Passive acoustic monitoring of the diel and annual vocal behavior of the Black and Gold Howler Monkey.

Patterns of vocal activity may involve information about vocalizations themselves as well as their function. In birds, vocal activity at the individual and population level is generally closely associated with breeding cycles, reaching the peak during territorial and mating competition, and decreasing with the onset of egg incubation and chick feeding. However, little is known about patterns of vocal activity in avian brood parasites that have unusual breeding cycles without parental care. Using passive acoustic monitoring, we determined the seasonal and diurnal patterns of population vocal activity in two avian brood parasites: the Common CuckooCuculus canorusand the Lesser CuckooC. poliocephalus. We found that both species and both sexes showed a similarly highly structured pattern of seasonal vocal activity, reaching a sharp peak in the early breeding season when birds compete for territories and mates, although males sang more frequently than females. Likewise, the diurnal patterns of vocal activity were similar in both species and both sexes of cuckoos, with peak activity occurring around dawn. Nocturnal calls by male cuckoos were also detected in both species, but only in the early breeding season. Collectively, the observed patterns of population vocal activity may suggest that the absence of parental care may not extend the period of vocal activity in these two species of brood parasites.

Patterns of vocal activity vary among tropical bird species, with some tending to sing before or close to dawn (“dawn chorus”) whereas others are more likely to vocalize later in the morning. Timing of vocal activity can, therefore, affect the results of bird counts which often rely heavily on vocalizations for species identification. Passive acoustic monitoring (PAM), which uses autonomous recording units (ARUs) to record vocalizations at a set schedule, allows birds to be sampled at multiple points simultaneously and can be set to record over extended time periods at single points. Thus, monitors provide an effective way to document vocal activity patterns during the morning when birds are typically most active. I used ARUs to record vocal activity of birds at a lowland forest site in eastern Ecuador during 2013-2017 on two 100-ha study plots. Monitors were set to record for 10-min periods followed by a 5-min break from 0545 to 0810. Species were identified by listening to the recordings, with presence of species noted during each 10-min period. Activity (number of species occurrences per period) was examined by strata (understory, canopy), guild, and by individual species. Overall patterns of activity (all species combined) increased rapidly from before dawn to about 0630 and then gradually decreased. The pattern was the same on both plots and consistent across years on each plot. Activity patterns differed among strata, guilds, and individual species. Understory birds peaked in activity before canopy birds and then declined to a point where there was less vocal activity than among canopy birds. Terrestrial granivores, omnivores, and frugivores all showed an early morning peak followed by a rapid decrease in contrast to arboreal species that increased in activity throughout the morning. Terrestrial insectivores did not differ from bark insectivores in their patterns of activity even though bark insectivores forage at higher strata. Substantial variation among species within different guilds also was apparent and illustrates that patterns of activity can vary even among species that forage in similar ways. Passive acoustic monitoring is a useful method for sampling bird activity because multiple monitors can be active at the same time across multiple points.

In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM is the generation of a significant amount of data, which often requires the use of machine learning tools for automated recognition. Here, we couple PAM with BirdNET, a free-to-use sound algorithm to assess, for the first time, the precision of BirdNET in predicting three tropical songbirds and to describe their patterns of vocal activity over a year in the Brazilian Pantanal. The precision of the BirdNET method was high for all three species (ranging from 72 to 84%). We were able to describe the vocal activity patterns of two of the species, the Buff-breasted Wren (Cantorchilus leucotis) and Thrush-like Wren (Campylorhynchus turdinus). Both species presented very similar vocal activity patterns during the day, with a maximum around sunrise, and throughout the year, with peak vocal activity occurring between April and June, when food availability for insectivorous species may be high. Further research should improve our knowledge regarding the ability of coupling PAM with BirdNET for monitoring a wider range of tropical species.

Chaco Chachalaca ( Ortalis canicollis) is a declining Neotropical bird, for which our current knowledge about its natural history is very limited. Here, we evaluated for first time the utility of passive acoustic monitoring, coupled with automated signal recognition software, to monitor the Chaco Chachalaca, described the vocal behavior of the species across the diel and seasonal cycle patterns, and proposed an acoustic monitoring protocol to minimize error in the estimation of the vocal activity rate. We recorded over a complete annual cycle at three sites in the Brazilian Pantanal. The species was detected on 99% of the monitoring days, proving that this technique is a reliable method for detecting the presence of the species. Chaco Chachalaca was vocally active throughout the day and night, but its diel activity pattern peaked between 0500 and 0900. The breeding season of Chaco Chachalaca in the Brazilian Pantanal, based on seasonal changes in vocal activity, seems to occur during the last months of the dry season, with a peak in vocal activity between August and October. Our results could guide future surveys aiming to detect the presence of the species, both using traditional or acoustic surveys, or to evaluate changes in population abundance using passive acoustic monitoring, for which recorders should be left in the field for a minimum period of nine days to obtain a low-error estimate of the vocal activity of the species. Our results suggest that passive acoustic monitoring might be useful, as a complementary tool to field studies, for monitoring other cracids, a family with several threatened species that are reluctant to human presence.

Species identification and recording in breeding bird surveys vastly rely on the registration of avian calls and songs. Despite comprehensive expert knowledge on species-specific activity patterns, data-based analyses of vocal activity patterns are lacking. Recent advances in passive acoustic monitoring allow the direct measurement of bird vocal activity at very high temporal resolution. We conducted a comprehensive survey, recording 25,000 h of audio data at 256 forest sites in Lower Saxony, Germany, to investigate vocal activity patterns of the European forest bird community. Our results reveal a high degree of inter-specific variability in seasonal and diel vocal activity patterns, including strong circular patterns along the day–night cycle and a significant seasonal component. Comparing acoustic detectability to species-specific survey recommendations revealed critical temporal discrepancies for 64.2% of species, and standard protocols (sunrise to 4 h after sunrise) showed discrepancies for 41.5% of species. This highlights the potential for temporal survey optimization to reduce imperfect detection and increase accuracy and precision. Emphasis should be given to the hours before and after sunrise and also sunset for sampling less detectable species. Combining observer-based surveys with passive acoustic monitoring might leverage the strengths of both methods. Our results also emphasize the potential of continuous recording schedules in passive acoustic monitoring to capture diverse temporal patterns. This study provides a baseline for future research on vocal activity patterns across habitats, throughout the year, and regarding anthropogenic impacts. Our findings may raise awareness among ornithologists about the sources of variation in acoustic detectability and its implications for breeding bird surveys, highlighting potential for methodological adjustments in survey timing and consequences for carful interpretation of bird surveys.

Monitoring the vocal behavior of owls is challenging because of their nocturnal habits and limited vocal activity. Here, we evaluated the use of passive acoustic monitoring coupled with automated signal recognition software to monitor the spontaneous vocal activity of the Ferruginous Pygmy-Owl (Glaucidium brasilianum) over a complete annual cycle at five recording stations in the Brazilian Pantanal. The vocal behavior of this species was concentrated during the crepuscular periods, with highest vocal activity in the hours prior to sunrise. The Ferruginous Pygmy-Owl was vocally active throughout the year, but the species showed a peak of activity from June to August. Paired Ferruginous Pygmy-Owl males tend to perform territorial calls less often during the nestling period, which may partly explain the significant decrease in the vocal activity after August. Our results suggest that the breeding period of the species starts in June, and the nesting phase probably occurs from September onwards, when the wet season starts. The first rains in seasonal tropical areas are usually associated with an increase in food availability, which may explain the species´ breeding period onset. Future surveys aiming to monitor the species, avoiding the use of broadcast calls, should be performed before sunrise between June and August, when the vocal activity was maximal.

Passive acoustic monitoring is a reliable method to study species behaviour and ecology, enabling the discovery of species activity patterns, especially in remote locations. Here, we conducted a year-long recording study to describe annual and circadian patterns in the vocal activity of two African barbet species, the Yellow-rumped Tinkerbird Pogoniulus bilineatus and the Western Tinkerbird Pogoniulus coryphaea. We used automated software to detect vocalisations of the two species from 4893 one-hour recordings taken in Cameroon. In total, we obtained 31,526 vocalisations of Yellow-rumped Tinkerbirds and 1318 vocalisations of Western Tinkerbirds. We used generalised additive mixed models to determine whether the month or hour of recording or meteorological conditions influenced the species’ vocal activity. Our results indicated that both tinkerbirds are likely seasonal breeders, as the highest vocal activity of both species was during the dry season. Both species exhibited smaller activity peaks in the wet season. Our study provides new information on the vocal activity patterns of two barbet species, which could be valuable in future monitoring and surveying efforts. This study provides an illustrative example of two useful technologies that facilitate studies in remote areas: passive acoustic monitoring for determining species’ activity patterns and automatic recognition software for the rapid analysis of large datasets.

Current knowledge regarding the vocal behavior in tropical non-passerines is very limited. Here, we employed passive acoustic monitoring to study the vocal activity of the white-tipped dove (Leptotila verreauxi) at three sites over a year in the Brazilian Pantanal. The diel pattern of vocal activity showed a bimodal pattern, with significantly higher vocal activity after sunrise than during the other hours of the day, in agreement with prior studies on this species and other members of Columbidae. The species was vocally active throughout the year, but vocal activity was maximum during May-June and lowest during January-February. Relative air humidity was positively associated with vocal activity, which may be related to the improvement of sound transmission under more humid conditions, but it could also be related to foraging efficiency due to a higher availability of invertebrates on wetter days. Vocal activity was not related to the mean air temperature or daily rainfall. Acoustic monitoring proved to be a useful tool for monitoring this shy forest species, for which a minimum number of three monitoring days was needed to detect a reliable vocal activity rate. Future studies should evaluate its use for monitoring other species of doves and pigeons that are secretive or threatened.

Passive acoustic monitoring (PAM) is an automatic and non-invasive method for long-term monitoring of bird vocal activity. PAM generates a large amount of data, and the automatic recognition of data poses significant challenges. BirdNET is a free-to-use sound algorithm. We evaluated the effectiveness of BirdNET in identifying the vocalizations of Chinese Bamboo Partridge (a Chinese endemic species) and proposed a random forest (RF) method to improve the result based on the detection of BirdNET. The diurnal and seasonal patterns of calling activity were described based on the identification results. The results showed that the recall of BirdNET-Analyzer was 16.6%, the precision of BirdNET-Analyzer-XHS was 50.8%, and the recall and precision of the RF model were 75.2% and 74.4%, respectively. The diurnal vocal activity of the Chinese Bamboo Partridge showed a bimodal pattern, with peaks around sunrise and sunset and low vocal activity during the central hours of the day. The seasonal vocal activity displayed a unimodal pattern, with a peak in vocal activity during April and May. This study used the Chinese Bamboo Partridge as an example and proposes an improved RF model, built on BirdNET recognition results, for species identification, providing a practical approach for recognizing the vocalizations of regional species.

Passive acoustic monitoring (PAM) is a promising, if underused, technology for primate conservation. Successful PAM requires an understanding of the target species' vocal activity patterns and the factors that influence them, but this information remains scarce for most vocal primates. This is true for sportive lemurs ( Lepilemur spp.), which are understudied but otherwise excellent candidates for PAM, being highly vocal and threatened. We deployed autonomous audio recorders to measure vocal activity in the Critically Endangered Nosy Be sportive lemur ( Lepilemur tymerlachsoni ), sampling a 4‐h window from twilight each night for two lunar cycles. Our objectives were to identify suitable call types for monitoring, evaluate a user‐friendly automated call detection algorithm, assess temporal variation in vocal activity, and examine how environmental variables and moon illumination influence vocal activity. Automated call detection found an estimated 38% of all target calls but generated a high rate of false positives (96%). Among three call types, “ouah” calls were common and had the highest detection rate (51%), making them suitable target calls. Call rates were highest in the fourth hour following twilight, increased with temperature and moon illumination, and decreased during rainfall. We also observed variation in vocal activity between recording dates and sites, highlighting the need for sufficient temporal and spatial replication. We present recommendations for improving survey design, detection probability, and population inferences from PAM. The recommendations are specific to L . tymerlachsoni and may guide similar work on other sportive lemurs, although species‐specific differences in vocal behavior and ecology must also be considered.

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.

Compared with traditional field techniques, automated and noninvasive bird monitoring techniques, such as passive acoustic monitoring, offer significant advantages. However, the extensive data collected through passive acoustic monitoring can be challenging to analyze and may require the use of machine learning algorithms for efficient processing. BirdNET is a user-friendly and ready-to-use machine learning tool that can recognize more than 6500 wildlife species, including several tropical species. However, the performance of BirdNET in tropical ecosystems has rarely been assessed. Here, we evaluate the effectiveness of BirdNET for monitoring the vocal activity of three Neotropical wetland species from recordings collected over a year in the Brazilian Pantanal: Green Ibis (Mesembrinibis cayennensis), Limpkin (Aramus guarauna), and Sunbittern (Eurypyga helias). BirdNET was able to detect the presence of the three species in 82–92% of the recordings with known presence. Similarly, BirdNET’s ability to correctly identify vocalizations was consistently greater than 77% (range 77–98%), confirming its effectiveness for monitoring these three tropical bird species. The peak vocal activity for the three species occurred during crepuscular periods, at the end of the rainy season, and during the receding season, a period when the risk of nest damage from flood pulses is low and food availability is high owing to the large presence of small water bodies. The use of machine learning algorithms such as BirdNET may improve bird monitoring in tropical areas but also facilitate research that improves our knowledge of birds’ natural history, which remains unknown for many tropical species.

Monitoring vulnerable species inhabiting mountain environments is crucial to track population trends and prioritize conservation efforts. However, the challenging nature of these remote areas poses difficulties in implementing effective and consistent monitoring programmes. To address these challenges, we examined the potential of passive acoustic monitoring of a cryptic high mountain bird species, the Rock Ptarmigan Lagopus muta. For 5 months in each of two consecutive years, we deployed 38 autonomous recording units in 10 areas of the Swiss Alps where the species is monitored by a national count monitoring programme. Once the recordings were collected, we built a machine‐learning algorithm to automate call recognition. We focused on studying the species' daily and seasonal calling phenology and relating these to meteorological and climatic data. Rock Ptarmigans were vocally active from March to July, with a peak of activity occurring between mid‐March and late April, 1 or 2 months earlier than the second half of May when the counts of the monitoring programme take place. The calling rate peaked at dawn before dropping rapidly until sunrise. Daily vocal activity demonstrated a consistent association with weather conditions and moon phase, whereas the timing of seasonal vocal activity varied with temperature and snow conditions. We found that the peak of vocal activity occurred when the snowpack was still thick and snow cover was close to 100% but with a local peak of high temperatures. Between our two study years, the peak of vocal activity occurred 30 days later in the colder year, suggesting phenological plasticity in relation to environmental conditions. Passive acoustic monitoring has the potential to complement conventional acoustic counts of cryptic birds by highlighting periods of higher detectability of individuals, and to survey small populations that often remain undetected during single visits. Moreover, our study supports the idea that passive acoustic monitoring can provide valuable data over large spatial and temporal scales, allowing decryption of hidden ecological patterns and assisting in conservation efforts.

Automated signal recognition as a useful tool for monitoring little-studied species: The case of the Band-tailed Nighthawk

The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of data that routinely exceeds the capacity for analysis. Computational advances, particularly the integration of machine learning approaches, will support data extraction efforts. However, the analysis and interpretation of these data will require parallel growth in conceptual and technical approaches for data analysis. Here, we use a large hand‐annotated dataset to showcase analysis approaches that will become increasingly useful as datasets grow and data extraction can be partially automated.We propose and demonstrate seven technical approaches for analyzing bioacoustic data. These include the following: (1) generating species lists and descriptions of vocal variation, (2) assessing how abiotic factors (e.g., rain and wind) impact vocalization rates, (3) testing for differences in community vocalization activity across sites and habitat types, (4) quantifying the phenology of vocal activity, (5) testing for spatiotemporal correlations in vocalizations within species, (6) among species, and (7) using rarefaction analysis to quantify diversity and optimize bioacoustic sampling.To demonstrate these approaches, we sampled in 2016 and 2018 and used hand annotations of 129,866 bird vocalizations from two forests in New Hampshire, USA, including sites in the Hubbard Brook Experiment Forest where bioacoustic data could be integrated with more than 50 years of observer‐based avian studies. Acoustic monitoring revealed differences in community patterns in vocalization activity between forests of different ages, as well as between nearby similar watersheds. Of numerous environmental variables that were evaluated, background noise was most clearly related to vocalization rates. The songbird community included one cluster of species where vocalization rates declined as ambient noise increased and another cluster where vocalization rates declined over the nesting season. In some common species, the number of vocalizations produced per day was correlated at scales of up to 15 km. Rarefaction analyses showed that adding sampling sites increased species detections more than adding sampling days.Although our analyses used hand‐annotated data, the methods will extend readily to large‐scale automated detection of vocalization events. Such data are likely to become increasingly available as autonomous recording units become more advanced, affordable, and power efficient. Passive acoustic monitoring with human or automated identification at the species level offers growing potential to complement observer‐based studies of avian ecology.