How to count bird calls? Vocal activity indices may provide different insights into bird abundance and behaviour depending on species traits

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.

ABSTRACTCapsule: Population changes of many moorland and heathland birds in southwest England show associations with environmental change, and the area supports notable breeding populations of species of conservation concern.Aims: To quantify changes in moorland and heathland breeding bird abundance in relation to changes in environmental variables.Methods: A two-visit moorland bird survey method was used to estimate the abundance of 23 target bird species in 2008 and 2014 in Exmoor National Park, southwest England. Data on changes in 25 environmental variables over the same period were used to test associations between changes in bird abundance and environmental change.Results: Fourteen species increased in abundance and nine decreased. Breeding species which wintered on or close to Exmoor were less likely to show negative population changes than species which migrated to winter in southern Europe or Africa. Change in abundance of 18 species was associated with change in at least one environmental variable, and these relationships were broadly consistent with the known ecology of these species.Conclusion: Although some moorland species have declined or gone locally extinct, this area of moorland and heathland in southwest England remains an important stronghold for several species of high conservation concern. The maintenance of semi-natural moorland and heathland habitats in areas such as Exmoor may provide important refuges for such species, both now and in the future.

Passive acoustic monitoring (PAM) provides new opportunities for assessing bird abundance and habitat preferences, yet its performance relative to traditional point-count surveys (PCO) remains insufficiently tested, especially for quiet and inconspicuous forest passerines. We compared the vocal activity and habitat associations of the Goldcrest Regulus regulus and Firecrest Regulus ignicapilla in a temperate forest ecosystem using PAM-derived and PCO-based indices. Across 30 monitoring points in the Romincka Forest (Poland), PAM yielded >33,000 recorded songs and revealed strong spatial variation in both species. Vocal activity measures obtained from PAM correlated positively with PCO detections and territories, confirming the reliability of PAM as a complementary abundance indicator. Goldcrest vocal activity showed a strong positive association with the proportion of coniferous trees—especially spruce—and with local tree-species richness, reflecting the species’ affinity for structurally diverse conifer-dominated stands. In contrast, Firecrest abundance was unrelated to forest structure in PAM data, while PCO detections indicated avoidance of pine and lower activity in species-rich stands. No significant relationship with stand age was observed for either species. The weak interspecific correlations in activity parameters highlight their distinct ecological niches despite overlapping ranges. Based on PCO Goldcrests proved to be more abundant, with a territorial ratio of 3:2 compared to Firecrests. Our study demonstrates that PAM effectively captures variation in abundance and habitat selectivity of both Regulus species and provides a scalable, efficient complement to traditional surveys in temperate forest ecosystems.

Habitat changes explain shifts in bird community composition in abandoned military training areas: Lessons for conservation

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.

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

The global road network, currently over 45 million lane‐km in length, is expected to reach 70 million lane‐km by 2050, while the number of vehicles utilizing it is expected to double. Roads have been shown to affect a range of wildlife, including birds, but most studies have been relatively small scale. We use data from across Great Britain to analyse the relationships between roads and the spatial distributions of bird populations. We model counts of 51 common and widespread species from the U.K. Breeding Bird Survey in relation to road exposure, which we calculated for each count site using the density, distance and traffic volume of all roads within a 5‐km radius. In these models, we incorporate other factors known to affect bird populations, including agricultural intensity, human population, habitat and climate. Importantly, we also account for differences in detectability of birds near to roads. The abundances of 30 species were strongly significantly related to exposure to either major or minor roads. Species were generally in higher abundances with increasing exposure to minor roads (20/28). In contrast, most significant associations between major road exposure and bird abundance were negative (7/8). For species with significant effects of road exposure, we assessed how estimated abundance changed across the central 50% of road exposure experienced for each species. The mean decrease in abundance was 19% and the mean increase was 47%. These changes in bird abundance were up to half as large as those associated with increasing agricultural intensity, a factor often cited as a major cause of bird population changes. Synthesis and applications. Our research shows many species to vary in abundance with increasing road exposure. This suggests that roads may modify bird populations on a national scale and that their potential as drivers of biodiversity change should not be overlooked. Our work highlights the need for appropriate mitigation of roads, particularly in areas important for avian biodiversity. This could include efforts to reduce impacts of road noise and/or collisions, such as reduced speed limits or quieter road surfaces in sensitive areas.

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.

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.

Most lowland rivers in the southwestern United States have been impounded, diverted, or dewatered. Lack of flooding due to river impoundments on the Middle Rio Grande has contributed to the spread of exotic vegetation such as Russian olive (Elaeagnus angustifolia) and saltcedar (Tamarix) associated with fuel loads of dense understory. Management has largely focused on thinning of understory vegetation to remove nonnative species and reduce fire risk, but it is unclear how these actions impact avian populations. Using distance-sampling methods, we quantified densities of five groups of birds (birds nesting in canopy, midstory, and understory; water-obligates; and spring migrants) across 12 types of vegetation spanning managed and nonmanaged stands. We used a space-for-time substitution model to estimate changes in abundance of birds from scenarios that applied four possible options for management at the landscape scale. One option, mechanical clearing of cottonwood understory, had severe detrimental impacts for abundances of the three nesting guilds and spring migrants when applied across the study area. A hand-thinning method to remove most exotics but retain native shrubs and the ground layer also negatively impacted birds nesting in understory but had positive or no effect on the other four groups of birds. Over the short term (5–10 years), not clearing would increase the proportion of native and nonnative understory and generally increase abundances of birds. With application of “no management” over a longer period (50–75 years), we assumed transition of most cottonwood (Populus deltoides var. wislizeii) stands to shrublands of Russian olive and projected that canopy-nesting birds would decrease but other groups would increase. A scenario of wetland restoration that converted 25% of open habitat to wetland increased abundances of understory-nesting birds slightly and water-obligate birds substantially. Our projections of changes in avian populations will help managers evaluate biological impacts of management being considered for the Middle Rio Grande.La mayoría de los ríos de tierra baja del suroeste de los Estados Unidos ha sido embalsada, desviada, o deshidratada. La falta de inundaciones debido a embalses de la parte media del río Bravo ha contribuido a la propagación de vegetación exótica como el olivo ruso (Elaeagnus angustifolia) y el cedro salado (Tamarix), asociada con cargas de combustible del sotobosque denso. El manejo se ha enfocado en disminuir la vegetación del sotobosque para remover las especies no nativas y reducir los riesgos de incendios, pero no es claro el impacto que estas acciones tienen sobre las poblaciones aviarias. Por medio de métodos de muestreo a distancia, cuantificamos las abundancias de cinco grupos de aves (pájaros que anidan en el dosel, el estrato medio y el sotobosque; obligados al agua; y especies migratorias primaverales) en 12 tipos de vegetación en parcelas manejadas y no manejadas. Utilizamos un modelo de sustitución de espacio por tiempo para estimar los cambios de abundancia de aves en escenarios que aplicaron cuatro posibles opciones para manejo a escala de paisaje. Una opción, la limpieza mecánica de álamos del sotobosque, tuvo impactos negativos severos sobre la abundancia de los tres grupos de aves según su anidación y las especies migratorias primaverales cuando se aplicó a toda el área de estudio. El método de limpieza manual para remover la mayoría de las especies exóticas y mantener los arbustos nativos y el sotobosque también impactó negativamente a las aves anidando en el sotobosque pero tuvo un impacto positivo o neutro en los otros cuatro grupos de aves. A corto plazo (5–10 años), no limpiar incrementaría la proporción del sotobosque nativo y no nativo y en general incrementaría la abundancia de aves. Con la aplicación de “no-manejo” a largo plazo (50–75 años), asumimos la transición de la mayoría de las parcelas de álamos (Populus deltoides var. wislizeii) a matorral

Passive acoustic monitoring (PAM) allows for cost-effective, unattended and non-invasive acoustic sampling over an extended period of time and is now an invaluable tool for acoustic monitoring of vocally active species. Its application is rapidly growing in studies covering multiple aspects of avian ecology and behaviour, including presence-absence surveys, population density estimations, threatened species monitoring and anthropogenic impacts on populations. However, the potential for information on year-round variation in male and female vocalisations and the factors affecting duetting behaviour to be derived from PAM has never been exploited. In the present study we deployed automatic recording units (ARU) to investigate long-term sex-specific life strategies based on the vocal activity of the Yellow-breasted Boubou Laniarius atroflavus, an Afromontane, duetting songbird. Using automatic detection we showed strong seasonality in singing performance with males producing solo songs at a higher rate during the breeding than non-breeding season whereas female solos peaked at the end of the breeding season. Duets were produced at a relatively stable rate throughout the year except the time encompassing the turn of the rainy and dry seasons when overall vocal activity was at a low level. In general, year-round singing patterns coincided with the rainy and dry seasons at the study site with vocal activity peaking in the dry season and gradually declining with the onset of rainfall. In addition, we found that boubous were slightly more vocally active when morning temperature was higher, especially in the rainy season. Sex-dependent variation in vocal activity in relation to life cycle stage may suggest that differences between males and females are of functional significance. Most likely, the seasonality of male solo songs could be explained on the basis of sexual selection pressure and that male and female joint vocalizations act as a cooperative behaviour playing a role in territory defence against conspecifics. Our PAM-based results provide new and important insights into how male–female solo songs and duet interactions may be related to year-round territoriality. This may help us to better understand the evolutionary significance of duetting. Furthermore, our findings highlight the link between life cycle events of a tropical songbird and seasonal changes in weather conditions. By tracking the effect of weather on vocal activity, PAM might provide an important indication of how changes in climate may affect bird behaviour.

Passive Acoustic Monitoring (PAM) has emerged as a pivotal technology for wildlife monitoring, generating vast amounts of acoustic data. However, the successful application of machine learning methods for sound event detection in PAM datasets heavily relies on the availability of annotated data, which can be laborious to acquire. In this study, we investigate the effectiveness of transfer learning and active learning techniques to address the data annotation challenge in PAM. Transfer learning allows us to use pre-trained models from related tasks or datasets to bootstrap the learning process for sound event detection. Furthermore, active learning promises strategic selection of the most informative samples for annotation, effectively reducing the annotation cost and improving model performance. We evaluate an approach that combines transfer learning and active learning to efficiently exploit existing annotated data and optimize the annotation process for PAM datasets. Our transfer learning observations show that embeddings produced by BirdNet, a model trained on high signal-to-noise recordings of bird vocalisations, can be effectively used for predicting anurans in PAM data: a linear classifier constructed using these embeddings outperforms the benchmark by 21.7%. Our results indicate that active learning is superior to random sampling, although no clear winner emerges among the strategies employed. The proposed method holds promise for facilitating broader adoption of machine learning techniques in PAM and advancing our understanding of biodiversity dynamics through acoustic data analysis.

North Atlantic right whale monitoring in Roseway Basin, Canada, is primarily based on short‐term (<14 d) visual surveys conducted during August–September. Variability in survey effort has been the biggest limiting factor to studying changes in the population's occurrence and habitat use. Such efforts could be enhanced considerably using passive acoustic monitoring (PAM). We sought to determine if variation in whale presence, relative abundance, demography, and/or behavior (estimated through visual surveys) could be explained by variation in three right whale call types in this habitat. A generalized linear model was fit to 23 d of concurrent PAM and visual monitoring during four summers within the Roseway Basin Right Whale Critical Habitat boundaries. The model revealed significant positive relationships between relative abundance, call counts and presence of surface‐active group behavior. PAM can refine daily right whale presence estimates. While visual observations (n= 23 d) implied a 40% decline in right whale presence during 2014–2015 relative to 2004–2005, PAM data (n= 211 d) showed right whales were present between 71%–85% of survey days throughout all years analyzed. We demonstrate that PAM is a useful tool to extend periods of right whale monitoring, especially in areas where visual monitoring efforts may be limited.

The effect that extreme natural events have on biological diversity is relatively poorly known. We used a before–after control‐impact (BACI) design to analyze changes in bird abundances and communities following Hurricane Gudrun, which struck southern Sweden in January 2005, felling 75 million m3 of forest and causing damage to 5% of forested areas (half a million hectares) in a few hours. We used recent measures of impact in combination with classical BACI contrasts to analyze bird count data from a monitoring program in Sweden. We investigated changes in the abundance of 17 species commonly found in forests, along with changes in species composition and functional structure of the bird community. In total, we considered 34 response variables and examined whether responses were immediate or long‐term. There was no evidence of a strong effect of the hurricane on the abundances of six species. Estimates of the effects on five species were too uncertain to draw inferences. We detected positive and negative effects of the hurricane on the abundances of the remaining six species, but the magnitude of effects often was small. Generally, the effects were in the expected direction: negative on birds associated with mature forest and positive on birds associated with open land or young forest. We found evidence of an increase in the proportion of species that nest on the ground and a decrease in the proportion of species that nest in cavities and trees. In contrast, the hurricane had no discernible effect on functional measures of diversity (richness, evenness or divergence), or on communities’ reproductive or morphological characteristics. Our results suggest that the hurricane affected bird populations and communities, but the magnitude of effects was generally small.

Passive acoustic monitoring (PAM) was used to generate a six‐week continuous time‐series of occupation of a segment of the St. Lawrence Estuary by belugas. The PAM data were acquired from a cabled hydrophone deployed at mid‐depth into the 300‐m deep Laurentian channel off Cap‐de‐Bon‐Desir during summer 2003. Beluga vocalisation activity time‐series was obtained from the [0.5‐5.0 kHz] bandpassed PAM data that were processed to filter out noise from the heavy shipping on the nearby St. Lawrence Seaway and from occasional whale watching boats, and low‐frequency traces of clicks, via adaptive spectral subtraction and image processing methods applied to the spectrogram. The remaining traces on the binary image of the spectrogram were summed up to generate a vocalisation index, which was essentially due to belugas in this region. The vocalisation index time‐series was then analysed for presence of circadian rhythms and correlations with the semi‐diurnal tidal cycle, currents from an acoustic Doppler current profiler and wind. Results are interpreted from the knowledge of the biological and physical oceanographic processes occurring in the monitored area.