustic data, and many of the shots are also captured by OOI hydrophones. This work aims to combine these two datasets to evaluate the acoustic behavior of airgun shots over a variety of conditions and examine the relationship between various environmental factors and acoustic propagation behavior. [Work supported by ONR.]

Environmental drivers of odontocete occurrence in a nearshore temperate habitat

Nearshore coastal waters off Northumberland, UK, are important habitats for marine megafauna including marine mammals and seabirds. The area also features extensive anthropogenic activities including shipping, marine renewables development, fisheries, and tourism. Meanwhile there is lack of baseline data on odontocete occurrence to allow assessment of the potential impact from human disturbance. A recent increase in the number of sightings of common bottlenose dolphins (Tursiops truncatus) in the area has provided impetus for new data to inform conservation managers and policy makers. To provide this information, acoustic data were collected using broadband passive acoustic recorders at three sites (Druridge Bay, Newbiggin-by-the-Sea, and St Mary's Island) off the Northumberland coast and analyzed to identify species-specific click trains of common bottlenose dolphin, white-beaked dolphin (Lagenorhynchus albirostris) and harbor porpoise (Phocoena phocoena). Detection data were modelled for each species as detection-positive hours (DPH) using Generalized Additive Models with Generalized Estimating Equations (GAM-GEE) to investigate the effects of environmental covariates, including day of year, diel phase, lunar phase, salinity, year, month and sea surface temperature, and their interaction with location. A total of 20,845 h were recorded during the study resulting in a total of 1660, 71, and 1111 DPH identified for bottlenose dolphin, white-beaked dolphin and porpoise, respectively. The final model for bottlenose dolphins retained all covariates except tidal index and an interaction between location and day of year after model selection and all covariates in the final model were significant. The final model for white-beaked dolphin retained month, year, location, diel index, temperature, lunar index and interactions between location and diel index and all covariates except lunar index were significant. The final model for porpoise retained all covariates and interactions between location and diel phase, day of year, lunar index, and salinity, however, salinity and the interaction between location and lunar index were not significant. Bottlenose dolphin clicks were detected in the area year-round but with two peak periods in occurrence coinciding with May and late September; white-beaked dolphin clicks were detected predominantly in July and August; and porpoise clicks were present year-round but with much greater probability of detection at the Druridge Bay site, where there were peaks in winter and in September. The results of this study provide important information for conservation and management actions that may be needed to reduce anthropogenic pressures on odontocetes in UK waters.

Acoustic presence and demographics of sperm whales (Physeter macrocephalus) off southern New England and near a US offshore wind energy area

Abstract Construction in the southern New England wind energy area (WEA), a large-scale offshore wind farm on the east coast of the United States, started in June 2023. Baseline data was collected from 2020 to 2022, with six passive acoustic recorders (SoundTraps) deployed at shallow (&amp;lt;60 m) sites in the vicinity of Nantucket Shoals and Cox’s Ledge. Data were analysed for sperm whale presence, and demographic composition was assessed using interclick intervals. Presence varied by site, season, and year. Sperm whales were detected year-round but the majority (78%) of days with acoustic occurrences were between May and August. Three demographic classes (putative social groups, adult males, and midsize animals) were detected across multiple seasons, with social groups detected most frequently. Sound propagation tests were conducted at two sites and predicted detection ranges within 20–40 km indicate that sperm whales were likely in proximity to the WEA. These results provide a baseline that will be used to assess ongoing sperm whale presence, especially that of social groups which may be more sensitive to disturbance. This study highlights why sperm whales, classed as endangered in US waters, should be considered in mitigation plans and permitting efforts for offshore wind energy.

Rice's whale occurrence in the western Gulf of Mexico from passive acoustic recordings

Rice's whale occurrence in the western Gulf of Mexico from passive acoustic recordings

Decomposing drivers in avian insectivory: Large‐scale effects of climate, habitat and bird diversity

AbstractAimClimate is a major driver of large‐scale variability in biodiversity, as a likely result of more intense biotic interactions under warmer conditions. This idea fuelled decades of research on plant‐herbivore interactions, but much less is known about higher‐level trophic interactions. We addressed this research gap by characterizing both bird diversity and avian predation along a climatic gradient at the European scale.LocationEurope.TaxonInsectivorous birds and pedunculate oaks.MethodsWe deployed plasticine caterpillars in 138 oak trees in 47 sites along a 19° latitudinal gradient in Europe to quantify bird insectivory through predation attempts. In addition, we used passive acoustic monitoring to (i) characterize the acoustic diversity of surrounding soundscapes; (ii) approximate bird abundance and activity through passive acoustic recordings; and (iii) infer both taxonomic and functional diversity of insectivorous birds from recordings.ResultsThe functional diversity of insectivorous birds increased with warmer climates. Bird predation increased with forest cover and bird acoustic activity but decreased with mean annual temperature and functional richness of insectivorous birds. Contrary to our predictions, climatic clines in bird predation attempts were not directly mediated by changes in insectivorous bird diversity or acoustic activity, but climate and habitat still had independent effects on predation attempts.Main ConclusionsOur study supports the hypothesis of an increase in the diversity of insectivorous birds towards warmer climates but refutes the idea that an increase in diversity would lead to more predation and advocates for better accounting for activity and abundance of insectivorous birds when studying the large‐scale variation in insect‐tree interactions.

Effects of temporal variations in ecotourist noise on an avian community: a case study from a UNESCO world heritage site

ABSTRACT Investigations into how ecotourist resorts and their visitors impact local ecosystems and their wildlife have produced mixed results. One characteristic of ecotourist infrastructure is anthropogenic noise, and especially how fluctuations of visitors contribute to it. Furthermore, we do not understand how these noise levels influence animal species richness and/or assemblages. Here, we used passive acoustic recording over nine months to quantify how variation in weekly and seasonal human activity influence local bird assemblages at a popular ecotourist resort situated on K’gari (Fraser Island), Australia, a UNESCO World Heritage site and an Important Bird Area (Birdlife International). Background noise levels were highest during peak tourist season, especially proximate to main buildings. This noise was primarily associated with machinery and vehicles, but not human voices or music. Bird species richness correlated with noise (levels and detections), but did not appear to be sensitive to peak seasons or human voices. Community composition, however, was influenced by temporal and spatial fluctuations across peak tourist seasons. This study has implications for the development and maintenance of ecotourist infrastructure attempting to support visitors seeking sightings of rare or sensitive bird species and highlights anthropogenic noise as an important consideration for the industry.

Distribution and seasonality of the Omura’s whale (Balaenoptera omurai) in Australia based on passive acoustic recordings

The Omura’s whale is one of the most recently described species of baleen whale. Initially known only from stranding and whaling specimens, it has now been identified in all ocean basins excluding the central and eastern Pacific. Unlike most baleen whales that migrate between the poles and the equator seasonally, the Omura’s whale is known to inhabit tropical to sub-tropical waters year-round. In Australian waters, there remain fewer than 30 confirmed visual sightings over the past decade. This study utilizes passive acoustic recordings from 41 locations around Australia from 2005 to 2023 to assess the distribution and seasonality of the Omura’s whale. The seasonal presence of Omura’s whale vocalizations varied by location, with higher presence at lower latitudes. Vocalizations were detected year-round in the Timor Sea and the Kimberley region. The most southerly occurrence of Omura’s whale vocalizations was recorded off the North West Cape, WA. In Australia, the vocalizations detected differ from those of other Indian Ocean Omura’s whale populations. Geographic variation of the vocalization was also observed within Australia, including vocalizations similar but not identical to those of the Omura’s whale detected in the Great Barrier Reef for the first time. The identified seasonal distribution and possible acoustic populations of the species in Australia provides valuable information to assess environmental and anthropogenic pressures on the Omura’s whale and to aid in creating management policies for the species.

Using the Soundscape Code to compare coastal marine habitats in the Gulf of Maine

Identifying similarities and differences in soundscape properties among coastal marine habitats is valuable for determining indicators of habitat composition, assessing functional connectivity among habitats, and informing management decisions regarding the soundscapes of these habitats. The “Soundscape Code,” proposed and developed by Dylan Wilford, enables rapid calculation of values for four salient soundscape properties: amplitude, impulsivity, periodicity, and dissimilarity. This enables multivariate statistical analyses to quantitatively compare soundscapes in different habitat types and geographic regions. The objective of the current work was to determine whether geographic region or habitat type accounts for more variability in coastal soundscape properties. Passive acoustic recordings were acquired in three different habitat types (sand, macroalgae, and eelgrass dominated substrates), in each of four different geographic locations along the New Hampshire/Maine coastline, to compare the soundscapes of habitats with varied biological and geophysical substrate composition. Results indicate that geographic location accounted for more variability in the soundscapes than habitat type, suggesting that habitats’ local connectivity outweighs acoustic and biological uniformity of the same habitat type over broader spatial scales. Future analyses will incorporate metagenomic data for predictive modeling of habitat composition through the combined use of passive acoustic monitoring and metabarcoding of seawater samples.