Ecology

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In line with the results of present study, we suggest that the exploitation of forage resources by agro-pastoralists occurs in a non-random manner. The use of forage resources is guided by a series of functional characters related to palatability and nutritional value, which determine preferential use due to the better quality of resource. At the same time, we understand that forage uses are complex and multifactorial in nature, and regulated in a substantial way by seasonality and ecological factors (Fig. 5), such as the availability of plant resources and local diversity.Figure 5Diagrammatic representation for the effects of vegetation cover and seasonality on forage resource selection in Dry Forests. Image created with Microsoft Office 2019 PowerPoint (www.office.com).Full size imageThe differences of plant species cited between areas reveal the positive effect of vegetation cover on the use and knowledge of plants by agro-pastoralists. Our findings reveal that the greater number of plant species mentioned by agro-pastoralists in Area II is directly associated with greater availability of resources in this area, as long as we consider vegetation cover as availability of resources, which allows different species to be used throughout the year. On the other hand, in regions with low vegetation cover (Area I), the low availability of resources limits the use and knowledge of plants by residents, which can lead to greater pressure on a small set of available species. Such findings reinforce the importance of vegetation cover for ecosystem provision of goods and services to human populations that depend directly or indirectly on these services.The most represented families found in the present study have also been reported in several other ethnobotanical studies6,16,17,29, with emphasis on Fabaceae and Poaceae, which are recognized for their high forage potential, which derives, above all, from high palatability and nutritional value30. Simultaneously, citations mostly for native species reflect the importance and potential of Caatinga resources as important components of the ruminant diet11, both for the woody and herbaceous strata, corroborating the estimate in the literature that 70% of vegetation has potential use as forage31.The characteristic seasonality of vegetation, on the other hand, represents a limiting factor for forage productivity, culminating in high fluctuations in quality and availability, as well as changes in the dominance of different strata and composition of forage species throughout the seasons11,32. The seasonal distribution of species explains the similarity of seasons between areas, with a higher similarity percentage for the dry seasons, since there is less availability of resources to be exploited compared to the rainy season. In this context, the potentially used species are commonly accessible woody species in both areas. However, during the rainy season, the high availability of herbaceous plants regulates different uses (Fig. 4), but even so, they also exhibit relatively similar patterns, mainly due to the woody component that denotes the common demand by ruminants at the beginning of this season.The effect of climatic variables on vegetation use patterns was documented by16,17, both of which showed greater richness in the use of herbaceous forage during the rainy season, a finding that reflects the seasonal distribution—restriction to that season—and decrease in the qualitative character of annual species33. At the same time, it also reflects the greater number of unique species for the rainy season. However, when compared to woody strata, significant differences in terms of richness are not found because although the diversity of herbaceous species in the Caatinga is greater24, it is much less known than that of the tree-shrub stratum11.Agro-pastoralists even characterize animal preferences for herbaceous stratum, but as its diversity is immense and ephemeral, they claim to have limited ability to identify the species. The high abundance of resources in the rainy season also reduces the concern with forage use, which implies less attention to the species that are consumed. In contrast, woody species, due to multiple uses and greater availability over time, tend to be better known10,34, with a different effect in the dry season making the optimal foraging pattern in this period inherent to the knowledge of agro-pastoralists35.In addition, according to the ecological appearance hypothesis, there is a general tendency for less apparent species to be neglected by populations36. Some studies have corroborated the hypothesis within the context of forage use, with woody species being cited more and having more uses6,15. In addition, people tend to focus on resources whose supply is given continuously10, which may explain why woody species are well represented in both seasons.Security in the provisioning of ecosystem services is an essential component for local populations, and thus woody species are highly valued because they reflect predictability of use15,35. This can be a particularly influential criterion because perennial or late leaf deciduous species, such as Cynophalla flexuosa and Myracrodruon urundeva, had significant amounts of citations and perceptions employing high valuation, as represented by some statements by some interviewees: “É um refrigero na seca” (it is savage in the dry season), “É uma ração boa na seca” (it is a good food in the dry season).In turn, differences in richness of the species cited by the two areas corroborate our first hypothesis that populations inserted in environments with greater vegetation cover tend to cite more species. In line with these findings, considerable floristic dissimilarity was also found between the two areas, given the exclusivity of species. Such dissimilarity may suggest particularities in the vegetation attributes of each area, such as greater floristic diversity7,37,38.Since anthropic processes are irregularly distributed in space, variation in the provisioning of ecosystem services by vegetation also occurs, and influences different collection profiles39. On the other hand, areas with greater species richness have been shown to have greater use patterns6,7. The larger number of species cited as woody and native for Area II is, therefore, associated with greater general richness, as well as herbaceous species present in the rainy season. In contrast, common species are reflected in trends of similar foraging patterns, as well as the presence of common species between areas38. In addition to different levels of disturbance, differences in floristic composition between areas may also be due to edaphic variation40.Our second hypothesis was refuted because the difference in the richness of exotic species between the areas. Plausible explanations for this finding are that, in general, exotic herbaceous species are commonly used for forage in the semi-arid region of Brazil41. Herbaceous species comprise the primary component of the ruminant diet. However, in the midst of their occurrence restricted to the short rainy period, exotic species, mainly of Fabaceae and Poaceae, have been introduced to increase the forage availability, which currently represents an important attribute of forage resources in the Caatinga41,42,43. At the same time, and to also increase the availability of forage resources, the cultivation of species by agro-pastoralists may be common in their properties44, mainly exotics, such as Prosopis juliflora, that have high adaptive potential and governmental incentives45.Regarding use patterns, according to the data presented here it is possible to state that agro-pastoralists ’ experiences with herding activities provide an accumulation of a vast knowledge about forage resources15. This knowledge allows forage resources to be characterized by their potential according to a variety of criteria associated with seasonal variation and qualitative attributes, as commonly found by other studies14,15,16,17,37. Such criteria are often revealed by qualitative approaches that define the valuation perception of resources. Thus, nutritional value and palatability can be implicitly associated with the definitions of “É uma ração boa” (it is a good food), “o bicho gosta muito” (the animals like it very much) and “Rico em proteínas” (rich in protein).It should be added that the establishment of intrinsic relationships with resources allows a particular understanding at a high level of detail15,35, such as changes in palatability throughout development with descriptions including chemical17 and structural changes. Studies confirm that some Caatinga species vary in their chemical composition during leaf maturation, which influences nutritional quality17,46.In addition to revealing the domain of information, this body of knowledge allows maximizing forage use based on nutritional properties weighted by availability14,37. Nunes37 confirmed that the forage species selected by informants and the criteria they adopted coincided with nutritional values measured by the literature, and that, as also found in the present study, younger plants were recognized as highly appreciated by animals. This appreciation is due to the greater palatability of plant organs at this stage47. This is a matter of concern for the sustainability of the Caatinga, since direct or indirect grazing has compromised the regeneration process12 since younger individuals are clearly more sensitive to damage48.Also, considering the potential of Caatinga, we suggest that investment through government actions encourage the cultivation of native species to ensure the production of forage and, consequently, guarantee the sustainability of livestock activity and the ecosystem in question. More

This study proposed a protocol to evaluate the behavior of geese reared outdoors in agroforestry systems. A data collection form (i.e., BOF) was developed and validated both in relation to its reliability and its validity. In this context, moreover, ABMs useful for a welfare assessment protocol could be defined, and changes in the behavior of geese due to daily time and environmental context could be identified.Behavioral observations, based on the capture of the major changes in an animal’s body language17, are used daily in the assessment of animal health and welfare. Body language is a type of dynamic expression of the interactions among conspecifics or between animals and their environment. Behavioral changes can happen quickly or as subtle shifts not easily detectable18. Indeed, especially in the case of direct observation in the field, it becomes difficult to identify each behavioral variation. Furthermore, the on-farm use of the BOF proposed in the present study involved focal subgroup sampling, as ten geese were simultaneously observed, which may increase the difficulties. Indirect observation by videos, which allow the review of a certain action several times and the focal-animal approach, is a useful tool to partially overcome these issues and thus improve the accuracy of observation. The validation process of the BOF adopted in this study, therefore, included the definition of both its interobserver reliability and correlation with indirect observations.In this study, the direct observations in the field were performed by both an expert (i.e., main observer) and an inexperienced trained observer. As expected, the main observer was able to detect a higher frequency of behaviors, especially the rarer ones. For example, the inexperienced observer did not report any examples of allo-grooming, squawking, wagging tail, stretching, or panting behavior. However, the two observers showed excellent interobserver reliability (ICC  > 0.75). Major agreements were found for walking, roosting, and foraging. Accordingly, several studies have shown that observers with little experience can also provide a valuable contribution in observational research19,20. Overall, these results support the reliability of the BOF even if the observer’s experience helps him or her to better grasp rarer behaviors, as these behaviors could play an important role as welfare indicators.In the last two decades, important technological developments have occurred in the livestock sector. The use of sensors, cameras, and other devices can generate objective information about individual behavior, thereby allowing its evaluation in large observation areas and for large groups of animals and resulting in the better detection of natural animal behavior. Thus, in our study, the data collected by a video recording system (Noldus XT) were used as a gold standard measure to define the criterion validity of the BOF. Our results indicated excellent agreement between direct and indirect observations, supporting the BOF criterion validity. A poor correlation was only found for 2 variables (i.e., squawking and wagging tail), which were more difficult to collect by direct observation. The use of the BOF involved the simultaneous observation of 10 animals, but the geese had a synchronized behavior and moved in groups within the grazing area. This greatly facilitated focal subgroup sampling, allowed all animals to always be under observation, and could explain the high correlation between the two observation methods. However, the comparison between the observations collected in the field by the main observer and those recorded using the computerized system confirmed the greater accuracy of the latter. The analysis of the video in continuous with the use of some tools, such as the zoom or slow-motion functions, and the focal-animal sampling provided an easier identification of some behaviors and, in general, greater accuracy. Due to its nonintrusive approach, video recording has become a common practice for behavior assessment21, but it can be expensive and time-consuming. On the other hand, direct observations made by the BOF were valid and less expensive, suggesting that it could be a feasible tool with which to evaluate the welfare principle of Appropriate behavior. As recommended for welfare assessment protocols22, the BOF ethogram included indicators of both positive and negative states; however, it would be necessary to integrate it with behavioral tests and other ABMs evaluating the human-animal relationship.As mentioned above, there is no standardized geese behavior ethogram. Thus, to verify the content validity of the BOF, its behavior variables were analyzed through a PCA. The 4 extracted PCs could represent the broad behavioral dimensions of geese. In particular, the geese’s activity reported in PC1 was characterized by locomotor, foraging, and exploratory behaviors, with opposite signs with respect to roosting. The positive correlation between explorative and grazing activities and their negative correlation with static behaviors has been widely demonstrated in chickens. Chicken genotypes characterized by low exploratory aptitude exhibited low kinetic behaviors but a high frequency of roost and rest behaviors23. Göransson et al.24 showed that 50% of the observed birds exhibited sitting behavior, whereas less than 10% performed foraging activity.PC2 included all the variables that characterized the geese’s social aspects, including both positive and negative interactions. Usually, greylag geese live in a large flock because the offspring remain with their parents for an entire year. Such groups are characterized by complex relationships based on social interactions25. The formation of a group is characterized by agonistic behaviors such as fighting, pecking, and threatening, as well as submissive behaviors such as avoiding contact, crouching, and escaping26 to establish a hierarchical order. After this phase, a tolerance status develops, and birds maintain their social interactions through the use of body postures and vocalizations. Accordingly, the variables reported in PC2 were related not only to aggressive behaviors but also to geese’s vocalization and posture, which probably helped to maintain flock stability. Therefore, a higher PC2 score could indicate the need to establish and maintain a hierarchical order within the group, resulting in high social interactions.PC3 reported comfort and body care behaviors. The opportunity to spend a lot of time on body care, which should also include access to water for bathing, is of paramount importance with regard to fulfilling the biological requirements of geese27. Thus, a higher loading of this PC means that animals showed a good degree of both welfare and adaptability. In our study, a high frequency of self-cleaning and wing flapping behaviors was recorded, and the geese often took advantage of the water tub. In contrast, a very low frequency of aggression behaviors was observed, suggesting that the groups of geese were quite stable and that the animals felt safe in the environment in which they were rear. These findings confirm that agroforestry has a favorable impact on bird welfare by allowing the display of the full range of behavior, improving the animals’ comfort28.PC4 was mainly represented by the neck forward behavior. This position only occasionally represents an attack behavior and is not utilized during the establishment of hierarchical order but when it is necessary to maintain and reinforce the order inside the group. Furthermore, a goose that assumes this posture often does so while continuing another activity29. The neck forward behavior was positively associated with the stretching behavior. Stretching is usually categorized as a comfort behavior for broilers30, but it could also be used when the animal needs to relax stress-related tension in their muscles31,32 or as an adaptive strategy for dealing with unknown contexts33. Neck forward and stretching were eventually considered social avoidance behaviors, although they could be ambivalent and thus require further study, case-by-case assessment, and perhaps a better description in the ethogram.Finally, some interesting results emerged regarding the comparison of geese’s behavior during the morning and afternoon and between the two different agroforestry systems. In particular, geese showed a higher frequency of active behaviors such as walking, foraging, drinking, neck forward, and feeding during the morning compared to the afternoon. All of these behaviors suggest that geese concentrate their grazing and exploration activities during the morning. When and where to move is crucial for the food search and to avoid both predators and adverse climate conditions34. Cartoni Mancinelli et al.35 included exploratory attitude, walking, and eating grass activities in a multifactorial score as important parameters to consider to evaluate the adaptability of different organically reared chicken genotypes. Thus, exploratory and kinetic behaviors are fundamental, especially in animals reared outdoors. Moreover, the positive correlation between walking and grazing behaviors is widely known36,37. In contrast, during the afternoon, geese showed higher frequencies of static behaviors such as resting, roosting, and self-grooming, suggesting that geese are more dedicated to comfort and body care activities during this time. These trials were performed in the hottest season; thus, the geese’s behavioral differences during the day could also depend on the fact that animals preferred to carry out active behaviors during the cooler hours (morning), while in the hottest hours (afternoon), they engaged in static activities. Active behaviors cause an increase in metabolism and body temperature38, whereas static behavior, such as roosting, is considered adaptative behavior to promote heat dissipation31,39.This could also explain why higher frequencies of walking and foraging and lower frequencies of static behaviors were found in the orchard system than in the vineyard system. Studies carried out on chickens have reported that, among different pasture enrichments, the presence of trees promotes walking animal activity compared with crop inclusion40,41. The cover provided by trees made the animals feel protected from predators and provided shade during the hottest part of the day40, thereby stimulating the animals to explore all the available space in the pen. Accordingly, geese reared in the apple orchard ingested more grass than those reared in a vineyard36. However, there were no differences between the two systems for social behaviors. Moreover, the highest frequency of roosting and self-cleanliness behaviors was recorded in the vineyard, suggesting that this space offered a comfortable environment and that both systems seem respectful of the biological needs and welfare of the geese.The behavioral assessment protocol proposed in this study involving the BOF ethogram was feasible, low-cost, fast, and responsive both over time and between housing systems. It could thus be used for the assessment of Appropriate behavior in a welfare assessment protocol for geese reared in outdoor or free-range systems, although it lacks indicators of the human-animal relationship, such as avoidance distance or handling tests; such a scoring system should be developed. Regarding the specific behaviors in the two agroforestry systems, it should also be noted that they are difficult to generalize, as the characteristics of the plants, the environment, and management could have influenced these traits. Specifically, the behaviors could have been affected by the temperatures; therefore, further trials at different altitudes, seasons (i.e., autumn and winter), and climate are necessary for external validation. More

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Sample collection and metagenomic sequencingWritten informed consent was obtained prior to participation in the project. The study protocol for the Japanese (Disease, Drug, Diet, Daily life) microbiome project was approved by the medical ethics committees of the Tokyo Medical University (Approval No: T2019-0119), National Center for Global Health and Medicine (Approval No: 1690), the University of Tokyo (Approval No: 2019185NI), Waseda University (Approval No: 2018-318), and the RIKEN Center for Integrative Medical Sciences (Approval No: H30-7). We conducted a prospective cross-sectional study of 4198 individuals participating in the Japanese 4D microbiome project, which commenced in January 2015 and is ongoing20.Participants registered in the project were those who visited hospitals in the area for disease diagnosis or a health checkup. Faecal samples are collected from both healthy and diseased participants. The eligibility criteria for participants are as follows: (1) born and raised in Japan; (2) age >15 years; (3) written informed consent provided; and (4) having an endoscopic diagnosis on colonoscopy; either having undergone a colonoscopy within the last 3 years or planning to undergo colonoscopy for colorectal cancer screening, surveillance, and diagnosis of various gastrointestinal symptoms. The exclusion criteria were as follows: (1) suspected acute infectious disease based on clinical findings (e.g., acute enterocolitis, pneumonia, tuberculosis etc.); (2) acute bleeding; (3) hearing loss; (4) unable to understand written documents; (5) unable to write and (6) limited ability to perform activities of daily living. No compensation was paid to participants.Participants collected faecal samples using a Cary–Blair medium-containing tube60 at home, and the samples were refrigerated for up to 2 days before the hospital visit. Immediately after participants arrived at the hospital, their faecal samples were frozen at −80 °C until DNA extraction. We avoided collecting samples within 1 month of administering bowel preparation for colonoscopy because it has a profound effect on the gut microbiome and metabolome61. Health professionals checked that the amount of stool was sufficient for analysis. Shotgun metagenomic sequencing was performed for 4241 faecal samples and quality controls were conducted20, from which 43 samples were excluded from further analyses due to the low number of high-quality reads (130 bp. Encoded genes in the contigs were predicted by MetaGeneMark (3.38)70. Assembled contigs were defined as phages if they passed all of the following six criteria.

1.

A genome size threshold was applied, and contigs less than 10 Kb were excluded, as typical dsDNA phages have genomes larger than >10 Kb71.

2.

Viral-specific k-mer patterns were checked by DeepVirFinder (v1.0)22. Contigs with p-values >0.05 were excluded from further analysis.

3.

To detect viral hallmark genes (VHGs) and plasmid hallmark genes, we performed a highly sensitive HMM-HMM search against the Pfam database72. First, the encoded genes were aligned to the viral protein database, collected from complete (circular) viral genomes (n = 13,628) in the IMG/VR v2 database30 using JackHMMER. The obtained HMM profiles were searched against the Pfam database using hhblits73 with a  >95% probability cut-off. These procedures were performed using the pipeline_for_high_sensitive_domain_search script (https://github.com/yosuken/pipeline_for_high_sensitive_domain_search)74,75. Contigs with plasmid hallmark genes or those without VHGs were excluded. The hallmark genes used in this analysis are summarised in Supplementary Data 3.

4.

The presence of housekeeping marker genes of prokaryotic species was checked by fetchMG (v1.0)76, and ribosomal RNA genes (5 S, 16 S and 23 S) were identified by barrnap (0.9) (https://github.com/tseemann/barrnap). Contigs with the marker genes and ribosomal RNA genes were excluded from further analysis.

5.

The encoded genes of each contig were aligned to the viral protein database and a plasmid protein database constructed from the reference plasmids in RefSeq (n = 16,136, in April 2020) using DIAMOND (v0.9.29.130)77 with the more-sensitive option. The number of genes aligned to each database was compared, and contigs with more genes aligned to the plasmid protein database were excluded from further analysis.

6.

The proportion of provirus regions was assessed by CheckV (v0.7)24, and contigs estimated with 70% and 10% contamination.To evaluate the performance of this custom pipeline, we applied the pipeline to reference phage genomes (n = 2609, as positive data) and plasmid sequences (n = 16,136, as negative data) in Refseq. The true positive rate was defined as the number of phages detected as phages by the pipeline divided by the number of reference phages. The false positive rate was defined as the number of plasmids detected as phages by the pipeline divided by the number of reference plasmids. DeepVirFinder22, VirSorter (v1.0.3)23 Virsorter2 (2.2.3)25, VIBRANT (v1.2.1)26, Seeker (v1.0.3)27 and ViralVerify (v1.1)28 were also applied to the same datasets with the default parameters, and the performance was compared among them.Analysis of phage genomesViral operational taxonomic units (vOTUs) were constructed by clustering phage genomes with a  > 95% identity29 using dRep (v2.2.3)78 with the default options. Representative sequences of each vOTU selected by dRep were further clustered with reference sequences in RefSeq, IMG/VR30, gut virome database (GVD)15, gut phage database (GPD)9, and metagenomic gut virus (MGV) database31 with >95% identity and >85% length coverage using aniclust.py script in the CheckV package to identify common sequences among the databases.To further construct broader viral clusters (VC), proportions of protein clusters shared between phages were assessed. First, to define protein clusters, similarity searches of all protein sequences from all the phages identified in this study were performed using DIAMOND with the more-sensitive option (e-value 20% of clusters were grouped as a VC, which corresponds approximately to family- or subfamily-level clusters7,37. Rarefaction curves of the vOTUs and VCs were estimated with the iNEXT function in the iNEXT package (v2.0.20)80. The similarity matrix of the phages based on the percentage of shared protein clusters was further projected by tSNE using the tsne function in the Rtsne package (v0.16).Taxonomy annotation of phages was performed with a voting approach described previously16 with minor modifications. First, the protein sequences of each phage were aligned to viral proteins detected from phage genomes in RefSeq (n = 2609, in April 2020) using DIAMOND with the more-sensitive option. Then, the best-hit taxonomy of each protein (family levels) was counted, and the most common taxonomy was assigned to the phage if >20% of proteins in the phage were aligned to the same taxonomy.Phage lifestyles (i.e. virulent or temperate) were predicted by BACPHLIP40 and alignments to reference bacterial genomes in the RefSeq. Phages were defined as temperate if the BACPHLIP score was >0.8 or the phage genome was aligned to any reference genomes with >1000 bp alignment length with >95% identity.Host predictionBacterial and archaeal genomes were downloaded from the RefSeq database (in April 2019). To reduce the redundancy of genomes from closely related strains in the same species (e.g. Escherichia coli), 10 genomes were selected randomly for species with more than 10 genomes, and other genomes were excluded from the dataset. The reference dataset consisted of 33,215 bacterial and 822 archaeal genomes.Host prediction of the identified phages was performed using CRISPR spacers81. CRISPR spacers were predicted from the reference microbial genomes and assembled contigs ( >10,000 bp) from the 4198 metagenomic datasets using PILER-CR (1.06)82. Short (100 bp) spacers were discarded. In total, 679,323 and 283,619 spacers were identified from the reference microbial genomes and assembled contigs, respectively. Taxonomy information was assigned to the assembled contigs if they were aligned to the microbial reference genomes with >90% identity and >70% length coverage thresholds using MiniMap283. The CRISPR spacers were mapped to the phage genomes using BLASTN with the option for short sequences: -a20 -m9 -e1 -G10 -E2 -q1 -W7 -F F81. CRISPR spacers, which were mapped with 100% identity or 1 mismatch/indel with >95% sequence alignment, were used for host assignment at the genus level. Assignments of host species were checked manually, and if any of the following non-human intestinal species were assigned, the host was excluded: Dickeya, Anaerobutyricum, Rubellimicrobium, Eisenbergiella, Harryflintia, Leucothrix, Photorhabdus, Spirosoma, Syntrophobotulus, Thermincola, Algoriphagus, Franconibacter, Kandleria, Lawsonibacter, Methylomonas, Provencibacterium, Pseudoruminoccoccus, Rhodanobacter, Romboutsia, Sharpea, Varibaculum and Thioalkalivibrio.Quantification of viral abundance and analysis of the virome profileTo quantify the viral abundances in each sample, metagenomic reads were mapped to the gene set of VHGs (Supplementary Data 3) of each representative vOTU using Bowtie2 with a  > 95% identity threshold, and reads per kilobase million (RPKM) were calculated for each vOTU. The reason for using only VHGs in the analysis was to avoid over-counting of viral reads, which could be caused by spurious mapping of reads from horizontally transferred genes of other phages or bacterial species. The α-diversity (Shannon diversity) of the vOTU-level viral profile was calculated using the diversity function in the vegan package. The β-diversity (Bray-Curtis distance) between individuals was assessed using the vegdist function, and the average distance against other individuals was calculated for each individual. The VC-level viral profile was obtained by summing all the RPKM of vOTUs for each VC.Phylogenetic analysis of novel VCsTo construct phylogenetic trees for the vOTUs and reference genomes, protein sequences of large terminases, portal proteins, and major capsid proteins (Supplementary Data 3), which are often used to construct phage phylogenetic trees7,9, were extracted from the vOTUs in the 10 most abundant VCs (VC_19, 1, 2, 24, 12, 15, 3, 44, 18, 6), and their homologues were searched for in the reference phage genomes in RefSeq using DIAMOND with the more-sensitive option (e-value 0.01% (n = 865) and genera with average relative abundance >0.5% (n = 32) were included in the analysis.Analysis of VLPs and whole metagenomes from 24 faecal samplesQuality filtering of sequenced reads from the 24 VLPs and whole metagenomes was performed using fastp (version 0.20.1)92 with the default parameters. Contamination with human (hg38) or phiX genomes was excluded by mapping the reads to the genomes using Bowtie2.To exclude bacterial DNA contamination in the VLP dataset, we performed further filtering. First, the VLP reads were assembled into contigs using MEGAHIT and the contigs were checked for virus or not. Contigs were defined as viral contigs if they were predicted as viruses by DeepVirFinder (P-value More

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