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ABSTRACT Introduction and AimLong‐term changes in wildlife habitats are fundamental for understanding biodiversity change and the ecological contexts that may shape opportunities for host contact or exposure. Avian influenza virus (AIV), one of the most pressing zoonotic threats, is maintained primarily in wild birds whose habitats are undergoing rapid transformation. Yet no globally consistent, temporally explicit habitat dataset tailored to AIV host species exists, leaving their long‐term habitat dynamics poorly documented. To address this gap, we developed the first global annual habitat maps of AIV host birds from 2000 to 2022. Main Variables IncludedWe developed a habitat classification framework specific to AIV host birds and produced the global annual terrestrial habitat maps by integrating satellite‐derived land cover, climate zones, biome information and topography. The dataset includes 8 Level‐1 and 34 Level‐2 habitat types, achieving overall accuracies of 0.84 (± 0.08) and 0.83 (± 0.12), respectively. Time CoverageThe maps span the years 2000–2022, with annual temporal resolution. Spatial CoverageThe dataset covers global terrestrial surfaces (excluding Antarctica) at a resolution of 300 m. TaxaWild bird species with confirmed AIV detections, with habitat preferences derived from IUCN species‐level associations. ApplicationsThis dataset provides a foundational environmental layer for improving host species distribution models and for examining how environmental change influences habitats used by AIV host birds. It can support downstream ecological and epidemiological analyses within a One Health framework and inform conservation planning and land‐use management. 

Abstract Expansion of impervious surface area (ISA) in urbanizing regions often leads to vegetation area losses, a direct impact of urbanization. Many activities driven by economic growth, population increases, targeted urban greening investments, environmental policies, and major sports events change vegetation composition, structure, and function, leading to substantial indirect (positive or negative) impacts on vegetation in urban area. In this study, we analyzed the spatial‐temporal dynamics of ISA, enhanced vegetation index (EVI), and gross primary production (GPP) in the Yangtze River Delta (YRD), China, over 2000–2020. Positive indirect impacts of urbanization on EVI and GPP surged after 2011, coinciding with China's Ecological Civilization Strategy. The concurrent increases of ISA, EVI, and GPP in the YRD provide an example for our society to work and advance the UN's Sustainable Development Goal #11, “Make cities inclusive, safe, resilient, and sustainable.” 

Abstract The wild to domestic bird interface is an important nexus for emergence and transmission of highly pathogenic avian influenza (HPAI) viruses. Although the recent incursion of HPAI H5N1 Clade 2.3.4.4b into North America calls for emergency response and planning given the unprecedented scale, readily available data-driven models are lacking. Here, we provide high resolution spatial and temporal transmission risk models for the contiguous United States. Considering virus host ecology, we included weekly species-level wild waterfowl (Anatidae) abundance and endemic low pathogenic avian influenza virus prevalence metrics in combination with number of poultry farms per commodity type and relative biosecurity risks at two spatial scales: 3 km and county-level. Spillover risk varied across the annual cycle of waterfowl migration and some locations exhibited persistent risk throughout the year given higher poultry production. Validation using wild bird introduction events identified by phylogenetic analysis from 2022 to 2023 HPAI poultry outbreaks indicate strong model performance. The modular nature of our approach lends itself to building upon updated datasets under evolving conditions, testing hypothetical scenarios, or customizing results with proprietary data. This research demonstrates an adaptive approach for developing models to inform preparedness and response as novel outbreaks occur, viruses evolve, and additional data become available. 

Abstract Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade‐offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision‐making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision‐making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision‐making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white‐nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision‐making, including competing objectives, risks, prediction uncertainty, and limited resources. 

Abstract Influenza A viruses in wild birds pose threats to the poultry industry, wild birds, and human health under certain conditions. Of particular importance are wild waterfowl, which are the primary reservoir of low‐pathogenicity influenza viruses that ultimately cause high‐pathogenicity outbreaks in poultry farms. Despite much work on the drivers of influenza A virus prevalence, the underlying viral subtype dynamics are still mostly unexplored. Nevertheless, understanding these dynamics, particularly for the agriculturally significant H5 and H7 subtypes, is important for mitigating the risk of outbreaks in domestic poultry farms. Here, using an expansive surveillance database, we take a large‐scale look at the spatial, temporal, and taxonomic drivers in the prevalence of these two subtypes among influenza A‐positive wild waterfowl. We document spatiotemporal trends that are consistent with past work, particularly an uptick in H5 viruses in late autumn and H7 viruses in spring. Interestingly, despite large species differences in temporal trends in overall influenza A virus prevalence, we document only modest differences in the relative abundance of these two subtypes and little, if any, temporal differences among species. As such, it appears that differences in species' phenology, physiology, and behaviors that influence overall susceptibility to influenza A viruses play a much lesser role in relative susceptibility to different subtypes. Instead, species are likely to freely pass viruses among each other regardless of subtype. Importantly, despite the similarities among species documented here, individual species still may play important roles in moving viruses across large geographic areas or sustaining local outbreaks through their different migratory behaviors.