Emerging technologies in citizen science and potential for insect monitoring

Emerging technologies are increasingly employed in environmental citizen science projects. This integration offers benefits and opportunities for scientists and participants alike. Citizen science can support large-scale, long-term monitoring of species occurrences, behaviour and interactions. At the same time, technologies can foster participant engagement, regardless of pre-existing taxonomic expertise or experience, and permit new types of data to be collected. Yet, technologies may also create challenges by potentially increasing financial costs, necessitating technological expertise or demanding training of participants. Technology could also reduce people's direct involvement and engagement with nature. In this perspective, we discuss how current technologies have spurred an increase in citizen science projects and how the implementation of emerging technologies in citizen science may enhance scientific impact and public engagement. We show how technology can act as (i) a facilitator of current citizen science and monitoring efforts, (ii) an enabler of new research opportunities, and (iii) a transformer of science, policy and public participation, but could also become (iv) an inhibitor of participation, equity and scientific rigour. Technology is developing fast and promises to provide many exciting opportunities for citizen science and insect monitoring, but while we seize these opportunities, we must remain vigilant against potential risks. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Animal-borne soundscape logger as a system for edge classification of sound sources and data transmission for monitoring near-real-time underwater soundscape

The underwater environment is filled with various sounds, with its soundscape composed of biological, geographical, and anthropological sounds. Our work focused on developing a novel method to observe and classify these sounds, enriching our understanding of the underwater ecosystem. We constructed a biologging system allowing near-real-time observation of underwater soundscapes. Utilizing deep-learning-based edge processing, this system classifies the sources of sounds, and upon the tagged animal surfacing, it transmits positional data, results of sound source classification, and sensor readings such as depth and temperature. To test the system, we attached the logger to sea turtles (Chelonia mydas) and collected data through a cellular network. The data provided information on the location-specific sounds detected by the sea turtles, suggesting the possibility to infer the distribution of specific species of organisms over time. The data showed that not only biological sounds but also geographical and anthropological sounds can be classified, highlighting the potential for conducting multi-point and long-term observations to monitor the distribution patterns of various sound sources. This system, which can be considered an autonomous mobile platform for oceanographic observations, including soundscapes, has significant potential to enhance our understanding of acoustic diversity.

Fin whale song characteristics and potential subpopulation identity in the New York Bight

Fin whale (Balaenoptera physalus) song can follow a highly consistent pattern, and regional differences in song patterns can be a valuable indicator of subpopulation identity and distribution. In the Northwest Atlantic, endangered fin whales are currently managed as a single stock despite previous identification of different regional song patterns, which indicates potential subpopulation structuring and vulnerability to anthropogenic disturbance if not managed accordingly. Here we document fin whale song in the New York Bight (NYB) from 2017 to 2020 using passive acoustic data to identify monthly and yearly trends in song patterns and to explore potential subpopulation structuring. The predominant song pattern observed was highly consistent with the pattern documented almost a decade prior in the NYB, with short inter-note intervals (INI) from fall-winter and long-INIs in the spring. However, in one song year the majority of songs were composed of long-INIs. This change in song pattern could be due to a shift in fin whale behavior or possibly multiple fin whale subpopulations using the NYB. Fin whales in the NYB may be particularly vulnerable to disturbance given the increasing anthropogenic pressures in this region, and further research into subpopulation structuring is needed to ensure adequate management of these endangered whales.

The Internet of Animals: what it is, what it could be

One of the biggest trends in ecology over the past decade has been the creation of standardized databases. Recently, this has included live data, formal linkages between disparate databases, and automated analytics, a synergy that we recognize as the Internet of Animals (IoA). Early IoA systems relate animal locations to remote-sensing data to predict species distributions and detect disease outbreaks, and use live data to inform management of endangered species. However, meeting the future potential of the IoA concept will require solving challenges of taxonomy, data security, and data sharing. By linking data sets, integrating live data, and automating workflows, the IoA has the potential to enable discoveries and predictions relevant to human societies and the conservation of animals.

NEAL: an open-source tool for audio annotation

Passive acoustic monitoring is used widely in ecology, biodiversity, and conservation studies. Data sets collected via acoustic monitoring are often extremely large and built to be processed automatically using artificial intelligence and machine learning models, which aim to replicate the work of domain experts. These models, being supervised learning algorithms, need to be trained on high quality annotations produced by experts. Since the experts are often resource-limited, a cost-effective process for annotating audio is needed to get maximal use out of the data. We present an open-source interactive audio data annotation tool, NEAL (Nature+Energy Audio Labeller). Built using R and the associated Shiny framework, the tool provides a reactive environment where users can quickly annotate audio files and adjust settings that automatically change the corresponding elements of the user interface. The app has been designed with the goal of having both expert birders and citizen scientists contribute to acoustic annotation projects. The popularity and flexibility of R programming in bioacoustics means that the Shiny app can be modified for other bird labelling data sets, or even to generic audio labelling tasks. We demonstrate the app by labelling data collected from wind farm sites across Ireland.

Oceanic giants dance to atmospheric rhythms: Ephemeral wind-driven resource tracking by blue whales

Trophic transfer of energy through marine food webs is strongly influenced by prey aggregation and its exploitation by predators. Rapid aggregation of some marine fish and crustacean forage species during wind-driven coastal upwelling has recently been discovered, motivating the hypothesis that predators of these forage species track the upwelling circulation in which prey aggregation occurs. We examine this hypothesis in the central California Current Ecosystem using integrative observations of upwelling dynamics, forage species' aggregation, and blue whale movement. Directional origins of blue whale calls repeatedly tracked upwelling plume circulation when wind-driven upwelling intensified and aggregation of forage species was heightened. Our findings illustrate a resource tracking strategy by which blue whales may maximize energy gain amid ephemeral foraging opportunities. These findings have implications for the ecology and conservation of diverse predators that are sustained by forage populations whose behaviour is responsive to episodic environmental dynamics.

Convergence of emerging technologies: Development of a risk-based paradigm for marine mammal monitoring for offshore wind energy operations

The ability to gather real-time and near real-time data on marine mammal distribution, movement, and habitat use has advanced significantly over the past two decades. These advances have outpaced their adoption into a meaningful, risk-based assessment framework so critically needed to support society's growing demands for a transition to increased reliance on renewable energy. Marine acoustics have the capacity to detect, identify, and locate vocalizations over broad areas. Photogrammetric and image processing increases the ability to visually detect animals from surface or aerial platforms. Ecological models based on long-term observational data coupled with static and remotely sensed oceanographic data are able to predict daily and seasonal habitat suitability. Extensive monitoring around anthropogenic activities, combined with controlled experiments of exposure parameters (i.e., sound), supports better informed decisions on reducing effects. Population models and potential consequence modeling provide the ability to estimate the significance of individual and population exposure. The collective capacities of these emerging technical approaches support a risk ranking and risk management approach to monitoring and mitigating effects on marine mammals related to development activities. The monitoring paradigm related to many offshore energy-related activities, however, has long been spatially limited, situationally myopic, and operationally uncertain. A case evaluation process is used to define and demonstrate the changing paradigm of effective monitoring aimed at protecting living resources and concurrently providing increased certainty that essential activities can proceed efficiently. Recent advances in both technologies and operational approaches are examined to delineate a risk-based paradigm, driven by a diversity of regional data inputs, that is capable of meeting the imperative for timely development of offshore wind energy. Integr Environ Assess Manag 2022;18:939-949. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Global collision-risk hotspots of marine traffic and the world's largest fish, the whale shark

Marine traffic is increasing globally yet collisions with endangered megafauna such as whales, sea turtles, and planktivorous sharks go largely undetected or unreported. Collisions leading to mortality can have population-level consequences for endangered species. Hence, identifying simultaneous space use of megafauna and shipping throughout ranges may reveal as-yet-unknown spatial targets requiring conservation. However, global studies tracking megafauna and shipping occurrences are lacking. Here we combine satellite-tracked movements of the whale shark, Rhincodon typus, and vessel activity to show that 92% of sharks’ horizontal space use and nearly 50% of vertical space use overlap with persistent large vessel (>300 gross tons) traffic. Collision-risk estimates correlated with reported whale shark mortality from ship strikes, indicating higher mortality in areas with greatest overlap. Hotspots of potential collision risk were evident in all major oceans, predominantly from overlap with cargo and tanker vessels, and were concentrated in gulf regions, where dense traffic co-occurred with seasonal shark movements. Nearly a third of whale shark hotspots overlapped with the highest collision-risk areas, with the last known locations of tracked sharks coinciding with busier shipping routes more often than expected. Depth-recording tags provided evidence for sinking, likely dead, whale sharks, suggesting substantial “cryptic” lethal ship strikes are possible, which could explain why whale shark population declines continue despite international protection and low fishing-induced mortality. Mitigation measures to reduce ship-strike risk should be considered to conserve this species and other ocean giants that are likely experiencing similar impacts from growing global vessel traffic.

Estimating Florida manatee (Trichechus manatus latirostris) abundance using passive acoustic methods

Manatees are difficult to detect, particularly cryptic populations that inhabit areas with limited water clarity. The effectiveness of using vocal detections to estimate manatee abundance was evaluated in a clear water spring where manatees congregate seasonally. Vocalizations were extracted by a detection classifier that clustered sounds with similar spectral properties. Vocalization counts from recordings in Blue Spring, FL, USA were strong predictors of manatee abundance. The link between independent visual counts and abundance estimates from passive acoustic monitoring was used to provide an estimate of 1.059 (95% confidence interval 0.963-1.127) vocalizations/manatee/5-min, which might be used elsewhere for cue counting of manatees.

Acoustic detection range of right whale upcalls identified in near-real time from a moored buoy and a Slocum glider

The goal of this study was to characterize the detection range of a near real-time baleen whale detection system, the digital acoustic monitoring instrument/low-frequency detection and classification system (DMON/LFDCS), equipped on a Slocum glider and a moored buoy. As a reference, a hydrophone array was deployed alongside the glider and buoy at a shallow-water site southwest of Martha's Vineyard (Massachusetts, USA) over a four-week period in spring 2017. A call-by-call comparison between North Atlantic right whale upcalls localized with the array (n = 541) and those detected by the glider or buoy was used to estimate the detection function for each DMON/LFDCS platform. The probability of detection was influenced by range, ambient noise level, platform depth, detection process, review protocol, and calling rate. The conservative analysis of near real-time pitch tracks suggested that, under typical conditions, a 0.33 probability of detection of a single call occurred at 6.2 km for the buoy and 8.6-13.4 km for the glider (depending on glider depth), while a 0.10 probability of detection of a single call occurred at 14.4 m for the buoy and 22.6-27.5 km for the glider. Probability of detection is predicted to increase substantially at all ranges if more than one call is available for detection.

Accounting for the Lombard effect in estimating the probability of detection in passive acoustic surveys: Applications for single sensor mitigation and monitoring

The detection range of calling animals is commonly described by the passive sonar equations. However, the sonar equations do not account for interactions between source and ambient sound level, i.e., the Lombard effect. This behavior has the potential to introduce non-linearities into the sonar equations and result in incorrectly predicted detection ranges. Here, we investigate the relationship between ambient sound and effective detection ranges for North Atlantic right whales (Eubalaena glacialis) in Cape Cod Bay, MA, USA using a sparse array of acoustic recorders. Generalized estimating equations were used to model the probability that a call was detected as a function of distance between the calling animal and the sensor and the ambient sound level. The model suggests a non-linear relationship between ambient sound levels and the probability of detecting a call. Comparing the non-linear model to the linearized version of the same model resulted in 12 to 25% increases in the effective detection range. We also found evidence of the Lombard effect suggesting that it is the most plausible cause for the non-linearity in the relationship. Finally, we suggest a simple modification to the sonar equation for estimating detection probability for single sensor monitoring applications.

An overview of North Atlantic right whale acoustic behavior, hearing capabilities, and responses to sound

Creating a baseline understanding of communicative signals and perceptual abilities is imperative for gaining insight into a species' life history. This is especially relevant for at-risk species, as it can aid in monitoring and conservation efforts. Marine mammals communicate predominately through acoustic modalities for a variety of functions, including foraging and reproduction. The acoustic signals produced by marine mammals, and their ability to perceive signals produced by conspecifics, are directly impacted by the level of ambient noise in the underwater environment in which they inhabit. Modern ocean noise levels are considerably louder than historical levels, and noise is therefore considered to be a threat to acoustically communicating marine mammal species. This review summarizes the documented acoustic signals, hearing abilities, and responses to sound of a critically endangered baleen whale, the North Atlantic right whale (Eubalaena glacialis), highlights gaps in the current body of literature, and identifies priorities for future research.

Acoustic tracking of fin whales: Habitat use and movement patterns within a Canadian Pacific fjord system

Fin whale 20 Hz calls were detected, localized, and tracked using a 10 km aperture network of three acoustic receivers deployed for 11 months in a Pacific Canadian fjord system. The area has been historically important for fin whales and is located along a route that tankers will begin using in 2024. A total of 6712 calls were localized, and trajectories were fitted for 55 acoustic tracks. Fin whale tracks occurred throughout the monitoring site. Call activity peaked in September and was low during winter months. Swimming characteristics varied significantly between day- and nighttime: at night, whales swam faster (7.1 vs 4.0 km/h median, +75.2%), which resulted in longer (+34.7%), less predictable (-70.6%) tracks as compared to daylight hours. Call frequencies varied between 16 and 32 Hz. Beside stereotypical song frequencies, fin whales also used irregular frequency components, which contributed the majority of calls in the summer but did not occur in the winter. The results suggest that the area is primarily used as a summer feeding ground, where fin whales follow a diel behavioral cycle. The observed activity patterns will aid in the assessment of strike risk and harassment mitigation and provide a baseline to document behavioral change.

Near real-time detection of low-frequency baleen whale calls from an autonomous surface vehicle: Implementation, evaluation, and remaining challenges

Mitigation of threats posed to marine mammals by human activities can be greatly improved with a better understanding of animal occurrence in real time. Recent advancements have enabled low-power passive acoustic systems to be integrated into long-endurance autonomous platforms for persistent near real-time monitoring of marine mammals via the sounds they produce. Here, the integration of a passive acoustic instrument capable of real-time detection and classification of low-frequency (LF) tonal sounds with a Liquid Robotics wave glider is reported. The goal of the integration was to enable monitoring of LF calls produced by baleen whales over periods of several months. Mechanical noises produced by the platform were significantly reduced by lubricating moving parts with polytetrafluoroethylene, incorporating rubber and springs to decelerate moving parts and shock mounting hydrophones. Flow noise was reduced with the development of a 21-element hydrophone array. Surface noise produced by breaking waves was not mitigated despite experimentation with baffles. Compared to a well-characterized moored passive acoustic monitoring buoy, the system greatly underestimated the occurrence of sei, fin, and North Atlantic right whales during a 37-d deployment, and therefore is not suitable in its current configuration for use in scientific or management applications for these species at this time.

Estimating effectiveness of speed reduction measures for decreasing whale-strike mortality in a high-risk region

Recent estimates of blue (Balaenoptera musculus) and humpback (Megaptera novaeangliae) whale ship-strike deaths on the US west coast are above the Potential Biological Removal limit determined by the National Marine Fisheries Service. Beginning in 2015, the National Oceanographic and Atmospheric Administration requested voluntary Vessel Speed Reductions (VSR) in the designated shipping routes off San Francisco, California, USA, in order to decrease whale mortality from ship strikes. We applied a ship strike model based on whale density and Automatic Identification System (AIS) vessel data. We bootstrapped speeds from vessels that transited when no VSR was in place to assess the effect of the VSR on strike mortality rates. Finally, we calculated the expected mortality for hypothetical compliance scenarios by programmatically imposing speed caps. Average predicted mortality for the region was 2.7 blue whales and 7.0 humpback whales in a 4 month period. Compared to years prior to the VSR (2012-2014), vessel speeds during the VSR were slower. This lowered blue whale deaths within the shipping lanes by 11-13% and humpback whale deaths by 9-10% in 2016-2017. If 95% of mariners adhered to recommended 10 knot (kn) limits in the shipping lanes alone, we predicted twice as many blue whale and 3 times as many humpback whale deaths would be avoided relative to current adherence. Adding a 10 kn speed limit (with 95% cooperation) at the ends of each of the lanes would result in about 5- and 4-fold reductions in blue whale and humpback whale mortality, respectively, relative to current practices. Our approach can evaluate ship strikes and mitigation measures for whale populations around the globe.

Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data

Six baleen whale species are found in the temperate western North Atlantic Ocean, with limited information existing on the distribution and movement patterns for most. There is mounting evidence of distributional shifts in many species, including marine mammals, likely because of climate-driven changes in ocean temperature and circulation. Previous acoustic studies examined the occurrence of minke (Balaenoptera acutorostrata) and North Atlantic right whales (NARW; Eubalaena glacialis). This study assesses the acoustic presence of humpback (Megaptera novaeangliae), sei (B. borealis), fin (B. physalus), and blue whales (B. musculus) over a decade, based on daily detections of their vocalizations. Data collected from 2004 to 2014 on 281 bottom-mounted recorders, totaling 35,033 days, were processed using automated detection software and screened for each species' presence. A published study on NARW acoustics revealed significant changes in occurrence patterns between the periods of 2004-2010 and 2011-2014; therefore, these same time periods were examined here. All four species were present from the Southeast United States to Greenland; humpback whales were also present in the Caribbean. All species occurred throughout all regions in the winter, suggesting that baleen whales are widely distributed during these months. Each of the species showed significant changes in acoustic occurrence after 2010. Similar to NARWs, sei whales had higher acoustic occurrence in mid-Atlantic regions after 2010. Fin, blue, and sei whales were more frequently detected in the northern latitudes of the study area after 2010. Despite this general northward shift, all four species were detected less on the Scotian Shelf area after 2010, matching documented shifts in prey availability in this region. A decade of acoustic observations have shown important distributional changes over the range of baleen whales, mirroring known climatic shifts and identifying new habitats that will require further protection from anthropogenic threats like fixed fishing gear, shipping, and noise pollution.

Near real-time marine mammal monitoring from gliders: Practical challenges, system development, and management implications

In 2017, an endangered North Atlantic right whale mortality event in the Gulf of St. Lawrence, Canada, triggered the implementation of dynamic mitigation measures that required real-time information on whale distribution. Underwater glider-based acoustic monitoring offers a possible solution for collecting near real-time information but has many practical challenges including self-noise, energy restrictions, and computing capacity, as well as limited glider-to-shore data transfer bandwidth. This paper describes the development of a near real-time baleen whale acoustic monitoring glider system and its evaluation in the Gulf of St. Lawrence in 2018. Development focused on identifying and prioritizing important acoustic events and on sending contextual information to shore for human validation. The system performance was evaluated post-retrieval, then the trial was simulated using optimized parameters. Trial simulation evaluation revealed that the validated detections of right, fin, and blue whales produced by the system were all correct; the proportion of species occurrence missed varied depending on the timeframe considered. Glider-based near real-time monitoring can be an effective and reliable technique to inform dynamic mitigation strategies for species such as the North Atlantic right whale.

Performance of a deep neural network at detecting North Atlantic right whale upcalls

Passive acoustics provides a powerful tool for monitoring the endangered North Atlantic right whale (Eubalaena glacialis), but robust detection algorithms are needed to handle diverse and variable acoustic conditions and differences in recording techniques and equipment. This paper investigates the potential of deep neural networks (DNNs) for addressing this need. ResNet, an architecture commonly used for image recognition, was trained to recognize the time-frequency representation of the characteristic North Atlantic right whale upcall. The network was trained on several thousand examples recorded at various locations in the Gulf of St. Lawrence in 2018 and 2019, using different equipment and deployment techniques. Used as a detection algorithm on fifty 30-min recordings from the years 2015-2017 containing over one thousand upcalls, the network achieved recalls up to 80% while maintaining a precision of 90%. Importantly, the performance of the network improved as more variance was introduced into the training dataset, whereas the opposite trend was observed using a conventional linear discriminant analysis approach. This study demonstrates that DNNs can be trained to identify North Atlantic right whale upcalls under diverse and variable conditions with a performance that compares favorably to that of existing algorithms.