Eco-epidemiological mechanisms of avian influenza and Newcastle virus co-infection: Spatial convergence and compatibility

Abstract

Understanding the mechanisms of viral co-infection in natural host populations is crucial for predicting pathogen dynamics and cross-species transmission risks. This study focuses on the co-infection of avian influenza virus (AIV) and Newcastle disease virus (NDV) in wild ducks, combining field monitoring data, ecological niche modeling, and statistical analysis to reveal distribution patterns and driving factors. Organ samples collected from wild ducks in key migratory habitats revealed that AIV exhibits a broader ecological niche than NDV, with both viruses primarily replicating in the respiratory system. Ecological niche overlap modeling indicated a high degree of preference overlap between AIV and NDV in organ-specific microenvironments, suggesting that ecological compatibility is a prerequisite for co-infection. This study underscores the critical role of ecological and spatial compatibility in shaping viral co-infection patterns and provides a theoretical framework for understanding virus interactions in complex ecosystems.

Introduction

Over the past decade, avian influenza viruses (AIV) have emerged as an increasing global threat, posing significant risks to public and animal health while incurring substantial socioeconomic losses (Kang et al., 2024). Based on their pathogenicity in chickens, AIVs are classified into highly pathogenic avian influenza viruses (HPAIV) and low pathogenic avian influenza viruses (LPAIV), with H5 and H7 subtypes being the primary representatives of HPAIVs (Liang et al., 2021). Wild birds play a crucial role in the transmission and evolution of AIVs, with wild ducks serving as key natural reservoirs in the epidemiology of AIVs (Qi et al., 2021; Verhagen et al., 2020).

Newcastle disease virus (NDV) is a highly contagious pathogen in poultry, affecting the respiratory, neurological, and gastrointestinal systems, and posing a significant threat to the poultry industry (Taghizadeh et al., 2024). Based on virulence assays, NDV strains are categorized as avirulent, low (lentogenic), moderate (mesogenic), and high (velogenic) virulence (Lu et al., 2022). Wild birds, particularly ducks and other waterfowl, serve as potential reservoirs of NDV but often exhibit no clinical signs upon infection. Low-virulence NDV is frequently transmitted from wild waterfowl to chickens through migratory pathways, with the potential to mutate into highly virulent strains during transmission (Dortmans et al., 2012; Fereidouni et al., 2009).

Both NDV and AIV are RNA viruses, classified respectively as type 1 avian paramyxovirus and type A orthomyxovirus, sharing certain common characteristics. Co-infections of NDV and AIV are frequently observed in epidemiological surveillance, complicating viral transmission dynamics and pathogenicity in avian hosts. A study by Fazel and Mehrabanpour (2018) showed that co-infection of 28-day-old chickens with lentogenic NDV (LaSota strain) and LPAIV (H9N2) resulted in AIV interfering with NDV replication, especially under simultaneous inoculation. Although viral replication dynamics were affected, no significant clinical symptoms were observed (Fazel and Mehrabanpour, 2018). Mary J. Pantin-Jackwood et al. confirmed that velogenic strains of NDV (vNDV) and LPAIV can co-infect domestic ducks. Their study revealed that LPAIV interfered with contact-mediated transmission of vNDV, reducing viral shedding despite the absence of clinical symptoms in the infected ducks (Pantin-Jackwood et al., 2015). Similarly, Costa-Hurtado et al. found that co-infection of highly virulent NDV and HPAIV in chickens led to NDV-mediated suppression of HPAIV replication, thereby reducing disease severity and mortality rates (Costa-Hurtado et al., 2014). Furthermore, Ge et al. utilized specific pathogen-free (SPF) chicken embryos as a model to investigate viral replication dynamics during co-infection with AIV (H5N1 and H9N2) and NDV (LaSota vaccine strain and the virulent F48E9 strain). Their findings indicated that co-infection resulted in viral interference, which was significantly influenced by factors such as viral virulence, inoculation timing, and infection dose (Ge et al., 2012). Similarly, Wenbo Liu et al. reported that NDV replication in chicken embryos was strongly inhibited by H9-subtype AIV. Although recent studies have provided preliminary insights into the interactions between NDV and AIV during co-infection, the precise mechanisms underlying viral interference and pathogenicity remain unclear. While some research has explored the interference effects at the mechanistic level, most studies have focused on clinical observations rather than detailed investigations into host-pathogen interactions and disease outcomes, further revealing their distribution patterns and driving factors.

The concept of the ecological niche was first defined by Grinnell in 1917 (GallopÍN, 1989), and niche theory has since played a pivotal role in elucidat ing species coexistence mechanisms and predicting community succession (Barbour, 2023; Hu et al., 2022). It has been widely applied in studies on species diversity (Zhang et al., 2024), community structure (Zhao et al., 2024), interspecific interactions (Lin et al., 2025), and evolutionary processes (Dowell and Hekkala, 2016). Broadly speaking, a species' niche reflects its capacity to exploit environmental resources, which can be quantified by analyzing its spatial distribution and resource utilization patterns. A broader niche indicates greater adaptability and more efficient resource utilization. Among the key niche characteristics, niche breadth represents the extent of a species' resource utilization (Feinsinger et al., 1981), while niche overlap quantifies the degree of resource-sharing and potential competition between species (Pastore et al., 2021). These two parameters collectively provide a framework for understanding species coexistence and competitive dynamics within ecological communities.

A species' niche reflects its capacity to exploit environmental resources, and in a similar way, organs within an animal can be considered distinct microhabitats. While co-infections of AIV and NDV have been increasingly reported in wild birds and poultry, existing studies have primarily focused on clinical manifestations, immune responses, or viral interference dynamics (Costa-Hurtado et al., 2015; Gowthaman et al., 2019; Pantin-Jackwood et al., 2015). However, the mechanisms underlying viral coexistence and tissue distribution within hosts remain insufficiently explored. In parallel, niche theory has been widely applied to analyze pathogen distribution patterns at population or geographical scales (Escobar and Craft, 2016; Peterson, 2006). Limited understanding of within-host viral ecology constrains mechanistic insights into how tissue microenvironments mediate virus-virus interactions, co-infection patterns, and organ-specific viral distribution. Addressing these questions is essential for elucidating the ecological strategies underlying viral coexistence and for informing more effective surveillance and intervention efforts in wild bird populations. Therefore, this study applies niche theory to interpret and analyze the spatiotemporal distribution of AIV and NDV in wild ducks. Specifically, we aim to (i) quantify the niche breadth of AIV and NDV within various organ environments, (ii) assess the degree of niche overlap between the two viruses at the tissue level, and (iii) explore how these ecological patterns may contribute to co-infection dynamics and viral coexistence within individual hosts.

Methods

Sample collection and virus isolation

The organ samples, including livers, rectums, throats, lungs, kidneys, hearts, thymus, spleens, and bursas, were collected from 12 wild ducks with AIV and NDV co-infection in Xianghai National Nature Reserve, Jilin, China in October 2011. The collected samples were preserved in phosphate-buffered saline (PBS, pH 7.2) supplemented with streptomycin, penicillin, and 10 % glycerol.

After vortexing and centrifugation, the supernatants were collected and in oculated into 10-day-old specific-pathogen-free (SPF) embryonated chicken eggs. Following a 72-hour incubation at 37°C, the allantoic fluid was harvested (Lv et al., 2023). Each organ sample was analyzed separately to determine tissue-specific viral load and distribution. Total RNA was extracted from 200 μL of allantoic fluid using TRIzol reagent (Ambion, USA), following the manufacturer's instructions. Briefly, samples were lysed in TRIzol, followed by chloroform-mediated phase separation and centrifugation at 12,000 r/min for 10 minutes at 4°C. The aqueous phase was collected, and RNA was precipitated with isopropanol, washed with 75 % ethanol, air-dried, and resuspended in DEPC-treated water for downstream analysis.

Plasmid standard for quantification

The viral DNA reverse-transcribed from the sample was amplified via PCR, and the product was purified and identified. A 4 μL aliquot of the purified PCR product was ligated to 1 μL of the pEASY-T3 vector and incubated at 25°C for 15 minutes. The ligation mixture was then introduced into 50 μL of competent cells for transformation. The transformed cells were plated, cultured overnight at 37°C in a constant-temperature incubator. A single colony was selected, cultured, and subjected to PCR for positive identification. Sequence analysis was performed using Primer Premier 5, and the appropriate restriction enzyme cleavage sites were identified. Positive clones were selected, and plasmids were extracted. The plasmids were single-cut with the restriction enzyme SalI and double-cut with EcoRI to verify the recombinant plasmids. After linearization, the digested products were purified using TE buffer. The concentrations of the linearized AIV and NDV plasmids were measured with a micro-nucleic acid analyzer, yielding concentrations of 101.8 ng/μL and 43.7 ng/μL, respectively. Based on the formula: plasmid mole number = concentration × 6.02 × 10¹⁴ / (DNA length × 660), the plasmid concentrations were calculated to be 2.29 × 10¹⁰ copies/μL and 1.12 × 10¹⁰ copies/μL.

The plasmid was serially diluted in a 10-fold gradient to final concentrations ranging from 2.29 × 10⁹ to 100 copies/μL for AIV and 1.12 × 10⁹ to 100 copies/μL for NDV. Using plasmid concentrations of 2.29 × 10⁵ copies/μL for AIV and 1.12 × 10⁶ copies/μL for NDV as templates, primer pairs were used at identical concentrations. The final concentrations of PCR primers were set at 100 nM, 200 nM, 300 nM, and 400 nM, with each primer tested in triplicate. Standard curves for AIV and NDV were generated using qPCR, with serial dilutions ranging from 10⁻³ to 10⁻⁹ for AIV and 10⁻² to 10⁻⁹ for NDV.

Specificity, sensitivity, and repeatability validation

Viral RNA of AIV and NDV was extracted from harvested allantoic fluid. Two sets of reverse transcription products were used as templates, and qPCR was performed using specific primer pairs: InfA-F/InfA-R for AIV and NDV-F2F/NDV-F2R for NDV. All reactions were conducted using the SYBR Green dye-based method . Primer sequences and their respective amplicon sizes are listed in Table 1. Specificity was evaluated using four primer combinations (InfA-F/InfA-R, InfA-F2F/F2R, NDV-F2F/F2R, NDV-F2F/InfA-R), with two replicates per group to validate the specificity of the qPCR reactions.

Table 1.

Primer sequences and amplicon sizes used for qPCR detection of avian influenza virus (AIV) and Newcastle disease virus (NDV).

Primers	Sequences (5′−3′)	Amplicon sizes/bp
InfA-F	GACCRATCCTGTCACCTCTGAC	70
InfA-R	AGGGCATTYTGGACAAAKCGTCTA
NDV-F2F	TGATGTTCGGCTGTATCTGTCC	160
NDV-F2R	TTCTGTTGTATGCCTCATTAGGG

The sensitivity assay was conducted in two phases: determining both the upper and lower limits of sensitivity. The AIV and NDV standards were serially diluted in 10-fold steps, from 10⁸ to 10¹ copies/μL for AIV and from 10⁹ to 10¹ copies/μL for NDV, followed by qPCR to identify the upper sensitivity limit. For the lower sensitivity limit, the standards were diluted from 10⁷ to 10° copies/μL for AIV and from 10⁸ to 10° copies/μL for NDV, with qPCR performed for detection (Wang et al., 2008).

Repeatability was assessed by performing each test in triplicate for both AIV and NDV standards. Additionally, the reproducibility of the standard curve was verified through three independent tests to ensure the consistency of results across multiple repetitions.

Niche breadth and niche overlap

The Levins' index was employed to quantify niche breadth and niche overlap, providing insights into resource utilization and interspecific competition.

$$B{i}^{{}^{\prime }}=\frac{1}{n\times {\sum }_{j=1}^n{P}_{ij}^2}$$ (1)

$$P_{ij}=\frac{X_{ij}}{{\sum }_{j=1}^n{X}_{ij}}$$ (2)

$$O_{ik}=\frac{{\sum }_{j=1}^n\left(P_{ij}{P}_{kj}\right)}{{\sum }_{j=1}^n{\left(P_{ij}\right)}^2}$$ (3)

In Formulas (1), (2) and (3), $B{i}^{{}^{\prime }}$ is the modified niche breadth of species $i$, $O_{ik}$ is the niche overlap value of species $i$ and species $k$, and $P_{ij}$ represents the proportion of individuals of species $i$ in the $j$th resource state to all individuals of the species (Levins, 1968; Wang et al., 2012; Wu et al., 2020).

Statistical analysis

Real-time PCR data were extracted from LightCycler software for the calculation of mean cycle threshold (Ct) values and standard deviations (SDs). Standard linear regression (Y = a+bX) was performed, where the log concentration of target DNA copies (Y) was plotted against the mean Ct values (X). PCR amplification efficiency ($Ef{f}_{slope}$) was determined from the slope of the standard curve using the equation $Ef{f}_{slope}=\left[{10}^{\left(-1/slope\right)}-1\right]$.

For ecological niche analysis, niche breadth and niche overlap of AIV and NDV were calculated using Levins’ index for each individual and each organ. Paired Wilcoxon signed-rank tests were applied to compare values between AIV and NDV at both the individual level and the organ level. Statistical significance was set at p < 0.05. All analyses were performed in R (v4.4.2) using the wilcox.test function (Li et al., 2008).

Results

Absolute standard curves

By analyzing the amplification curves, the final primer concentrations for AIV and NDV detection were determined to be 300 nM and 400 nM, respectively. Based on the research by Fereidouni et al. (2009) and the results from the sensitivity assay, the average Ct value for the lower sensitivity limit of the AIV standard was 35.156, while the average Ct value for the NDV standard was 32.547 (Fereidouni et al., 2009). Accordingly, the AIV-F2F/F2R and NDV-M2F/M2R primers, with Ct values of 36.013 and 34.111, respectively, were confirmed as negative. These findings confirm that the fluorescent quantitative primers used in this study meet the required specificity criteria. The amplification efficiency of the standard curve is generally required to be between 90 % and 110 %, which is also used as a criterion for assessing sensitivity. If the amplification efficiency exceeds this range when adjusting the upper or lower limits, it indicates that the detection has reached its threshold. The standard deviation (SD) value represents the variability of the samples; in this experiment, all SD values were less than 0.5, demonstrating that the established standard curve exhibits good reproducibility. Based on these experimental results, the standard curve was determined to meet the criteria for specificity, sensitivity, and repeatability.

In this study, the standard curve for AIV detection was defined as $Y=-3.3422\log X+40.08$, with a correlation coefficient (R²) of 0.9922 and an amplification efficiency of 99.17 %. Similarly, the standard curve for NDV detection followed the equation $Y=-3.3882\log X+37.302$, with an R² value of 0.9926 and an amplification efficiency of 97.31 % (Figure 1). These results demonstrate high linearity and robust amplification efficiency, confirming the reliability and accuracy of the qPCR assay for quantifying AIV and NDV.

Fig. 1.

Standard curves for avian influenza virus (AIV) and Newcastle disease virus (NDV) quantification. Standard curves were generated by plotting the cycle threshold (Ct) values (Y-axis) against the logarithm (log₁₀) of DNA copy numbers (X-axis). Serial 10-fold dilutions of plasmid standards were used to construct the curves. Linear regression was performed to obtain the standard curve equations and corresponding correlation coefficients (R²), which reflect the linearity and amplification efficiency of the qPCR assays for AIV and NDV.

Organ-specific distribution of AIV and NDV in wild ducks

Fluorescence-based quantitative analysis was conducted on organ samples, including the liver, rectum, throat, lungs, kidneys, heart, thymus, spleen, and bursa of Fabricius, collected from 12 wild ducks co-infected with AIV and NDV. The obtained Ct values for each organ was quantified and converted into copy concentrations using the corresponding standard curve. To provide a more intuitive visualization of the distribution of AIV and NDV across different organs in wild ducks, we constructed combined violin-box-jitter plots and heatmaps (Figure 2 and 3). As shown in Figure 2A, AIV exhibits a relatively uniform distribution in infected birds. However, the heatmap (Figure 2B) reveals higher viral loads in the throat and spleen, which may be attributed to the limited sample size. In contrast, both Figure 3A and 3B indicate that NDV is predominantly distributed in the throat and lungs. These findings suggest that AIV and NDV primarily localize in the respiratory system of wild birds, with AIV infection potentially triggering an immune response in the spleen.

Fig. 2.

Organ-specific distribution of avian influenza virus (AIV) and Newcastle disease virus (NDV) RNA in wild ducks. (A) Violin-box-jitter composite plot illustrating the distribution of AIV and NDV RNA concentrations (log₁₀ copies/μL) across nine distinct organs, including the liver, rectum, throat, lungs, kidneys, heart, thymus, spleen, and bursa of Fabricius. Violin plots depict the probability density of the data, with superimposed boxplots indicating the median and interquartile range (IQR). Whiskers extend to 1.5 × IQR. Each dot corresponds to a single wild duck sample. Color codes correspond to specific organs, as indicated in the legend. (B) Hierarchical clustering heatmap of AIV and NDV RNA abundance across organs. Heatmap showing the normalized and Z-score–transformed viral RNA levels of AIV and NDV across nine organs. Each row represents an individual sample, and each column represents a distinct organ. Colors indicate relative expression levels (Z-score), with red denoting higher and blue denoting lower values. Both rows and columns were hierarchically clustered using Euclidean distance and complete linkage to reveal sample and organ-specific patterns of viral distribution.

Fig. 3.

Niche breadth and niche overlap of avian influenza virus (AIV) and Newcastle disease virus (NDV) at individual and organ levels. (A) Niche breadth at the individual level. (B) Niche breadth at the organ level. (C) Niche overlap at the individual level. (D) Niche overlap at the organ level. Index values range from 0 to 1, with higher values indicating broader tissue utilization (niche breadth) or greater similarity in tissue occupancy (niche overlap). Paired Wilcoxon signed-rank tests were used for comparisons, with p-values shown on each panel. At both levels, AIV exhibited significantly greater niche breadth and niche overlap than NDV (*p < 0.05).

Niche breadth and niche overlap

The ecological niche breadth and niche overlap of AIV and NDV in wild ducks were quantitatively assessed using the Levins' index, providing insights into their spatial distribution within host tissues and potential interviral interactions. Across both the individual and organ levels, paired Wilcoxon signed-rank tests demonstrated significant differences (p < 0.05) in niche breadth and niche overlap between AIV and NDV, with AIV exhibiting greater values for both metrics than NDV. As shown in Figure 3A and 3B, AIV generally exhibited a broader niche than NDV during co-infection, with the exception of sample 119. NDV niche breadth values were typically below 0.7, except in samples 119, 233, and 255. Notably, samples 233 and 255 exhibited viral loads below 100 copies/μL, whereas sample 119 displayed both high NDV abundance and broad tissue distribution, potentially attributable to individual differences in immune status. Data from Figure 3B further demonstrate that AIV and NDV exhibit distinct tissue tropisms.

Figure 3C highlights individual variability in viral co-localization. High niche overlap values from the perspective of AIV indicate substantial spatial co-distribution with NDV, whereas lower overlap values from the NDV perspective reflect its inherently narrower tissue niche. Figure 3D further reveals organ-specific co-localization patterns, with greater overlap observed in the bursa of Fabricius and rectum. Collectively, these findings suggest heterogeneous virus-tissue interactions and potential competitive or synergistic dynamics in co-infected organs.

Discussion

Migratory birds serve as major reservoirs for the evolution and mutation of AIV and NDV, significantly contributing to viral transmission into poultry and causing substantial economic and public health losses (Chen et al., 2006; Rasmussen et al., 2023). Evidence of AIV and NDV co-infection in both poultry and wild birds has raised concerns in various countries. Despite the increasing prevalence of co-infections, research on the interaction between AIV and NDV remains limited, particularly in the context of their transmission dynamics among millions of migratory birds worldwide (El Zowalaty et al., 2011; França et al., 2014; Hanson et al., 2005; Rosenberger et al., 1974).

Our findings provide novel insights into the ecological distribution and co-infection dynamics of AIV and NDV in wild ducks, a natural reservoir of avian pathogens. The detection of AIV and NDV co-infections in a significant proportion of samples underscores the ecological compatibility of these viruses within the wild duck host. Co-infection not only raises concerns regarding inter-viral interactions but may also enhance opportunities for genetic exchange and viral reassortment, especially in the case of segmented viruses like AIV (Chauhan and Gordon, 2022; Dunham et al., 2009; Ma et al., 2009). Previous studies have suggested that co-infection can modulate host immune responses, either through synergistic suppression or immunomodulation (Costa-Hurtado et al., 2014; Ge et al., 2012; Pantin-Jackwood et al., 2015). Our results, while ecological in focus, support the hypothesis that shared ecological space can serve as a precondition for such interactions.

Ecological niche modeling revealed that AIV and NDV occupy distinct ecological spaces. Niche breadth analysis further demonstrated that AIV exhibits a broader ecological niche than NDV, indicating greater adaptability to environmental heterogeneity. This is consistent with AIV’s high mutation rate and frequent genetic reassortment, which likely facilitate replication across diverse organ microenvironments (Dankar et al., 2011; Gao et al., 2013). In contrast, the narrower niche breadth of NDV suggests a stronger dependency on specific ecological conditions, potentially limiting its persistence outside optimal environments. Viral load analysis showed that both viruses primarily replicate in the respiratory system, with AIV predominantly detected in the throat and spleen, while NDV was concentrated in the throat and lungs-findings consistent with previous reports (Alexander and Allan, 1974; Ranjbar et al., 2019). The elevated AIV load in the spleen may reflect its interaction with host immune system, where lymphocytes and viral particles accumulate, potentially enhancing immune activation (Mota and Madden, 2022). In contrast, NDV showed limited splenic replication and a restricted systemic distribution, likely due to the low virulence of the strains in this sample set. Only when the fusion (F) protein precursor F0 is cleaved into F1 and F2 by host proteases does NDV become infectious. Virulent NDV strains possess multiple basic amino acids flanking the cleavage site, enabling systemic replication via a broad range of host proteases. Avirulent strains, lacking these cleavage motifs, require trypsin-like enzymes and are thus confined to the respiratory and gastrointestinal tracts. Molecular characterization confirmed the lentogenic of our NDV samples, with F protein cleavage site motifs such as ¹¹²GKQGRL¹¹⁷ or ¹¹²ERQGRL¹¹⁷, and mean death time (MDT) exceeding 96 hours, consistent with lentogenic NDV molecular features (Gaikwad et al., 2016; Nakaya et al., 2004; Panda et al., 2004).

Although AIV and NDV occupy distinct ecological niches, with AIV exhibiting a broader niche breadth than NDV within individual hosts (Figures 3A and 3B), our results reveal a apparent ecological overlap between the two viruses (Figures 3C and 3D), especially in the lungs. These findings are consistent with previous studies. For example, histopathological and immunohistochemical analyses of ducks and turkeys co-infected with AIV and NDV confirmed the presence of both viral antigens in certain respiratory and immune organs, suggesting potential tissue-level co-localization of the two viruses (Costa-Hurtado et al., 2014; França et al., 2014). Such ecological overlap may facilitate frequent co-infections. Field data have shown that co-infection with low pathogenic NDV and low pathogenic AIV (H9N2) in commercial chicken flocks may exacerbate disease severity and increase susceptibility to other respiratory or immunosuppressive pathogens (Gowthaman et al., 2017), potentially impacting viral transmission dynamics and evolutionary trajectories. These findings underscore the importance of intensified ecological surveillance, particularly during migration seasons when host aggregation and viral transmission are heightened along major flyways (Fourment et al., 2017; Gass et al., 2023). However, as this study was based on co-infection data from a specific host population with a limited sample size, further research is needed before extrapolating the findings to broader ecological or evolutionary contexts.

From a disease ecology perspective, our findings carry several important implications. First, the spatial and ecological overlap of AIV and NDV in wild ducks suggests that wild birds may serve as potential reservoirs for future outbreaks, highlighting the need for integrated surveillance across wild and domestic avian populations. Second, future virological and immunological studies should prioritize the effects of co-infection on viral adaptability, pathogenicity, and cross-species transmission. In 2024, China reported a human case of co-infection with avian influenza A (H10N5) and seasonal influenza A (H3N2). Epidemiological investigation indicated that the infection likely resulted from contact with live poultry infected with AIV and consumption of duck meat infected with AIV.This case represents a illustrative example of cross-species co-infection and reinforces the critical need for comprehensive monitoring of co-infection across wild and domestic bird populations. Finally, our work offers a methodological framework for incorporating niche theory into the study of viral co-infection-an underexplored yet critical frontier in emerging infectious disease research.

In summary, this study applies classical ecological niche theory to elucidate the co-distribution of AIV and NDV within wild ducks, highlighting the complex interplay among host, pathogen, and organ-specific environments. Future experiments employing single-virus infections as controls will be essential to determine whether AIV and NDV interact competitively or symbiotically within host tissues. Continued surveillance of AIV-NDV co-infections is also warranted to generate more granular data, which is critical for anticipating and mitigating future zoonotic threats.

Ethics statement

Ethics statement. This study was performed according to the recommendations detailed in the Guide for the Care and Use of Laboratory Animals of the Ministry of Science and Technology of the People's Republic of China. All procedures involving wild duck sampling were approved by the Animal Ethics Committee of Northeast Forestry University under permit number 2025075. Fieldwork and sample collection were conducted in accordance with relevant national and local regulations on wildlife protection and ethical standards for animal research.

CRediT authorship contribution statement

Sipei Zhang: Data curation, Investigation, Methodology, Software, Validation, Writing – original draft. Xiaolin Zhang: Data curation, Resources, Validation. Boyu Liu: Data curation, Software. Chuanxiu Li: Methodology, Software. Xinyu Zhang: Validation. Xiang Li: Writing – review & editing. Xinru Lv: Writing – review & editing. Yi Li: Methodology. Mengdan Fei: Investigation. Qing An: Software. Yang Xiu: Validation. Zhuoyan Li: Data curation. Jingxin Liu: Software. Linhong Xie: Methodology. Hongliang Chai: Formal analysis, Methodology, Resources, Writing – review & editing.

Disclosures

The authors declare that there is no conflict of interest regarding the publication of this manuscript.