Holger Klinck

The vocal activity and detectability of tropical birds are subject to high levels of temporal heterogeneity, but quantifying patterns of diel and day‐to‐day variation in complex systems is challenging with traditional point count methods. As a result, research concerning stochastic temporal effects on tropical bird assemblages is limited, typically offering only broad conclusions, such as that overall activity is highest in the first few hours of the morning and some species are active at different times of the day. Passive acoustic monitoring introduces several advantages for studying temporal variation, particularly by enabling simultaneous and continuous data collection across adjacent sites. Here, we employed autonomous recording units to quantify temporal variation in bird vocal activity and observed species richness at an Amazonian reserve in Madre de Dios, Peru, a region featuring some of Earth's richest, most complex bird assemblages. We manually annotated 18 dawn hour recordings, collected simultaneously from three separate days at the same six sites, which represent various microhabitats and bird community compositions. We documented significant and consistent temporal variation in avian vocal activity levels and observed species richness within the dawn hour and across days. We found that temporal effects were stronger for vocal activity than for observed species richness and that vocal activity patterns over the course of the dawn hour varied between species. Our results indicate that overlooked temporal variation in Amazonian soundscapes may obfuscate the results of surveys that do not sufficiently account for temporal variables with simultaneous monitoring. While manual analysis of large volumes of soundscape data remains challenging, such data should be collected to supplement traditional surveys whenever possible. Rapid advances in the automated processing of acoustic data could lead to more efficient methods for reducing temporal bias and improving the calibration and accuracy of tropical bird surveys.

Many odontocetes produce whistles that feature characteristic contour shapes in spectrogram representations of their calls. Automatically extracting the time × frequency tracks of whistle contours has numerous subsequent applications, including species classification, identification, and density estimation. Deep-learning-based methods, which train models using analyst-annotated whistles, offer a promising way to reliably extract whistle contours. However, the application of such methods can be limited by the significant amount of time and labor required for analyst annotation. To overcome this challenge, a technique that learns from automatically generated pseudo-labels has been developed. These annotations are less accurate than those generated by human analysts but more cost-effective to generate. It is shown that standard training methods do not learn effective models from these pseudo-labels. An improved loss function designed to compensate for pseudo-label error that significantly increases whistle extraction performance is introduced. The experiments show that the developed technique performs well when trained with pseudo-labels generated by two different algorithms. Models trained with the generated pseudo-labels can extract whistles with an F1-score (the harmonic mean of precision and recall) of 86.31% and 87.2% for the two sets of pseudo-labels that are considered. This performance is competitive with a model trained with 12 539 expert-annotated whistles (F1-score of 87.47%).

Understanding niche partitioning of closely related sympatric species is a fundamental goal in ecology. Acoustic communication space can be considered a resource, and the acoustic niche hypothesis posits that competition between species may lead to partitioning of communication space. Here, we compare the calling behavior of two sympatric Bornean hornbill species—the rhinoceros hornbill ( Buceros rhinoceros ) and the helmeted hornbill ( Rhinoplax vigil )—to test for evidence of acoustic niche partitioning. Both hornbill species emit calls heard over many kilometers and have similar habitat preferences which is predicted to result in interspecific competition. We collected acoustic data on sympatric populations of both hornbill species using 10 autonomous recording units in Danum Valley Conservation Area, Sabah, Malaysia. We found that there was substantial spectral overlap between the calls of the two species, indicating the potential for competition for acoustic space. To test for evidence of acoustic niche partitioning, we investigated spatial and temporal patterns of calling in each species. Both hornbills were strictly diurnal and called throughout the day, and we were equally likely to detect both species at each of our recorders. We did not find evidence of temporal acoustic avoidance at a relatively coarse timescale when we divided our dataset into 1 h bins, but we did find evidence of temporal acoustic avoidance at a finer timescale when we used null models to compare the observed duration of overlapping calls to the expected amount of overlap due to chance. Abstract in Malay is available with online material.