Many South-East Asian bird species are in rapid decline due to offtake for the cage-bird trade, a phenomenon driven largely by consumption in Indonesia and labelled the 'Asian Songbird Crisis'.Interventions aimed at reducing this offtake require an understanding of the spatial and temporal dynamics of the trade. We surveyed the bird-keeping habits of over 3,000 households from 92 urban and rural communities across six provinces on Java, Indonesia, and compared prevalence and patterns of bird keeping with those from surveys undertaken a decade ago. We estimate that one-third of Java's 36 million households keep 66-84 million cage-birds. Despite over half of all birds owned being non-native species, predominantly lovebirds (Agapornis spp.), the majority of bird-keepers (76%) owned native species.Ownership levels were significantly higher in urban than rural areas, and were particularly high in the eastern provinces of the island. Overall levels of bird ownership have increased over the past decade, and species composition has changed. Notably, lovebirds showed a seven-fold increase in popularity while ownership of genera including groups with globally threatened species such as leafbirds (Chloropsis spp.) and white-eyes (Zosterops spp.) also rose sharply. The volume of some locally threatened birds estimated to be in ownership (e.g., >3 million White-rumped Shama Kittacincla malabarica) cannot have been supplied from Java's forests and research on supply from other islands and Java's growing commercial breeding industry is a priority. Determining temporal and spatial patterns of ownership is a crucial first step towards finding solutions to this persistent, pervasive and adaptive threat to the regional avifauna.

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Demand for cage birds is highly prevalent and increasing across Indonesia, as wild bird populations across Asia decline. To find ways to reduce demand, it is important to understand the motivations and psychographic drivers to keep (or not keep) birds, and how demographic characteristics and public attitudes influence such decisions. Based on surveys with over 3,000 people, we found few people citing health, sanitary, or welfare concerns as reasons for not keeping birds, whereas most people started keeping birds to enjoy their beauty or song, or to keep up with peers. Pet-keepers ("Hobbyists") commonly started doing so opportunistically; song contest participants ("Contestants") and breeders and trainers ("Breeders") did so for financial gain. Bird-keepers and non-bird-keepers disagreed on birds' environmental importance, longevity in captivity, and endangerment by trade. Older respondents were less concerned that keeping birds endangers them and few felt birds to be an important part of the environment. Hobbyists were least likely to consider wild bird population health a major concern. Efforts to dissuade potential bird-keepers should focus on public concern for the environment and the threat bird-keeping poses to wild populations. The importance of peer pressure among bird-keepers presents an opportunity to promote sustainable bird-keeping activities among key groups.

The COVID-19 pandemic led to unparalleled pressure on healthcare services. Improved health-care planning in relation to diseases affecting the respiratory system has consequently become a key concern. We investigated the value of integrating sales of non-prescription medications commonly bought for managing respiratory symptoms, to improve forecasting of weekly registered deaths from respiratory disease at local levels across England, by using over 2 billion transactions logged by a UK high street retailer from March 2016 to March 2020. We report the results from the novel AI explainability variable importance tool Model Class Reliance implemented on the PADRUS model. PADRUS is a machine learning model optimised to predict registered deaths from respiratory disease in 314 local authority areas across England through the integration of shopping sales data and focused on purchases of non-prescription medications. We found strong evidence that models incorporating sales data significantly out-perform other models that solely use vari-ables traditionally associated with respiratory disease (e.g. sociodemographics and weather data). Accuracy gains are highest (increases in R2 between 0.09 to 0.11) in periods of maximum risk to the general public. Results demonstrate the potential to utilise sales data to monitor population health with information at a high level of geographic granularity.

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