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.

Integrating Citizen Scientist Data into the Surveillance System for Avian Influenza Virus, Taiwan.

The continuing circulation and reassortment with low-pathogenicity avian influenza Gs/Gd (goose/Guangdong/1996)-like avian influenza viruses (AIVs) has caused huge economic losses and raised public health concerns over the zoonotic potential. Virologic surveillance of wild birds has been suggested as part of a global AIV surveillance system. However, underreporting and biased selection of sampling sites has rendered gaining information about the transmission and evolution of highly pathogenic AIV problematic. We explored the use of the Citizen Scientist eBird database to elucidate the dynamic distribution of wild birds in Taiwan and their potential for AIV exchange with domestic poultry. Through the 2-stage analytical framework, we associated nonignorable risk with 10 species of wild birds with >100 significant positive results. We generated a risk map, which served as the guide for highly pathogenic AIV surveillance. Our methodologic blueprint has the potential to be incorporated into the global AIV surveillance system of wild birds.

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.

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.

GABB: A global dataset of alpine breeding birds and their ecological traits.

Alpine ecosystems represent varied climates and vegetation structures globally, with the potential to support rich and functionally diverse avian communities. High mountain habitats and species are under significant threat from climate change and other anthropogenic factors. Yet, no global database of alpine birds exists, with most mountain systems lacking basic information on species breeding in alpine habitats, their status and trends, or potential cryptic diversity (i.e., sub-species distributions). To address these critical knowledge gaps, we combined published literature, regional monitoring schemes, and expert knowledge from often inaccessible, data-deficient mountain ranges to develop a global list of alpine breeding bird species with their associated distributions and select ecological traits. This dataset compiles alpine breeding records for 1,310 birds, representing 12.0% of extant species and covering all major mountain regions across each continent, excluding Antarctica. The Global Alpine Breeding Bird dataset (GABB) is an essential resource for research on the ecological and evolutionary factors shaping alpine communities, as well as documenting the value of these high elevation, climate-sensitive habitats for conserving biodiversity.

Range map datasets for terrestrial vertebrates across Taiwan.

Accurate data describing the geographic distribution of specific species form the basis for effective conservation management policies. However, for most species the freely available distributional information is usually confined to either expert maps or purely theoretical maps constructed by using a variety of modeling frameworks. These maps usually do not provide enough resolution for conservation applications or do not accurately describe the current distribution status. In this study, we constructed a novel workflow designed to integrate data from various species distribution models and expert knowledge into a single unified modeling process. Under this workflow, we systematically constructed current distribution maps for a selection of terrestrial vertebrates found across Taiwan. We used species distribution modeling as the base and then aggregated multiple open datasets describing species occurrence and environmental factors as data sources. Thereafter, we estimated the primary broad-scale and high spatial resolution species range maps using the MaxEnt modeling algorithm, and then consulted experts on each taxa to refine these maps. This dataset provides up-to-date species distribution maps for 379 terrestrial vertebrates in Taiwan, with members from across four taxa (27 amphibians, 52 reptiles, 264 birds, and 36 mammals). This dataset helps to fill the spatial knowledge gaps for conservation concerns and improves our understanding of the geographic distribution of more than half (61%) of the vertebrate species of Taiwan. Furthermore, by stacking the range maps of multiple species, we can identify vertebrate diversity hotspots and identify priority areas for conservation.

A trait dataset for Taiwan's breeding birds.

BACKGROUND: Species traits affect how a species interacts with the environment and other species and thus determine the role of the species in an ecosystem. They affect not only population dynamics of a species across space and over time, but also community structure and function through their key role in the community assembly processes. Information on species traits is also highly relevant for conservation planning as they determine the adaptive ability of a species in the face of environmental changes. However, information on species traits is usually scarce and sparsely distributed amongst diverse types of literature and sources. Difficulty in accessing comprehensive information on species traits has formed an essential knowledge gap, limiting our understanding of biodiversity patterns and ecosystem functioning and preventing effective conservation. Even for birds, a well-studied taxon, comprehensive trait information is still unavailable or distributed across different sources for many species. NEW INFORMATION: In this study, we compiled information from a variety of sources on 23 traits for all breeding birds, including 157 resident and 14 summer visiting species, in Taiwan and surrounding islands. The 23 traits include those related to the movement patterns, morphology, geographic distributions, activity patterns, feeding behaviour, habitat use, and breeding behaviour and strategies of the species. The trait information was obtained, not only from published literature and datasets, but also from unpublished banding records and specimen measurements. The dataset also contains derived traits, including the elevation and temperature boundaries of species distribution ranges in Taiwan. In addition, structured information on nest characters, which is seldom compiled in trait datasets, has been made available, for the first time, for the breeding birds in Taiwan. Therefore, the most comprehensive trait dataset to date on breeding birds in Taiwan will allow trait-based research and applications in diverse topics and thus enhance our understanding of the patterns and dynamics of breeding bird diversity and its functions in Taiwan.