There is very little information on the diurnal and seasonal patterns of Cuculidae species’ calling activity in subtropical areas. In this study, we used passive acoustic monitoring to investigate the diurnal and seasonal patterns of calling activity of seven Cuculidae species at three sites in eastern China’s forest over a year. Our results showed that these species exhibited significant diurnal variations except for Cuculus micropterus. Eudynamys scolopaceus and Cuculus saturatus increased their vocal activity at dawn and dusk, whereas the other four species peaked their calling activity in the morning. Five species showed significant seasonal variations with a peak in vocal activities earlier in the season (late May or early June) and a gradual decline after that, except for Clamator coromandus, which displayed two peaks in seasonal calling activity. As for Cuculus micropterus, its calling activity was not significantly related to the season. Our study has provided basic knowledge about the calling patterns of seven Cuculidae species, and based on seasonal changes in vocal activity we propose that the breeding season of these species in east China begins in late May and ends in July.

The vocal behaviour of birds can change daily and seasonally. Understanding the daily and seasonal acoustic activity patterns of birds helps to understand their behavioural ecology and is crucial for designing effective monitoring protocols. We studied daily and seasonal acoustic activity patterns for a complete annual cycle of three bird species: Alström's Warbler Phylloscopus soror, Brownish-flanked Bush Warbler Horornis fortipes and Streak-breasted Scimitar Babbler Pomatorhinus ruficolli at three different sites in Yaoluoping National Nature Reserve, Anhui Province, East China. All three species displayed a peak in singing activity in the morning; however, they differed in their acoustic activity during the daytime. Alström's Warbler was vocally active for five months (April–August), with higher singing activity earlier in the season. Brownish-flanked Bush Warbler was vocally active for six months (March–August) and showed a significant variation in singing activity over time. Streak-breasted Scimitar Babbler was vocally active for nine months (March–November) and displayed a slight variation in singing activity among months at different sites. The acoustic activity of the Alström's Warbler and Brownish-flanked Bush Warbler was negatively correlated with precipitation, whereas in Streak-breasted Scimitar Babbler there was no relationship with precipitation. Overall, we found large temporal variation in singing activity within a day and between seasons. To better understand the function of the daily and seasonal pattern of singing, future studies should investigate the functioning of different song types used during daytime and morning.

There is an abundance of bird species in subtropical areas, but studies on the vocal behavior of non-passerines in subtropical regions are limited. In this study, passive acoustic monitoring was used to investigate the temporal acoustic patterns of the vocal activities of the Oriental Turtle Dove (Streptopelia orientalis) in Yaoluoping National Nature Reserve (YNNR) in eastern China. The results show that the vocal production of the Oriental Turtle Dove exhibited a seasonal variation, peaking in the period April–August. Additionally, its diurnal vocal activity displayed a bimodal pattern in late spring and summer, with the first peak in the morning and a secondary peak at dusk. Among weather factors, temperature significantly affected the temporal sound pattern of the Oriental Turtle Dove, instead of humidity and precipitation. This study, which was focused on sound monitoring technology, provides knowledge for further research on bird behavior and ecology. In the future, long-term sound monitoring could be used for managing and conserving bird biodiversity.

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.