Virus transmission between host species underpins disease emergence. Both host phylogenetic relatedness and aspects of their ecology, such as species interactions and predator–prey relationships, may govern rates and patterns of cross-species virus transmission and hence zoonotic risk. To address the impact of host phylogeny and ecology on virus diversity and evolution, we characterized the virome structure of a relatively isolated island ecological community in Fiordland, New Zealand, that are linked through a food web. We show that phylogenetic barriers that inhibited cross-species virus transmission occurred at the level of host phyla  as well as at lower taxonomic levels. By contrast, host ecology, manifest as predator–prey interactions and diet, had a smaller influence on virome composition, especially at higher taxonomic levels. The virus–host community comprised a ‘small world’ network, in which hosts with a high diversity of viruses were more likely to acquire new viruses, and generalist viruses that infect multiple hosts were more likely to infect additional species compared to host specialist viruses. Such a highly connected ecological community increases the likelihood of cross-species virus transmission, particularly among closely related species, and suggests that host generalist viruses present the greatest risk of disease emergence. Sampling the viromes of vertebrates, arthropods and plants on an island ecosystem shows that viral transmission between species is strongly affected by phylogeny but less affected by predator–prey relationships and that generalist viruses pose the greatest zoonotic risk. Determining how viruses move through ecosystems is central to understanding their occasional emergence as pathogens. Recent metagenomic studies suggest that only a small proportion of viruses cause serious disease and mortality, with apparently healthy wildlife species commonly infected by multiple viruses2. In reality, viruses are a key component of global ecosystems, regularly moving between species in the absence of overt disease3. As such, a full understanding of infectious disease emergence requires an ecosystem-level approach4, in which the viromes of entire interacting communities are investigated.Cross-species virus transmission is a near-universal feature of viruses5. Rates of cross-species virus transmission are expected to be higher among closely related host species, reflecting a greater similarity in the virus and host proteins that underpin successful virus–cell relationships such as host-cell binding6. A fundamental difference in virus–host cell relationships in part explains why viruses from vertebrates and invertebrates are usually phylogenetically distinct7, even though the latter are often dietary components of the former. A second proposed factor is that ecological properties of the host play a major role in virus movement between species by determining the probability of virus exposure8. Each time species interact, they provide opportunities for cross-species virus transmission. Hence, the more interactions, the greater the probability of host jumping. For example, changes in land use have increased human–animal interactions, driving disease emergence events in humans9. Predator–prey interactions are common exposure events, with the consumption of prey providing direct contact with viruses during digestion. Consequently, the structure of an ecosystem food web may have a large impact on the flow of viruses through communities. With the exception of marine microbial food webs in which viral lysis of microbial hosts impacts food web structure10, the prediction is yet to be tested.Two factors have been proposed to govern virus movement from one host species to another, shaping similarities and differences in virus composition among taxa and hence determining virome structure at the ecosystem scale. First, it is possible that host phylogenetic relatedness directly impacts the frequency and pattern of cross-species virus transmission2, allowing a more precise description of viral diversity and the increasingly rapid discovery of novel viruses11. Far less attention has been directed toward understanding how viruses move within ecosystems. Research on food webs including viruses has commonly focused on single pathogens of a species of interest12 or on the microbial subset of the food web10. The virome of an entire food web is yet to be characterized, in part because the large number of species in most ecological communities makes this impractical. However, in small island forest ecosystems, such as Pukenui/Anchor Island in the Fiordland region of southwestern New Zealand , endangered mohua  and critically endangered Te Kakahu/Chalky Island skink (Oligosoma tekakahu). The Pukenui/Anchor Island community also has a relative decoupling of phylogenetic relatedness and ecological niches. Some species that are distantly related  possess a similar ecological niche and regularly interact. For example, yellow-crowned parakeets  were frequently observed with passerines , travelling and foraging in multi-species flocks , as observed elsewhere in New Zealand13.The unique evolution of New Zealand wildlife  means that unlike other forest ecosystems the Pukenui/Anchor Island ecological community is dominated by birds, with no terrestrial mammals, and only one species of reptile. However, despite being small, isolated and having a low diversity , this community contains a breadth of ecological niches, including a variety of diets: carnivores, insectivores, plant-eaters , piscivores and omnivores. This ecological community also contains threatened species that may be vulnerable to disease emergence via cross-species virus transmission: for example, the critically endangered kākāpō  sequencing to document the virome of each host in the community, including viruses that directly infect the host, bacteriophage and those present in the host diet. Hence, we use the total viral diversity of each host, rather than only those viruses that have established a true infection, to provide a broader view of virus transmission. Viruses that have established infection in their hosts could represent transient spill-over events or sustained cross-species transmissions and result in acute or chronic infections. If host phylogeny were the key driver of viral diversity, we would expect viromes to cluster according to the major host phyla , as well as at lower taxonomic levels, with more closely related hosts having more similar viromes than hosts that are more distantly related Fig. . By contOur sampling of the Pukenui/Anchor Island forest community included all key vertebrate species in addition to representative invertebrates and plants . Non-metric multidimensional scaling plots similarly revealed that, overall, viral communities clustered to the level of host phyla . In addition, host phylogenetic order explained the most variation between viral communities (R2 = 0.80) compared to host phylum (R2 = 0.13) and class (R2 = 0.30), when used as the dependent variable in the PERMANOVA test (n = 49).We identified 16,633 viral sequences (assembled contigs) with an abundance of 360 million reads from a total of 112 different viral families. The number of viral families in each library ranged from 3 to 45, with a mean of 18.9 (±11.0 s.d.). Viral richness was dependent on host taxonomy  but not whether the hosts ate insects  or plants . When controlling for host order, PERMANOVA tests revealed that insectivores had significantly different viromes from non-insectivores, and plant-eaters had significantly different viromes from non-plant-eaters  than diet . A similar result was obtained when including only viruses likely to infect chordates, with all comparisons significant , and host order explaining more variation (R2 = 0.48) than host diet .Within Chordata (for which most host resolution is available), we used host order and host diet to compare how much variation in the viromes is explained by each factor. Host diet was described using two separate binary factors based on the primary diet types , even after controlling for host relatedness . By contrast, there was no significant correlation between host relatedness and viral community .To further determine the relative impact of host phylogeny and ecology on virus community composition within the Chordata, we assessed the degree of correlation between three dissimilarity matrices: viral community (a Bray–Curtis dissimilarity matrix), host relatedness (generated using the sum of branch lengths from a host phylogeny) and host ecological similarity (using the dietary factors described above). In this case, host ecological similarity and viral community were significantly correlated . Module 1 contained only invertebrates , module 2 predominately comprised plants (75% plants and 25% invertebrates), while modules 3 and 4 largely comprised chordates   and identified four modules using a community detection algorithm. Notably, the modules follow host phylogenetic groupings, with a Pearson’s chi-squared test of independence indicating that host phylum and module were highly correlated , module 2 containing the highest number of virus families 36) and module 3 containing the lowest number of both hosts (7) and virus families (17). The communities within each module were significantly different from one another (6 and modP > 0.05) of Pc(k) = k0.08kk/57)−( for the hosts and Pc(k) = k−0.12kk/32)− = k0.17kk/30)−( and Pc(k) = k0.34kk/14)− in comparison to a network generated with a null model . The cumulative degree distributions for the host and virus nodes followed a truncated power-law distribution . Within the Chordata, host order but not host diet significantly impacted degree, betweenness and eigenvector centrality  and grey warbler, with over 40 viral families in each) differed in their connectivity in the network  based on the Bray–Curtis dissimilarity matrix, which we refer to here as the ‘host community network’ Fig. . This neork Fig. , with th14. Our analysis also revealed that if a virus were to move through this network, it would first move within a host phylum, then most likely from plants to arthropods, and to/from arthropods and chordates. Strikingly, direct links between plants and chordates were far less common, which may reflect the relatively small number of chordates on the island that eat plants, compared to those that eat invertebrates  was high (3.1), while the path length ratio  was similar (1.1), with a ‘small-worldness’ index of 2.7. This signifies a small world network: both highly clustered and highly connected. Most nodes are a small number of links away from every other node, enabling viruses to easily move between speciesParvoviridae (module 1), Caulimoviridae (module 2), Fiersviridae (module 3) and Caliciviridae (module 4), chosen for their high viral diversity across multiple host species  and slater/woodlouse  library, strongly indicating a food web interaction  host–virome network based on the presence or absence of each virus family in each host library, and the (unipartite) host community network based on the level of virome similarity between hosts.15. Similarly, host phylogeny plays an important role in shaping the diversity of bacteria and eukaryotic parasites17, such that it likely has wide-ranging impacts on microbial diversity. Despite this, the strength of the host phylogenetic trend observed here is surprising given that the viromes characterized comprised all the actively transcribed viruses identified, not just those directly infecting the host in question. There were, necessarily, differences in sampling method between chordates , invertebrates (body tissue) and plants (leaf tissue) which, while unavoidable, could have resulted in artificial similarities in the virome between similar sample types. However, cloacal swabs should have biased the results toward food web interactions as they represent a sample of the host digestive tract which often contains diet-associated viruses18. Moreover, we found significant differences between viral communities at lower host phylogenetic levels (class and order) where the sampling method was consistent, suggesting the differences observed were not due to the sampling method.This analysis revealed a strong effect of host phylogeny on the host–virome network, with more viral families shared within than between host phyla. Host taxonomy also impacted viral richness and node level properties within the host–virome network. To our knowledge, this is the first such evidence from an ecosystems-scale analysis, and hence supports analyses of specific viral families and hosts that have shown host phylogenetic distance to be a key constraint on the cross-species transmission of viruses and hence on disease emergenceRhipidura fuliginosa, passerine) and the tawaki   all influence host viromes, although to a lesser extent than phylogeny23. Despite the limited influence of host ecology on viral diversity, we found clear examples of host predator–prey interactions at the level of virus species, showing that viruses do move between species via these processes. Overall, our results suggest that virus traffic from predator–prey interactions between distantly related species is only transient  and hence unlikely to result in productive infections. Our results therefore suggest that increases in host connectivity via predator–prey interactions would not result in increased rates of infection.Within the Chordata, predator–prey interactions were not associated with high levels of cross-species viral transmission in this island community, again likely reflecting phylogenetic constraints. It is possible, however, that more predator–prey viral transmissions would be detected in ecological communities where such interactions occur more frequently between closely related species, thereby reducing the effect of host phylogeny, and that viruses absent from chordate cloacal swabs may be subject to different evolutionary patterns. Indeed, studies of the human gut have provided limited evidence for host diet impacting virome structure25, suggesting they have similar constraints in network construction. Our host–virome network followed a truncated power-law distribution that had lower cut-off values than expected by chance. This suggests that the structure is influenced by assembly mechanisms . Power-law distributions occur when nodes are likely to get more links the greater the number they already have, such that the ‘rich get richer’24. Accordingly, hosts that already have a high diversity of viruses are more likely to acquire new viruses than hosts with a low diversity of viruses. This could be partly driven by species abundance24, with more abundant host species more likely to be exposed to viruses within the network. Similarly, viruses with many hosts are more likely to acquire more hosts, which may inform zoonotic risk assessments26. In particular, rather than assigning the highest risk score to viruses that are similar to those that have already emerged, it may be of greater utility to give preference to the most generalist viruses. This power-law distribution is truncated when the underlying model no longer accurately predicts the distribution beyond a certain cut-off, effectively preventing the rich from getting richer beyond that point. The higher this cut-off, the greater the number of highly connected hosts and viruses27. In our models, this cut-off value was higher than for the null network, indicating that the host–virome network contains more highly connected hosts and viruses  than expected by chance. Truncated power-law distributions are often found in mutualistic networks , with the truncation thought to be due to ‘forbidden’—physically impossible—links28. In our network, forbidden links could be due to phylogenetic barriers preventing cross-species transmission.Both the host–virome network (bipartite) and host community network (unipartite) had similar structures to those observed in other ecological networks, including a truncated power-law distribution and small world network, structures that are commonly found in food-web and plant–pollinator networks25. This is common in ecological networks, with most species only two links apart on average in complex food webs29. The combination of strong modularity in our host virome network and high connectivity in our host community network implies that a pathogenic virus could rapidly move through the network, particularly once the virus crossed from one module to another, without requiring a direct consumer relationship between hosts. The host community network showed an especially strong clustering within chordates, with a high degree of similarity between hosts. This cluster included endangered species such as the mohua and Te Kakahu skink, suggesting that on Pukenui/Anchor Island these species are vulnerable to disease emergence from other chordates within that cluster. By contrast, the kākāpō was less closely connected to this cluster, suggesting it may be less vulnerable.The host community network (unipartite) was a ‘small world’ network. Although clear clusters emerge, most nodes (hosts) can be reached via a small number of connections to other nodesOur study shows that the phylogenetic relatedness of hosts is a strong driver of viral diversity in this ecological community. Phylogenetic barriers between distantly related hosts may prevent frequent virus movement despite exposure events via predator–prey interactions. The ecological community studied is highly connected, presenting risks for disease emergence in vulnerable species. This work sheds light on the processes that dictate viral movement through ecosystems and could be expanded to include sampling over multiple time points to further understand these processes. More research is needed on how the disruption of these networks impacts disease emergence.2), located in Dusky Sound, Fiordland, New Zealand  on the south-west coast of the South Island and is over 80 km to the nearest township by air. Following the eradication of invasive mammals in the early 2000s, the island became a key habitat for endangered native species, including the kākāpō (S. habroptila). The island is also an important habitat for seabirds that nest in the forest, including the tawaki/Fiordland crested penguin (E. pachyrhynchus). The temperate rainforest consists predominately of beech and podocarp (conifer) trees, with an understory/forest floor including shrubs, vines and mosses. To our knowledge, the only non-native permanent inhabitant of the island is the invasive German wasp (Vespula germanica).Pukenui/Anchor Island is a small island 11.4 km1.4 km2, This research was conducted under a Department of Conservation Wildlife Act Authority authorization number 86173-FAU and authority for research and/or collection of material on public conservation land authorization number 86172-RES and had ethics approval from the University of Auckland reference number 002198.Fieldwork was undertaken on Anchor Island, Fiordland, New Zealand, between 17 February and 14 March 2021. The 18 bird and 1 skink species were caught using four different methods, depending on the species in question. Small, flighted birds were caught using low-canopy mist netting, while larger flighted birds were caught with high-canopy mist nets. Bird calls were used to attract the birds to the area and into the nets. Non-flying birds were caught by hand or hand net. Skinks were caught using gee-minnow traps. Once caught, the animals were weighed, and a cloacal swab was taken, using a sterile nylon flocked swab FLOQswab (Copan), either mini-tip or regular tip depending on the size of the animal. The entire tip of the swab was inserted into the cloaca and swabbed with two to four circular motions while applying gentle pressure against the mucosal surfaces. The swab was then cut using scissors sterilized with 70% alcohol and placed into a tube with 1 ml of RNAlater. Samples were kept at −20 °C for the duration of the fieldwork, then frozen at −80 °C.Leaves were collected from each plant species by cutting the stem with sterile scissors and placing the leaves into a sterile collection bag . At the fieldwork base, a leaf from each individual plant was chopped into approximately 5 mm × 5 mm pieces and placed into 1 ml of RNAlater. The total volume of the solution and plant material was no more than 1.3 ml, to ensure preservation of all the RNA. To ensure the RNAlater permeated into the tissue, samples were left at 4 °C for approximately 12 h before being transferred to −20 °C. Samples were kept at −20 °C for the duration of the fieldwork, then frozen at −80 °C.Invertebrates were collected by manual search and by extraction from soil. At five sites on Anchor Island, the area within a 5 m × 5 m square was intensively searched for invertebrates . When an invertebrate was found, it was placed alive into a sterile pottle with damp moss or leaf litter from the site. At the same site, a sterilized spade was used to cut a soil core approximately 2 l in volume. The core was placed into a sterilized 2 l container. The invertebrates and soil cores were kept at 4 °C for the duration of the fieldwork. They were then transferred to Massey University, New Zealand. The invertebrates collected by hand were examined live under a dissecting microscope using sterile tools and identified to the lowest classification level possible . Invertebrates from the soil cores were extracted using Berlese funnels into RNAlater and identified in the same way. Once identified, the invertebrates were individually stored at −80 °C.RNA was extracted using the RNeasy plus mini extraction kit (Qiagen) and QIAshredders (Qiagen). The tube containing the swab in RNAlater was thawed and the swab removed from the tube using sterile forceps and placed in 600 µl of extraction buffer. The swab and buffer were vortexed for 2 min at maximum speed. The swab and buffer were then placed into a QIAshredder and centrifuged for 5 min at maximum speed. The flowthrough was retained (avoiding the cell debris pellet) and used in the extraction following the standard protocol in the kit. The RNA was eluted into 50 µl of sterile water. Extractions were pooled by host species for sequencing. About 25 µl of each extraction was used in each pool, and this was concentrated using the NucleoSpin RNA Clean‑up XS, Micro kit for RNA clean up and concentration (Macherey-Nagel). The concentrated RNA was eluted into 20 µl of sterile water.RNA was extracted using the Rneasy plant mini extraction kit (Qiagen). The plant material in RNAlater was thawed just enough to remove approximately 20–30 mg. This was placed into a tube with a sterile stainless steel 6 mm bead. The tube, sample, bead and adapter set were then cooled at −80 °C for 30 min. After cooling, the plant tissue was disrupted by beating using the TissueLyser II (Qiagen) at 30 Hz for 2 min. The kit protocol was then followed for the remainder of the extraction. The RNA was eluted in 50 µl of sterile water. Extractions were pooled and concentrated as described above. Before concentrating, the pooled RNA was treated with DNase, using the rDNase Set (Macherey-Nagel).RNA was extracted using the RNeasy plus mini extraction kit (Qiagen). For small invertebrates (<30 mg), the whole body was used in the extraction. For larger invertebrates, a 30 mg piece of the abdomen was used. The frozen tissue was placed into a tube with a sterile stainless steel 6 mm bead, and 300–600 µl of buffer was added, depending on the amount of material. The tissue was disrupted by beating using the TissueLyser II (Qiagen) at 30 Hz for 4 min. The kit protocol was then followed for the remainder of the extraction. The RNA was eluted in 50 µl of sterile water. Extractions were pooled and concentrated as described above.Complementary DNA libraries were prepared using the Stranded Total RNA Prep with Ribo-Zero Plus (Illumina) for cloacal swabs and invertebrates and the TruSeq Stranded Total RNA with Ribo-Zero Plant (Illumina) for plants. Libraries were sequenced on the Illumina Novaseq platform at Auckland Genomics, University of Auckland, and the Australian Genome Research Facility, with invertebrates, plants and vertebrates sequenced entirely independently . One blank negative control library  was sequenced with each sequencing run .30, adapters and bases below a quality of 5 were trimmed, using a sliding window approach with a window size of 4. Bases were cut if below a quality score of 3 at the beginning and end of the reads. Using bbduk in BBtools (bbmap 37.98)31, sequences less than 100 nucleotides in length or below an average quality of 10 were removed.Using Trimmomatic (0.38)32. Viruses were identified by comparing the assembled contigs to the National Center for Biotechnology Information (NCBI) nucleotide database (nt) and non-redundant protein database (nr) using Blastn (blast+ 2.1.2)33 and Diamond Blastx (Diamond 2.0.9)34. Contigs were retained that had hits to viruses and an open reading frame greater than 300 nucleotides for nr hits . Sequence similarity cut-off values of 1 × 10−5 and 1 × 10−10 were used for the nt and nr databases, respectively, to prevent false positives. Virus transcript abundance was estimated using Bowtie2 (2.2.5)35. Viruses that met the following conditions were assumed to be contaminated as a result index-hopping from another library, and removed: (1) viruses were sequenced on the same lane, (2) the total read count was <0.1% of the read count in the other library, and (3) viruses were >99% identical at the nucleic acid level. Any virus found in the blank negative control libraries was assumed to have resulted from contamination and similarly removed from all libraries and analyses.Reads were de novo assembled using Megahit (1.2.9)36. Viruses were grouped into viral families, as classified by the International Committee on Taxonomy of Viruses or NCBI. Viruses not classified to family level were included provided they had an order-level classification . We did not group at a lower classification as it resulted in many viruses being excluded: only 56% of the viruses could be classified to the genus level, whereas 84% could be classified to the family level. An operational taxonomic unit table  was visualized at the virus family level using non-metric multidimensional scaling with a Bray–Curtis dissimilarity matrix, presented as an ordination plot using the R packages phyloseq v1.34.0 (ref. 38) and scatterplot3d v0.3-41 (ref. 39). Bray–Curtis dissimilarity is a statistic ranging from 0 to 1 that reflects the dissimilarity of communities between libraries, with 0 meaning both libraries have an identical community, and 1 meaning the libraries have no virus families in common. Pairwise permutational analyses of variance  and pairwise PERMANOVA test from pairwiseAdonis v0.4 (ref. 41)) were used to test for differences in the community based on host phylogenetic groupings and host ecology, using the Bray–Curtis dissimilarity matrix and an alpha of 0.05 following a Bonferroni correction. Where multiple terms were used, the marginal effects of each term were tested using by=“margin” in adonis2.All analyses were conducted in R (4.0.5)40. The host relatedness dissimilarity matrix was generated using the sum of branch lengths from a phylogenetic tree created using the Open Tree of Life and R package rotl v3.0.14 (ref. 42). The ecological similarity matrix was created using two host dietary factors, ‘eat invertebrates yes/no’ and ‘eat plants ‘yes/no’, combined into a matrix using the function vegdist in the package vegan40, with the Manhattan dissimilarity index. A partial Mantel test was used to test the correlatedness of ecological similarity and viral community while controlling for host relatedness.To explore the effect of host phylogeny and diet on the viral community at lower taxonomic levels within Chordata, we conducted Mantel tests to determine the degree of correlation between three dissimilarity matrices—viral community (the Bray–Curtis dissimilarity matrix as described above), host relatedness and host ecological similarity—using the package vegan43 and visualized using visNetwork44 based on the presence or absence of each virus family in each host library. Two sets of nodes were defined: virus families and host species. One link in the network corresponded to a virus node inhabiting a host node whenever the virus family was found in the host library. This host–virome network comprised 49 host libraries, 112 virus families and 926 interactions between these two node sets. Modules  were identified to infer the community structure in the host–virome network. To do this, we used the DIRTLPAwb+ community detection algorithm45 in the bipartite package46 that identifies partitions with high modularity scores by maximizing weighted modularity, weighted using log abundance (not RPM). DIRTLPAwb+ uses multiple iterations of the LPBwb+ algorithm, based on Barber’s modularity47 equation  = 1 if the modules are the same, and 0 if different46.in which Pairwise PERMANOVA tests were used to test for differences in community composition between modules, using module as an independent factor and an alpha of 0.05 following Bonferroni correction.48. We performed pairwise PERMANOVA tests on 100 rarefied data sets of the original data using the modules detected in our host–virome network as an independent variable. We rarefied the data to the size of the smallest library (630 contigs) using the rrarefy function of the vegan package40. This was repeated 100 times independently to obtain 100 different rarefied data sets, using the replicate function in base-R v4.0.5. We then analysed each of these rarefied data sets with the pairwise PERMANOVA test and averaged the result across the 100 rarefied data sets. In this way, we tested whether the communities identified by the modularity analysis remained consistently significantly different when rarefied down to the lowest sequencing depth.We evaluated the robustness of the modules identified by rarefying to the lowest sequencing depth following the methods used by Lurgi et al.k is the cumulative degree distribution, i is the power-law decay exponent and k−(k/z) is the exponential cut-off for the truncation, where z is the cut-off value beyond which the power-law distribution no longer fits. We then compared this distribution to a null model of a random bipartite graph created using the Erdos–Renyi model, with the same number of host and virus nodes and interactions but with the interactions randomized. The impact of host taxonomy and host diet on node level properties of the network  were interrogated using general linear models.Using the modules obtained from the community detection algorithm, we evaluated the roles of individual species in the network by analysing the degree distribution (the distribution of the number of links from each node). To assess the distribution of links between nodes in the host–virome network, we also calculated the cumulative degree distribution and fitted a truncated power law, using nls and the Kolmogorov–Smirnov test to evaluate whether it fitted the power law distribution. The truncated power law distribution was as follows in equation :2\docume49), which uses the transitivity  and average shortest path length to compare the network to 1,000 randomly generated networks. This generates a ‘small-worldness’ index as developed by Humphries and Gurney51, which determines whether the network is significantly different from 1,000 randomly generated null networks and whether the network can be deemed a ‘small world’.To examine the strength of connections between hosts created by shared viromes, we created a unipartite network  based on the Bray–Curtis dissimilarity matrix, using the phyloseq v1.34.0 and igraph v1.2.11 R packages. Given the Bray–Curtis statistic ranges from 0 to 1, a cut-off was required to determine how similar two communities need to be to link them in the network. We chose a cut-off of 0.9, which is the lowest that still creates a cohesive network . However, this cut-off does create nine singletons (nodes with no connections), which were removed. The network (referred to as the ‘host community’ network) comprised 40 hosts and 99 interactions. The ‘small world’ properties of this network were examined using the distance_table functions in igraph, including calculating the shortest paths  and average shortest path length . We used the smallworldness function in qgraph v1.9.2 53. Individual maximum likelihood phylogenetic trees for each virus family were estimated using IQ-TREE (1.6.12)54, with the best-fit substitution model determined by the program and node robustness assessed by using the approximate likelihood ratio test with 1,000 replicates. Phylogenetic trees were visualized using APE (5.4)55 and ggtree (2.4.1)56 in R.The patterns of virus diversity within viral families were visualized using phylogenetic trees with one viral family per module examined in detail, out of a total of 112 viral families. Amino acid sequences were aligned using MAFFT (7.402)Further information on research design is available in the Supplementary InformationSupplementary Tables 1–7.Reporting SummarySupplementary Data 1Operational taxonomic unit table of raw virus abundance levels.


Decreases in sequencing costs combined with technological advances, such as automation and robotics, allow for efficient processing and analysis of numerous samples in plant disease clinics, tissue culture laboratories, and breeding programs. There are many opportunities for translating virome analysis to support plant health. For example, virome analysis can be employed in the development of biosecurity strategies and policies, including the implementation of virome risk assessments to support regulation and reduce the movement of infected plant material. A challenge is to identify which new viruses discovered through HTS require regulation and which can be allowed to move in germplasm and trade. On-farm management strategies can incorporate information from high-throughput surveillance, monitoring for new and known viruses across scales, to rapidly identify important agricultural viruses and understand their abundance and spread. Virome indexing programs can be used to generate clean germplasm and seed, crucial for the maintenance of seed system production and health, particularly in vegetatively propagated crops such as roots, tubers, and bananas. Virome analysis in breeding programs can provide insight into virus expression levels by generating relative abundance data, aiding in breeding cultivars resistant, or at least tolerant, to viruses. The integration of network analysis and machine learning techniques can facilitate designing and implementing management strategies, using novel forms of information to provide a scalable, replicable, and practical approach to developing management strategies for viromes. In the long run, these management strategies will be designed by generating sequence databases and building on the foundation of pre-existing knowledge about virus taxonomy, distribution, and host range. In conclusion, virome analysis will support the early adoption and implementation of integrated control strategies, impacting global markets, reducing the risk of introducing novel viruses, and limiting virus spread. The effective translation of virome analysis depends on capacity building to make benefits available globally.Virome analysis  Advances in high-throughput sequencing (HTS) technologies have expanded our understanding of virus communities and their impacts on ecosystems, including the ongoing COVID-19 pandemic . EmerginThe virome concept emerged in parallel with the general microbiome concept . Virome Climate change is shifting the distribution of plant-host and insect-vector populations . The conOur ability to characterize the virome of an individual plant, a plant community, or an ecological niche is a major scientific advance, creating many possibilities for translating virome information to improve crop disease management across scales, methodologies, and disciplines Figure 1New-generation diagnostic tools based on HTS offer extensive qualitative and quantitative information to understand virome ecological properties. This data can be analyzed using machine learning to help design precise intervention strategies. HTS has higher sensitivity that can help rapidly identify virus species that pose a risk for disease emergence, providing better understanding of previously unimportant viruses  that mayVirome data, in terms of both virus incidence and associations, can be translated to target on-farm management strategies for particularly important viruses and virus combinations in a region. Ultimately, as sequencing becomes less and less expensive, diagnostic agencies and farm managers may screen their crop by implementing new generation diagnostic tools that can be used in surveillance strategies for viruses in agriculture and other systems . Virome via true seed, exchange of germplasm among breeding programs is also complicated by seed-transmissible viruses, and could benefit from greater sensitivity of diagnosis  and sugarcane mosaic virus   of the causal agent as a novel virus, yam bean mosaic virus, followed rapid amplification-based diagnostic development to eradicate infected plants and seed lots . Anothertyvirus) . MCMV antyvirus) . Diagnostyvirus) . A survetyvirus) . The Inttyvirus) . Howevertyvirus) .Another example where virome analysis can have an important role is the cross-continental epidemic of cassava mosaic disease CMD, Figure 3Pathogen surveillance can be conducted with multiple interconnected goals, including detection of outbreaks, pathogen characterization, and geographic characterization – along with characterizing the current phytosanitary measures and quarantine regulations and whether they are sufficient . Effectivia botanical seeds and vegetative planting material for distribution locally or globally, affecting smallholder farmers and large production areas. Recent proposals for a global surveillance system to mitigate pathogen introductions are being discussed  evaluate the potential for introduction, establishment and spread of pathogens and pests, the magnitude of risk, and the economic impact for a specific region based on key risk variables such as environmental suitability, vector availability, crop area, and productivity . The mosPRAs need to account for global change factors such as climate and trade. Global climate change and extreme weather events drive crop losses, and can damage agricultural infrastructure, along with changing distributions of arthropod vectors and weeds that act as hosts . When naThe application of new diagnostic technologies raises a key question: which viruses among those observed in a plant virome should be regulated? The implementation of virome studies accelerated the discovery of viruses at a pace that surpasses the speed of experiments to characterize virus species and their host range. Phytosanitary regulations need to incorporate automated surveillance effectively in real-time, which represents a challenge in terms of methodologies, policies, and phytosanitary standards . The capOur understanding of the ecology of viruses across scales is growing, from individual samples to studies comprising multiple host, farms, and regions. This information can be complemented with agroecological metadata for a geographic location, cultivar, growth stage, weather, and other variables, generating large datasets. For example, individual plant samples can reveal virus-virus interactions that may explain symptomatology . SurveilLarger and more complex viromes with multiple layers of information can be analyzed with machine learning algorithms such as decision trees, Bayesian and neural networks, and other support vector machines . Virus cAs a simpler illustration of the potential for the use of VMUs Figure 4Box 1Potyvirus, Polerovirus, Machlomovirus, and Mastrevirus. In this example, potyviruses and machlomoviruses are vectored by aphids and beetles, respectively, and both groups are mechanically transmitted. Poleroviruses are only aphid-transmitted but not mechanically transmitted. Seed transmission is reported for potyviruses and machlomoviruses, but not for poleroviruses and mastreviruses. Sanitation would work for mechanically transmitted viruses like potyviruses and machlomoviruses, but not for poleroviruses, while using virus-free seed would contribute to management of seed-transmitted viruses. Lack of information represents a challenge during classification, such that some VMUs may be defined in the short-run without classification information.Machine learning can identify groups of viruses that can be managed together according to their most effective control strategy. We use the term “virome management unit” (VMU) to refer to a subset of viruses within a virome that have similar characteristics relevant to management needs. This concept can be used to simplify management planning for viromes with many virus species, such as in the context of whole-farm or regional virome management. VMUs can be evaluated based on information about virus transmission, vector types, and management strategies used for each virus and their effectiveness, including tools such as sanitization, insecticides, and resistant or tolerant cultivars. As an illustration, consider the virome associated with MLN in Kenya , with 15party package . Four distinct VMUs were identified in this maize virome. VMU1 consists of maize streak virus, an African native grass virus. VMU2 includes only potyviruses. VMU3 includes the poleroviruses. VMU4 includes the machlomovirus MCMV. VMU2 which are seed transmitted and mechanically transmitted can benefit from use of certified virus-free seed and sanitation practices. VMU4 could primarily benefit from using varieties resistant or tolerant to the MCMV virus, and virus-free seed. In some cases, such as VMU1, more biological information is needed to identify effective management strategies with high confidence. Resistance genes were identified as relevant for MCMV in VMU 4; maize with tolerance to MCMV can help mitigate the effects of MLN epidemics. Although there are maize hybrids resistant to SCMV, based on resistance genes like Scmv 1 and 2 (In this illustration of the VMU concept, VMUs were delimited using a supervised machine learning algorithm in R , a decis package . In this 1 and 2 , this clVirus indexing is the testing of germplasm to ensure it is free of viruses, a particularly important step for vegetatively propagated crops and tissue culture propagation, especially when germplasm is moving between breeding programs and companies, and between countries. The most common virus indexing methods include grafting of plants on indicator hosts, use of serological methods such as ELISA, and molecular detection through conventional PCR and qPCR . One of When considering how to manage viromes in germplasm or seed systems, it is important to distinguish between virus species that are already present in a region and those that may represent new introductions. For those viruses that are already endemic in a region, quality declared seed that maintains viruses at a low level may be the best economic value when seed completely free of a virus is impractical to produce for the time being . It may via true seeds, exchanging germplasm among breeding programs is similarly complicated by seed transmissible viruses (Systems ensuring virus-free germplasm support trade and seed distribution and are particularly important for developing resistance to viral diseases and reducing their impact on crop yields. Germplasm sharing among breeding programs in regions where different viruses are present may have helped to spread viruses globally , and new viruses . Screeni viruses . IndexinHTS in crop breeding has been used to fingerprint the genomes of plant accessions and to characterize the diversity of breeding populations . For exaUndoubtedly the role of HTS-diagnostics and virome analysis in plant pathology will continue to expand in the future. But to have an impact on plant viral epidemics in real world settings, more is needed than technology alone. Frameworks for technology application must consider supporting factors including local access to equipment, computing power and data storage capacity, and staff capacity building for the interpretation of the complicated outputs of modern bioinformatic tools.Australia and New Zealand, island nations with unique endemic biota, apply stringent biosecurity practices to reduce catastrophic introductions of invasive species . New ZeaAnother global trend is the emphasis on reforestation to sequester carbon and provide other ecosystem services . RelatedAccess to tools and support for virome-based analyses is scarcer in low-income countries. Many countries still struggle with maintenance of conventional plant health surveillance and screening systems, which demand significant resources to operate, even for valuable commercial crops like cassava in Southeast Asia, a cash crop for more than 8 million farmers . InternaFor now, a dose of realism and humility is required given the track record of adoption of current, comparatively simple methods, such as PCR-based checks, in countries with limited resources. Plant virus epidemics are increasingly a globalized phenomenon . WidesprFully embracing virome technologies means not only the development of increasingly user-friendly tools for accessibility, but also dedication to long-term training and capacity building for implementers around the world, and supporting critical infrastructure for sampling, analysis, and interpretation. People remain at the core of the application of these technologies.RIAB and KG conceptualized the synthesis. All authors contributed content for the synthesis. RIAB and KG developed a final document with comments from all authors. All authors contributed to the article and approved the submitted version.


Dear Editor,Arenaviridae. This virus resembles a circular or round morphology with a usual approximate diameter of 110–130 nm. LASV requires an intricate nucleotide diversity among strains, which is intertwined with the subgroups of strains across geographical locations. As a result, six main LASV clades or lineages have been identified: clades I–III in Nigeria; clade IV covers up the countries of Sierra Leone, Guinea, and Liberia; and clade V in southern Mali; and a more contemporary clade VI, emerging from Togo. The capacity of these strains to change over time is a special featureMastomys natalensis is suspected to be the animal reservoir for LASV. It’s a rodent from the Mastomys genus, which is common and reproduces a majority in West AfricaMastomys rats infected with LASV is the cause of LASV infection in humansLassa virus (LASV) is a zoonotic, acute, fatal, disease-causing virus, a causative agent of Lassa fever (LF) which is a type of viral hemorrhagic fever that is endemic in various parts of West Africa including Liberia, Nigeria, Guinea, and Sierra Leone. Past research indicated that although the first primary incidence of LF was noticed in 1969, the zoonotic transfer of this viral agent to humans has been occurring for more than a centenary. LASV or Lassa virus is a single-stranded, encapsulated, bipartite RNA virus that predominantly corresponds to the family of [44] Since the early symptoms and indications of LF are frequently mild and similar to those of other enteric fevers, LF clinical diagnosis can be difficult. Patients from West Africa or those who have recently been to an area where LF is known to be endemic who have a fever over 38°C (100.4°F) and who are not improving after receiving antimalarial or antibiotic treatment should be believed to have the disease.A considerable section of LF cases is primarily instituted by rodent-to-human LASV transmissions associated with close proximity with infected animals or animal excreta, needless to say, there are high possibilities of human-to-human transmissions. Even though LASV infection can cause symptoms in patients, it is thought to cause asymptomatic infection in its natural animal reservoirs. The peak of the LF in West Africa is during dry seasons and the lowest is in the wet season. The incubation period for LF is somewhere between 7 and 21 days after exposure. The WHO estimates that 80% of LASV-infected people have no symptoms, whereas 20% of infected people have severe, multisystem illnesses.The first identified case of LF occurred in the Yedseram river valley of Lassa, a remote town in North-Eastern Nigeria and henceforth, the viral disease has been named ‘Lassa fever’Scanty and sporadic data are available for Nigerian LF for years before 2000 because of a lack of systematic testing. There has always existed a wide gap between the widespread occurrence of the infection in Nigeria and the lack of comprehensive data on the trends of the infection. However, the existing reports suggest that the outbreaks of this fatal disease have ameliorated dimensionally and temporally and are expected to take an even greater catastrophic form for the natives of Nigeria and other parts of West Africa.The factors contributing to the re-emergence of LF epidemics in Nigeria are nosocomial transmission, travel and migration, the public health system, social factors, effects of civil War and conflicts and the COVID-19 pandemicTwo such identified compounds are lacidipine and phenothrin which inhibits the entry of the virus by blocking the low-pH-induced membrane fusion. They also inhibited the entry of viruses, the SSP-GP2 interface being the target of entry. The entry if blocked, blocks the replication of the virus and its spread at an early stageNot applicable.None.S.M., A.D., J.B., S.S., and S.H.: designed the study. S.M., J.B., S.K., and S.N.: made the first draft. P.S., S.P., and N.M. updated the manuscript. S.M. and S.S.: reviewed the final draft. All authors have critically reviewed and approved the final draft and are responsible for the content and similarity index of the manuscript.The authors declare that they have no financial conflict of interest with regard to the content of this report.Not applicable.Sumira Malik and Sarya Swed.


Margaritifera falcata) and the Western ridged mussel (Gonidea angulata) in the Chehalis River and Columbia River watersheds in the Western USA. We characterized viromes of mussel hemolymph from 5 locations in 2018 and 2020 using metagenomic methods and identified 557 viruses based on assembled contiguous sequences, most of which are novel. We also characterized the distribution and diversity of a previously identified mussel Gammarhabdovirus related to pathogenic finfish viruses. Overall, we found few consistent associations between viruses and mussel health status. Variation in mussel viromes was most strongly driven by location, with little influence from date, species, or health status, though these variables together only explained ~1/3 of variation in virome composition. Our results demonstrate that Western freshwater mussels host remarkably diverse viromes, but no single virus or combination of viruses appears to be associated with morbidity or mortality during MMEs. Our findings have implications for the conservation of imperiled freshwater mussels, including efforts to enhance natural populations through captive propagation.Freshwater mussels (Unionida) are globally imperiled, in part due to largely unexplained mass mortality events (MMEs). While recent studies have begun to investigate the possibility that mussel MMEs in the Eastern USA may be caused by infectious diseases, mussels in the Western USA have received relatively little attention in this regard. We conducted a two-year epidemiologic investigation of the role of viruses in ongoing MMEs of the Western pearlshell ( North American freshwater mussel populations play critical roles in ecosystem health and functioning  but haveFreshwater mussel mass mortality events (MMEs), in which a population experiences an acute die-off affecting a significant proportion of the population, are a particularly enigmatic subset of mussel population declines. While some MMEs result from obvious stressors , most lack clear causes. In such cases, infectious disease has long been suspected of playing a role ,8, thougMalacoherpesviridae) often causes mortality rates up to 100% in larvae and juveniles associated with changes in host metabolism and loss of energy reserves [Vibrio spp. and environmental factors  strongly influence OsHV-1 mortality rates [Nimaviridae), infectious hypodermal and hematopoietic necrosis virus , and infectious myonecrosis virus  have therefore been studied in detail. While evidence from recent freshwater mussel MME investigations suggests that infectious diseases  may play a contributing role [An infectious disease often plays a primary role in marine invertebrate MMEs ,17, thoureserves . Secondaty rates ,21. In sty rates ,23. Penaing role ,12, the Western freshwater mussel populations from the Pacific Region are similarly in decline, but they have received less attention, even though Western USA freshwater mussels play the same critical roles in ecosystem functioning and face many of the same anthropogenically driven population declines as their Eastern USA counterparts . Recent M. falcata a novel gammarhabdovirus  chemarfal virus 1 (CHMFV-1), closely related to pathogenic finfish viruses in the genus Novirhabdovirus, which are of global concern for wild fisheries and aquaculture [Here, we describe the results of a two-year epidemiologic study to characterize the role of viruses in Western mussel MMEs in OR and WA, USA. By pairing field data on mussels during MMEs and at control sites with metagenomic data on mussel viromes, we examine whether particular viruses or combinations of viruses are associated with the health status of mussels. Our results reveal a large number of viruses in Western mussel populations but, unlike in many Eastern US populations, few associations between viruses and MMEs. Also, we previously identified in aculture . We descG. angulata mass mortality in the Crooked River in 2014 from a site (CRO) within Smith Rock State Park near Terrebonne, OR  in 2018 and 2020  surveyed. Live G. angulata were also present throughout the survey area. The Owyhee River is a tributary of the Snake River, in Nevada, Idaho, and Oregon. In 2019 the Xerces Society for Invertebrate Conservation and Oregon state fish biologists surveyed the Owyhee River in response to a public report of numerous recently dead shells. The survey found unburied mussels and many recently dead shells of M. falcata, G. angulata, and Anodonta oregonensis from RM 43 to RM 24. Additional resurveys in 2019 at several sites (RM 24 and RM 33) did not successfully relocate any live G. angulata, despite having observed 21 live animals at RM 24 and 5 live animals at RM 33 in 2017. Surveys in 2017 at RM 76 documented a large  bed of M. falcata, but resurveying at RM 76 in 2019 revealed evidence of substantial die-off, with an estimated 42% mortality rate of M. falcata. A small number of live and dead G. angulata were also observed in 2020 but were not observed at all during a 2022 survey. The Chehalis River originates in Southwestern WA and drains into Gray’s Harbor and the Pacific Ocean. A Washington state fish biologist first reported a large-scale mussel MME in the Chehalis River near Oakville, WA in 2015. Follow-up surveys in 2017–2020 confirmed mass mortality of M. falcata bed.The Skookumchuck River is a tributary of the Chehalis River in WA. While an ongoing mussel MME has been documented in the Chehalis River above and below the Skookumchuck’s confluence with the Chehalis River, we have not observed any unusual levels of mussel mortality in the Skookumchuck. However, in 2022 in another tributary, the Newaukum River, Xerces Society biologists investigated a MME impacting a large G. angulata in the Crooked River from the Smith Rock State Park site (CRO). Mussels were collected whole, wrapped in damp paper towels, and placed in coolers over wet ice with barriers to keep them chilled without freezing. Samples were shipped overnight to a laboratory for processing, where we extracted hemolymph from the anterior adductor muscle sinus, as previously described [G. angulata during a MME in the Owyhee River (OWY) in July 2018 using the same methods. On 8 October 2018, we collected samples from 5 moribund and 5 apparently healthy escribed . SamplesG. angulata. We also sampled 3 apparently healthy G. angulata in the Chehalis River in August 2020 during sampling of the Chehalis River MME site LCM1 described below. All 2020 samples were collected non-lethally in the field using the same method of extracting hemolymph from the anterior adductor muscle sinus. Hemolymph samples were stored immediately in microcentrifuge vials on dry ice in the field before overnight shipping and transfer to −80 °C freezers in the laboratory where they were preserved until further processing. A summary of samples is provided in We returned to the same Crooked River sample site (CRO) on 4 September 2020 and collected an additional 5 moribund and 6 apparently healthy M. falcata from a MME site at RM44 (LCM2) in the Chehalis River on 26 September 2018 near Oakville, OR. On the same day, we sampled 5 apparently healthy M. falcata from the nearby Skookumchuck River at RM2.5 (SKO). On 26 August 2020, we sampled a MME site in the Chehalis River (LCM1) just upstream of the confluence with the Newaukum River near RM75.5, collecting samples from 12 moribund and 13 apparently healthy M. falcata  to extract total nucleic acids and converted RNA to double-stranded cDNA using the Superscript IV system  with random hexamers and NEBNext Ultra II Non-Direction RNA Second Strand Synthesis Module. We prepared DNA libraries using the Nextera XT DNA Library Preparation Kit . We sequenced libraries using a MiSeq instrument.Samples from 2018 were sequenced with V3 chemistry, 600 cycle kit, while 2020 samples were sequenced with V2 chemistry, 300 cycle kit . Samples were sequenced on a total of 7 separate runs. We prepared mussel hemolymph samples for virus sequencing using previously described methods . Briefly−20 to minimize false positive identifications if they did not have equivalent or better matches to eukaryotic, bacterial, or bacteriophage sequences. We annotated putative viral contigs using Cenote-Taker 2 in annotation mode [We demultiplexed reads and quality-trimmed them to ≥Q30 using CLC Genomics Workbench v23.0.2  and we discarded short reads (length < 50 nt). We then used public  and in-hion mode  and the ion mode . We remoion mode ,34.Margaritifera falcata). To avoid confusion in future taxonomic revisions, we did not include taxonomic descriptors in virus names, as per published guidelines [For identified virus contigs, we estimated taxonomy using the DIAMOND BLASTx + MEGAN pipeline ,36 with idelines . For virPhyloseq [We mapped trimmed, quality-filtered reads >50 nt from each individual to all identified virus contigs with a stringency of ≥95% nt identity over ≥95% read length using the “map reads to reference” function in CLC Genomics Workbench. We counted a virus as present in a sample if it contained ≥2 reads mapping to a contig at the specified thresholds. We chose the ≥2 read threshold to avoid counting viruses as present in a sample if they contained only a single mapped read in which the corresponding paired read was discarded due to quality filtering. Read mapping results were used to calculate a measure of viral read abundance normalized by contig length to account for differing target sequence lengths for each virus . The resPhyloseq  in R andG. angulata dataset, we used 2-tailed t-tests to evaluate whether individual viruses were present at higher abundance in case vs. control mussels for both the 2018 and 2020 samples. We then used ANOVA to determine if virus richness differed between any of the case vs. control groups for each year in the Crooked River or the G. angulata group collected from the Chehalis River. We also used a machine learning approach to assess the potential importance of individual viruses in distinguishing case vs. control mussels in the G. angulata dataset. We used the function randomForest  followed by the importance function of the randomForest package [To screen for viruses associated with mortality in each population, we first separated the datasets by species. We used an epidemiologic case–control approach, in which moribund mussels were classified as cases and apparently healthy mussels were classified as controls. Cases were characterized by mussels found to be unburrowed and lying atop the river bottom, often adjacent to other normal burrowed and open, filtering mussels. These mussels tended to respond only slowly when touched, if at all, and appeared to gape slightly open without actively filtering. Controls were observed to be securely burrowed into the river substrate and reactive, closing when handled. For the  package  in R ver package  to modelM. falcata dataset, we first used 2-tailed t-tests as a screening tool to evaluate whether individual virus abundances were significantly different between (1) case vs. control M. falcata sampled in the Chehalis River in 2020; (2) case Chehalis River M. falcata and control Skookumchuck River M. falcata in 2020; and (3) case Chehalis River M. falcata and control Skookumchuck River M. falcata in 2018. We also used ANOVA to assess whether virus richness differed significantly between mussel samples from the following groups: 2018 control Skookumchuck River; 2020 control Skookumchuck River; 2018 case Chehalis River; 2020 control Chehalis River; and 2020 case Chehalis River. As with the G. angulata dataset, we used random forests  to model the relationship between the abundance of each virus and mussel health status.For the metaMDS function  of the vegan package [varpart function of the vegan package in R) to explore the relative influence and proportion of virome variation that could be attributed to collection year, sample site, species, and health status.We used a multivariate approach to assess the complete dataset of all sites, health conditions, species, and years to explore factors influencing the viromes of all mussels in the study. We used nonmetric multidimensional scaling (NMDS) implemented via the  package  in R to Rhabdoviridae family by the International Committee on Taxonomy of Viruses [To investigate the diversity of chemarfal virus 1 within and between populations, we conducted an analysis of the rhabdovirus L gene, consistent with phylogenetic methods designated for the  Viruses . For all Viruses ) to calcG. angulata, 50 M. falcata) from 5 sample sites in 2018–2020. After quality trimming and length filtering, the average sequence depth was 2,065,369 (standard deviation (SD) 1,103,334) reads per individual. From these reads, we identified 557 viral contigs ranging from 1016–16,281 nt  in the Dicistroviridae, and the remaining 5% of Picornavirales distributed among the Picornaviridae, Iflaviridae, Caliciviridae, Noraviridae, and Polycipiviridae. Next, 20% of all contigs were placed within the ssDNA Shotokuvirae, 93% of which were unclassified members of the Cressdnaviricota (Nodaviridae (6%), Tombusviridae (6%), Picobirnaviridae (3%), Circoviridae (2%), Solemoviridae (2%), Astroviridae (1%), Botourmiviridae (1%), and Parvoviridae (1%). These revised taxonomies allowed us to confirm all 557 contigs as viruses and reduced the number of contigs listed as “viruses, unknown” to 8% of the full dataset. 7% of the dataset remained as “Riboviria, unclassified” and 1% of contigs were assigned to the unclassified Cruciviruses-like group. The majority of contigs were only distantly related to known viruses. Initial taxonomic assignments based on the DIAMOND + MEGAN pipeline placed 19.2% (n = 107) of contigs into the “not assigned” group. Of the remaining contigs, the lowest levels of classification placed 11.1% (n = 62) as “Viruses”, 14.7% (n = 82) as “Riboviria” and 18.3% (n = 102) as unclassified viruses most closely related to those of Shi et al.  Figure . Our furviricota . Other tG. angulata, with 21.9% (n = 122) of those exclusively observed in G. angulata; 78.1% (n = 435) of viruses were found in M. falcata, with 32.9% (n = 183) of those unique to M. falcata; 45.2% (n = 252) of viruses were observed in at least 1 individual of both species; 32.0% (n = 178) of viruses were found exclusively from a single study site, while 68.4% (n = 381) of viruses were found in two or more sites. By year of sampling, 71.3% (n = 397) of viruses were observed in 2018, while 91.2% (n = 508) were observed in 2020; 62.5% (n = 348) of viruses were observed in both years.A total of 67.1% (n = 374) of viruses were found in Analysis of the chemarfal virus 1 L gene found 12 individuals with complete coverage, 1 of which was the sample originally used to characterize the virus . The aveG. angulata in the 2018 Crooked River samples. Chemarfal virus 122  and chemarfal virus 1 were found in all 5 cases and in none of the 5 control animals . Chemarfal virus 122 was a nearly complete genome with 98.5% nt sequence similarity to clictilig virus 2, a partial dicistrovirus genome we previously identified in [p = 0.029). None of these viruses were observed in any individuals from the 2018 Owyhee River cases, 2020 Crooked River controls, or 2020 Crooked River cases. Chemarfal virus 122 was observed in all three 2020 LCM1 controls, but neither of the other two viruses was observed in these samples. Both chemarfal virus 122 and chemarfal virus 1 were observed in numerous M. falcata samples from both 2018 and 2020. Only 1 virus  had significantly different viral read depths between cases and controls in the 2020 Crooked River samples, with higher levels observed in the controls (p = 0.038). Crogonang virus 158 was not observed in any other G. angulata samples in 2018 or 2020, though it was present in a single M. falcata sample from the 2020 LCM1 dataset; 16 viruses were observed exclusively in the 3 individuals sampled from the Owyhee River. All vRPM/kb values for individual viruses and G. angulata samples are listed in G. angulata was 80.7 ± 50.9 and did not differ between any of the sample groups (ANOVA p = 0.864)  estimate of error rate of 40.7%, with classification error rates for cases at 38.5% and controls at 42.9%. Variable importance rankings revealed that crogonang virus 1 had a much higher contribution to the model, followed by crogonang virus 117, crogonang virus 159, and crogonang virus 6. All other viruses—including chemarfal virus 1 (ranked as 8th most important)—had relatively lower contributions to distinguishing cases from controls and showed a consistent decrease in importance  read co= 0.864) . Random portance . G. angulata cases collected from the Owyhee River (samples OGH01 through OGH03), we observed 40, 55, and 72 viruses in each individual. We found 83 viruses total from the OWY site, 16 of which were never observed in any other samples from the study. Samples OGH01 and OGH02 had similar viral read distributions, primarily driven by picorna-like viruses of the Pisuviricota followed by the tombus-like and noda-like viruses of the Kitrinoviricota. OGH03 differed from the other two, with the largest proportion (74%) of relative viral reads attributed to ssDNA viruses of the Cressdnaviricota.For the 3 M. falcata dataset, nine viruses had significantly different viral read depths between cases and controls in the 2020 LCM1 samples. Two of these, flactilig virus 1, Nodaviridae and flactilig virus 7, Picornaviridae, had significantly higher levels in the control group, while seven had higher levels in cases. Both flactilig virus 1 and flactilig virus 7 were found at 100% prevalence in LCM1 controls and 0% prevalence in LCM1 cases and were never observed in G. angulata . When comparing 2020 M. falcata viruses between pooled LCM1 samples  vs. 2020 SKO controls, 183 viruses were significantly different between the 2 groups, with 59 at higher levels in the SKO controls and 124 higher in the LCM1 cases. For the 2018 LCM2 cases vs. 2018 SKO controls, 25 viruses were significantly different, with 11 found at higher levels in the SKO controls and 14 at higher levels in the LCM2 cases. Five viruses were significantly elevated in Chehalis River  cases vs. SKO controls in both study years: chemarfal virus 122 (Dicistroviridae), chemarfal virus 265 (Picornaviridae), chemarfal virus 24 (Marnaviridae), chemarfal virus 194 (Cressdnaviricota), and chemarfal virus 273 (Dicistroviridae). None of these 5 viruses differed significantly between 2020 cases and controls at the LCM1 site. Overall mean (±SD) virus richness for M. falcata was 102.3 ± 78.3 and did not differ between any of the sample groups (ANOVA p = 0.064) (p = 0.02), with a prevalence of 100% in cases (n = 6) versus 40% in controls (n = 5). However, this association was not observed in subsequent 2020 sampling, where average virus abundance in SKO controls was slightly higher than—but not significantly different from—that of LCM1 cases (p = 0.53). Similarly, the chemarfal virus 1 level did not differ significantly between LCM1 cases and controls collected from the same site (p = 0.27). All vRPM/kb values for individual viruses and M. falcata samples are listed in M. falcata cases and controls based on virus abundance data performed better than those of the G. angulata models, with an OOB estimate of an error rate of 14%. Classification error for the M. falcata random forest model was 27.8% for cases and 6.3% for controls. Variable importance rankings showed that flactilig virus 1 and chemarfal virus 122 were more important than other viruses. Chemarfal virus 265, crogonang virus 6, and flactilig virus 7 were slightly more important than other viruses, and the remaining viruses had relatively even importance values that steadily decreased throughout the dataset (In the = 0.064) . Chemarf dataset .M. falcata control from SKO in 2020).For both species, the distribution of relative viral read abundances among phyla was generally consistent with their observed richness. For example, the Pisuviricota contained 41% of the 557 contigs we observed and averaged 38% of relative read abundances across all individuals in the study . The CreR2 value of 0.31. Individual R2Adj values and R2Adj values uniquely attributable to individual variables (in parentheses) were as follows: sample site = 0.283 (0.131), species = 0.128 (0.006), sample year = 0.052 (0.013), and health status = 0.024 (0.008). The sample site was the only variable with a large proportion of unique variation explained (13.1%), as most of the 12.8% of variation explained by species occurred in combination with the sample site (10.2/12.8%). Proportions of unique and shared variation explained by each component are shown in NMDS ordination and subsequent visualization of the virome data for all samples are shown in M. falcata and showed significant associations with the control mussels. Both of these viruses were previously identified at high prevalence in multiple populations of mucket (Actinonaias ligamentina) from the Midwestern US across 3 consecutive years, suggesting that they may represent common members of a core virome for some mussel species. We found Western US mussels had high virus richness in comparison to our previous investigations of viruses in MMEs of Eastern US mussels. For example, in a study of 29 muckets (A. ligamentina) from three watersheds in the Midwestern and Eastern US over several years, we found 38 viruses using similar methods [M. falcata was 102.3 ± 78.3, and 80.7 ± 50.9 for G. angulata. While differences in sequencing depth, number of study sites and species, and other factors may partially account for some of the differences observed in virus richness between studies, it is possible that Western US mussels harbor higher viral diversity than mussels of the Eastern US.We identified 557 virus contigs from 77 mussels sampled at 5 sites throughout several major Northwestern US watersheds in 2018–2020. The majority of these viruses are uncharacterized (+)ssRNA viruses and are distantly related to viruses found in previous studies of invertebrates (including freshwater mussels)  and aqua methods . In thatG. angulata and M. falcata. In particular, chemarfal virus 1 was significantly higher in 2018 cases than controls for both species. While chemarfal virus 1 was present at very high levels in many samples, our 2020 samples found it in a number of controls from MME sites as well as controls from sites with no mortality observed. Indeed, in the full study, we found no viruses that were significantly elevated in G. angulata cases vs. controls across both years. Similarly, for the M. falcata data, no viruses were consistently significantly elevated in cases vs. controls across all years and sites. Collectively, our statistical analyses of the two species’ viromes found few differences between cases and controls. Virus richness did not differ between any of the sample groups in the study. For both mussel species, random forest misclassifications for cases were relatively high , though classification was much more accurate for controls in the M. falcata dataset (6.3%) than in the G. angulata dataset (42.9%). This is likely due to sampling design, as the M. falcata dataset included 14 samples from a separate control site (the Skookumchuck River). When comparing the results of cooccurring cases and controls, we found no consistent differences in virus abundance that suggested a potential etiologic role for viruses in the observed MMEs.Our initial results from 2018 suggested several potential viruses were associated with cases for both Vesiculovirus Indiana (vesicular stomatitis Indiana virus) is a well-studied virus that causes disease in livestock and wild mammals but causes little or no cell death in its persistently infected insect vectors [Our results show that the rhabdovirus chemarfal virus 1 (CHMFV-1) is not likely associated with disease and regularly occurs at high levels in individuals and populations exhibiting no outward signs of morbidity or mortality. This observation concords with previous data on rhabdoviruses in invertebrates. Many rhabdoviruses are vector-borne, with arthropods serving as vectors that transmit the viruses to animals or plants . Althoug vectors . Similar vectors . The low vectors .Picornaviridae in wild rodents exhibited fluctuations in richness and evenness over time that were associated with changes in host density, ambient temperature, rainfall, and humidity. Campbell et al. [Albula vulpes) differed across areas of the Caribbean, with viral richness and load highest in the most anthropogenically impacted locations. In a study of the cave-dwelling freshwater mussel Congeria kusceri from the Balkan Peninsula, Scapolatiello et al. [We found that approximately 1/3 of the variation in mussel virome composition could be explained by species, site, date of sampling, and health status (case versus control). The majority of this variation was explained by location (sampling site). When accounting for the effects of other variables, the variation uniquely attributable to time, species, and health status totaled only 2.6%. Many large-scale studies of viromes are based on cross-sectional surveys at a specific place and time with little ecological context , but thil et al.  found sil et al.  found tho et al.  found a o et al. . While wo et al. , histopao et al. , metabolo et al. , and anao et al. . Ostreid herpesvirus 1 (OsHV-1) and its variants [Emerging viruses have been implicated in wild and captive animal mortality events worldwide. Mass mortality events due to infectious disease in shrimp aquaculture cost USD ~1 billion in losses annually  and are variants . These ovariants . Indeed,variants ,10 and bvariants ,12, withRapid population declines and the growing list of extinct freshwater mussels represent a major global conservation crisis. Population augmentation via captive propagation and/or wild animal translocations is the primary tool for preventing extinctions and restoring populations . The nee


Global food production is at risk from many abiotic and biotic stresses and can be affected by multiple stresses simultaneously. Virus diseases damage cultivated plants and decrease the marketable quality of produce. Importantly, the progression of virus diseases is strongly affected by changing climate conditions. Among climate-changing variables, temperature increase is viewed as an important factor that affects virus epidemics, which may in turn require more efficient disease management. In this review, we discuss the effect of elevated temperature on virus epidemics at both macro- and micro-climatic levels. This includes the temperature effects on virus spread both within and between host plants. Furthermore, we focus on the involvement of molecular mechanisms associated with temperature effects on plant defence to viruses in both susceptible and resistant plants. Considering various mechanisms proposed in different pathosystems, we also offer a view of the possible opportunities provided by RNA -based technologies for virus control at elevated temperatures. Recently, the potential of these technologies for topical field applications has been strengthened through a combination of genetically modified (GM)-free delivery nanoplatforms. This approach represents a promising and important climate-resilient substitute to conventional strategies for managing plant virus diseases under global warming scenarios. In this context, we discuss the knowledge gaps in the research of temperature effects on plant-virus interactions and limitations of RNA-based emerging technologies, which should be addressed in future studies. Global warming has been predicted to continually worsen this century due to increasing greenhouse gases in the atmosphere  was shown to increase in a linear fashion as temperature increased from 13 °C to 23 °C -infected N. tabacum  disease in chili was correctly predicted based on simulation of temperature effects on Elevated temperatures can affect plants, viruses, and their interactions, which may influence within-host virus dynamics  and in turn affect symptoms  in the defence against RNA viruses  at higher temperature of 27 °C but not below 24 °C  transgenic N. tabacum at 26–29 °C  , which underwent natural recovery 23–25 days post-infection (dpi). Results showed that the amount of full-length viral RNA was similar in all infected tissues, including recovered leaves. However, when green fluorescent protein (GFP)-silenced transgenic plants were infected with ORMV that encodes the strong VSR p125, a non-fluorescent phenotype was established in recovered leaves and a fluorescent phenotype persisted in symptomatic mature leaves. These results suggest that recovery from symptoms was concomitant with reduction of VSR activity, but not with viral RNA clearance , rdr2, and nuclear RNA polymerase D (nrpd) (Pol IV subunit) led to non-recovered or weakly recovered plants. On the contrary, enhanced recovery was observed in dcl3 mutant plants. In addition, this enhanced recovery was impaired in dcl3- dcl4 double mutants. This suggests that the recovery is associated with DCL4-dependent PTGS instead of DCL3-dependent TGS. Interestingly, even though recovery symptoms were independent of TGS pathway, the involvement of the other two TGS components, RDR2 and PolIV is required. Given the important function of RDR2 and PolIV in maintenance and spread of silencing -infected A. thaliana   type. These NLR proteins contain a nucleotide-binding (NB) domain, a C-terminal leucine-rich repeat (LRR) domain and a N-terminal domain Cesari . During Environmental variables, including temperature, need to be considered as factors affecting viral pathogenicity and degrees of plant resistance when plants carry major-effect resistance genes in different genetic backgrounds  or the typical TSWV systemic symptoms at 32 °C  was suppressed at 34 °C. Interestingly, instead of completely compromising resistance at this elevated temperature, plants retained resistance to TMV through enhanced antiviral RNA silencing and exhibited no HR on inoculated leaves  is a non-coding gene that is transcribed into non-coding RNA containing a sequence that is partially complementary to miR399; three nt mismatches are located in the expected miR399 cleavage site. When miR399 pairs with IPS1 transcripts, the 3 nt mismatches in IPS1 transcripts form a central bulge to block the activity of miR399. Therefore, miR399 is arrested by TM (IPS1) and fails to cleave its intended target PHOSPHATE2 , has been used to attract or decoy sRNAs instead of amplifying them -associated protein 9 (CRISPR-Cas9) is a dual-factor system that consists of single guide RNA (sgRNA) and Cas9 DNA endonuclease , termed “BioClay”, have been successfully combined with dsRNAs and applied to protect tobacco plants from virus infection. Rather than being internalized, BioClay is broken down when exposed to atmospheric carbon dioxide and moisture. Therefore, this carrier served to protect dsRNAs from degradation on leaves rather than facilitate penetration of dsRNAs across the plasma membrane , vector behaviors , virus movement and host plant traits. Notably, virus accumulation in individual plants can influence not only symptom expressions but also viral transmission, which emphasizes the importance of plant-virus interactions in virus epidemics. Given these considerations, gaining an overview of temperature effects on plant-virus interactions is highly critical for developing virus control strategies that are resilient to global warming.The development of virus-resistant crop varieties has been a traditional and effective way to manage plant virus diseases in agriculture for decades. However, both conventional breeding strategies and strategies augmented with molecular techniques, including QTL mapping and marker-assisted selection, are laborious and time-consuming endeavours. Considering projected elevated temperature effects, it should be noted that plant breeding may be under more immense pressure than previously due to negative effects of higher temperatures on R gene-mediated resistance to viruses in many pathosystems. This anticipated breakdown of R gene-mediated resistance to viruses may decrease resistance durability, which in turn poses challenges for controlling plant virus diseases through breeding strategies at elevated temperatures. Owing to this concern, climate-resilient approaches, and robust platforms for capturing the most complete picture of genetic variations are needed to speed up the development of ‘temperature-insensitive’ virus-resistant crops or to lower the rate of resistance-breakdown at elevated temperatures.Research that aims to increase plant resistance to viruses by applying nanocarrier-mediated modifications can potentially overcome the above challenges. Nanoplatforms for delivering biomolecules are also considered as promising substitutes to plant genetic engineering. As noted earlier, genetic engineering can be a relatively efficient approach to improve plant resistance or tolerance to biotic or abiotic stresses. However, generation of GM crops by genetic engineering is lacking broad public acceptance even though some GM crops have been commercialized for many years and shown to be safe. Considering this public acceptance issue and the challenges for conventional breeding, GM-free and less time-consuming NP delivery systems, therefore, attract much scientific and commercial attention. So far, sprayable RNA biopesticides that trigger RNAi are the most well-studied cargos delivered by NPs into plants for controlling plant viruses. Since antiviral RNAi has been shown to be enhanced by elevated temperatures in either virus-susceptible or virus-resistant plants, exogenous application of NP-conjugated RNA for SIGS induction appears to be a promising strategy for plant virus management in global warming scenarios. Interestingly, the choice of cargo for conjugated NPs may not be limited to siRNAs or dsRNAs, but may be expanded to miRNAs, CRIPR/Cas9 plasmids, or ssRNAs, such as STTM, making nanocarriers a versatile platform for virus control during climate change.Due to the reality of inevitable changes of our climate, an increased effort to understand temperature effects on virus epidemiology and molecular plant-virus-vector interactions is needed. Considering the growing and comprehensive knowledge of molecular plant-virus-vector interactions, we believe a combination of RNA-based virus resistance technologies and NPs will provide promising strategies for virus control at elevated temperatures. Based on RNA-based analyses or capturing the landscape of genetic diversity, this strategy can be applied to trigger antiviral RNAi, regulate the expression of endogenous genes, or perform gene editing without the need to generate GM crops. Future studies should concentrate on understanding the efficiency of NP-mediated RNA-based technology at both lab and field scales, evaluating off-target risks, and optimising application time, to allow this strategy a sustainable success at agricultural field scale in the foreseeable future.