Environmental metagenomics enhances detection of circulating viruses from live poultry markets in Cambodia

Domain: biology
Published: 2026-01-12
Editorial outcome: major_revision

Abstract
Environmental surveillance has emerged as a pivotal strategy for early detection of pathogens that pose a threat to humans. In Asia, live-bird markets (LBMs) are key human-animal interfaces for zoonotic virus transmission. Traditional sampling strategies are time-consuming, expensive and carry significant biosafety risks. Here, we assess the performance of metagenomics on environmental samples (ES) versus traditional poultry swabs for detecting viral pathogens in two Cambodian LBMs between January 2022 and April 2023. ES, including air (n = 35), cage swabs (n = 17), carcass wash water (n = 17) and drinking water (n = 9) are collected alongside oropharyngeal and cloacal swabs from chickens (n = 30) and ducks (n = 29). ES is sensitive in detecting 40 viruses from pathogen families including Orthomyxoviridae and Coronaviridae. Air samples capture the greatest diversity of poultry viruses. Viral contigs from ES show high sequence identity to poultry swab contigs when aligned to the same gene. We show ES outperforms poultry samples in detecting the highly pathogenic influenza A/H5N1, including clades 2.3.4.4b and 2.3.2.1c, which are found in the environment but are missed by poultry swabs. Our findings show metagenomics on ES replicates traditional surveillance, offering broader coverage and improved pathogen detection. This approach could be pivotal for mitigating zoonotic spillover and enhancing pandemic preparedness.

Introduction
Environmental surveillance has become an increasingly important strategy for early detection and monitoring of viral pathogens1–5. Several major disease outbreaks in humans have resulted from zoonotic spillover from animals6–11 making pathogen surveillance at human-animal interfaces such as live-bird markets (LBMs) critical.

LBMs are common in Asia, holding critical socio-economic significance. However, they are also marked by the prevalence of both endemic and emerging infectious diseases of One Health importance, including highly pathogenic avian influenza A virus (AIV) H5N12,12,13. Highly pathogenic A/H5N1 has a mortality rate exceeding 50% in humans14,15 while also imposing a significant economic burden on global poultry industries16,17. Outbreaks of pathogens like A/H5N1 in poultry often lead to severe economic losses affecting livelihoods reliant on small scale poultry production18–20. Other pathogenic avian viruses, including members of the Rotavirus21,22, Avastrovirus23, Gammacoronavirus24 and Alpharetrovirus genera25 also cause substantial losses in domestic poultry. Improving pathogen surveillance systems at LBMs is essential to infectious disease monitoring, risk mitigation, and outbreak response, especially in developing regions of the world.

Classical surveillance and pathogen monitoring at LBMs has traditionally relied on random testing of individual animals via oropharyngeal and cloacal swabs. However, this approach may miss circulating pathogens if the selected bird is not infected when sampled, thus limiting the probability of detection. In addition, traditional sampling strategies are time-consuming, costly, and pose significant occupational biosafety risks while compromising animal welfare26,27. ES, particularly air, can provide a more comprehensive, safe, and easier approach to pathogen surveillance. Furthermore, it is hypothesized that poultry virus shedding can result in contamination of the surrounding environment, with the environment becoming a reservoir of pathogens exacerbated by insufficient biosecurity measures at LBMs28,29. Previously, we demonstrated through air sampling that workers at an LBM in Cambodia were exposed to influenza subtypes A/H5N1 and A/H9N230 and this approach has also proven successful in accurately detecting pathogens in other settings31–34. In addition, ES can be used to detect influenza A virus subtypes from sources such as the wash water used to clean bird carcasses after de-feathering at LBMs30.

We hypothesize that ES could supplement traditional poultry pathogen surveillance and provide insight into risks of environmental exposure for LBM workers and consumers. This information is crucial for mitigating risks of pathogen transmission between birds and throughout the poultry supply chain, and for preventing spillover to humans. However, it remains unknown whether metagenomic data from ES in LBMs accurately reflects data from traditional poultry samples used for pathogen surveillance.

To date, environmental pathogen surveillance has largely relied on PCR-based methods, often used for monitoring of select human pathogens (e.g., influenza A and SARS-CoV-2) in wastewater and sewage35,36. This conventional approach for pathogen monitoring has been widely used in Cambodian LBMs for a number of years with a focus on avian influenza subtypes37–42. Although these approaches offer high sensitivity and specificity, they are limited in the range of viruses they can detect. Unbiased metagenomics has emerged as a method with a much broader target range for virus identification and surveillance, allowing detection of a diverse array of known and novel pathogens43. One major challenge using metagenomics is the low amount of viral nucleic acid present in most sample types, compared to higher levels of host and environmental nucleic material. Methods to reduce non-target sequences in metagenomic data have been explored, but vary in effectiveness44. However, viral enrichment using probe hybridization methods has been shown to significantly improve sensitivity across different sample types, enabling the accurate detection of low abundance and/or low-frequency virus variants45–48.

In this study, we compared the performance of targeted virus capture probe-based metagenomics on several different ES types versus traditional poultry swab samples from LBMs for the detection and surveillance of avian pathogens. We hypothesize that using metagenomics on ES may offer a more robust approach to facilitate the early detection of virus transmission at high-risk human-animal interfaces, allowing for more rapid and effective risk mitigation measures to take place.

Results
Air detects more virus species than poultry swabs
To evaluate the performance of metagenomics for viral pathogen detection using ES compared to poultry swabs, we first calculated the detection rates of poultry viruses among both sample types. In total 84 different poultry virus species were identified over the course of the study (Fig. 1A). We showed that the majority of viruses found in poultry swabs could be directly recaptured in corresponding ES at the exact same time point with detection rates consistently ranging between 70% and 90% (Fig. 1A and Supplementary Fig. 3A, B). This finding was generally consistent across both locations and the majority of sampling dates when comparing the performance between individual ES and all four poultry swab types (Supplementary Fig. 4). Common poultry respiratory viruses including avian influenza and avian coronavirus were frequently identified in different poultry swab types and corresponding ES (Fig. 1A and Supplementary Fig. 5–8). While this was also true for other pathogens including Rous sarcoma virus, avian leukosis virus, avian orthoreovirus (Retroviridae) and chicken astrovirus (Astroviridae).

Despite the consistent overall performance of ES in capturing the same viruses found in poultry at each time point (70-90%), we observed variability in the performance of how individual ES types recaptured viruses specific to each of the four poultry swabs. By combining data from all twelve timepoints we show that air (slaughter and holding area) and cage swabs showed no significant differences in the number of viruses recaptured from chicken oropharyngeal and cloacal swabs (Supplementary Fig. 9 and Supplementary Data 6 and 7). Other ES including wash water, drinking water and air from outside the market recaptured significantly less viruses relative to both chicken sample types. Using this same approach, we show that all ES recaptured significantly less viruses than both duck swabs (Supplementary Fig. 10 and Supplementary Data 8 and 9). These findings highlight variation in performance across individual ES types, as well as differences depending on the poultry species targeted. Despite this, we found that most virus species directly identified in poultry were also detected across two or more ES types (Supplementary Fig. 11). However, at most timepoints, different ES groups uniquely detected poultry viruses that were not captured by any other ES group at that sampling date (Supplementary Fig. 11). Each ES group was implicated at least once in directly increasing the number of viruses recaptured. This suggests that independent of overall performance, a variety of sample types may be required to capture as many viruses as possible.

Viruses found in poultry swabs but failed to be recaptured in ES had a significantly lower abundance (Supplementary Fig. 12A) and were in circulation at the LBMs less often than those successfully detected through corresponding ES (Supplementary Fig. 12B). Importantly, there was no association between taxonomic family and the ability of ES to capture viruses identified in poultry swabs. For example, avian sarcomavirus CT10 could not be identified in ES despite appearing in poultry swabs on two separate occasions. However, ES successfully identified several other members of Retroviridae family including avian leukosis virus and rous sarcoma virus.

Despite variation in ES ability to directly recapture the exact same viruses found in poultry, it also frequently detected between 10 and 30 additional poultry viruses that were not identified through bird sampling at the same time point (Fig. 1A, Supplementary Fig. 5-10 and 13). Building on this we show that air (slaughter and holding area) and cage swabs had a significantly higher total virus diversity than all poultry samples (Fig. 1B, Supplementary Fig. 14 and Supplementary Data 10-13). In addition, cage swabs and air samples (from slaughter and holding areas) had significantly higher alpha-diversity than wash water and drinking water thus highlighting differences in ES performance (Fig. 1B). Using a similar approach we next quantified total virus diversity relative to each of the four poultry swabs (i.e., only including viruses that were detected at least once in that specific poultry sample type). Air from the slaughter area detected significantly more viruses than duck samples and chicken oropharyngeal swabs but not cloacal swabs (Fig. 1C–F, and Supplementary Fig. 15 and Supplementary Data 14-17). Air collected from the holding area also captured more viruses than chicken oropharyngeal swabs, but no significant differences were observed when comparing to other bird samples.

ES analysed using targeted metagenomics revealed an additional 50 viruses which were not found through poultry sampling at any time during the study (and thus not included in previous analyses) (Supplementary Fig. 16). While most of these viruses may be from alternative sources (such as arboviruses or porcine viruses), 5 additional poultry viruses were also detected, including goose aviadenovirus A, duck adenovirus 4, pigeon circovirus, shelduck picobirnavirus II and avian dependoparvovirus 1. Metagenomics on ES also detected the arbovirus dengue virus 3 as well as the common human respiratory pathogen Rhinovirus A1 (Supplementary Fig. 16). This highlights a major advantage of ES in facilitating the detection of viruses which may be missed by traditional sampling methods or circulating pathogens from other sources.

Overall, these findings suggest that the ES conducted at LBMs in Cambodia enhances detection of poultry virus species at LBMs in Cambodia.

Performance of environmental samples is dependent on poultry host species and sample type
Principal coordinate analysis (PCoA) of β-diversity (Jaccard distance) focusing only on the poultry viruses identified in this study (Fig. 1A) revealed statistically significant virome separation between the groups after controlling for location and time (PERMANOVA < 0.001*** and R2 = 0.037) (Fig. 2A). Pairwise PERMANOVA of each individual comparison revealed no significant differences between ES including cage swabs, drinking water and air from the holding area and slaughter area (Supplementary Data 18). Air samples from outside the LBM, as well as wash water, were significantly different from all other sample types. Both cloacal and oropharyngeal swabs collected from chickens had significantly different virome compositions compared to the same swab types from ducks (Supplementary Fig. 5). Given the observed differences in virome composition between poultry swab types we wanted to evaluate the relationship/performance with each sample type individually. Thus, based on the PCoA in Fig. 2A, we calculated the median coordinates (PCo1 and PCo2) (Fig. 2B) of each poultry swab (cloacal and oropharyngeal from both domestic ducks and chickens) and subsequently measured the distance of all samples from each point (Fig. 2C–F and Supplementary Data 19–22). Using this quantitative comparative evaluation of virome composition we show that no significant difference could be detected between oropharyngeal samples from chickens and ES of drinking water, cage swabs and air (slaughter and holding area) (Fig. 2C). Similar findings were observed when comparing cloacal swabs from chickens with drinking water, cage swabs and wash water, although air samples were a significantly larger distance by this measure (Fig. 2D). Interestingly, no difference could be detected between wash water and oropharyngeal or cloacal swabs from ducks when comparing their respective distances from each median centroid (Fig. 2E, F).

To better understand why specific viruses were not detected in ES, we first identified the poultry swab type most associated with taxa which failed to be captured in ES at the exact same they were identified in poultry. To do this, we calculated Spearman correlations between virus abundance (RPM) and the distance of each sample from poultry swab median centroid as calculated for Fig. 2B–F. The results of this analysis were then compared between poultry viruses successfully recaptured by ES and those which went undetected as shown in Fig. 1A (Fig. 2G–J). By employing this approach, we found that viruses which could not be detected through ES had significantly more positive correlations than viruses accurately recaptured when examining data for cloacal swabs in chickens (Fig. 2H). This result indicates that taxa undetected by ES tended to have a higher abundance the greater the distance from the median centroid thus highlighting a negative association with chicken cloacal swabs. Conversely, taxa successfully recaptured by ES tended to have more negative correlations than undetected taxa suggesting this group had a higher abundance the shorter the distance from the median centroid. This indicates that cloacal swabs from chickens are positively associated with taxa successfully recaptured. The opposite finding was true for oropharyngeal and cloacal swabs in ducks thus suggesting these sample types had a greater association with undetected taxa (Fig. 2I, J).

Using a Bayesian approach, we conducted source tracking analysis to estimate the proportion of viruses in ES that are likely to have come from each poultry swab type (Fig. 2K). We found that cloacal swabs from chickens were identified as the dominant source of virus composition for all ES. The probability of cloacal swabs from chickens contributing to the environmental virome composition was significantly higher compared to oropharyngeal swabs from chickens as well as both sample types from ducks (Fig. 2K). Typically, more chickens are processed at LBMs than ducks in Cambodia. This may in part explain our findings as the environment is contaminated with a greater number of chicken viruses.

Overall, these findings highlight the differential performance of ES with respect to poultry species and sample type. ES conducted at LBMs performs better at detecting viruses from chickens than those infecting ducks, while cloacal-derived chicken viruses appear to be a major source for the environmental pathogens in LBMs.

Environmental contigs cover the majority of virus genes in poultry with a high sequence similarity
Next, we aimed to assess whether de novo assembled viral contigs from ES were an accurate representation of those found in poultry swab samples. At each independent timepoint, poultry contigs were aligned individually to the corresponding ES assemblies to calculate the percentage of poultry contigs recovered in ES. We found that the majority of contigs from direct poultry samples mapped to those from ES (Supplementary Fig. 17). For both oropharyngeal and cloacal swabs obtained from chickens, the highest percentage of viral contigs was shown to map to the ES air (slaughter and holding areas) and cage swabs (Supplementary Fig. 18 and Supplementary Data 23 and 24). Large variation in the percentage of viral contigs mapped was observed between air samples collected from outside the LBMs (Supplementary Fig. 18) highlighting substantial variability. No significant differences could be determined between the percentage of viral contigs mapped between ES and duck (Supplementary Fig. 19 and Supplementary Data 25 and 26).

To further assess the similarity between poultry and environmental sequences, all contigs were aligned to the full set of poultry virus genes which were detected in at least one poultry sample across the length of the study (see “methods” section). Applying the same methodology as the species-level analysis in Fig. 1, we demonstrate that most virus genes detected in poultry swabs were also recovered in at least one corresponding environmental sample at the same timepoint (Fig. 3A). The majority of genes identified belonged to species in a handful of highly abundant genera including Coronaviridae, Orthomyxoviridae, Rotavirus and Orthoreovirus (Supplementary Figs. 20–23). While ES broadly recapitulated the poultry swab virome with gene-level resolution we show marked variation in performance among individual ES types. Across all twelve sampling dates, air collected from the slaughter area consistently recovered the highest number poultry virus genes (Supplementary Fig. 24) with no significant difference reported relative to each bird sample (Supplementary Fig. 25-26 and Supplementary Data 27-30). Cage swabs and air (holding area) performed similarly for chicken oropharyngeal and cloacal swabs but not duck samples (Supplementary Fig. 25-26). In contrast, carcass wash water and drinking water recovered markedly fewer viral genes than any of the four poultry swab types (Supplementary Fig. 25-26). Although these findings persisted at each sampling date, carcass wash water uniquely captured a subset of genes that no other ES type detected on multiple different occasions, underscoring its surveillance value (Supplementary Fig. 27).

ES frequently recovered a substantial number of additional poultry virus genes often that were missed by concurrent bird sampling (Supplementary Fig. 28, 29). Aggregating data across all twelve sampling dates and restricting the analysis to viral genes relative to each bird sample individually we show that air (from slaughter and holding areas) and cage swabs exhibited significantly greater viral gene diversity compared to both duck swabs (Supplementary Fig. 30 and Supplementary Data 31, 32). Air from the slaughter area yielded a significantly higher number of detected viral genes compared to chicken cloacal swabs (Supplementary Fig. 31 and Supplementary Data 33-34). Notably, no other pairwise comparisons reached statistical significance, suggesting that wash water and drinking water performed comparably to poultry swabs in terms of total virus gene diversity (Supplementary Fig. 30, 31). Collectively, these findings further confirm that virus gene recovery varies by ES type and poultry host, underscoring the need for a multi-sample strategy to enhance surveillance.

To understand how accurate environmental sampling was in capturing the genetic makeup of poultry virus contigs, we next calculated pairwise percent identity (PID) and coverage for every poultry environment contig pair that aligned to the same gene. By calculating the difference in PID (poultry PID – ES PID) for contig pairs aligned to the same gene we show that most environmental contigs diverge from their poultry counterparts by ≤1%, indicating that the majority of sequences are near-identical (Fig. 3B–D). This finding was remarkably consistent when comparing each ES type to each poultry swab individually (Supplementary Fig. 32–35). Applying the same analysis to sequencing coverage revealed that, for contig pairs mapped to the same gene, coverage differences were minimal in most cases (Fig. 3E). Across all poultry swab types, air samples (slaughter and holding area) together with cage swabs exhibited the highest number of sequences which accurately paired to corresponding poultry contigs (Fig. 3F, Supplementary Fig. 36–38 and Supplementary Data 35–38). ES contigs with a corresponding poultry counterpart were primarily represented by species in the Rotavirus or Coronaviridae genus (Fig. 3F and Supplementary Fig. 39). Similarly, most paired poultry contigs belonged to the Orthomyxoviridae or Coronaviridae genus (Fig. 3F and Supplementary Fig. 39). Notably, a high proportion of poultry contigs which were not accurately represented by a corresponding environmental contig belonged to the Orthomyxoviridae family (A/Influenza) (Supplementary Fig. 40, 41). Closer inspection of each individual gene revealed the majority were of these genes were PB1, PB2 and PA for Orthomyxoviridae or ORF1ab for Coronaviridae (Fig. 3G). This finding may in part be explained by the virion having PB1, PB2 and PA buried within the internal polymerase complex rendering them vulnerable to high levels of degradation once outside the host.

Overall, these findings highlight that ES enhances detection of poultry virus genes. De novo assembled contigs have a remarkably similar coverage and percent identity when aligned to the same gene further highlighting the accuracy of this approach.

Environmental samples yielded highly pathogenic influenza A contigs more often than poultry swabs
Across the length of the sampling period, at both LBM locations, we identified four different influenza hemagglutinin (HA) subtypes in ducks and chickens including H5, H6, H7 and H9 (Fig. 4A, B and Supplementary Fig. 42, 43). H5 clade 2.3.4.4b was found on three separate visits to the market (Fig. 4A, C). However, this H5 clade was jointly detected by poultry swab sampling and ES on only one sampling date. By contrast, the same H5 clade 2.3.4.4b was detected in wash water, cage swabs, and air samples on two additional occasions without corresponding poultry detections (Fig. 4C). Similar findings occurred for clade 2.3.2.1c, which was detected at LBMs on four separate sampling visits (Fig. 4A, C). On one occasion (December 2022), both clade 2.3.2.1c and 2.3.4.4b were detected in eight different ES (4 air, 2 cage swabs and 2 wash water samples), suggesting high prevalence in birds coming into the market, and significant environmental contamination. However, no sequences were identified through direct animal sampling on this occasion, highlighting the versatility of environmental sampling. This event did not coincide with any festival period, even though earlier work in Cambodia has shown that increased poultry traffic at LBMs and increased H5N1 virus circulation are directly linked to major festival periods49.

In total, HA subtype H6 was found in 27 samples across the study, with both the ST2853-like and HN57-like clades detected. The HN57-like clade was found in ducks but was not detected in corresponding ES (Supplementary Fig. 42), and this was the only detection of the HN57-like clade of H6 influenza in this study. On six separate sampling visits, the ST2853-like H6 clade was successfully sequenced from poultry swabs as well as air samples and carcass wash water (Supplementary Supplementary Fig. 42). On one visit, H6 was detected in air but not in the corresponding poultry swabs (Supplementary Fig. 4D). The Eurasian avian lineage of H7 HA was only found once and was sequenced from both air and poultry samples at the same time and place (Supplementary Fig. 43). H9 HA was the predominant HA subtype detected, with a total of 43 positive samples across all 12 visits (Fig. 4B). On seven different sampling visits, H9 was identified in both poultry samples and ES. Importantly, on four separate sampling visits, H9 sequences were found in data from ES but were not identified in data from poultry swabs (Fig. 4B). The opposite trend was also reported, whereby H9 sequences were detected in duck samples but were not observed in any ES. Overall, metagenomics using ES at LBMs detected circulating influenza HA subtypes more often than metagenomics using traditional poultry swabs.

Three different neuraminidase (NA) gene subtypes were identified at LBMs in this study including N1, N2 and N6 (Fig. 4C–F). In line with H5 HA results, we identified both the 2.3.4.4b and the 2.3.2.1c clade of N1 (Fig. 4C). The 2.3.4.4b N1 clade was only detected in poultry swabs and ES (drinking water and air) on two visits (Fig. 4B, C). Six different ES yielded the detection of both H5 and N1 genes (representing the 2.3.4.4b and 2.3.2.1c clades) on December 23, 2022, at the LBM in Takeo province. N2 was the most common NA subtype found throughout the study and was consistently co-detected in both poultry swabs and ES (Fig. 4B, D). In line with other findings, N6 sequences of the H5 Eurasian lineage were also consistently co-detected in both poultry samples and ES (Fig. 4B, F).

Hierarchal clustering based on influenza HA and NA subtype detection revealed that the HA H9 commonly co-occurred with N2 at both LBMs (Fig. 4B). Using this approach, we were also able to infer that HA H6 (ST2853-like clade) co-occurred mostly with N6 while both H5 clades 2.3.2.1c and 2.3.4.4b co-occurred with N1 clades of the same subtype. While by nature metagenomic sequencing and de novo assembly of viral influenza A contigs from the environment and pooled poultry samples makes full length genome identification impossible for segmented RNA viruses, hierarchal clustering of detection patterns allows us to infer (co)-circulating subtypes.

Methods
All research conducted in this study complied with relevant ethical regulations. Sample collection protocols were approved by the National Ethics Committee for Health Research (NECHR) under the Ministry of Health, Cambodia (approval numbers NECHR013, NECHR143, NECHR149, and NECHR320, approved in 2021–2022). Cambodia does not have a dedicated animal ethics review board; however, all animal sample collections were carried out in collaboration with the National Animal Health and Production Institute and conducted with authorization from the General Directorate of Animal Health and Production, Ministry of Agriculture, Forestry and Fisheries.

Study design and sample collection
Between January 2022 and April 2023 samples were collected on 12 occasions in Cambodia at Orussey market, Phnom Penh province and Daun Keo market, Takeo province (Supplementary Fig. 1). Orussey market was chosen as a sampling site for this study because is the largest LBM in Cambodia and serves as a major hub for poultry trade with birds arriving from a broad geographic area. This centralization of poultry trade increases the potential for pathogen transmission and amplification positioning Orussey market as a key site for surveillance. In contrast, Daun Keo market in Takeo province was selected as a study site due to its smaller scale, lower trade volume, and primarily regional bird sourcing which includes cross-border poultry trade. It is representative of the many small-scale, decentralized live bird markets dispersed throughout Cambodia. Individual animal samples consisted of oropharyngeal and cloacal swabs collected from ten domestic ducks and chickens. Birds were randomly selected from the entire population present at the market, ensuring that no two birds originated from the same cage/stall. Samples were combined into pools of ten individual cloacal or oropharyngeal swabs for sample processing, with ducks and chickens pooled separately. ES comprised air, carcass wash water, cage swabs, and drinking water. Air samples were collected for three hours at three locations at each market (the slaughter area, the poultry holding/storage area, and a “control area” - either the food storage area located in a separate building or >50m away from the LBM) using AerosolSenseTM samplers (Thermo Fisher Scientific). Air was sampled through an omnidirectional inlet at a rate of 200 L/min. Only one air sample from each location within the market was collected at each timepoint (Supplementary Data 1). Collected material was eluted from each air sampler by rinsing and vortexing the collection matrix in 2 mL of viral transport medium. Carcass wash water (50 mL) used to wash poultry carcasses after slaughter and de-feathering processes was collected from the slaughter area. Up to two wash water samples was collected per timepoint, as the slaughter area in each LBM typically has no more than two carcass wash containers (Supplementary Data 1). Cage swabs were collected from five randomly selected cages in the poultry holding area of the market. All five cage swabs were subsequently pooled for sequencing. Where available, up to five 2 mL samples of poultry drinking water from inside poultry cages was collected at random (Supplementary Data 1). Samples were collected using polyester swabs and placed into viral transport medium (VTM) which was prepared in-house and consists of 2.95% tryptose phosphate broth, 145 mM NaCl, 5% gelatin, 54 mM Amphotericin B, 106 U/L penicillin-streptomycin, and 80 mg/L gentamicin. All samples were transported immediately and on ice to Institut Pasteur du Cambodge located in Phnom Penh. Samples were aliquoted into tubes on arrival and stored at −80 °C until further processing.

RNA extraction, library preparation and next-generation sequencing
Samples were vortexed for 15–30 seconds before proceeding directly to RNA extraction using the Direct-zol RNA Miniprep Kit (Zymo Research, California, USA) according to the manufacturer’s protocol and eluted in 50 uL of nuclease-free water. We used 200 µL of sample per extraction. Swab samples were pooled by type and species: ten samples per pool for oropharyngeal and cloacal swabs from chickens and ducks, five samples per pool for cage swabs, up to two samples pooled for wash water samples and five samples pooled for drinking water. Sequencing libraries were generated using the Twist Library Preparation Enzymatic Fragmentation (EF) kit 2.0 (Twist Bioscience, San Francisco, California, USA) and the Twist Comprehensive Viral Research Panel (CVRP), according to the manufacturer’s protocol (Twist Total Nucleic Acids Library Preparation EF Kit 2.0 for Viral Pathogen Detection and Characterization Protocol and Twist Target Enrichment Standard Hybridization v1 protocol). Briefly, extracted RNA was converted to cDNA using the ProtoScript II First Strand cDNA Synthesis kit (New England Biolabs, Ipswich, Massachusetts, USA) and Random Primer 6 (S1230S) from New England Biolabs (NEB). The NEB Next Ultra II Non-Directional RNA second Strand Synthesis kit (E6111S) was subsequently used to convert the single-stranded cDNA to double stranded DNA (dsDNA). Illumina TruSeq-compatible libraries were generated using the Twist Library Preparation EF kit 2.0. Library preparation included DNA fragmentation at 37 °C for 5 minutes, followed by 10 cycles of PCR amplification, and 22 cycles for post-capture PCR amplification. The pooled libraries were enriched by hybridization capture using the Twist Comprehensive Viral Research Panel (CVRP). Following enrichment, libraries were pooled in equimolar ratios, diluted and denatured prior to sequencing according to the standard MiSeq System Denature and Dilute Libraries Guide (Document # 15039740 v10). The library was then sequenced to generate paired-end 75 bp reads using a MiSeq V3 reagent kit (Illumina, MS-102-3001) on the Illumina MiSeq System. In order to quantify potential contamination, nuclease-free water was included as a negative control and processed alongside both poultry and environmental samples from the point of extraction through to library preparation, and subsequently sequenced on the same Illumina sequencing run as the study samples. Sequencing results from the negative controls are presented in Supplementary Fig. 2. We demonstrate that negative controls contained few detectable viral contigs none of which was shared with other samples in this study. This effectively rules out any contamination and provides a meaningful baseline for interpreting true viral signals in study samples.

Processing of viral sequencing data
Raw sequencing data was processed using the CZ ID pipeline (https://czid.org) which is an open-source cloud-based metagenomics pipeline for global pathogen detection and monitoring74. Briefly, the Illumina bioinformatics pipeline employed through CZ ID (https://chanzuckerberg.zendesk.com/hc/en-us/articles/360034790554-Illumina-Pipeline-Details) first quality filters and trims raw sequencing reads with fastp (Supplementary Data 2)75. Host-derived sequences were then excluded by aligning quality-controlled reads to the human, chicken, and duck reference genomes using Bowtie 276. The remaining non-host reads were assembled de novo into contigs using SPAdes77. Taxonomic classification of contigs was performed via DIAMOND blastx78 against the NCBI nr protein database. Finally, reads were mapped back to the assembled contigs using Bowtie 2 for abundance estimation. Viral abundance was estimated as reads per million (RPM) relative to the total number of reads which mapped to virus sequences, while de novo assembled contigs were directly downloaded from the CZ ID website. In order to remove potential false positives virus contigs were only retained for downstream analysis if they accounted for greater than 0.1% of reads in at least one sample.

Bioinformatic analysis
All biostatistical analyses were carried out in R studio (version 4.4.0)79. Alpha-diversity was estimated using the estimate_richness function from the phyloseq package in R. Beta-diversity (Jaccard Index) was calculated using the vegdist function in the vegan package (version 2.7)80. To statistically test for differences between sample types we employed a Permutational Analysis of Variance (PERMANOVA) test which was calculated using the adonis function also in the vegan package. Pairwise PERMANOVAs were calculated using the pairwise.adonis function. To visualise virome composition between sample types we used principal coordinate analysis (PCoA) using the dudi.pco function of the ade4 package (version 1.7)81. To perform a quantitative comparative evaluation of virome composition we employed a median centroid testing methodology based on the Jaccard distances from a PCoA. This approach sees the median PCo1 and PCo2 coordinates calculated (median centroid) for each poultry swab sample type and the distance of all other samples from this point is subsequently determined. Spearman correlations were calculated using the corr.test function in the psych package82. In order to estimate the proportion of viruses in ES that are likely to have come from each poultry swab type source tracking analysis was conducted using the R package SourceTracker83. SourceTracker is a Bayesian approach that estimates the proportional contribution of potential source environments to a given sink sample, based on community composition. The model uses Gibbs sampling to infer the probability distributions of sources contributing to each sink, allowing for the inclusion of an “unknown” source category to account for uncharacterized variation. All plots used to visualise data were produced using the ggplot2 package in R84.

Heatmaps were produced using either the ComplexHeatmap package85 or the circlize package86. For visualisation using heatmaps, virus abundance in RPM was rank scaled using a custom function in R. For a given array of values ‘x’ of length k, denoted by x = {x₁, x₂, …, xk}, the values were first ranked. The corresponding rank-scaled values, denoted by xᴿ = {x₁ᴿ, x₂ᴿ, …, xkᴿ}, were then calculated using the formula: xᵢᴿ = [Rank(xᵢ) – Minimum(Rank(x))] / [Maximum(Rank(xᵢ)) – Minimum(Rank(x))]. This transformation involves ranking the original values and rescaling those ranks to fall within the [0, 1] range. Statistical significance was determined using the non-parametric Wilcox test between two comparative groups. When statistical significance was being determined between three or more comparative groups the non-parametric Kruskal-Wallis with Dunns post-hoc test was employed using the PMCMRplus package. All P values obtained were corrected for false-discovery rate (FDR) using the Benjamini-Hochberg method (p.adjust function in R). P values are annotated as follows unless otherwise stated: P < 0.05*; P < 0.01**; P < 0.001***.

Sequence identity and coverage
Contigs de novo assembled from poultry swabs were first mapped to those recovered from environmental samples with BWA-MEM287, and for each environmental sample we used Samtools88 to calculate the proportion of poultry contigs that had at least one homologous environmental counterpart (number of mapped poultry contigs divided by the total poultry contigs, expressed as a percentage). To extend the comparison to the gene level, we extracted the top DIAMOND blastx hit for every poultry contig after taxonomic assignment, retrieved the corresponding GenBank proteins, and compiled a custom reference set of ~4000 viral genes detected in at least one poultry sample (Supplementary Data 3). All quality-filtered contigs were then queried against this database with DIAMOND blastx; coverage, alignment length, number of mismatches and percent identity were recorded (Supplementary Data 4). Only alignments with an e-value ≤ 1×10⁻⁵ were retained. When contigs from a poultry sample and its paired environmental sample both aligned to the same reference gene, their percent identity and coverage values were compared, whereas genes with no contig meeting the e-value threshold in each sample were recorded as undetected. Differences in percent identity (PID) were determined by comparing poultry and environmental contigs that aligned to the same reference gene, with the PID of the environmental contig subtracted from that of the poultry contig.

Phylogenetic tree reconstruction
For phylogenetic tree reconstruction of influenza gene segments from de novo assembled contigs all available reference sequences were downloaded from the NCBI Influenza database and GISAID (Supplementary Data 5) from January 1, 2014 to July 1, 2024. From our metagenomic data, only influenza contigs de novo assembled through the CZ ID pipeline and greater than 200bp were included for analysis. Contig taxonomy was determined using blast89. Reference datasets were subsampled to 500 sequences using SMOT90 and combined with sequences identified in the current study. SeqKit was used for sequence length filtering and FASTA file manipulation91. Multiple nucleotide sequence alignments were carried out using Clustal Omega with default settings92. The resulting multiple sequence alignments were trimmed to ensure alignment accuracy using trimAl89,93. After trimming, alignments were manually inspected and any remaining erroneous or ambiguous regions were removed. Maximum likelihood phylogenies for each gene segment or genome were individually reconstructed using IQ-TREE (version 2.1.4)94 with the best fit nucleotide substitution model. The phylogeny was inferred using 1000 bootstrap replicates, and branches were filtered based on bootstrap support. Specifically, terminal branches whose immediate parental node had support < 70% were pruned using the ape::drop.tip() function in R. Internal branches with bootstrap support < 70% were retained to maintain overall topological structure. Phylogenetic trees were plotted in R using the ggtree package95.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Cronin et al. examined the utility of using environmental sampling at live bird markets for surveillance of influenza and other viral pathogens of concern. Zoonotic pathogens pose a serious threat to human health, and live animal markets are especially hazardous when it comes to zoonoses. Environmental sampling is less expensive and easier logistically than directly swabbing animals. They found that most of the viruses detected from direct swabs were found through environmental sampling, although there were differences in the viruses found in each environmental sample type. Environmental sampling was better at detecting the chicken virome than the duck virome. Environmental sampling is an important surveillance tool and it is critical to understand how well it performs compared to more traditional methods\nMajor Issues-\nThe usefulness and impact of this study would be greatly improved if recommendations on optimal environmental sampling were given. In lines 247-250 it states that “a variety of sample types are required to accurately capture the most viral viruses from poultry”. It would be helpful if this was followed by how combining specific environmental sampling types increases the ability to recapture the virome from the direct swabs.\nThe title doesn’t accurately reflect the conclusions. Air sampling alone isn’t sufficient to detect the range of viruses and environmental sampling better reflects chicken than duck viromes.\nSomething like “Environmental sampling closely recapitulates direct chicken swabs for avian viruses circulating in live poultry markets” would be better.\nThe github link doesn’t currently work.\nThe figures overall are very nice, but there are a few issues that impede the ability to interpret them. All of the air samples are colored the same despite being treated as separate categories for analysis. This is an issue when not all data points are labeled, such as in Figure 1C, Supplementary Figure 4, and Supplementary Figure 7. The following issues are more minor.\nWithin the figures the order of the samples isn’t consistent and takes a little extra effort to read (i.e Figure 2 D-G and Figure 3H). For Figure 2A, the circle is unnecessary, and the figure would be easier to interpret if it was linear. Figure 4 – It would be easier to see what is going on if the tip shapes were a bit smaller or slightly transparent.\nMinor issues\nLine 298 -301: Exactly how many additional avian viruses were detected\nSupplementary Figure 5 is labeled as Supplementary Figure 4.", "reviewer_2": "The authors present a new, robust and more inclusive approach to the surveillance of avian viral pathogens in high-risk settings, environmental metagenomic surveillance, which provides a better overview of the viral diversity in such settings due to its ability to detect several pathogens at the same time, using only minimal resources in the collection of environmental samples at the same time.\nThe authors demonstrate that the same pathogens can be detected accurately by metagenomics on environmental samples as by metagenomics on poultry samples. However, the authors also acknowledge that several different environmental sample types may need to be combined to achieve a similar level of coverage to poultry samples.\nThe introduction covers all the important aspects related to different surveillance strategies, but it could provide some more context about the specific study location. The proposed approach is methodologically and technically coherent and demonstrates the authors' expertise in the application of the described laboratory and (bio-)statistical methods. All methods are described in detail and reproducible. The results are underpinned by numerous useful illustrations. The discussion addresses all the key points raised in the text, although some limitations in the interpretation and follow-up of the results need to be discussed. For example, it remains unclear which specific intervention strategies can be chosen based on the results and where they should be applied. In addition, aim to prove the superiority of the proposed approach over “conventional pathogen surveillance”, but it remains unclear what this baseline is.\nIntroduction:\n-Could you provide a short summary of studies looking into avian viral pathogens in the study location by “conventional pathogen surveillance”?\n-Could you please explain what your understanding of “conventional pathogen surveillance”, to which you are comparing your approach, is? At the beginning of the discussion, you seem to compare your approach to “targeted probe-enriched metagenomics on direct poultry swab samples”, but it does not become clear from the text before.\nMethodology:\n-Why were exactly those two live bird markets chosen?\n-In some instances throat swabs are mentioned, in others oropharyngeal swabs. Which ones were used and why?\nOropharyngeal swabs would be the preferred choice for the detection of avian influenza viruses. Was viral transport medium used and what were the transport conditions on the way to the laboratory? All this may impact the probability of extracting sufficient high-quality RNA for sequencing.\nDiscussion:\n-It would be useful to discuss how the use of different sample collection methods, sample types and transport conditions may have impacted amount and quality of the RNA extracted.\n-Why in your opinion are air samples performing better than other environmental samples?\n-Why do you think is the approach better at detecting chicken than duck viruses?\n-Why do you think carcass wash water provided statistically significantly different results?\n-The authors claim that the proposed approach is more cost-effective. Is there any evidence or an example of what kind of resources may be saved?\n-Can the proposed approach realistically be implemented on a wider scale in Cambodia or similar settings? Do laboratories in such settings typically have the necessary equipment and expertise to carry out this type of analysis compared to, e.g., conventional PCR testing?\n-How can the proposed approach complement rather than replace existing approaches?\n-The authors claim that the proposed approach allows for more rapid and effective risk mitigation. Which specific mitigation strategies can be chosen based on the results and where should they be applied? It would be important to discuss here that the proposed approach is not able to identify the infected population within the setting, which would be necessary to design targeted mitigation strategies, e.g. for specific poultry production systems. Given that infection cannot be traced back to a certain population, intervention at the source is not possible. For example, avian influenza viruses are found in most live bird markets in Southeast Asia, so how would a positive test result be used?\nOther comments:\n-Is the title appropriate given that the authors are talking about more environmental samples than just air samples?\n-The data, code and analysis files under https://github.com/PeterCx/air-sampling-zoonotic-virus are not available.\n-Table 1 is not mentioned in the text.\n-Lines 48-49: please check for some newer references on wastewater surveillance.\n-Line 82: the results do not indicate at which point along the poultry supply chain interventions are appropriate.\n-Lines 292-294: does this include pathogens not typically associated with the respective host (chickens or ducks)?\n-Lines 341-344: maybe the season played a role? Or some sort of festivity?", "reviewer_3": "Overall comments:\nThis paper investigates approaches for virus surveillance in live bird markets. It describes the comparison of viruses detected in cloacal and throat swabs from poultry to those detected from environmental samples (air, carcass wash, cage swabs). Similar viruses were found in the environmental samples compared to the poultry swabs, including some viruses that were likely not from the targeted poultry species (chickens and ducks). This indicates that environmental surveillance approaches could be useful for conducting surveillance of viruses in live animal market settings.\nWhilst use of environmental surveillance for zoonotic and veterinary pathogens is not new, this study provides a potentially powerful direct comparison of results from environmental samples and swabs. There are however currently some major weaknesses in the study design or reporting. I detail these concerns below, but key areas include a severe lack of clarity and detail in reporting of methods (e.g., numbers of samples per time point), apparent absence of negative control samples, and phylogenetic estimation from virus contigs that have been created from environmental samples that likely represent chimeras of virus genomes from many different individuals. I am also concerned about some elements of statistical analysis, such as little effort to statistically control for differences in the number and type of samples per time point. I hope that the authors can address these concerns.\nMajor comments:\nWhat appropriate blank negative controls were sequenced here (e.g., a blank swab taken in the market, an unused air filter)?\nGiven that the aim is to compare different sample types, it would be extremely important here to confirm that the diversity of viruses observed are not the result of contamination between samples during sequencing. Metaviromic studies can also easily detect viruses present via other contamination in the lab or reagents, and this can be excluded with appropriate controls.\nLine 111: The number of samples collected is not present in the methods. The information is roughly summarised in Table 1, but this is never referred to in the text. In any case, it surely needs to be more detailed, i.e., broken down per market, per time point, and per sample type and species. It’s not clear how bird samples/stalls were selected – were these taken from both the slaughter and the poultry holding area as well? How many stalls? Without this information, it’s impossible to evaluate whether environmental sampling is better, of whether it simply targets broader or different locations.\nLine 157: The bioinformatic information is not sufficiently detailed. Please provide information on the mapping approaches (e.g., what is the reference database used here), as well as information on sequence length filtering etc (e.g., what parameters). What number of reads or contig length is considered a “detection”, and how does this compare to negative controls?\nLine 192: GISAID is mentioned and sequences are used in phylogenies. Yet there is no acknowledgements table of the submitting and originating labs, or other equivalent evidence of information to appropriately acknowledge the authors sharing data on this platform. This would be required according to the terms of use of GISAID. (To be clear, I recognise that NCBI data does not require such acknowledgement according to the terms of use).\nParagraphs line 152 and line 193: It is problematic to build contigs from environmental or pooled data and rely on this for phylogenetic tree estimation. This is because chimeras that represent similar infections in many different hosts can be easily generated. At a minimum, it would be needed to perform bootstraps and provide support for grouping of environmental and non-environmental samples, and provide some comment on the limitation of this approach in terms of possible inaccuracies in estimated divergence and the potential challenges of relying on chimera sequences for public and veterinary health surveillance.\nLine 193: It’s unclear whether the authors used alignments that included both full length CDS and also shorter sequences (200bp) when building phylogenies. This could deeply skew phylogenetic tree estimation and phylogenetic placement. It is possible that better handling of these data is implied by “manually curated and…. trimmed to ensure alignment accuracy”. Please provide additional details of this.\nEthics: How were human reads screened in and removed from the metagenomic data, particularly the air sampling data, before submission to GenBank? Bait capture would not necessarily exclude these.\nFigure 1: it’s a shame that Figure 1A isn’t broken down into different sample types. The difference between a carcass wash – which will presumably also pick up blood-borne pathogens – and an air sampler is quite profound. I’m not convinced that directly comparing throat/cloacal samples to the whole collection of these other sample types is very fair here, especially given the number of samples per group isn’t known.\nFigure S1 – some of these viruses have only 1 RPM. How many million reads were generated per sample? There must be some minimal cut-off of RPM below which these data cannot be trusted. What do the sequencing negative controls suggest? Avian viruses could presumably be from other birds, e.g., wild birds or domestic geese.\nDiscussion: I was surprised that the discussion didn’t mention many limitations or weaknesses, especially compared to existing sampling approaches. A key limitation would surely be the inability to quantify prevalence per market. In addition, there would be an inability to detect reassortment segmented viruses in environmental samples, compared to individual birds, and also the fact that assembly could result in chimeric viruses. Assessing the implications of detected variants in such chimeric viruses would be more challenging.\nSection line 311: What is the value of testing mapping of contig-to-contig here? Particularly when contigs are short and sparse, a contig from, e.g., a capsid found in a throat swab, would obviously not map to a contig of an RdRp found in an environmental sample. So is this proportionality more related to how much of the total genome was recovered in the environmental sample, rather than anything interesting about diversity? It would be useful to clarify this to the reader. If so, perhaps % of genome per type would be a better metric.\nIn general, there are statistical issues drawing conclusions about the ability of one sample type to capture another, when the number and type of samples is variable per time point. For example, Section line 330: Making such comparisons is challenging given the relatively limited information on sampling. For example, looking at Figure 3, no ducks were swabbed in Takeo in 23rd December 2022. It's perhaps unsurprising then that no H5 viruses were found in bird samples, because H5 viruses are highly duck-adapted rather than chicken adapted. Vice versa, no chicken samples were taken on 21st December – which probably explains the lack of N2s (from chicken-adapted H9N2s). Grouping “poultry swabs” together isn’t reasonable, given these different adaptations. Were none of these birds present in the market on these times? I’m sympathetic to the realities of fieldwork, but I do think that these comparisons are misleading/oversimplified as currently presented in the text.\nLine 392: “Given that de novo…”. There’s little data presented on whether the de novo assembled sequences do accurately represent those detected in poultry swabs. Whilst we see that they indeed identify the same viruses, the accuracy of the genome information below the broader clade level is not shown. For example, in Figure 3 it’s unclear which are new environmental and which are new poultry swab data, and how similar these are. There are some quite long branches in some of the new data, and I would feel somewhat reassured if these weren’t in the environmental samples because of the risk of de novo assembly producing strange chimeras. It would have been useful to have some artificial pools here, e.g., comparing results from sequencing fake “environmental samples” made of pooled swabs, to those obtained from sequencing individual swabs.\nMinor comments:\nThe title suggests that air sampling is sufficient to capture the diversity of zoonotic viruses circulating in the market. Yet there is little clarity provided that any pathogen detected here is zoonotic, with the (implied) exception of avian influenza virus. It would be useful to have some added discussion of the diversity of the zoonotic viruses observed to justify this title.\nLine 24: It is wrong that environmental surveillance has not been used for zoonotic pathogens. The abstract itself defines ES as including air and cage swabs, etc. There is extensive evidence of the use of environmental sample types being used to detect zoonotic pathogens including influenza since at least the 1970s, e.g., see https://doi.org/10.1111/tbed.13633. This is recognised by the authors at line 77.\nLine 108: Given that the markets are named, it could be useful to see the point locations on Fig S1.\nLine 120: Additional details should be provided to allow reproducibility, e.g., the type of swab, type of VTM, length of vortex etc. The Direct-zol RNA Miniprep manufacturer’s protocol provides various different conditions for different sample types, and it would be useful to understand which was followed for each sample type, as I imagine that this is different for the pooled swab and the liquid samples. How the samples were pooled into libraries and how many reads per sample and sample type would also be important in the Supplementary Information. The number of PCR cycles in the Twist protocol would be helpful too.\nLine 178: the R package SourceTracker will not be familiar to all readers, and it would be useful to add some brief details of the approach of this package.\nLine 204: Why was no bootstrapping or other estimates of nodal support done?\nData availability: Will the consensus sequences be shared individually, at least for those from the individual hosts? Or is this just SRA? I’m glad these data will be shared, but I can’t evaluate how appropriately this has been done without access to the project or the Github.\nLine 221: were any novel viruses found? Twist approaches would be able to detect novel viruses, at least for some families. What is used to define whether a virus is “found”, e.g., what if only a short contig of 200bp is present, or not all genes? This is especially important thinking about retroviruses, given the possible similarity to host DNA.\nLine 231: is gull coronavirus definitely proven as respiratory, not fecal-oral?\nLine 248: I don’t understand this conclusion. Figure 1C shows the number of viruses detected, but not the diversity. So it’s not clear whether more sample types would be necessary, or, e.g., if wash water contains only a subset of air sample data.\nLine 277 to 280: I don’t understand why this approach was used rather than something simpler, please clarify to the reader.\nLine 283 “tended to have more negative correlations”, is also unclear.\nLine 306 – “performs better at detecting chicken viruses” – perhaps this conclusion could be phrased as “performs better at detecting viruses from the chickens, which were also the most abundant host in this market”, or equivalent. Whilst I note your Discussion point about this, I think it’s useful to add this note here or at least a link to the Discussion. Adding information on the total estimated relative numbers/proportion of individuals per species in each market would be critical here.\nFigure 3H doesn’t appear to have any text associated with it in the legend. Should it also say N6, not H6?\nLine 403: Correct to “Gamma”"}, "author_response": "Response to reviewers’ comments\nPlease note that the line numbers mentioned in our response refer to the revised manuscript with\ntracked changes (all markup). Please see our responses in blue, and the line numbering follows the\nclean version.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nCronin et al. examined the utility of using environmental sampling at live bird markets for surveillance\nof influenza and other viral pathogens of concern. Zoonotic pathogens pose a serious threat to human\nhealth, and live animal markets are especially hazardous when it comes to zoonoses. Environmental\nsampling is less expensive and easier logistically than directly swabbing animals. They found that most\nof the viruses detected from direct swabs were found through environmental sampling, although there\nwere differences in the viruses found in each environmental sample type. Environmental sampling was\nbetter at detecting the chicken virome than the duck virome. Environmental sampling is an important\nsurveillance tool and it is critical to understand how well it performs compared to more traditional\nmethods\nMajor Issues\nThe usefulness and impact of this study would be greatly improved if recommendations on optimal\nenvironmental sampling were given.\nWe thank the reviewer for raising an important point. We agree and to address this we have now revised\nthe manuscript to include recommendations on optimal environmental sampling. This addition has been\nincorporated into the Discussion section (lines 1048 to 1058). In response to this and related comments\nin the rebuttal, we have added a substantial number of new figures to help clarify what the optimal\nstrategy is in this context.\nIn short, ES sampling should not replace but complement existing poultry swab sampling for\nsurveillance. ES of air and cage surfaces should be prioritized, as these sample types consistently\ndetected the highest diversity of poultry viruses (Figure 1). However, other ES types should also be\nincluded. As demonstrated in Supplementary Figure 11, every different environmental sample type is\nimplicated at least once in capturing a poultry virus no other environmental sample type could detect\n(see next comment). Figure 2 also highlights that ES performs better in recapturing chicken viruses\nthan duck viruses. This analysis suggest that in addition to ES, limited but targeted oropharyngeal and\ncloacal swabs especially from ducks remain important for detecting species-specific viruses that may\nnot persist in the environment. While wash water detected the lowest number of poultry viruses (species\nrichness) compared to other sample types it was the shortest distance (Jaccard beta-diversity) from\noropharyngeal and cloacal swabs in ducks (Figure 2). This indicates that while wash water is less\ndiverse it shared a higher proportion of their fewer viruses with duck samples highlighting its importance.\nThe same point can be made about drinking water which tended to detect less poultry viruses than\nother ES (Figure 1). However samples in this group were the shortest distance from the chicken\noropharyngeal median centroid again suggesting that it shared a higher proportion of their fewer viruses\nwith chicken oropharyngeal swabs. While there are multiple layers to all of this we believe it is clear that\na hybrid approach is most beneficial to broaden surveillance coverage. Multiple ES types should be\nused in combination with poultry swabs (ducks prioritised).\nThis optimal sampling strategy described here is of course context dependent. Other human animal\ninterfaces have different animal species, different viruses and ES needs to be validated/evaluated in\nother settings as the optimal strategy may be different.\nIn lines 247-250 it states that “a variety of sample types are required to accurately capture the most\nviral viruses from poultry”. It would be helpful if this was followed by how combining specific\nenvironmental sampling types increases the ability to recapture the virome from the direct swabs.\nWe would like to thank the reviewer for their constructive feedback. As briefly mentioned in the previous\ncomment we have conducted a new analysis presented in Supplementary Figure 11 to address this.\nThese figures display the total number of viruses detected in each poultry swab across timepoints,\nalongside a stacked bar plot representing detections in environmental samples. If a virus was identified\nin multiple environmental sample types at a given timepoint, it is shown in the lower portion of the bar\nlabelled in the legend as >2 ES (coloured in grey). In contrast, viruses detected exclusively in a single\nenvironmental sample type at a given timepoint are represented in the upper portion of the bar and\ncolour coded accordingly. Every different environmental sample type is implicated at least once in\ncapturing a poultry virus no other sample type could detect. Consistent with our original statement, this\nanalysis demonstrates that while the majority of viruses can be detected across multiple environmental\nsample types, a subset of viruses are uniquely identified in specific samples. Therefore, incorporating\nmultiple sample types enhances the overall recovery of viral diversity.\nIn addition, when considering the total number of poultry viruses detected (not just those recaptured)\nwe show in Figure 1B that air from the slaughter and holding areas, as well as cage swabs have a\nhigher alpha-diversity than other groups. In Figure 2D we show that these three ES types are the\nshortest distance from the median centroid of chicken cloacal swabs. While in Figure 2C cage swabs\nare shown as the group with the second shortest distance from oropharyngeal swabs in chickens.\nTogether these findings based on both alpha-diversity and beta-diversity analysis indicate that air\n(slaughter and holding area) and cage swabs enhance poultry virus surveillance and thus must be\nincluded. In Figure 2E-F we see that wash water was the shortest distance from oropharyngeal and\ncloacal swabs in ducks suggesting it performed best at detecting viruses from this host. This is important\nas in Figure 1 and associated Supplementary we often see wash water detecting less viruses than other\nES types. This tells a story of quality over quantity while wash water is less diverse (alpha-diversity) it\nshared a higher proportion of their fewer viruses with duck samples. This analysis also captures\ntemporal consistency, as it incorporates data from all twelve sampling visits to the live bird market. The\nsame can be said for drinking water which was the shortest distance from oropharyngeal swabs in\nchickens (Figure 2C).\nFive out of six ES types in this study contributed meaningfully to poultry virus detection, supporting the\ninclusion of diverse sample types to maximize surveillance performance. Based on these findings and\ncomments from other reviewers we have now described the optimal sampling strategy in the discussion\non lines 1048 to 1058.\nWe believe the new figures and analysis better supports the statement. The manuscript text has also\nbeen updated at lines 438-445, 458-463 and 510-552 describing these findings.\nThe title doesn’t accurately reflect the conclusions. Air sampling alone isn’t sufficient to detect the range\nof viruses and environmental sampling better reflects chicken than duck viromes.\nSomething like “Environmental sampling closely recapitulates direct chicken swabs for avian viruses\ncirculating in live poultry markets” would be better.\nWe agree with the reviewer and have now updated the title accordingly. We have changed air to\nenvironmental sampling. We have however not opted to specifically mention chickens rather than\nducks. Yes the performance for chickens was better than ducks but as mentioned in response to the\nfirst comment this approach is meant to “enhance” not replace. With respect to this we believe the new\ntitle accurately reflects the content of the manuscript.\nThe github link doesn’t currently work.\nThis has now been resolved. The GitHub is publicly available with all relevant data and R scripts. Please\nnote that the link has been updated to - https://github.com/PeterCroninX/air-sampling-zoonotic-virus.\nThe figures overall are very nice, but there are a few issues that impede the ability to interpret them. All\nof the air samples are colored the same despite being treated as separate categories for analysis. This\nis an issue when not all data points are labeled, such as in Figure 1C, Supplementary Figure 4, and\nSupplementary Figure 7.\nWe greatly appreciate the reviewer’s detailed comments. In response, we have updated all figures in\nthe manuscript with each of the 10 groups having a distinct colour.\nThe following issues are more minor.\nWithin the figures the order of the samples isn’t consistent and takes a little extra effort to read (i.e\nFigure 2 D-G and Figure 3H). For Figure 2A, the circle is unnecessary, and the figure would be easier\nto interpret if it was linear. Figure 4 – It would be easier to see what is going on if the tip shapes were a\nbit smaller or slightly transparent.\nWe thank the reviewer for their helpful suggestions. We understand the concern regarding the ordering\nof groups in Figures 2D–G and 3H. The figures being referred to are now Figure 2C-F in the latest draft.\nThe ordering of these figures was intentional and we believe it is important to maintain this order for\ngood reason. This is because the groups are ranked by their distance to the median centroid, with the\ngroup closest to the centroid appearing first. Given the median centroid is calculated solely based off\npoultry swabs this group always appears first. The second group in each case corresponds to the\nenvironmental sample that is the shortest distance from the median centroid of the respective poultry\nswab (in other words most similar based on the PCoA of beta-diversity). We acknowledge that this\nrationale was not clearly explained in the original manuscript, and we have now updated the figure\nlegend for Figure 2 (lines 1472-1473) to clarify this. We believe having a standardised order in this case\nonly causes confusion as it may not be clear which group is most closely associated with each poultry\nswab type. However, outside of this one case we have ensured that the ordering of all other figures is\nconsistent throughout the manuscript.\nFor Figure 4C (previously Figure 3H) we have now also updated the order. From top to bottom the\nheatmap is broken down by date while poultry swabs always appear first followed by environmental.\nFurthermore, circular heatmaps have been replaced with more conventional linear representations, and\nthe phylogenetic trees in Figures 4 have been revised by adjusting the tip shapes (shapes made smaller\nand also distinct shape for poultry and environment) for improved clarity. We agree with the reviewer\nthat these modifications enhance the overall readability of the figures.\nFigure 4 being referred to here has been removed in line with comments from other reviewers and new\nanalysis included this time around (associated with Figure 3). However, phylogenetic trees are still\nincluded for avian influenza (now in Figure 4). With respect to this comment we have now changed the\naesthetics of the trees reducing tip size.\nMinor issues\nLine 298 -301: Exactly how many additional avian viruses were detected. Supplementary Figure 5 is\nlabelled as Supplementary Figure 4.\nWe thank the reviewer for their feedback. Specifically, 5 additional poultry viruses were detected, and\nwe have updated the manuscript accordingly at lines 475 to clarify this.\nWe would like to emphasize what we originally meant by \"additional avian viruses\" in this context,\nespecially as we have added several new figures and made substantial revisions to the original draft.\nIn the original draft, \"additional avian viruses\" refers to viruses detected in environmental samples that\nwere never detected in poultry swabs at any timepoint. We identified 5 “additional” poultry viruses by\nthis definition. This figure is still included as Supplementary Figure 16 and associated text shown at\nlines 471 to 481.\nIn the latest draft of the manuscript when we refer to “poultry viruses,” we specifically mean the 84\nviruses that were detected in at least one poultry swab at one or more timepoints in this study. Except\nfor Supplementary Figure 16 all analysis presented in the study is based on these viruses. Importantly,\nthere are many instances where ES detects some of these viruses but poultry swabs fail to do so. It’s\nimportant to mention this as in the original draft much of the analysis focused on the ability of ES to\ndirectly recapture exactly what was present in poultry swabs with little attention given to this detail. In\nline with other reviewers comments we have updated all figures to reflect this.\nWe have also ensured that all Supplementary Figures are correctly labelled.\nReviewer #2 (Remarks to the Author):\nThe authors present a new, robust and more inclusive approach to the surveillance of avian viral\npathogens in high-risk settings, environmental metagenomic surveillance, which provides a better\noverview of the viral diversity in such settings due to its ability to detect several pathogens at the same\ntime, using only minimal resources in the collection of environmental samples at the same time.\nThe authors demonstrate that the same pathogens can be detected accurately by metagenomics on\nenvironmental samples as by metagenomics on poultry samples. However, the authors also\nacknowledge that several different environmental sample types may need to be combined to achieve a\nsimilar level of coverage to poultry samples.\nThe introduction covers all the important aspects related to different surveillance strategies, but it could\nprovide some more context about the specific study location. The proposed approach is\nmethodologically and technically coherent and demonstrates the authors' expertise in the application of\nthe described laboratory and (bio-)statistical methods. All methods are described in detail and\nreproducible. The results are underpinned by numerous useful illustrations. The discussion addresses\nall the key points raised in the text, although some limitations in the interpretation and follow-up of the\nresults need to be discussed. For example, it remains unclear which specific intervention strategies can\nbe chosen based on the results and where they should be applied. In addition, aim to prove the\nsuperiority of the proposed approach over “conventional pathogen surveillance”, but it remains unclear\nwhat this baseline is.\nIntroduction:\n-Could you provide a short summary of studies looking into avian viral pathogens in the study location\nby “conventional pathogen surveillance”?\n-Could you please explain what your understanding of “conventional pathogen surveillance”, to which\nyou are comparing your approach, is? At the beginning of the discussion, you seem to compare your\napproach to “targeted probe-enriched metagenomics on direct poultry swab samples”, but it does not\nbecome clear from the text before.\nWe thank the reviewer for their input. We agree this was not clear in the original draft. Firstly we have\nupdated the introduction at lines 113 to 115 citing studies which have used convention pathogen\nsurveillance methods in Cambodia.\nWhen we refer to conventional or traditional pathogen surveillance in the manuscript we were referring\nto the random collection of poultry swabs (both oropharyngeal and cloacal) which are then typically\nsubject to PCR-based methods for the screening of one or few pathogens (e.g., Influenza A and\nCoronaviruses). While this approach offers high sensitivity and specificity it is limited in the number of\nviruses it can detect. Metagenomics has emerged as a method with a much broader target range for\nvirus identification and surveillance, allowing detection of a diverse array of known and potentially novel\npathogens. Independent of the surveillance method (PCR/Metagenomics) the conventional sampling\nstrategy has always been random poultry swab sampling which may miss circulating pathogens if the\nselected bird is not infected when sampled, thus limiting the probability of detection. All of this is\nmentioned in the introduction at lines 111 to 126. With respect to all of this, the current study aimed to\ncompare targeted virus capture probe-based metagenomics on several different ES types versus the\nconventional sampling strategy of random poultry swabs collected from LBMs.\nWe agree that in the original draft it was unclear what we were comparing against. We have now\nupdated the manuscript at lines 36 to 37 in the abstract and lines 127 to 129 in the introduction to\nspecifically state what we are comparing against. We believe these changes now make it clear to the\nreader what our approach is.\nMethodology:\n-Why were exactly those two live bird markets chosen?\n-In some instances throat swabs are mentioned, in others oropharyngeal swabs. Which ones were used\nand why? Oropharyngeal swabs would be the preferred choice for the detection of avian influenza\nviruses. Was viral transport medium used and what were the transport conditions on the way to the\nlaboratory? All this may impact the probability of extracting sufficient high-quality RNA for sequencing.\nWe thank the reviewer for their feedback. Orussey market, located in Phnom Penh, is the largest live\nbird market in Cambodia and serves as a major hub for poultry trade. Birds are brought to the market\nfrom a broad geographic area, leading to high animal density and extensive inter-regional mixing. This\ncentralization of poultry trade increases the potential for pathogen transmission and amplification which\nmakes Orussey market a key site for avian influenza virus surveillance and research in Cambodia. In\ncontrast, Takeo market was selected as a study site due to its smaller scale, lower trade volume, and\nprimarily regional bird sourcing including cross border poultry trade. It is representative of the many\nsmall-scale, decentralized live bird markets dispersed throughout Cambodia. Studying such a market\nprovides critical insight into local transmission dynamics and complements findings from larger, urban\ncentres like Phnom Penh. Together, these sites offer a broader understanding of virus circulation across\ndifferent market types. We have now updated the methods section at lines 138 to 147 highlighting the\nrationale for surveillance in both of these markets.\nOriginally when we used the word “throat” we were referring to oropharyngeal swabs. We have now\nremoved the word “throat” from the manuscript replacing with oropharyngeal.\nWe have also updated the methods at line 166 to 178 describing the sample transport\nmedium/conditions used in this study. Specifically, the VTM used in this study was prepared in-house\nand consists of 2.95% tryptose phosphate broth, 145 mM NaCl, 5% gelatin, 54 mM Amphotericin B,\non ice to the lab located in Phnom Penh. No liquid nitrogen was used to avoid a freeze-thaw cycle.\nDiscussion:\n-It would be useful to discuss how the use of different sample collection methods, sample types and\ntransport conditions may have impacted amount and quality of the RNA extracted.\nThank you for this valuable suggestion. We agree that variations in sample collection methods, sample\ntypes, and transport conditions can significantly impact the amount and integrity of RNA recovered,\nparticularly when comparing environmental samples to direct poultry swabs.\nTo address this, we have revised the Discussion section (lines 1000–1004) to acknowledge these\nlimitations and their potential influence on downstream sequencing and virus detection outcomes.\nSpecifically, we now note that environmental samples, such as air, water, and surface swabs, may yield\nlower viral loads and be more susceptible to RNA degradation or inhibitory substances, which could\npartially account for their lower virus detection rates relative to direct avian samples. These differences\nhighlight both the technical challenges and interpretive considerations when integrating environmental\nmetagenomics with host-based surveillance.\n-Why in your opinion are air samples performing better than other environmental samples?\nWe believe that the relatively strong performance of air samples may be attributed to their low\nbackground contamination. Compared to other environmental samples (e.g., animal swabs or feces),\nair typically contains less bacterial and host material, reducing nonspecific nucleic acids and enhancing\nviral enrichment and sequencing efficiency. We have now updated the manuscript in the discussion at\nlines 958 to 961 stating why we believe air performed better than other sample types.\n-Why do you think is the approach better at detecting chicken than duck viruses?\nOne likely explanation is that a significantly higher number of chickens pass through these markets on\na daily basis compared to ducks, which reflects the greater demand for chicken meat relative to duck\nmeat. As a result, the market environment is more heavily contaminated with chicken-associated\nviruses, leading to increased detection rates. The lower number of ducks means there is a lower chance\nof the environment becoming saturated with viruses from this host. We have addressed this in the\ndiscussion at lines 1018-1032.\n-Why do you think carcass wash water provided statistically significantly different results?\nWash water likely captures oropharyngeal viruses efficiently due to the release of respiratory fluids\nduring slaughter, direct rinsing of the upper airway, and the pooling effect across multiple carcasses,\nresulting in a concentrated representation of respiratory virus in this sample type. We have now\nmentioned this in the discussion at lines 972 to 1000.\n-The authors claim that the proposed approach is more cost-effective. Is there any evidence or an example of what kind of\nresources may be saved?\nWe appreciate this insightful question. To clarify and support our claim, we have expanded the\nDiscussion section (lines 1094–1112) to explain how environmental sampling, particularly when paired\nwith metagenomics, can substantially reduce both logistical and financial costs associated with\ntraditional surveillance. Rather than individually sampling and testing potentially thousands of birds to\ncharacterize viral circulation in live-animal markets, a small number of environmental samples, such as\nair, water, or surface swabs, can capture viral signals from multiple species and sources simultaneously.\nThis pooled exposure effect reduces the number of samples that must be collected, transported,\nprocessed, and sequenced, resulting in significant savings in labour, consumables, and laboratory time.\nFurthermore, the use of the CZ ID platform, which is open-source and designed to function with minimal\ncomputational infrastructure and bioinformatics expertise, eliminates the need for specialized software\nlicenses or high-performance computing resources, further lowering barriers to implementation. Taken\ntogether, this integrated approach enables faster, broader, and more cost-effective surveillance,\nparticularly in resource-limited settings such as Southeast Asia. These revisions have now been\nreflected in the updated manuscript.\n-Can the proposed approach realistically be implemented on a wider scale in Cambodia or similar\nsettings? Do laboratories in such settings typically have the necessary equipment and expertise to carry\nout this type of analysis compared to, e.g., conventional PCR testing?\nWe thank the reviewer for raising this important point. We believe, and have shown, the proposed\napproach is both realistic and feasible for broader implementation in Cambodia and similar resource-\nlimited settings across Southeast Asia. All wet lab procedures and sequencing in this study were\nconducted at the Institut Pasteur du Cambodge (IPC), a World Health Organization (WHO) Reference\nLaboratory equipped with an Illumina MiSeq platform. This demonstrates that sequencing-based\nsurveillance can be successfully conducted within-country, without requiring outsourcing to international\nfacilities.\nHowever, even in settings where in-country sequencing capacity is not yet established, next-generation\nsequencing is increasingly accessible through external commercial or institutional services. These\nplatforms offer relatively rapid turnaround times and continually decreasing costs, in part due to the\nlong-surpassed limits of Moore’s Law in sequencing technology. The cost per genome and data\nprocessing time have dropped significantly over the past decade, making external NGS services a\nviable and scalable option for many low- and middle-income countries.\nTo further lower the technical barrier, we used the CZ ID platform for downstream analysis. CZ ID is\nopen-source, freely available, and optimized to run on minimal computational infrastructure with limited\nbioinformatics expertise—making the entire workflow accessible to labs without dedicated\nbioinformatics personnel or high-performance computing. While conventional PCR testing remains\nvaluable for targeted diagnostics, metagenomic sequencing offers a broader, unbiased detection\ncapability that can identify both known and novel pathogens. These additions have been incorporated\ninto the revised Discussion section (lines 1106–1112).\n-How can the proposed approach complement rather than replace existing approaches?\nWe believe the proposed approach is best positioned as a complement rather than a replacement for\nexisting surveillance methods. Our findings show that environmental samples tend to have higher alpha\ndiversity for poultry viruses compared to poultry swabs, allowing for the detection of a broader range of\nviral species. However, environmental samples were only able to recapture approximately 70–90% of\nthe viruses detected in poultry swabs. This indicates that while environmental metagenomics offers\nbroad coverage, it may miss certain viruses present in individual hosts. In addition, we found that\nenvironmental metagenomics performed better for chickens than for ducks, likely due to the greater\nnumber of chickens present at the market. This suggests that direct sampling may be particularly\nimportant for animal groups present in lower numbers at human-animal interfaces to ensure their viral\ndiversity is adequately captured. Therefore, integrating environmental sampling with poultry swab\nmetagenomics provides a more comprehensive surveillance strategy casting a wider net and\nmaximizing the likelihood of detecting diverse pathogens circulating in live poultry markets. We have\nupdated the manuscript in the discussion section at lines 1057 to 1066 to address this.\n-The authors claim that the proposed approach allows for more rapid and effective risk mitigation. Which\nspecific mitigation strategies can be chosen based on the results and where should they be applied? It\nwould be important to discuss here that the proposed approach is not able to identify the infected\npopulation within the setting, which would be necessary to design targeted mitigation strategies, e.g.\nfor specific poultry production systems. Given that infection cannot be traced back to a certain\npopulation, intervention at the source is not possible. For example, avian influenza viruses are found in\nmost live bird markets in Southeast Asia, so how would a positive test result be used?\nWe thank the reviewer for this insightful and important comment. We agree that environmental\nmetagenomic surveillance does not allow for identification of specific infected animals or tracing\ninfections to particular production systems. However, we believe the approach still enables timely and\nactionable mitigation strategies in high-risk settings such as live bird markets, especially when applied\nin combination with targeted follow-up actions.\nTo clarify this point, we have added a paragraph to the Discussion (lines 1067–1093) outlining specific,\nrealistic interventions that can be guided by environmental data. For instance, detection of elevated\nviral loads on cage swabs may prompt enhanced disinfection of cages and holding areas. High levels\nof viral contamination in air samples can support operational changes such as covering defeathering\nmachines or modifying ventilation systems. Repeat environmental sampling can then be used to assess\nthe effectiveness of these interventions and determine whether further measures, such as poultry\nquarantine (e.g., 48–72 hours) or temporary market closure, may be warranted.\nFurthermore, when novel or high-consequence viruses are detected, targeted responses such as\nconfirmatory PCR, host-specific swabbing, or upstream supply chain tracing can be initiated. In this\ncontext, environmental surveillance serves as a scalable and cost-effective early warning system. While\nit cannot pinpoint the infection source at the individual or flock level, it enables risk-based decision-\nmaking and complements existing animal surveillance strategies. These clarifications have now been\nincorporated into the revised manuscript.\nOther comments:\n-Is the title appropriate given that the authors are talking about more environmental samples than just\nair samples?\nWe agree and we have now updated the title to more accurately reflect the findings of the study.\n-The data, code and analysis files under https://github.com/PeterCx/air-sampling-zoonotic-virus are not\navailable.\nThis has now been updated accordingly. The GitHub is publicly available with all relevant data and R\nscripts. This link has been updated to - https://github.com/PeterCroninX/air-sampling-zoonotic-virus.\n-Table 1 is not mentioned in the text.\nTable 1 has also been updated to a more detailed version included as Supplementary Table 1 which\nshows the number of samples collected at each timepoint and market. It is now referred to in the text\nat lines 157, 162 and 166.\n-Lines 48-49: please check for some newer references on wastewater surveillance.\nWe have now added several new references at line 70 for wastewater surveillance as requested.\n-Line 82: the results do not indicate at which point along the poultry supply chain interventions are\nappropriate.\nThis has now been addressed in a previous response above (regarding risk mitigation). We have\nincluded details of this in the discussion at lines 1067–1093.\n-Lines 292-294: does this include pathogens not typically associated with the respective host (chickens\nor ducks)?\nThis analysis was conducted for all 84 viruses detected on at least one market visit and in at least one\npoultry swab across the study. The viruses in question can be seen in Figure 1A and mentioned in the\ntext at lines 364-365. Some of these viruses were only found once and in a lower abundance so they\nmay not be typically associated with the host.\n-Lines 341-344: maybe the season played a role? Or some sort of festivity?\nWe thank the reviewer for raising an important point. It has been previously shown that there is an\nincrease in avian influenza virus at live-bird markets in Cambodia which directly corresponds to the four\nmajor Cambodia festival periods. However when these samples were taken (December 2022) this\nwould have been a month after the November Bon Om Touk (water festival) and two months before\nFebruary Lunar New Year. Thus there is no evidence it is associated with a festivity.\nWe have added new text to the manuscript at lines 777 to 779.\nReviewer #3 (Remarks to the Author):\nOverall comments:\nThis paper investigates approaches for virus surveillance in live bird markets. It describes the\ncomparison of viruses detected in cloacal and throat swabs from poultry to those detected from\nenvironmental samples (air, carcass wash, cage swabs). Similar viruses were found in the\nenvironmental samples compared to the poultry swabs, including some viruses that were likely not from\nthe targeted poultry species (chickens and ducks). This indicates that environmental surveillance\napproaches could be useful for conducting surveillance of viruses in live animal market settings.\nWhilst use of environmental surveillance for zoonotic and veterinary pathogens is not new, this study\nprovides a potentially powerful direct comparison of results from environmental samples and swabs.\nThere are however currently some major weaknesses in the study design or reporting. I detail these\nconcerns below, but key areas include a severe lack of clarity and detail in reporting of methods (e.g.,\nnumbers of samples per time point), apparent absence of negative control samples, and phylogenetic\nestimation from virus contigs that have been created from environmental samples that likely represent\nchimeras of virus genomes from many different individuals. I am also concerned about some elements\nof statistical analysis, such as little effort to statistically control for differences in the number and type of\nsamples per time point. I hope that the authors can address these concerns.\nMajor comments:\nWhat appropriate blank negative controls were sequenced here (e.g., a blank swab taken in the market,\nan unused air filter)? Given that the aim is to compare different sample types, it would be extremely\nimportant here to confirm that the diversity of viruses observed are not the result of contamination\nbetween samples during sequencing. Metaviromic studies can also easily detect viruses present via\nother contamination in the lab or reagents, and this can be excluded with appropriate controls.\nWe appreciate the reviewer’s concern regarding potential contamination and the importance of including\nappropriate negative controls in virus metagenomic studies. Originally we sequenced five negative\ncontrol samples to monitor for potential contamination. These controls were not included in the original\nmanuscript, as no evidence of cross-contamination or background viral signal was detected. In line with\nother virus metagenomic studies we only counted reads if they mapped to an assembled contig\n(methods section lines 240 to 241). Of the five negative controls sequenced, only one produced\nassembled contigs, which were all identified as Totiviridae_sp. a virus known to infect fungi.\nSaccharomyces cerevisiae is a known host of this virus species. This virus was not detected in any\nother sample in our dataset. Ultimately, this means that aside from a single fungal virus, all negative\ncontrols showed zero counts for every species detected whether environmental or poultry-associated.\nThis rules out any contamination from the lab/reagents. To address the reviewers concerns we have\nincluded a new Supplementary Figure 2 in the manuscript which is a heatmap showing all species\ndetected in the study. We have also updated the manuscript at lines 211 to 217 in the describe these\nfindings.\nHowever, the specific examples suggested (i.e., a \"blank swab taken in the market\" or an \"unused air\nfilter\") are difficult to interpret in the context of our study. Once a swab is exposed to the market\nenvironment, it can no longer be considered a true negative control, and therefore cannot provide a\nmeaningful baseline. Similarly, the suggestion of sequencing an unused air filter is unlikely to be\ninformative, as such filter commercially manufactured by Thermo Fisher Scientific are supposedly\nsterile. Even if these filters are contaminated with viral nucleic acid during the manufacturing process it\nis highly unlikely they are contaminated specifically with a plethora of poultry pathogens. In our view,\nsuch materials do not constitute appropriate or realistic negative controls for this type of work.\nLine 111: The number of samples collected is not present in the methods. The information is roughly\nsummarised in Table 1, but this is never referred to in the text. In any case, it surely needs to be more\ndetailed, i.e., broken down per market, per time point, and per sample type and species. It’s not clear\nhow bird samples/stalls were selected – were these taken from both the slaughter and the poultry\nholding area as well? How many stalls? Without this information, it’s impossible to evaluate whether\nenvironmental sampling is better, of whether it simply targets broader or different locations.\nWe thank the reviewer for their valuable feedback. We have now clarified the sampling strategy in the\nmethods section at lines 147 to 166. Table 1 has been moved to Supplementary Table 1 and shows the\nnumber of samples collected for each group at each LBM visit. Birds and cages were chosen randomly\nfor sample collection. It’s important to note that poultry swabs are only taken from live birds. Typically\nat the market once birds arrive they are kept in the holding area either in cages or occasionally on the\nground adjacent cages. Once live birds are transferred to the slaughter area they are culled almost\nimmediately. With respect to this, poultry swabs can only be obtained from live birds within the holding\narea. Furthermore, like poultry swabs, cage swabs in this study were pooled. 5 cages were randomly\nselected and swabbed at each LBM visit. Five drinking water samples were also randomly collected.\nDrinking water is contained within the cage but not all cages typically have drinking water. With respect\nto this drinking water was often not collected from the same cages that were swabbed. Up to two\ncarcass wash water sample was collected at each timepoint because typically there is only no more\nthan two carcass wash water containers in the slaughter area of LBM.\nLine 157: The bioinformatic information is not sufficiently detailed. Please provide information on the\nmapping approaches (e.g., what is the reference database used here), as well as information on\nsequence length filtering etc (e.g., what parameters). What number of reads or contig length is\nconsidered a “detection”, and how does this compare to negative controls?\nWe thank the reviewer for their response. We agree with the reviewer and have now made several\nchanges to the methods section providing greater detail for the bioinformatics methods used at lines\n220 and 246. We have also included Supplementary Table 2 which shows the number of reads retained\nafter each QC step in the CZ ID pipeline. The reason that the bioinformatic methods were not sufficiently\ndetailed in the original manuscript was because we used the CZ ID pipeline. This is an open source\nfree to use platform which has previously been published. In the original manuscript we provided\nreferences and a link to this pipeline. The full details are explained here\n(https://chanzuckerberg.zendesk.com/hc/en-us/articles/360034790554-Illumina-Pipeline-Details).\nLBMs in Southeast Asia are often located in areas where access to substantial computational resources\nfor data analysis is limited. This is a key reason why we selected this pipeline, as the framework is\nstraightforward to apply and adaptable to resource-limited settings.\nBriefly, raw sequencing reads were first processed with fastp to remove adapter sequences and filtered\nbased on quality and a minimum read length threshold of 35 bp. Host-derived sequences were then\nexcluded by aligning quality-controlled reads to the human, chicken, and duck reference genomes using\nBowtie2. The remaining non-host reads were assembled de novo into contigs using SPAdes. Taxonomic\nclassification of contigs was performed via DIAMOND alignment against the NCBI Non-Redundant\nProtein database. Finally, reads were mapped back to the assembled contigs using Bowtie2 for\nabundance estimation.\nFor the majority of analysis in this manuscript contigs of any length were included. However, raw\nabundance estimations from CZ ID were subject to a 0.1% relative abundance filter in order to remove\nfalse positives and more robustly capture the proportion of viral signal within each sample. Thus a\ndetection in this study includes contigs of any length but is only included if greater than 0.1% of reads\nmap to this contig in at least one sample. The only exception to this was for phylogenetic tree\nconstruction where contigs had to be 200bp or greater to be included. This was done to avoid biasing\nphylogenetic inference as short fragments may lack sufficient phylogenetic signal and can distort tree\ntopology. We acknowledge a mistake in the original draft of this manuscript as we had written in the\nresults section that contigs of greater than 200bp were used for mapping poultry contigs to the\nenvironment. However the 200bp cutoff was only used for phylogenetic trees. Poultry contigs were\nmapped to ES contigs if they passed the filtering criteria mentioned above.\nWith respect to all of this information, numerous changes have been made to the methods section at\nlines 220 to 339.\nNegative controls have now been addressed in response to the first comment.\nLine 192: GISAID is mentioned and sequences are used in phylogenies. Yet there is no\nacknowledgements table of the submitting and originating labs, or other equivalent evidence of\ninformation to appropriately acknowledge the authors sharing data on this platform. This would be\nrequired according to the terms of use of GISAID. (To be clear, I recognise that NCBI data does not\nrequire such acknowledgement according to the terms of use).\nWe have now included GISAID acknowledgments for all sequences used in Supplementary Table 5.\nParagraphs line 152 and line 193: It is problematic to build contigs from environmental or pooled data\nand rely on this for phylogenetic tree estimation. This is because chimeras that represent similar\ninfections in many different hosts can be easily generated. At a minimum, it would be needed to perform\nbootstraps and provide support for grouping of environmental and non-environmental samples, and\nprovide some comment on the limitation of this approach in terms of possible inaccuracies in estimated\ndivergence and the potential challenges of relying on chimera sequences for public and veterinary\nhealth surveillance.\nWe thank the reviewer for raising this important point. We are aware of this limitation which is incredibly\ndifficult to resolve. Originally the phylogenetic tree estimation was presented as a method of showing\nsimilarity between poultry and ES contigs for important viruses while also allowing us to visualise what\nsubtype or clade of each virus that was in circulation at the market. Based on this and other comments\nwe have conducted a new analysis presented in Figure 3 and associated Supplementary Figures 17-\n41 which is an alternative method of showing similarity between contigs. We believe this revised\nanalysis is more comprehensive and clearly demonstrates strong concordance between poultry and\nenvironmental contigs in terms of gene presence, gene coverage, and sequence identity.\nWe first identified the top DIAMOND blastx hit for each poultry contig after taxonomic assignment.\nCorresponding reference genes were retrieved from GenBank to build a custom database of\napproximately 4,000 poultry virus genes. A full list of GenBank accessions and associated annotations\n(genus, species, and gene type) is provided in Supplementary Table 3. All contigs that passed filtering\n(as previously described) were then mapped to this reference gene set to calculate coverage and\npercent identity as reported by DIAMOND (Supplementary Table 4). Only DIAMOND hits with a\nsignificant e-value threshold greater than 1e-5 were retained for downstream analysis. Where poultry\nand ES contigs had significant hits against the same Genbank entry the difference in sequence identity\nand coverage was reported. If no contig in a given sample aligned to a gene with an e-value ≤ 1e-5, the\ngene was considered undetected in that sample. Ultimately this new analysis shows that environmental\ncontigs align to the majority of poultry virus genes with little difference in coverage or percent identity.\nThis analysis is described in the new results section from lines 612 to 753.\nThis gene-level mapping approach provides a clearer and more conservative measure of genetic\nsimilarity than phylogenetic trees. However, we have opted to retain phylogenetic trees only for\ninfluenza A and these trees were rebuilt with 1000 bootstrap replicates, and we display only branches\nwhose parent node is supported by ≥70% bootstrap, ensuring clade assignments are more robust. The\ninfluenza trees therefore serve solely to contextualise circulating clades at the LBMs, while the gene-\nlevel analysis delivers the principal evidence of sequence concordance. The methods have been\nupdated accordingly at lines 336 to 338.\nWhile this analysis does not completely resolve the possibility of chimeras from closely related strains\nwe have included this as a limitation in the discussion from lines 1038 to 1044. Where the detected\nsequence divergence of key viruses could have pathogenic implications, targeted strain-specific PCR\nassays should be undertaken to independently verify the assembly-derived variants\nLine 193: It’s unclear whether the authors used alignments that included both full length CDS and also\nshorter sequences (200bp) when building phylogenies. This could deeply skew phylogenetic tree\nestimation and phylogenetic placement. It is possible that better handling of these data is implied by\n“manually curated and…. trimmed to ensure alignment accuracy”. Please provide additional details of\nthis.\nWe thank the reviewer for this important observation. We confirm that all contigs ≥200 bp were included\nin phylogenetic analyses. Our intention was not to perform full-length phylogenetic reconstruction, but\nrather to illustrate that viral sequences identified across sample types are separated by short\nphylogenetic distances. Thus showing that environmental contigs closely resemble those from poultry\nswabs. While we do have a number of full-length genes assembled, we opted not to exclude shorter\ncontigs, as we believed they still contribute meaningful data to the overall signal.\nImportantly, IQ-TREE constructs trees based on a per-column maximum likelihood approach, and is\nspecifically designed to accommodate partial sequences without systematically biasing topology,\nprovided alignments are well curated. The presence of shorter sequences may affect resolution at\ndeeper nodes but does not deeply skew tree estimation when the focus is on relative placement among\nclosely related sequences.\nWe have however reconstructed the influenza phylogenetic trees this using 1000 boostrap replicates\nand only parent branches with support >70% were included in the final tree. This is now mentioned in\nmethods at line 336 to 338.\nRegarding alignment quality, our previous phrasing (\"manually curated and trimmed to ensure\nalignment accuracy\") referred to the removal of highly divergent regions suspected to result from\nsequencing artifacts. We used a more reproducible process using the tool trimAl\n(https://github.com/inab/trimal), which removes poorly aligned or divergent positions based on objective\ncriteria. Following this, we manually reviewed the alignments to confirm overall quality and to ensure\nno major misalignments remained. The methods has been updated accordingly at lines 321 to 334 to\nprovide further details of this.\nEthics: How were human reads screened in and removed from the metagenomic data, particularly the\nair sampling data, before submission to GenBank? Bait capture would not necessarily exclude these.\nWe thank the reviewer for their comments. The TWIST CVRP used in this study uses probes designed\nto target viruses only so host nucleic acid should be low. Nonetheless, the CZ ID pipeline removes host\nreads specific to the selected specimen but also automatically filters out human-derived sequences.\nThis detail has now been incorporated into the methods section at lines 225 to 227.\nFigure 1: it’s a shame that Figure 1A isn’t broken down into different sample types. The difference\nbetween a carcass wash – which will presumably also pick up blood-borne pathogens – and an air\nsampler is quite profound. I’m not convinced that directly comparing throat/cloacal samples to the whole\ncollection of these other sample types is very fair here, especially given the number of samples per\ngroup isn’t known.\nWe thank the reviewer for their comment. In response, we have now included new Supplementary\nFigures 5–8, which presents the same data shown in Figure 1A but broken down by individual sample\ntype as requested. This provides a clearer view of virus detection patterns with respect to the different\npoultry swab types. Importantly, we have updated the heatmap presentation to address a limitation in\nthe original version, which focused only on the ability of environmental samples to recapture viruses\ndetected in poultry swabs. The revised figures now also highlight viruses that were uniquely detected\nin environmental samples, offering a more comprehensive view of the dataset.\nWhile we agree that this breakdown provides useful granularity, we also recognize that it adds\nsubstantial complexity given the large number of groups and timepoints. For this reason, Figure 1A in\nthe main text was originally designed to address a focused question whether poultry viruses can be\ndetected in environmental samples by providing a simplified summary. We continue to believe that\nretaining this summary in the main figure is helpful for clarity, especially as more detailed breakdowns\nare now available in the supplementary material. In light of the new Supplementary Figures and\nupdating of Figure 1A we have made a substantial number of text changes at lines 362 to 375.\nWe have also broken down Figure 1C (now Supplementary Figure 4 in the latest draft) by poultry virus\nso each comparison can be better visualised.\nFigure S1 – some of these viruses have only 1 RPM. How many million reads were generated per\nsample? There must be some minimal cut-off of RPM below which these data cannot be trusted. What\ndo the sequencing negative controls suggest? Avian viruses could presumably be from other birds, e.g.,\nwild birds or domestic geese.\nWe thank the reviewer for their comments. We presume the reviewer is referring to Supplementary\nFigure 7 (not 1?). Nonetheless this is now presented as Supplementary Figure 16 in the latest draft. We\nagree that the approach used in this figure was not described originally we have made changes to\naddress it.\nFirstly this heatmap being referenced shows abundances as ranked RPM which is not the same as raw\nRPM. For a given array of values ‘x’ of length k, denoted by x = {x₁, x₂, x₃, x₄, ..., xₖ}, the values were\nfirst ranked. The corresponding rank-scaled values, denoted by xᴿ = {x₁ᴿ, x₂ᴿ, x₃ᴿ, x₄ᴿ, ..., xₖᴿ}, were\nthen calculated using the formula: xᵢᴿ = [Rank(xᵢ) – Minimum(Rank(x))] / [Maximum(Rank(xᵢ)) –\nMinimum(Rank(x))]. This transformation involves ranking the original values and rescaling those ranks\nto fall within the [0, 1] range. First, the input vector x is replaced by its ranks, where the smallest value\ngets rank 1, the next gets rank 2, and so on; ties are assigned the average of their ranks. These ranks\nare then normalized using min-max scaling so that the lowest rank becomes 0, the highest becomes 1,\nand all other values are proportionally distributed in between. The function returns this vector of scaled\nrank values, allowing for comparison of relative positions across a consistent 0–1 scale. It is simply a\nmethod to normalise data for visualisation on a heatmap. In the case of Supplementary Figure 7 this\nrank scaling was done only on the non-poultry viruses shown in figure. In this case many of the values\nappear as rank (RPM) = 1 because they were the only non-poultry virus detected across all\nenvironmental samples. Ultimately, this approach serves as a way to scale the data for heatmap\nvisualization. Using raw RPM values would be impractical, as one virus may appear at 50 RPM while\nanother exceeds 250,000 RPM. Without applying a scaling method, it would be impossible to represent\nthis range meaningfully using a single colour palette. This information about rank scaling has been\nadded to the methods section at lines 284 to 290.\nIt's important to mention that the smallest RPM value included in this study after filtering for false\npositives (described below) is over 500 RPM. With respect to the total read counts we have now added\na Supplementary Table 2 which highlights to the reader the total raw reads and total reads after QC\n(including host/human read removal).\nWe fully agree that low-abundance viral detections require careful interpretation and that applying a\nminimum threshold is essential to avoid false positives. To address this, we applied a more stringent\n0.1% relative abundance filter, which more robustly captures the proportion of viral signal within each\nsample. This method has been described in relation to several comments. We have now updated the\nmethods section at lines 243 to 246 to explain this.\nNegative controls have now been addressed in response to the first comment.\nYes poultry viruses detected in Supplementary Figure 7 could presumably be from other birds. This\nhighlights how environmental sampling is robust and can detect viruses which would otherwise be\nmissed by poultry sampling. This is surely a positive finding especially given that we have shown there\nto be no contamination.\nDiscussion: I was surprised that the discussion didn’t mention many limitations or weaknesses,\nespecially compared to existing sampling approaches. A key limitation would surely be the inability to\nquantify prevalence per market. In addition, there would be an inability to detect reassortment\nsegmented viruses in environmental samples, compared to individual birds, and also the fact that\nassembly could result in chimeric viruses. Assessing the implications of detected variants in such\nchimeric viruses would be more challenging.\nWe thank the reviewer for this important observation. We agree that environmental metagenomic\nsurveillance has several limitations compared to traditional host-based sampling, and we have now\nadded a paragraph to the Discussion (lines 1057–1066) to explicitly address these. Most notably,\nenvironmental sampling does not allow for direct estimation of virus prevalence within individual animals\nor flocks, nor can it be used to quantify infection rates at the market level. This limits its utility for certain\nepidemiological analyses, such as determining attack rates or understanding host-specific infection\ndynamics. Additionally, environmental samples often represent pooled viral signals from multiple\nspecies and individuals, making attribution to a specific host or population impossible without follow-up\nsampling. Despite these limitations, we emphasize that this approach is not intended to replace\nconventional sampling, but rather to complement it, offering a broad, scalable, and cost-effective means\nof early detection, particularly in complex, high-risk settings like live bird markets.\nWe also agree that for segmented viruses, such as influenza, environmental sampling does not allow\nfor full genome reconstruction or accurate reassortment analysis, and we have now explicitly added this\npoint to the Discussion (lines 1044 to 1047). It’s important to mention that there are often individual\nbirds identified with coinfections of H5N1 and H9N2 which also prevents the reliable detection of\nreassortment events.\nWe have also revised the manuscript to address the potential for chimeric contigs resulting from\nassembly artifacts and the resulting challenges in interpreting detected variants (lines 1038 to 1044).\nThe later has been discussed in more detail in response to other comments in this rebuttal.\nSection line 311: What is the value of testing mapping of contig-to-contig here? Particularly when\ncontigs are short and sparse, a contig from, e.g., a capsid found in a throat swab, would obviously not\nmap to a contig of an RdRp found in an environmental sample. So is this proportionality more related\nto how much of the total genome was recovered in the environmental sample, rather than anything\ninteresting about diversity? It would be useful to clarify this to the reader. If so, perhaps % of genome\nper type would be a better metric.\nWe thank the reviewer for their feedback. As mentioned in response to a previous comment we have\nnow greatly expanded on this section (Figure 3).\nWe first identified the top DIAMOND blastx hit for each poultry contig after taxonomic assignment.\nCorresponding reference genes were retrieved from GenBank to build a custom database of\napproximately 4,000 poultry virus genes. A full list of GenBank accessions and associated annotations\n(genus, species, and gene type) is provided in Supplementary Table 3. All contigs that passed filtering\n(as previously described) were then mapped to this reference gene set to calculate coverage and\npercent identity as reported by DIAMOND (Supplementary Table 4). Only DIAMOND hits with a\nsignificant e-value threshold greater than 1e-5 were retained for downstream analysis. Where poultry\nand ES contigs had significant hits against the same Genbank entry the difference in sequence identity\nand coverage was reported. If no contig in a given sample aligned to a gene with an e-value ≤ 1e-5, the\ngene was considered undetected in that sample. Ultimately this new analysis shows that environmental\ncontigs align to the majority of poultry virus genes with little difference in coverage or percent identity.\nThis analysis is described in the new results section from lines 612 to 753.\nWe have retained the original analysis being referred to as Supplementary Figures 16 and 17.\nIn general, there are statistical issues drawing conclusions about the ability of one sample type to\ncapture another, when the number and type of samples is variable per time point. For example, Section\nline 330: Making such comparisons is challenging given the relatively limited information on sampling.\nFor example, looking at Figure 3, no ducks were swabbed in Takeo in 23rd December 2022. It's perhaps\nunsurprising then that no H5 viruses were found in bird samples, because H5 viruses are highly duck-\nadapted rather than chicken adapted. Vice versa, no chicken samples were taken on 21st December –\nwhich probably explains the lack of N2s (from chicken-adapted H9N2s). Grouping “poultry swabs”\ntogether isn’t reasonable, given these different adaptations. Were none of these birds present in the\nmarket on these times? I’m sympathetic to the realities of fieldwork, but I do think that these\ncomparisons are misleading/oversimplified as currently presented in the text.\nWe thank the reviewer for their feedback. In line with one of the previous comments, the number of\nsamples taken at each timepoint and for each individual group was not made clear in the original\nmanuscript. We have now added a table as previously requested to make this clear (Supplementary\nTable 1). The data being referred to is now Figure 4 in the latest manuscript draft.\nAt each timepoint irrespective of location there is at least one representative sample from each group.\nThis means that on 23rd of December ducks were sampled (both throat and cloacal) and chickens were\nalso samples on 21st December (throat and cloacal). Figure 3H has caused confusion because it only\nshows samples that were positive for at least one avian influenza HA (H5, H6, H9) or NA (N1, N2, N6).\nThus meaning that H5 was detected in the environment on 23rd of December but not in ducks or other\npoultry swabs. We have updated this heatmap (Figure 4C) to show all samples across all timepoints.\nWe understand that different viruses are better adapted to different poultry hosts and thus individual\ncomparisons are incredibly important and informative. As previously mentioned any figure which shows\na summary of overall poultry versus ES (such as Figure 1A) is now also broken down into individual\ncomparisons relative to poultry species and sample type and presented as Supplementary Figures.\nLine 392: “Given that de novo…”. There’s little data presented on whether the de novo assembled\nsequences do accurately represent those detected in poultry swabs. Whilst we see that they indeed\nidentify the same viruses, the accuracy of the genome information below the broader clade level is not\nshown. For example, in Figure 3 it’s unclear which are new environmental and which are new poultry\nswab data, and how similar these are. There are some quite long branches in some of the new data,\nand I would feel somewhat reassured if these weren’t in the environmental samples because of the risk\nof de novo assembly producing strange chimeras. It would have been useful to have some artificial\npools here, e.g., comparing results from sequencing fake “environmental samples” made of pooled\nswabs, to those obtained from sequencing individual swabs.\nWe thank the reviewer for their feedback. This has been addressed in relation to several comments\nalready. Figure 3 and associated text at lines 612 to 753 now shows that the majority of environmental\ncontigs align to the same gene as poultry contigs with little difference in sequence identity or coverage.\nRegarding the influenza phylogenetic trees that were retained in the latest draft of the manuscript.\nBranch support was assessed with 1,000 bootstrap replicates, and only branches showing ≥ 70 %\nsupport are displayed. The extended branches in the phylogenies were attributable to very short contigs\nfrom both environmental samples and poultry swab sequences whose limited length can distort tree\ntopology (as you have previously suggested). After applying a ≥ 70 % bootstrap filter, these few\nuninformative tips were removed. Nevertheless, environmental sampling still matches or outperforms\npoultry swabs in virus detection. Its important to mention that some short sequences still remain in the\ntree and have been kept as long as they pass the filters described.\nMinor comments:\nThe title suggests that air sampling is sufficient to capture the diversity of zoonotic viruses circulating in\nthe market. Yet there is little clarity provided that any pathogen detected here is zoonotic, with the\n(implied) exception of avian influenza virus. It would be useful to have some added discussion of the\ndiversity of the zoonotic viruses observed to justify this title.\nWe thank the reviewer for their comments. We have now updated the title to more accurately reflect the\nfindings if this paper.\nLine 24: It is wrong that environmental surveillance has not been used for zoonotic pathogens. The\nabstract itself defines ES as including air and cage swabs, etc. There is extensive evidence of the use\nof environmental sample types being used to detect zoonotic pathogens including influenza since at\nleast the 1970s, e.g., see https://doi.org/10.1111/tbed.13633. This is recognised by the authors at line\n77.\nWe thank the reviewer for their feedback. We have now altered this statement at line 28.\nLine 108: Given that the markets are named, it could be useful to see the point locations on Fig S1.\nWe have included a new map for Supplementary Figure 1 which shows the specific locations in each\nprovince.\nLine 120: Additional details should be provided to allow reproducibility, e.g., the type of swab, type of\nVTM, length of vortex etc. The Direct-zol RNA Miniprep manufacturer’s protocol provides various\ndifferent conditions for different sample types, and it would be useful to understand which was followed\nfor each sample type, as I imagine that this is different for the pooled swab and the liquid samples.\nHow the samples were pooled into libraries and how many reads per sample and sample type would also\nbe important in the Supplementary Information. The number of PCR cycles in the Twist protocol would be\nhelpful too.\nWe thank the reviewer for their helpful suggestions. We have now updated the methods section\naccordingly (lines 166 to 178 and lines 181 to 182) to include additional details of the experimental\nprotocol used. Briefly, samples were collected using polyester swabs and placed into viral transport\nmedium (VTM) containing 2.95% tryptose phosphate broth, 145 mM NaCl, 5% gelatin, 54 mM\nAmphotericin B, 106 U/L penicillin-streptomycin, and 80 mg/L gentamicin (Sigma-Aldrich, Steinheim,\nGermany). Samples were vortexed for 15–30 seconds before proceeding directly to RNA extraction\nusing the Direct-zol RNA Miniprep Kit. We used 200 µL of sample per extraction. Swab samples were\npooled by type and species: 10 samples per pool for oropharyngeal and cloacal swabs from chickens\nand ducks, 5 samples per pool for cage swabs, and 2–5 samples per pool for water samples. Library\npreparation included DNA fragmentation at 37 °C for 5 minutes, followed by 10 cycles of PCR\namplification, and 22 cycles for post-capture PCR amplification.\nLine 178: the R package SourceTracker will not be familiar to all readers, and it would be useful to add\nsome brief details of the approach of this package.\nWe thank the reviewer for this suggestion. We have now added a brief description of the SourceTracker\nmethod to the revised manuscript at lines 277 to 281. Briefly, SourceTracker is a Bayesian approach\nthat estimates the proportional contribution of potential source environments to a given sink sample,\nbased on community composition. The model uses Gibbs sampling to infer the probability distributions\nof sources contributing to each sink, allowing for the inclusion of an “unknown” source category to\naccount for uncharacterized variation. A reference to the original publication and the package repository\nhas also been included.\nLine 204: Why was no bootstrapping or other estimates of nodal support done?\nWe have now performed 1000 bootstrap replicates for all phylogenetic trees, and only parent branches\nachieving support values of ≥ 70 % are shown in Figure 4 (and associated Supplementary Figures 42-\n43).\nData availability: Will the consensus sequences be shared individually, at least for those from the\nindividual hosts? Or is this just SRA? I’m glad these data will be shared, but I can’t evaluate how\nappropriately this has been done without access to the project or the Github.\nThe raw FASTQ reads will be made publicly available under accession number PRJEB83776. The\nGitHub is live and the working data is now available there also. It’s important to note that no consensus\nsequences (only assemblies) were generated in this study.\nLine 221: were any novel viruses found? Twist approaches would be able to detect novel viruses, at\nleast for some families. What is used to define whether a virus is “found”, e.g., what if only a short contig\nof 200bp is present, or not all genes? This is especially important thinking about retroviruses, given the\npossible similarity to host DNA.\nWe thank the reviewer. We are aware of TWIST capability to identify novel viruses however all\nassembled contigs successfully mapped to the nr database with DIAMOND blastx. Thus we could not\ndetect any potential novel viruses.\nA virus was considered detected if it had an assembled contig of any length with a substantial number\nof reads mapping to it, defined as exceeding 0.1% of the total reads in the sample. This provides\nsufficient phylogenetic signal while minimising false positives. Yes even if the contig is short but still\nmeets the threshold it is included. In addition not all genes from a virus had to be recovered for that\nvirus to be included. Regarding the latter, our aim was to determine whether environmental samples\n(ES) recover the same viral taxa and genes as poultry swabs not to assemble complete genomes.\nMetagenomic studies routinely accept partial genomes especially for segmented or highly variable RNA\nviruses and none require every gene to be present for a detection to be reported.\nIf a high risk virus X is found at an LBM in Cambodia but its only a 200bp contig or not all genes for that\nvirus are found should this just be disregarded? The main goal of virus surveillance is risk mitigation so\nit shouldn’t matter what the length of the contig is or if all genes for a given virus can be found. Of course\nwe completely understand concerns about false positives and we have already filtered our dataset to\ndeal with this potential issue.\nFrom a risk mitigation perspective, the appropriate next step would not be dismissal but rather a\nstructured follow-up investigation, including classical molecular diagnostics (e.g., targeted PCR),\nconfirmatory sequencing, and potentially additional environmental and host sampling. These secondary\nefforts would help validate the signal, assess whether the virus is replicating and circulating, and\ndetermine the appropriate level of response. Excluding findings solely based on contig length risks\nmissing early indicators of emerging threats. In surveillance for pandemic-potential viruses, even weak\nsignals, if well-controlled, can be critical to catching the threat before it escalates.\nLine 231: is gull coronavirus definitely proven as respiratory, not fecal-oral?\nWe thank the reviewer for raising this important point. While avian coronaviruses, including those found\nin gulls, have been primarily associated with respiratory and enteric tracts, definitive experimental\nevidence establishing the exclusive transmission route (respiratory vs. fecal-oral) for gull coronavirus\nremains limited. We have now removed this sentence.\nLine 248: I don’t understand this conclusion. Figure 1C shows the number of viruses detected, but not\nthe diversity. So it’s not clear whether more sample types would be necessary, or, e.g., if wash water\ncontains only a subset of air sample data.\nWe thank the reviewer for these comments. Overall we agree that Figure 1C in the original manuscript\ndraft was not clear and failed to adequately support the statement being referred to here.\nTo address this concern we have included a new Supplementary Figure 11 which is a stacked bar plot\nwhich breaks down viruses detected in multiple (≥2) environmental sample types (shown at the base of\neach bar) and those unique to a single environmental sample type (stacked on top). Importantly, this\nanalysis only focuses on the direct recapture of viruses found in poultry at each specific timepoint. This\nprovides a clearer picture of how virus detection is distributed across sample types. It can be seen that\nfor the majority of visits to the LBM that the total number of species recaptured for each poultry swab is\nincreased by detections unique to specific environmental samples at that given time. In this current\nstudy each environmental sample type is shown to contribute to this at least once by increasing the\nnumber of viruses recaptured. Thus in order to recapture as many viruses as possible a variety of\nenvironmental sample types should be used.\nIn addition, when considering the total number of poultry viruses detected (not just those recaptured)\nwe show in Figure 1B that air from the slaughter and holding areas, as well as cage swabs have a\nhigher alpha-diversity than other groups. In Figure 2D we show that these three ES types are the\nshortest distance from the median centroid of chicken cloacal swabs. While in Figure 2C cage swabs\nare shown as the group with the second shortest distance from oropharyngeal swabs in chickens.\nTogether these findings based on both alpha-diversity and beta-diversity analysis indicate that air\n(slaughter and holding area) and cage swabs enhance poultry virus surveillance and thus must be\nincluded. In Figure 2E-F we see that wash water was the shortest distance from oropharyngeal and\ncloacal swabs in ducks suggesting it performed best at detecting viruses from this host. This is important\nas in Figure 1 and associated Supplementary we often see wash water detecting less viruses than other\nES types. This tells a story of quality over quantity while wash water is less diverse (alpha-diversity) it\nshared a higher proportion of their fewer viruses with duck samples highlighting their importance. This\nanalysis also captures temporal consistency, as it incorporates data from all twelve sampling visits to\nthe live bird market. The same can be said for drinking water which was the shortest distance from\noropharyngeal swabs in chickens (Figure 2C).\nFive out of six ES types in this study contributed meaningfully to poultry virus detection, supporting the\ninclusion of diverse sample types to maximize surveillance performance. Based on these findings and\ncomments from other reviewers we have now described the optimal sampling strategy in the discussion\non lines 1048 to 1057.\nLine 277 to 280: I don’t understand why this approach was used rather than something simpler, please\nclarify to the reader. Line 283 “tended to have more negative correlations”, is also unclear.\nWe are not entirely certain what is meant by a “simpler” analysis in this context, but we appreciate the\nopportunity to clarify our approach. In line with comments from other reviewers and the large number\nof changes made since the first draft, this analysis is now presented as Figure 2G-J and the associated\ntext is on lines 553 to 571. We have made significant changes to the text and we believe the results\ndescribed are now with clearer.\nUltimately a simple correlation analysis calculated between two types of data is all that has been done\nhere. On one hand we have the distance of each sample from each poultry median centroid (four\nseparate distance measures) as is presented in Figure 2C-F. On the other hand we have virus\nabundance in RPM. The resulting correlation matrix has the four distance measures as rows and the\nvirus species as columns. Based on analysis in Figure 1 we generated a list of taxa which were\nsuccessfully recaptured and those which failed to be detected by ES. So taxa are split into two groups.\nThus in Figure 2G-J the spearman correlation coefficient obtained for each virus is represented as a\ndatapoint.\nNegative correlation indicates that two values are moving in the opposite direction. So using this\napproach if any one virus has a negative correlation it means that as the distance to the median centroid\nbecomes shorter (low value) the abundance of the virus increases (high value). This scenario is a\npositive association between the virus and the group represented by the median centroid. Conversely,\nfor the same virus, if the distance from the median centroid increases (high value) the abundance of\nthis virus decreases (low value). This is also a positive association between the virus and the median\ncentroid.\nOn the other hand, given that positive correlation indicates two values are moving in the same direction.\nIf one virus has a positive correlation it means that the distance to the median centroid becomes longer\n(high value) as the abundance of the virus increases (high value). This is a negative association\nbetween the virus and the median centroid. On the other hand if the distance from the median centroid\nbecome shorter (low value) the abundance of the virus decreases (low value). This is also a negative\nassociation.\nThus using our approach, negative correlations are positive associations while positive correlations and\nnegative associations.\nFocusing now on Figure 2H we see that viruses successfully “recaptured” by ES have a significantly\nlower spearman distance than viruses “undetected”. Just by visualising the boxplot we see that for\n“recaptured” taxa the 75th percentile (Q3) is around 0. Thus the majority of viruses in this group\n(“recaptured”) have a negative spearman correlation coefficient with the distance from the median\ncentroid of chicken cloacal. Thus for the majority of recaptured viruses the shorter the distance from the\nmedian centroid of chicken cloacal the higher their abundance (positive association). For undetected\ntaxa the opposite is true as the 0 line is somewhere between the 25th and 50th percentile. This means\nthe majority of viruses in this group have positive correlations. Thus viruses undetected by ES have a\nnegative association with chicken cloacal swabs. Clearly we can see the opposite is true for ducks and\nagain this finding is statistically supported.\nWe believe that this beta-diversity analysis combined with median centroid testing and correlations is\nvery informative approach for identifying associations between ES performance and poultry\nhost/sample type. We hope this clarification resolves any confusion and have revised the relevant\nsections in the manuscript to explain this more clearly.\nLine 306 – “performs better at detecting chicken viruses” – perhaps this conclusion could be phrased\nas “performs better at detecting viruses from the chickens, which were also the most abundant host in\nthis market”, or equivalent. Whilst I note your Discussion point about this, I think it’s useful to add this\nnote here or at least a link to the Discussion. Adding information on the total estimated relative\nnumbers/proportion of individuals per species in each market would be critical here.\nWe thank the reviewer for their feedback. We have now updated the text at lines 604 to 606. We have\nalso now included the estimated numbers in the discussion (lines 1020 to 1021). This is based on an\nFAO report which is cited in the discussion. Between 10 and 100 chickens are slaughtered per day\nwhile between 1-10 ducks are.\nFigure 3H doesn’t appear to have any text associated with it in the legend. Should it also say N6, not\nH6?\nThe figure legend for panel 3H (now Figure 4H) has been updated for clarity at lines 1548 to 1554.\nAdditionally, we have corrected a labelling error, changing \"H6\" to \"N6\" as originally intended.\nLine 403: Correct to “Gamma”\nThis analysis has now been removed."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revisions fully address all of my concerns.\nA minor note on some of the supplemental figure labels. Many of the labels from figures with corresponding tables of statistical results have this at the end:\n\" P values for all pairwise comparisons can be found in Supplementary File X-Y. P values are annotated as follows: P < 0.05\n*; P < 0.01 **; P < 0.001***\"\nThe actual table numbers are missing and the asterisks are missing from the figures.", "reviewer_3": "Thank you to the authors for thoroughly addressing most of my comments. The additional detail, new analyses and new figures have really improved the quality of this paper, and the ability of readers to fully understand the data being presented. I have only a few outstanding comments:\n• Thanks for adding Figure S2. It is useful to see that no poultry-virus reads were identified in the negative controls. However, please add detail comment about what the negative control sample was (for example, was this a water control, processed from the point of extraction alongside all other samples analysed here? Or a completely separate run? Or something else?). The choice of negative control has important implications for interpretation. If the negative control was not processed alongside these samples, that is a significant weakness.\n(Please note that I disagree with the author’s comment in the rebuttal that “sterile” air filters are unlikely to be informative as a negative control. Sterile implies inactivation of pathogens, and does not imply RNA/DNA free status. I agree that it’s unlikely that filters would contain poultry pathogens, but use of a blank negative control that uses all of the plasticware and reagents and is run alongside real samples is best-practice for metagenomic studies including facilitating subsequent reanalysis of SRA data where people may not just be interested in poultry viruses. But I agree that it’s not critical here - it’s just critical to state what the actual choice of negative control was as noted above).\n• Thanks for adding information on the number of samples per time point. Please confirm in the text that samples from individual birds each came from a different stall (i.e., the birds were randomly sampled from all birds at the market). This is important because birds from the same stall/cage are more likely to share the same viruses, compared to environmental samples from different stalls. This sampling design is valuable for the reader to understand, especially when many field studies do not do this as it’s harder to sample in this way in the field.\n• To be clear to the editor and the authors, my question about “were any novel viruses found” is not intended to “dismiss” viruses where only short contigs are found as the authors imply. I fully agree that short contigs can be informative. However, it is important to provide clarity to the reader about what was counted as a “detection” here and whether any effort was made to discriminate between known and novel viruses. This clarity is necessary when interpreting the manuscript for readers who may be interested in virus diversity in these settings. The rebuttal now states that they do not attempt to detect novel viruses. To aid the reader’s understanding, it should be clearly stated somewhere in the manuscript - perhaps the Discussion? - that just because a virus has a close hit in nr using DIAMOND, it does not mean that the contig is 100% conclusively that virus. This is a recognised problem with metagenomic studies (I am reminded of https://doi.org/10.1016/j.cels.2015.07.008!).\nSimilarly, I would recommend removing the new text in the concluding paragraphs that states that “environmental samples enable detection of both known and novel viruses across multiple hosts and sample types”.\n• Thanks for stating that you now use bootstrapping on trees. However, I don’t understand how removal of parental branches with bootstraps <70 was done on Figure 4 and S42/S43 – do NONE of the internal branches have bootstraps <70? I can understand how pruning of external branches and corresponding samples could have been done, but it seems unlikely that removal of internal branches with bootstraps <70 would be possible without leaving a partially unconnected tree. Please check, and make the process of this filtering clear to the reader with a caveat somewhere about the potential issues that can be caused by phylogenetic estimation on mixed length sequences where aligned regions may come from different genome sections in different viruses.\n• Figure S16: the rebuttal says that “this heatmap being referenced shows abundances as ranked RPM which is not the same as raw RPM”. Thanks for adding the information on rank scaling to the methods. Please can you modify the legend slightly to make this clearer to the reader? “Heatmap showing the abundance of non-poultry viruses detected, represented as reads per million” implies raw to me, even though Rank(RPM) is on the figure.\n• There are a lots of typos generally in the newly added sections (e.g., “racptrured” in S5 legend, “environmental” multiple times in S11). Please check.\n• In the phylogenies:\no it’s almost impossible to see out the tip shape in some of the phylogenies where the tip shapes are overlapping. Perhaps the trees could also be provided as separate nexus/newick formats files, to help with this?\no The tip shape legends in all trees in the main text and supplementary are hard to understand (e.g., the Figure 4 legend doesn’t mention the square tip. Are the poultry samples not generated here as “new sequences”? If not new, what differentiates these “poultry” from “reference”?\no Please make all the trees use the same tip shape for the same tip type. For example, it really confused me for a long time that in 4A, triangles are \"reference\" but in 4B, triangles are \"new sequence\". This makes it hard to read, but also inconsistent with the legend text.\no I don't really understand the red arrow or what they mean. They seem to point to quite vague points on the tree, whereas generally I think that highlighting a small clade is the intention. Consider, perhaps, changing the tip opacity for the samples from these markets instead (i.e., more transparent for samples from the reference dataset might work well, and more clearly show the clustering of different sample types from this study)."}, "author_response": "Response to reviewers’ comments\nPlease note that the line numbers mentioned in our response refer to the revised manuscript with\ntracked changes (all markup). Please see our responses in blue, and the line numbering follows the\nclean version.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe revisions fully address all of my concerns.\nA minor note on some of the supplemental figure labels. Many of the labels from figures with\ncorresponding tables of statistical results have this at the end:\n\"P values for all pairwise comparisons can be found in Supplementary File X-Y. P values are\nannotated as follows: P < 0.05 *; P < 0.01 **; P < 0.001***\". The actual table numbers are missing and\nthe asterisks are missing from the figures.\nWe thank the reviewer for their feedback. We have now updated all Supplementary Figure legends to\ninclude the correct references to the corresponding Supplementary Tables.\nTo clarify to the editor, given the large number of pairwise comparisons in some figures, we selectively\nincluded asterisks to highlight only the most relevant or representative comparisons directly on the\nplots. The full list of P-values and significance levels for all pairwise tests is provided in the\ncorresponding Supplementary Tables as indicated in the updated figure captions.\nReviewer #3 (Remarks to the Author):\nThank you to the authors for thoroughly addressing most of my comments. The additional detail, new\nanalyses and new figures have really improved the quality of this paper, and the ability of readers to\nfully understand the data being presented. I have only a few outstanding comments:\n• Thanks for adding Figure S2. It is useful to see that no poultry-virus reads were identified in the\nnegative controls. However, please add detail comment about what the negative control sample was\n(for example, was this a water control, processed from the point of extraction alongside all other\nsamples analysed here? Or a completely separate run? Or something else?). The choice of negative\ncontrol has important implications for interpretation. If the negative control was not processed\nalongside these samples, that is a significant weakness.\n(Please note that I disagree with the author’s comment in the rebuttal that “sterile” air filters are\nunlikely to be informative as a negative control. Sterile implies inactivation of pathogens, and does not\nimply RNA/DNA free status. I agree that it’s unlikely that filters would contain poultry pathogens, but\nuse of a blank negative control that uses all of the plasticware and reagents and is run alongside real\nsamples is best-practice for metagenomic studies including facilitating subsequent reanalysis of\nSRA data where people may not just be interested in poultry viruses. But I agree that it’s not critical here\n- it’s just critical to state what the actual choice of negative control was as noted above).\nWe thank the reviewer for their feedback. We have updated the Methods section (lines 180–184)\naccordingly. Nuclease-free water was included as a negative control and processed alongside both\npoultry and environmental samples from the point of extraction through to the library preparation. In\naddition, to be clear, negative controls were then included on the same Illumina sequencing run as the\nactual samples themselves.\n• Thanks for adding information on the number of samples per time point. Please confirm in the text\nthat samples from individual birds each came from a different stall (i.e., the birds were randomly\nsampled from all birds at the market). This is important because birds from the same stall/cage are\nmore likely to share the same viruses, compared to environmental samples from different stalls. This\nsampling design is valuable for the reader to understand, especially when many field studies do not\ndo this as it’s harder to sample in this way in the field.\nWe agree that this clarification is important. Chickens and ducks were indeed randomly sampled from\nacross the entire market rather than from individual cages/stalls. We have now updated the\nmanuscript text at lines 125–126 to specify this.\n• To be clear to the editor and the authors, my question about “were any novel viruses found” is not\nintended to “dismiss” viruses where only short contigs are found as the authors imply. I fully agree\nthat short contigs can be informative. However, it is important to provide clarity to the reader about\nwhat was counted as a “detection” here and whether any effort was made to discriminate between\nknown and novel viruses. This clarity is necessary when interpreting the manuscript for readers who\nmay be interested in virus diversity in these settings. The rebuttal now states that they do not attempt\nto detect novel viruses. To aid the reader’s understanding, it should be clearly stated somewhere in\nthe manuscript - perhaps the Discussion? - that just because a virus has a close hit in nr using\nDIAMOND, it does not mean that the contig is 100% conclusively that virus. This is a recognised\nproblem with metagenomic studies (I am reminded of https://doi.org/10.1016/j.cels.2015.07.008!).\nSimilarly, I would recommend removing the new text in the concluding paragraphs that states that\n“environmental samples enable detection of both known and novel viruses across multiple hosts and\nsample types”.\nWe thank the reviewer for this helpful clarification. We agree that, although all contigs showed\nsignificant similarity to known viruses in the nr database, sequence similarity does not always\nguarantee complete certainty about viral identity, particularly for short or partial contigs. We have\nclarified this point in the Discussion (lines 666–668) to ensure readers understand that taxonomic\nassignments are based on best available similarity matches, and we have removed the statement\nreferring to the detection of “novel” viruses to avoid any potential confusion.\n• Thanks for stating that you now use bootstrapping on trees. However, I don’t understand how\nremoval of parental branches with bootstraps <70 was done on Figure 4 and S42/S43 – do NONE of\nthe internal branches have bootstraps <70? I can understand how pruning of external branches and\ncorresponding samples could have been done, but it seems unlikely that removal of internal branches\nwith bootstraps <70 would be possible without leaving a partially unconnected tree. Please check,\nand make the process of this filtering clear to the reader with a caveat somewhere about the potential\nissues that can be caused by phylogenetic estimation on mixed length sequences where aligned\nregions may come from different genome sections in different viruses.\nWe thank the reviewer for this helpful comment and the opportunity to clarify our approach. Following\nbootstrap resampling, we filtered the phylogeny by removing only terminal branches (tips) whose\nimmediate parental node had bootstrap support below 70. This was performed in R using the ape and\nggtree packages. For clarity, the ape::drop.tip() function automatically prunes these low-support tips\nand reconnects the remaining nodes, preserving a single, fully connected tree. We have now clarified\nthis procedure in the Methods section (lines 290–295) and note the caveats associated with\nphylogenetic estimation on mixed-length alignments where some regions may represent different\ngenome sections (lines 674 and 677).\n• Figure S16: the rebuttal says that “this heatmap being referenced shows abundances as ranked\nRPM which is not the same as raw RPM”. Thanks for adding the information on rank scaling to the\nmethods. Please can you modify the legend slightly to make this clearer to the reader? “Heatmap\nshowing the abundance of non-poultry viruses detected, represented as reads per million” implies raw\nto me, even though Rank(RPM) is on the figure.\nWe have now updated the Figure Legend for S16 as requested.\n• There are a lots of typos generally in the newly added sections (e.g., “racptrured” in S5 legend,\n“environmental” multiple times in S11). Please check.\nWe have now revised all Supplementary Figures and Figure legends to fix these typos.\n• In the phylogenies:\no it’s almost impossible to see out the tip shape in some of the phylogenies where the tip shapes are\noverlapping. Perhaps the trees could also be provided as separate nexus/newick formats files, to help\nwith this?\no The tip shape legends in all trees in the main text and supplementary are hard to understand (e.g.,\nthe Figure 4 legend doesn’t mention the square tip. Are the poultry samples not generated here as\n“new sequences”? If not new, what differentiates these “poultry” from “reference”?\no Please make all the trees use the same tip shape for the same tip type. For example, it really\nconfused me for a long time that in 4A, triangles are \"reference\" but in 4B, triangles are \"new\nsequence\". This makes it hard to read, but also inconsistent with the legend text.\no I don't really understand the red arrow or what they mean. They seem to point to quite vague points\non the tree, whereas generally I think that highlighting a small clade is the intention. Consider,\nperhaps, changing the tip opacity for the samples from these markets instead (i.e., more transparent\nfor samples from the reference dataset might work well, and more clearly show the clustering of\ndifferent sample types from this study).\nWe thank the reviewer for these valuable suggestions regarding the phylogenetic trees. The tree files\nused to plot Figure 4 are already available on the GitHub along with other data analysis files and\ncode. To improve the clarity and consistency of the phylogenies, we have updated all trees so that\nglobal tips now includes a thin black outline to enhance definition and distinguish overlapping\nsymbols. New sequences from this current study have been given a red tip outline. The legends have\nbeen corrected and standardized across all figures, squares now represent poultry sequences, circles\nrepresent environmental sequences, and triangles represent reference sequences downloaded from\nGISAID or NCBI. To further improve visibility, poultry and environmental tips have been made slightly\nlarger than reference tips, and the opacity has been adjusted such that reference sequences appear\nlighter while poultry and environmental tips appear darker, as suggested. As you mentioned, the red\narrows were originally intended to draw attention to the location of poultry and environmental\nsequences within each tree; we have now removed these. We agree that these modifications\nsubstantially improve the readability, consistency, and interpretability of the phylogenies."}]

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