Deforestation within breeding ranges may still drive population trends of migratory forest birds in the East Asian Flyway.

The East Asian Flyway (EAF) is the most species diverse of global flyways, with deforestation in its migratory landbird's non-breeding range suspected to be the main driver of population decline. Yet range-wide habitat loss impact assessments on EAF migratory landbirds are scarce, and seasonal variation in habitat preference of migratory species further increases the complexity for conservation strategies. In this study, we reviewed population trends of migratory forest breeding birds in the EAF along with their seasonal habitat preference from the literature and assessed the impact of forest cover change in species' breeding and non-breeding ranges on population trends. We found that 41.3% of the bird species with trend data available are declining, and most have higher forest preference in the breeding season. Despite 93.4% of the species experienced deforestation throughout their annual cycle, forest cover change in the non-breeding range was not identified as the main driver of population trend. However, forest cover change in species' regional breeding range interacts positively with the degree of breeding season forest preference in predicting population trends. We therefore stress that regional breeding habitat protection may still be important while following the call for cross-border collaboration to fill the information gap for flyway conservation.

An acoustic detection dataset of birds (Aves) in montane forests using a deep learning approach.

Background: Long-term monitoring is needed to understand the statuses and trends of wildlife communities in montane forests, such as those in Yushan National Park (YSNP), Taiwan. Integrating passive acoustic monitoring (PAM) with an automated sound identifier, a long-term biodiversity monitoring project containing six PAM stations, was launched in YSNP in January 2020 and is currently ongoing. SILIC, an automated wildlife sound identification model, was used to extract sounds and species information from the recordings collected. Animal vocal activity can reflect their breeding status, behaviour, population, movement and distribution, which may be affected by factors, such as habitat loss, climate change and human activity. This massive amount of wildlife vocalisation dataset can provide essential information for the National Park's headquarters on resource management and decision-making. It can also be valuable for those studying the effects of climate change on animal distribution and behaviour at a regional or global scale. New information: To our best knowledge, this is the first open-access dataset with species occurrence data extracted from sounds in soundscape recordings by artificial intelligence. We obtained seven bird species for the first release, with more bird species and other taxa, such as mammals and frogs, to be updated annually. Raw recordings containing over 1.7 million one-minute recordings collected between the years 2020 and 2021 were analysed and SILIC identified 6,243,820 vocalisations of seven bird species in 439,275 recordings. The automatic detection had a precision of 0.95 and the recall ranged from 0.48 to 0.80. In terms of the balance between precision and recall, we prioritised increasing precision over recall in order to minimise false positive detections. In this dataset, we summarised the count of vocalisations detected per sound class per recording which resulted in 802,670 occurrence records. Unlike data from traditional human observation methods, the number of observations in the Darwin Core "organismQuantity" column refers to the number of vocalisations detected for a specific bird species and cannot be directly linked to the number of individuals.We expect our dataset will be able to help fill the data gaps of fine-scale avian temporal activity patterns in montane forests and contribute to studies concerning the impacts of climate change on montane forest ecosystems on regional or global scales.

Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests.

Background: From passive acoustic monitoring (PAM) recordings, the vocal activity rate (VAR), vocalizations per unit of time, can be calculated and is essential for assessing bird population abundance. However, VAR is subject to influences from a range of factors, including species and environmental conditions. Identifying the optimal sampling design to obtain representative acoustic data for VAR estimation is crucial for research objectives. PAM commonly uses temporal sampling strategies to decrease the volume of recordings and the resources needed for audio data management. Yet, the comprehensive impact of this sampling approach on VAR estimation remains insufficiently explored. Methods: In this study, we used vocalizations extracted from recordings of 12 bird species, taken at 14 PAM stations situated in subtropical montane forests over a four-month period, to assess the impact of temporal sampling on VAR across three distinct scales: short-term periodic, diel, and hourly. For short-term periodic sampling analysis, we employed hierarchical clustering analysis (HCA) and the coefficient of variation (CV). Generalized additive models (GAMs) were utilized for diel sampling analysis, and we determined the average difference in VAR values per minute for the hourly sampling analysis. Results: We identified significant day and species-specific VAR fluctuations. The survey season was divided into five segments; the earliest two showed high variability and are best avoided for surveys. Data from days with heavy rain and strong winds showed reduced VAR values and should be excluded from analysis. Continuous recordings spanning at least seven days, extending to 14 days is optimal for minimizing sampling variance. Morning chorus recordings effectively capture the majority of bird vocalizations, and hourly sampling with frequent, shorter intervals aligns closely with continuous recording outcomes. Conclusions: While our findings are context-specific, they highlight the significance of strategic sampling in avian monitoring, optimizing resource utilization and enhancing the breadth of monitoring efforts.

Different Habitat Types Affect Bird Richness and Evenness.

More studies are needed on the mechanism and effective prediction of bird diversity in various habitats. The primary purpose of this study was to explore the difference in the species richness and evenness of various habitats. The secondary purpose was to explore which habitat types and compositions predict a high bird diversity. The 2010-2016 Taiwan Breeding Bird Survey was used to analyze the relationship between landscape habitat and bird ecology. Landscape habitat type was divided into seven categories and 26 sub-types: forestland, farmland, grassland, freshwater wetland, aquaculture pond and saltpan, coastland, and building area. Four ecological indexes were used: the number of bird individuals, the number of species, the Margalef Richness Index, and the Pielou Evenness Index. The result indicated that forestland decreased bird numbers, except in a windbreak forest. Natural and farmland-related habitats increased bird species richness. Similarly, the natural habitat increased species evenness. Urban greenspace could not replace the effect of forestland on species richness and evenness. Conifer forest, bamboo forest, windbreak forest, mixed tree, tall grassland, and orchard were important habitats for promoting higher species richness and evenness.