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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 Highly pathogenic avian influenza A(H5N1) viruses of clade 2.3.4.4b underwent an explosive geographic expansion in 2021 among wild birds and domestic poultry across Asia, Europe, and Africa. By the end of 2021, 2.3.4.4b viruses were detected in North America, signifying further intercontinental spread. Here we show that the western movement of clade 2.3.4.4b was quickly followed by reassortment with viruses circulating in wild birds in North America, resulting in the acquisition of different combinations of ribonucleoprotein genes. These reassortant A(H5N1) viruses are genotypically and phenotypically diverse, with many causing severe disease with dramatic neurologic involvement in mammals. The proclivity of the current A(H5N1) 2.3.4.4b virus lineage to reassort and target the central nervous system warrants concerted planning to combat the spread and evolution of the virus within the continent and to mitigate the impact of a potential influenza pandemic that could originate from similar A(H5N1) reassortants. 

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. 

Abstract Coastal wetlands provide essential ecosystem goods and services but are extremely vulnerable to sea‐level rise, extreme climate, and human activities, especially the coastal wetlands in large river deltas, which are regarded as “natural recorders” of changes in estuarine environments. In addition to the area (loss or gain) and quality (degradation or improvement) of coastal wetlands, the information on coastal wetland structure (e.g., patch size and number) are also major metrics for coastal restoration and biodiversity protection, but remain very limited in China's four major river deltas. In this study, we quantified the spatial–temporal dynamics of total area (TA) and patch number (PN) of coastal wetlands with different sizes in the four deltas and the protected areas (PAs) and assessed the effects of major driving factors during 1984–2020. We also investigated the effectiveness of PAs through the comparison of TA and PN of coastal wetlands before and after the years in which PAs were listed as Ramsar Sites. We found both TA and PN experienced substantial losses in the Liaohe River Delta and Yellow River Delta but recent recoveries in the Yangtze River Delta. The coastal wetlands had a relatively stable and variable trend in TA but had a continually increasing trend in PN in the Pearl River Delta. Furthermore, reduced coastal reclamation, ecological restoration projects, and rapid expansion of invasive plants had great impacts on the coastal wetland structure in various ways. We also found that PAs were effective in halting the decreasing trends in coastal wetland areas and slowing the expansion of reclamation, but the success of PAs is being counteracted by soaring exotic plant invasions. Our findings provide vital information for the government and the public to address increasing challenges of coastal restoration, management, and sustainability in large river deltas. 

One Health initiatives have advanced zoonotic disease management by recognizing the interconnectedness of three sectors of governance (human, ecosystem, and animal) and by identifying options that can improve full‐system health. Although One Health has had many successes, its full realization may be inhibited by a lack of strategies to overcome simultaneous impediments in decision making and governance. Decision impediments that hinder management may include uncertainty, risk, resource limitations, and trade‐offs among objectives. Governance impediments arise from disparities in costs and benefits of disease management among sectors. Tools and strategies developed from decision science, collaboration, and negotiation theory can help articulate and overcome coinciding decision and governance impediments and enhance multisectoral One Health initiatives. In cases where collaboration and negotiation are insufficient to address disparities in cross‐sector costs and benefits, altering incentive structures might improve disease‐specific outcomes and improve the realization of One Health. 

Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security, water resource management, and transmission of zoonotic infectious diseases. Optical image-based paddy rice mapping studies employed the unique spectral feature during the flooding/transplanting period of paddy rice. However, the lack of high-quality observations during the flooding/transplanting stage caused by rain and clouds and spectral similarity between paddy rice and natural wetlands often introduce errors in paddy rice identification, especially in paddy rice and wetland coexistent areas. In this study, we used a knowledge-based algorithm and time series observation from optical images (Sentinel-2 and Landsat 7/8) and microwave images (Sentinel-1) to address these issues. The final 10-m paddy rice map had user’s accuracy, producer’s accuracy, F1-score, and overall accuracy of 0.91 ± 0.004, 0.74 ± 0.010, 0.82, and 0.98 ± 0.001 (± value is the standard error), respectively. Over half (62.0%) of the paddy rice pixels had a confidence level of 1 (detected by both optical images and microwave images), while 38.0% had a confidence level of 0.5 (detected by either optical images or microwave images). The estimated paddy rice area in northeast China for 2020 was 60.83 ± 0.86 × 103 km2. Provincial and municipal rice areas in our data set agreed well with other existing paddy rice data sets and the Agricultural Statistical Yearbooks. These findings indicate that knowledge-based paddy rice mapping algorithms and a combination of optical and microwave images hold great potential for timely and frequently accurate paddy rice mapping in large-scale complex landscapes.