# Beneath the surface: Unsolved questions in soil virus ecology

Christina Hazard, Karthik Anantharaman, Luke Hillary, Uri Neri, Simon Roux, Gareth Trubl, Kurt Williamson, Jennifer Pett-Ridge, Graeme Nicol, Joanne Emerson

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## To cite this version:

Christina Hazard, Karthik Anantharaman, Luke Hillary, Uri Neri, Simon Roux, et al., Beneath the surface: Unsolved questions in soil virus ecology. Soil Biology and Biochemistry, 2025, 205, pp.109780. 10.1016/j.soilbio.2025.109780. hal-05409008

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Submitted on 10 Dec 2025

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Beneath the surface: Unsolved questions in soil virus ecology

Christina Hazard<sup>a, *, †</sup>, Karthik Anantharaman<sup>b, †</sup>, Luke S. Hillary<sup>c</sup>, Uri Neri<sup>d</sup>, Simon Roux<sup>d, †</sup>,
Gareth Trubl<sup>e</sup>, Kurt Williamson<sup>f</sup>, Jennifer Pett-Ridge<sup>e,g,h</sup>, Graeme W. Nicol<sup>a</sup>,
Joanne B. Emerson<sup>c, **</sup>

ARTICLE INFO

Keywords:

Soil viruses

RNA viruses

Viral ecology

Metagenomics

Viromics

Biogeochemical cycling

A B S T R A C T

Soil virus ecology is an exciting but still nascent field of research in soil microbiology. While there has been a recent surge in soil virus research studies, many fundamental questions remain unanswered, and a range of technical and bioinformatic challenges need to be overcome. In this perspective article, we present a series of key questions that highlight fruitful research areas for ongoing and future efforts. These include describing the challenges involved in understanding soil viral abundance and activity, spatiotemporal dynamics, life strategy prevalence, virus-mediated biogeochemical impacts, viral protein function, host prediction, and soil RNA virus discovery. In the near term, combining approaches (e.g., cultivation-based, meta-omics, biogeochemical, experimental, and bioinformatic) will be key to assessing the ecological and biogeochemical impacts of soil viruses from the microscopic to the field and global scales. Still, we stress that results must be tempered by current methodological limitations and highlight knowledge gaps that are most pressing to fill via new methods or measurements, such as the prevalence of different viral replication strategies across soils, the fate of microbial necromass carbon after viral lysis, the frequency of virus-host encounters that do not lead to successful infections yet could be bioinformatically mistaken as infections, and the diversity and ecological impacts of RNA viruses in soil.

**1. Introduction**

Soils are inhabited by an enormous diversity of microorganisms,
which contribute to soil and plant health and global biogeochemical
cycles. Soil viruses are approximately as abundant and diverse as their
microbial hosts (Williamson et al., 2017; Roux and Emerson, 2022), but
they have received comparatively less attention. Understanding the
impact of soil viruses on host physiology and ecological processes is
approximately a decade behind research conducted in marine systems.
The physicochemical heterogeneity of soils, combined with high levels
of previously uncharacterized viral diversity and complex, multitrophic

microbiomes, makes it difficult to extract and enumerate viruses and
unambiguously identify and assemble viral genomic signatures. Soil
virus ecology is therefore a nascent field of study but has gained
considerable momentum in recent years. The rapid development of
bioinformatic tools for identifying viral genomes in metagenomes and
metatranscriptomes (Kieft et al., 2020; Guo et al., 2021), together with
advancements in viromics (sequencing of the viral fraction) (Roux and
Emerson, 2022), has led to the discovery of vast soil virus diversity (Neri
et al., 2022; Ma et al., 2024) and facilitated prediction of the potential
functional importance of viruses in soil microbiomes and biogeochem-
ical cycles (Emerson et al., 2018; Trubl et al., 2018; Starr et al., 2019).

This article is part of a special issue entitled: Soil virus ecology published in Soil Biology and Biochemistry.

* Corresponding author.

** Corresponding author.

E-mail addresses: christina.hazard@univ-lyon1.fr (C. Hazard), jbemerson@ucdavis.edu (J.B. Emerson).

<footnote><sup>a</sup> Université Claude Bernard Lyon 1, CNRS, INRAE, VetAgro Sup, Laboratoire d'Ecologie Microbienne, Villeurbanne, 69622, France</footnote>

<footnote><sup>b</sup> Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53705, USA</footnote>

<footnote><sup>c</sup> Department of Plant Pathology, University of California Davis, Davis, CA, 95616, USA</footnote>

<footnote><sup>d</sup> Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA</footnote>

<footnote><sup>e</sup> Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA</footnote>

<footnote><sup>f</sup> Biology Department, College of William & Mary, Williamsburg, VA, 23185, USA</footnote>

<footnote><sup>g</sup> Department of Life and Environmental Sciences, University of California Merced, Merced, CA, 95343, USA</footnote>

<footnote><sup>h</sup> Innovative Genomics Institute, University of California Berkeley, Berkeley, CA, 94720, USA</footnote>
Despite this recent burst in soil virus ecology research, fundamental questions remain, and many technical and bioinformatic challenges must be overcome. These range from quantifying viral abundance, activity and turnover in soil, to explaining spatiotemporal distribution patterns and identifying impacts on microbial communities and biogeochemical processes. The continued development of computational approaches to facilitate detection of DNA and RNA virus populations and for comprehensive identification of host-virus linkages and virus protein function remains a significant obstacle. In this perspective, we present a series of key questions to highlight the requirement for innovative scientific efforts for advancing the state-of-the-art in the next decade of soil virus ecology research, and we present our outlook on the current and future state of the field.

## 2. How abundant are viruses in soils?

The first estimates of viral particle abundance in soils were published in 2003, initially using electron microscopy (Ashelford et al., 2003) and eventually shifting to epifluorescence microscopy to visualize and quantify virus-like particles extracted from soils. Using this approach, viral abundances have been reported for a wide variety of soils, including agricultural (Williamson et al., 2005, 2007; Swanson et al., 2009; Amossé et al., 2013; Chen et al., 2014), forested (Williamson et al., 2005, 2013; Helsley et al., 2014), peatlands (Trubl et al., 2016), hot desert (Gonzalez-Martin et al., 2013), cold desert (Williamson et al., 2007), and others (Williamson et al., 2017). While earlier reports relied upon single observations, more recent studies have begun to include time series over annual cycles (Narr et al., 2017; Roy et al., 2020; Cornell et al., 2021; Jacoby et al., 2024), establishing an understanding of viral dynamics (largely in agricultural soils). A meta-analysis (Williamson et al., 2017) and time series studies (Roy et al., 2020; Cornell et al., 2021; Jacoby et al., 2024) all found significant correlations between soil viral and bacterial abundances, which seem to suggest the majority of soil viruses infect bacterial hosts. However, this correlation is likely skewed since epifluorescence-based counts are biased toward icosahedral virions (virus particles) with larger dsDNA genomes, and unlikely to include filamentous virions and virions with genomes <6 kb in size, particularly neglecting ssDNA and RNA viruses (Steward et al., 2013). While important modifications have been reported to maximize particle extraction from different soils (Trubl et al., 2016) there have been few recent developments in microscopic approaches for estimating viral particle abundance in soil extracts in the past two decades. Modifications, optimizations, or new techniques in visualizing virus particles extracted from soils will be instrumental in improving our understanding of viral abundance in soils and the factors that govern it. Beyond microscopy-based approaches, viral sequence-based abundances (i.e., viral OTU abundances) and viromic DNA yields can also provide relative comparisons of viral abundance between soils within a study (Santos-Medellín et al., 2023) but it is less clear if such approaches can be used to compare across independent studies. Regardless of the approach, the proportion of viruses that are systematically missed in current estimates of viral particle abundance in soils is unknown.

Viral abundance in soils is likely the product of interactions between several poorly constrained factors, including host abundance, viral production rate, and viral decay or loss rate. The proportion of viral abundance as measured by epifluorescence or sequence-based counts that represents infectious particles capable of replicating within a susceptible host is unidentifiable. Mechanisms by which extracellular viral particles encounter and infect new hosts in the highly structured soil microenvironment, and the rate at which new viruses are produced, and the time it takes for the current populations of viruses in a given volume of soil to decay and be replaced by the production of new particles is largely unknown. We are in need of creative and novel approaches to estimate *in situ* viral infection and production rates in soil. While limited in scope and scale, one recent study attempted to quantify viral decay rates in soil (DiPietro et al., 2023), reporting that infectious

bacteriophages can persist in soils for several weeks in the absence of host cells. This is, on average, twice as long as the persistence of aquatic bacteriophage isolates in sterile aquatic microcosms (DiPietro et al., 2023). Beyond individual phage isolates, community-level estimates of marine viroplankton decay rates indicate that virus particles only persist in the water column for 1–2 days (Noble and Fuhrman, 2000). Such results suggest significant differences between aquatic and terrestrial phage ecology, particularly with respect to the balance between viral production and loss rates, and the importance — or lack thereof — of viral-mediated bacterial mortality. Much work remains in determining the factors and processes that regulate viral abundance in soils.

## 3. Why do soil viral communities turnover so rapidly over space and time?

Viromic analyses have revealed extreme soil viral community heterogeneity over space and time, including temporal turnover over hours to days and spatial turnover over meters (Santos-Medellín et al., 2021; Ter Horst et al., 2021, 2023; Durham et al., 2022; Santos-Medellín et al., 2022, 2023; Fudyma et al., 2024). Particularly within habitats — where heterogeneity in both biotic and abiotic environmental factors is low, compared to between habitats — viral communities tend to follow distance-decay patterns, becoming increasingly dissimilar over spatial and/or temporal distances (Santos-Medellín et al., 2021; Santos-Medellín et al., 2022, 2023). Over larger spatial scales, evidence suggests that soil viral communities are more similar within than between habitats, broadly defined to include soil physicochemical properties, plant community composition and phenology, and host (microbial) ecology and physiology (Srinivasiiah et al., 2015; Emerson et al., 2018; Trubl et al., 2018; X. Liang et al., 2020; Chen et al., 2021; Ter Horst et al., 2021; Durham et al., 2022; Ter Horst et al., 2023). For example, soil viral communities vary according to known patterns for other soil microbiota, including by pH, moisture content, depth, and between bulk and rhizosphere soil habitats (Adriaenssens et al., 2017; Fierer, 2017; Emerson et al., 2018; Starr et al., 2019; Van Goethem et al., 2019; X. Liang et al., 2020; Lee et al., 2021; Santos-Medellín et al., 2021; Ter Horst et al., 2021; Wu et al., 2021b; Lee et al., 2022; Roux and Emerson, 2022). Although more explicit temporal sampling designs are needed, significant shifts in soil viral community composition and/or abundance have been observed over months in agricultural fields, forests, grasslands, and peatlands (Roy et al., 2020; Cornell et al., 2021; Santos-Medellín et al., 2021; Ter Horst et al., 2021; Trubl et al., 2021) and over hours to days in soil microcosms (Van Goethem et al., 2019; Nicolas et al., 2023; Santos-Medellín et al., 2023). How soil viral community successional patterns proceed beyond a few days or months, and how viral populations, communities and successional patterns proceed predictably year-to-year in most soil habitats is unknown.

The strength of the soil viral distance-decay relationship is thus far generally not observed in host prokaryotic communities from the same samples (Santos-Medellín et al., 2021; Santos-Medellín et al., 2022, 2023), suggesting that viruses and their hosts have different turnover times, community assembly processes, and/or perhaps reflect different scales of measurement. For example, 'species' definitions are inherently different for viruses and hosts, so comparing patterns for both at the species level may or may not be appropriate. While stronger distance-decay relationships for viruses compared to prokaryotic hosts seem to be consistent across phylogenetic scales thus far (to the extent that different phylogenetic scales can be measured for viruses (Bolduc et al., 2017; Bin Jang et al., 2019)), strain-level differences in infection dynamics and host range have been demonstrated in many pure culture studies, and microbial microheterogeneity measurements (sub-population dynamics) over space can show distance-decay relationships (Cho and Tiedje, 2000; Andam et al., 2016; Crits-Christoph et al., 2020), suggesting that fine-scale phylogenetic resolution of host populations may reveal more tightly coupled spatiotemporal dynamics between viruses and their hosts. We are also likely measuring different scales of
activity in viromes and total DNA, in that viromes (derived from DNA contained inside virions) seem to capture very recent infections, consistent with short residence times for viral particles (Noble and Fuhrman, 1997; Bongiorni et al., 2005; Corinaldesi et al., 2010; Dell'Anno et al., 2015), while measurements from total DNA include dead and dormant organisms (Carini et al., 2016, 2020; Lennon et al., 2018). This activity disconnect would benefit from further investigation, as already considered for comparisons of distance-decay relationships in prokaryotic and tree communities (Meyer et al., 2018), for example with paired viromes and active microbiota measurements, such as transcriptomics and stable isotope probing (SIP) on the same soil samples. Another intriguing possibility is that the observed differences in virus and prokaryotic host spatiotemporal patterns are real, for example, reflecting spatially compartmentalized infections of microcolonies that drive local blooms in viral population abundances but that do not reach other microcolonies, resulting in an ephemeral pulse of the viral population but no measurable difference in the host population over the spatial scale of a ~1–10 g soil sample.

Measuring viral impacts on important ecosystem processes, such as biogeochemical cycling, is complicated when the dominant signals in *in situ* datasets from the field so often seem to be space and time. Highly replicated microcosm experiments that start with homogenized soils, followed by perturbations of interest seem essential and have already been demonstrated for interrogating soil viral responses to wet-up of dry soil (Van Goethem et al., 2019; Roy et al., 2020; Nicolas et al., 2023; Santos-Medellín et al., 2023), as well as carbon (C)-cycling (Lee et al., 2021; Albright et al., 2022) and nitrogen (N)-cycling (Lee et al., 2023) processes. For field-scale measurements, coarse-grained network analyses that group viruses at taxonomic levels much higher than species can be leveraged to reveal taxonomically conserved trait-based responses to environmental conditions (e.g., an increase in the abundance of putative actinobacteriophages in low-moisture grasslands (Santos-Medellín et al., 2022)) that might otherwise be masked by the spatial and temporal structuring of soil viral communities. Overall, new laboratory microcosm experiments that consider a range of temporal scales (particularly from days to months) and field sampling efforts to capture inter-annual time scales (e.g., weekly-to-monthly sampling for several years), as well as field collection and analysis across spatial scales (particularly from centimeters to tens of meters) would substantially advance our understanding of the spatiotemporal scales of soil viral community turnover.

## 4. How prevalent are different viral life cycles (lysis, lysogeny, others) in soils?

While extreme viral diversity is emerging as the rule in soil (Roux and Emerson, 2022), maintenance of that diversity is puzzling, given that most viral particles decay quickly (Heldal and Bratbak, 1991; Noble and Fuhrman, 1997; Bongiorni et al., 2005; Corinaldesi et al., 2010; Dell'Anno et al., 2015; Roy et al., 2020). Soil microbial cells can go dormant, forming seed banks that help to buffer populations from local extinction in fluctuating environments (Fierer and Lennon, 2011; Lennon and Jones, 2011; Lennon et al., 2021), and until very recently, the assumption in soil has been that viral populations piggyback on microbial dormancy in harsh environments (such as dry soils) via lysogeny as integrated prophages (Pantastico-Caldas et al., 1992; Marsh and Wellington, 1994; Ghosh et al., 2008; Kimura et al., 2008; Emerson, 2019; Wu et al., 2021b). However, most known phages are exclusively lytic and incapable of lysogeny, which requires specific genes for integration/excision from the host genome (Howard-Varona et al., 2017), and thus far, lysogeny genes (e.g., integrases, excisionases, and/orRESSORS) seem to be at low abundance in soil viromic sequencing data (Emerson et al., 2018; Trubl et al., 2018, 2021; Nicolas et al., 2023; Santos-Medellín et al., 2023). This would seem to suggest that lysogeny could be less prevalent in soil than previously assumed. A little-studied phenomenon, pseudolysogeny (arrested infection), may provide a

mechanism for lytic virus desiccation tolerance, whereby viral genomes remain inside host cells, stalled in mid-infection. Stalled pseudolysogenic infections can resume when conditions allow, and they are a hallmark of phage infections under nutrient-limiting conditions in culture, including in soil *Bacillus* phage-host systems (Schwartz et al., 2022). Pseudolysogeny offers the potential for lytic viral dormancy and seed banks that promote viral persistence and diversity in soil. If pseudolysogeny (arrested infections inside host cells) is a common soil viral survival mechanism, as we suggest here, then exclusively lytic viruses may dominate in soil, driving substantial microbial turnover, just as they do in the oceans (Suttle, 2007; Danovaro et al., 2011). The hypothesis of virulent viral dominance in soil is consistent with the observed high spatiotemporal dynamics — in order to observe so many different viral species over space and, particularly, time, there must be rampant boom and bust cycles.

We expect lytic infections to dominate in active soil microbial communities and for arrested lytic infections via pseudolysogeny to be the dominant viral survival strategy in dry soils, but temperate phages are certainly also present in soils (with some evidence for a high incidence of lysogeny in relatively deep soils (X. Liang et al., 2020)), and how to methodologically investigate these processes is an open question. To allow for a more thorough investigation of the relative proportions of different life strategies across terrestrial systems and over space, time, and environmental conditions, we can take advantage of the hallmarks of each infection strategy to recognize and track different life strategies across datasets. For example, integrated prophages can be putatively assigned as temperate and currently in a lysogenic state (Howard-Varona et al., 2017) if they are flanked by host genomic regions in metagenomes (of course, defective prophages may be difficult to distinguish), pseudolysogeny should include viral genomes inside host cells but separate from host genomes, and free viral particles (in the virion stage) can be inferred through detection in viromes. Testing the prevalence of pseudolysogeny in complex soil community datasets will be challenging, as some lysogenic viral genomes are extrachromosomal (Howard-Varona et al., 2017), divergent genes for lysogeny may be unrecognizable, and assembly may be insufficient to observe host genome flanks of some integrated proviruses in total metagenomes. We posit that pseudolysogeny can be putatively inferred as likely if most dry season viral genomes that are relatively complete (Roux et al., 2019; Nayfach et al., 2021) are in cellular (as opposed to viromic) fractions, not integrated in host genomes, and do not contain known genes for lysogeny.

## 5. What does viral activity mean in the context of soils?

Understanding the “activity” of viruses in soil ecosystems is challenging. For microorganisms, activity typically refers to growth or metabolism, and researchers commonly use terms like fast or slow grower and dormant to indicate various levels of activity (Lennon and Jones, 2011; Joergensen and Wichern, 2018; Marschmann et al., 2024). While viruses reproduce, they do not have their own metabolism, and instead, hijack their host’s metabolism to replicate, meaning that we cannot readily use metabolism as a measure of viral activity.

Although there is no viral metabolism, viral manipulation of host metabolism could provide useful insights into viral activity. The extent to which a virus manipulates its host's metabolism depends on several factors, including the type of infection (e.g., lysogenic or lytic) (Howard-Varona et al., 2017), the efficiency of the infection (e.g., pseudolysogeny) (Łoś and Węgrzyn, 2012), the quantity of viral genes expressed (Howard-Varona et al., 2020), the differential expression of host genes (Howard-Varona et al., 2020), and whether the virus carries auxiliary metabolic genes (AMGs) (Sun et al., 2023). These factors collectively influence viral turnover, which refers to the rate at which viruses infect host cells, replicate, release new virions into the environment and degrade.

Viral turnover rates, to the extent that they are measurable, could
also be a useful activity metric. While the number of virions in a soil ecosystem may not represent viral activity in terms of current infections, in some cases, the presence of a virion could indicate recent viral production (recent activity), as previously suggested, given likely high viral turnover rates in some soils (Santos-Medellín et al., 2023). Two important aspects of viral turnover are how long a virion can remain intact in the environment and its burst size, or the number of new viruses released from a single infected host cell. Virion stability appears to be specific to the virus type and burst size can also vary widely depending on the virus type, host species, and environmental conditions. Due to the limited number of isolated viruses from soils and difficulties with direct enumeration of viruses in soils, little is known about the range of soil virus burst sizes (Trubl et al., 2020). To overcome this limitation, a recent study modeled several scenarios of viral contributions to microbial mortality using a range of viral burst sizes (based on data obtained for isolated soil viruses) (Nicolas et al., 2023), and further empirical and modeling-based estimates will help to constrain the range of viral burst sizes in soil.

The use of stable isotopes of carbon substrates (e.g.,¹³C organic (Barnett and Buckley, 2023)), inorganic substrates (Lee et al., 2021), or ¹⁸O-enriched H₂O (Trubl et al., 2021) can be used to examine active viruses *in situ*. As viruses are entirely composed of host-assimilated elements, the transfer of isotopically enriched elements to a virus is a valuable approach for demonstrating activity, particularly when used over suitable temporal scales. The presence of stable isotopes in virus nucleic acid (usually dsDNA) can indicate recent cell mortality and the release of newly formed virions (when they are confidently predicted to be derived from virulent viruses), or can demonstrate potential activity of integrated viruses contributing to host metabolism when present in isotopically enriched integrated provirus genomes (Lee et al., 2023). The use of stable isotopes may be a helpful tool for examining virus persistence or turnover, e.g., after soil rewetting (Nicolas et al., 2023) or the removal of a substrate.

## 6. What effect does the viral shunt have on the soil carbon cycle?

Given the staggering abundance and diversity of soil viruses, many researchers have hypothesized a major role in soil C-cycling (Kuzyakov and Mason-Jones, 2018; Sokol et al., 2022; Nicolas et al., 2023), analogous to marine systems, where viruses sustain up to 55% of bacterial production via the ‘viral shunt’ (Wilhelm and Suttle, 1999; Suttle, 2007). Lysis of bacterial cells releases microbially-derived C, which is either quickly mineralized by other microbes (and thus potentially ‘shunted’ away from higher trophic levels) or interacts with soil minerals. In marine systems, the released C has also been proposed to contribute to a ‘viral shuttle’ (Weinbauer, 2004; Guidi et al., 2016; Sullivan et al., 2017), whereby sticky microbial lysates facilitate aggregation and sinking of C and viral particles to the deep ocean. Although sedimentation would not occur analogously in soil, necromass from virus-lysed microbial cells could contribute to soil aggregation. But how viral lysis influences the composition and C-cycle functionality of host microbes, the rate of C-cycling, the specific pools of C that interact with soil minerals, and consequently, the persistence of C in soil remains largely unknown.

The amount of soil C released due to the activity of soil viruses depends on highly complex ecological interactions and environmental dynamics that facilitate or hinder infections and/or trigger lysogenic to lytic transitions. Viral infections have been characterized by Lotka-Volterra and Red-Queen models in marine systems (Thingstad, 2000; Ignacio-Espinoza et al., 2020), potentially leading to pulses of C release. Hints of analogous patterns have been observed in soil, for example, a considerable estimated proportion of microbial cell death was attributed to viral activity in the week after a ‘wet-up’ of extremely dry soil (Nicolas et al., 2023); this was concurrent with a large pulse of mineralization-driven carbon dioxide (CO₂) loss (‘Birch Effect’) (Birch,

1958). It is tempting to link at least some of this soil C loss (which can equal the system's net annual CO₂ exchange (Xu et al., 2004)) to viral liberation of microbial biomass C. Other studies have observed higher organic matter-derived CO₂ loss, and changes in microbial C use efficiency, in soils where the viral load was experimentally increased (Albright et al., 2022; Osburn et al., 2024). However, empirical evidence on soil viral pools and fluxes is sorely lacking, and there is a compelling need for more direct measurements of soil viral biomass, active viruses, lytic activity and the number (or biomass) of hosts affected; these datapoints are critical to accurate upscaling and modeled estimates. New research on the rates of bacterial C release caused by viruses may benefit from combining isotope tracing and meta-omics data with quantitative biogeochemical modeling.

Viral infection may also shape the soil C-cycle by changing host community composition or functionality. In an arctic soil incubated at the brink of metabolic viability (−1.5 °C and anoxic), a measurable CO₂ efflux was found, and a small portion of the microbial community was actively involved in fermentation and organic matter degradation (Trubl et al., 2021). In contrast, there were many active viral populations — one third of which could be directly linked to the active bacterial populations. Other viral features may promote metabolic functional diversity and shape host microbes' phenotypes through more indirect means, by introducing new genetic material and regulating the host's mobilome (mobile genetic elements such as transposable elements, plasmids, and genomic islands). These can carry AMGs that benefit the host organism, e.g., by promoting catabolism of organic pollutants (van der Meer and Sentchilo, 2003), nitrogen fixation (Sullivan and Ronson, 1998), biofilm formation (Drenkard and Ausubel, 2002), or antibiotic resistance (Li and Wang, 2021). Several AMGs may influence the soil C-cycle by generating the ability to break down complex carbohydrates (Emerson et al., 2018; Trubl et al., 2018) or affecting methane metabolism (Zhong et al., 2024).

The stock of organic C in Earth's soils is enormous — larger than the combined carbon pool of atmospheric and terrestrial vegetation (Jobbagy and Jackson, 2000; Todd-Brown et al., 2013; Georgiou et al., 2022) — yet the factors that control its dynamics remain elusive (Georgiou et al., 2024). In soils, microbes die in many ways - viral lysis, predation, autophagy, abortive infection, cytolysis, apoptosis — and it seems probable that the mechanism of microbial death may influence necromass biochemistry (Ankrah et al., 2014) and persistence of cellular C. It is increasingly recognized that residues of dead microbial cells, ‘necromass’, may be as important to soil C turnover and persistence as the activities of living microorganisms (Fernandez and Kennedy, 2016; Kallenbach et al., 2016; C. Liang et al., 2020). Whether particles of viral detritus (empty capsids, nucleic acids) are also a meaningful component of soil necromass remains unclear, although tantalizing to consider. Soil virions are colloidal in size and certainly small enough to become entrapped within nano-pores (Liang et al., 2017; Kuzyakov and Mason-Jones, 2018). While the size of the soil viral detritus pool might be quantitatively small, it is likely a relevant stock of soil phosphorus (Jover et al., 2014), and the C sink for otherwise labile materials (lysis-generated particles that become mineral sorbed) is likely stronger in soil than in marine environments. Once sorbed to mineral surfaces, viral detritus and particles of lysed host biomass may be partly susceptible to microbial degradation, or may be trapped within stable soil aggregate structures that occlude and protect it (Golchin et al., 1994; Kleber et al., 2015). Soil mineral characteristics likely play a role in these phenomena, as minerals shape the microbial communities that colonize mineral surfaces, and serve as a reservoir for microbial products and necromass (Keiluweit et al., 2012; Whitman et al., 2018; Creamer et al., 2019; Neurath et al., 2021; Xie et al., 2024). In laboratory culture, virions show a range of sensitivities to pH, soil moisture, and temperature (Pollard and Solosko, 1971; Hurst et al., 1980; Kimura et al., 2008; Guglielmotti et al., 2011; Jonczyk et al., 2011; Bertrand et al., 2012) and possess a range of physical traits — such as size, morphology and surface properties — that may govern virion transport, decay and sorption to
mineral particles or host cells (You et al., 2022). These viral “traits”
likely affect virion transport, sorption, and decay with the soil organic
matter pool and may also be predictive of virus-host interactions and
their effects on soil biogeochemistry.

7. Can viruses be exploited for controlling microbially-mediated soil processes?

While empirical measurements of viral contributions to process rates in soil are still generally lacking, augmenting virus loads in soil has been shown to modulate C and N fluxes (Braga et al., 2020; Albright et al., 2022), suggesting potential for their use to manipulate specific microbial processes in soil. There is growing interest in using phage-based approaches for controlling microbial populations in the medical, food, and agriculture sectors. ‘Phage therapy’ has been used in medicine for almost a century (Venturini et al., 2022), and developing its use in clinical settings is now a multi-billion dollar industry, largely due to a requirement for novel antimicrobials arising from antibiotic resistance (Johnson, 2023). There is also increasing interest in the use of viruses for environmental engineering challenges (Schwarz et al., 2022), leading to considerations of whether using such approaches could be exploited for augmenting functions of soil microorganisms with deleterious environmental impacts (Wang et al., 2019).

Manipulating soil microbiomes through the application of viruses would have greater challenges compared to medical applications. While phage therapy in clinical settings requires relatively small preparations, application of phages in agriculture may be difficult to scale (Das et al., 2015), together with potential issues of viability in soil and ensuring host-virus interaction following application. However, if targeted soil virus cultivation could be routinely established, this could represent a natural approach for impacting host activity. Such an approach would require a framework with detailed knowledge of the microbial populations being targeted and which contribute to a specific process, and requirement for cultivating viruses with appropriate host range (Holtappels et al., 2023). The host range of viruses for specific taxonomic groups in soils is largely unknown. Though chromosome cross-linking (Hi-C or proximity ligation) (Wu et al., 2023) and clustered regularly interspaced short palindromic repeats (CRISPR) spacer matching (Lee et al., 2021; Trubl et al., 2021) suggests there are soil viruses that may infect multiple hosts. Also requiring investigation is coevolution dynamics and fitness costs for host and virus populations in soils. While *in vitro* model studies demonstrate that viruses coevolve quickly with their hosts, there is a lack of studies in natural ecosystems (Koskella et al., 2022).

A soil phage therapy approach may be more readily applicable to
processes that are performed by a taxonomically restricted range of
microbial populations. One example could be nitrification, a process
which involves the sequential oxidation of ammonia to nitrate, via ni-
trite, and is dominated by specialized groups of ammonia- and nitrite-
oxidizing bacteria and archaea (Prosser et al., 2020). Critically, nitrifi-
cation is central to losses of applied fertilizer N and therefore contributes
to nitrous oxide and nitrate pollution. While viruses of nitrifiers have not
been extensively studied, they have been recently shown to be diverse
and active during nitrification (Lee et al., 2023, Lee et al., 2024), and the
first virulent phage infecting the classical laboratory ammonia oxidizing
bacteria strain *Nitrosomonas europaea* was recently reported, demon-
strating that viruses can be isolated that augment nitrifying activity
(Quirós et al., 2023). The viability of using cultivated viruses to reduce
microbial activity in soil will be more challenging compared to other
industrial applications. However, it could represent a natural and highly
targeted approach for reducing deleterious microbial activities in soils,
in addition to advantageously establishing virus-host model systems for
understanding viral impacts on biogeochemical cycling.

8. How can we decode viral genomes, proteins, and metabolic impacts in soils?

Soil ecosystems pose significant challenges to studying viruses, due to their heterogeneity and high physical, chemical, and biological complexity (Williamson et al., 2017; Kuzyakov and Mason-Jones, 2018). Despite challenges, metagenomics- and metatranscriptomics-based approaches are valuable in studying viruses from highly complex soil microbiomes. A simple computational workflow to study viruses from soils often involves (i) identifying virus genomes from viromic/meta-genomic and/or metatranscriptomic data (Fang et al., 2019; Kieft et al., 2020; Ren et al., 2020; Guo et al., 2021; Camargo et al., 2023), (ii) annotating virus proteins (Tisza et al., 2021; Bouras et al., 2023), and (iii) interpreting roles of viruses such as in manipulating host metabolism and biogeochemistry (Kieft et al., 2020; Shaffer et al., 2020). Transformational advances are necessary with these approaches to revolutionize soil viral ecology and truly integrate it with other facets of soil science.

Future advances in virus identification are predicated on a combi-
nation of improvements in sequencing and computational approaches.
Improvements in sequencing, such as higher quality and yields of longer
sequence lengths (currently also requiring protocols for high-quality,
relatively high molecular weight nucleic acid extracts (Zablocki et al.,
2021)) can significantly aid virus identification. Computational ap-
proaches for virus identification have mostly relied on approaches to
differentiate viruses from other biological forms, using a combination of
homology, statistical patterns, and machine learning to identify virus
genomes (Fang et al., 2019; Kieft et al., 2020; Ren et al., 2020; Guo et al.,
2021; Camargo et al., 2023). However, these approaches often perform
comparatively poorly in high complexity and heterogeneity, which are
hallmarks of soil ecosystems (Bi et al., 2022; Roux and Emerson, 2022).
Therefore, advances in virus identification would benefit from a focus on
deep learning approaches and the use of large language models (LLMs).
LLMs are proving increasingly useful in biological applications, such as
for protein structure (Lin et al., 2023) and can allow the detection of
‘remote homology’, that is, deciphering homology in cases of low
sequence similarity. For virus sequences, this can enable the identifi-
cation of novel, previously unidentified viruses and viruses with
different properties from known representatives.

One of the biggest obstacles to soil viral ecology remains our poor knowledge of viral protein function. It is estimated that >75% of all viral proteins have no known function (Edwards and Rohwer, 2005; Ko and Hatfull, 2020). This drawback also impedes our understanding of viral AMGs. These host-derived genes employed by viruses manipulate their hosts for fitness and evolutionary benefits. AMGs putatively associated with metabolic and biogeochemical impacts have increasingly been described from soil ecosystems (Emerson et al., 2018; Trubl et al., 2018; Wu et al., 2022; Sun et al., 2023; Graham et al., 2024). Yet there is a lack of understanding of when and how are AMGs important in biogeochemical and ecosystem contexts in soils, and if our inability to annotate viral proteins leads to an unnecessary focus on a small number of proteins that can be annotated. A potential two-pronged approach to tackle these problems involves *in silico* approaches such as LLMs to cluster viral proteins of unknown function with those of known function based on remote homology of proteins or genomic context (Brandes et al., 2022; Flamholz et al., 2024) and *in vitro* experiments to test and validate these predictions, potentially in a high-throughput manner.

9. What does “host” mean in the context of soil viromics?

Most viral impacts on biological processes are directly linked to the infection and takeover of individual microbial cells by viruses. With most soil viruses not being cultivated and instead known only from metagenomic contigs or metagenome-assembled genomes, virus-host interactions have to be predicted, most often *in silico*. Given these constraints, establishing robust connections between soil viruses and their
(potential) host(s) remains challenging.

Multiple tools have been developed for host prediction, leveraging, e. g., matches between CRISPR spacers and protospacers or similarities in k-mer frequencies between virus and host genomes (Edwards et al., 2016; Coclet and Roux, 2021; Versoza and Pfeifer, 2022). More recently, several approaches integrating multiple signals rather than relying on a single method showed significant improvement in the quality and reliability of host predictions (Wang et al., 2020; Zhou et al., 2022; Roux et al., 2023). Nevertheless, these approaches remain limited in scope and resolution, especially for soil. Pragmatically, this means (i) many viruses still lack any predicted host and (ii) most predictions can only be made to a putative host taxon (e.g., a phylum or sometimes a genus) and not to a specific microbial population. These two limitations are critical to keep in mind to avoid overestimating and/or over-stating the certainty or resolution of these analyses.

Beyond cultivating more soil viruses, one way these *in silico* host predictions will be improved will be through the development of *in vitro* approaches that can connect uncultivated viruses to potential hosts. Several such approaches have been proposed and are being adapted for soil microbiomes, including Hi-C, single-cell genomics, and other methods enabling targeted single-cell sequencing of marker genes using, e.g., microfluidics devices and/or microdroplet encapsulation (Labonté et al., 2015; Spencer et al., 2016; Jarett et al., 2020; Sakowski et al., 2021; Hwang et al., 2023; Wu et al., 2023).

More fundamentally however, it is somewhat of an oversimplification to call these analyses, whether *in silico* or *in vitro*, "host predictions". Indeed, "host prediction" methods considered as the most robust, such as Hi-C, CRISPR spacer matches, or detection in single-cell amplified genomes, technically only reflect physical co-localization of a viral genome near a bacterial genome. While the presence of a viral genome in a microbial cell can obviously be linked to an ongoing infection, the frequency at which viruses inject their genomes into non-hosts or into cells to which they are poorly adapted, and the frequency with which viral genomes can enter a cell outside of a typical infection cycle, e.g., through natural transformation, remains essentially unknown, especially in complex environments like soils (Hwang et al., 2023). Hence, a more quantitative and systematic characterization of these processes across ecosystems will be needed to better interpret *in silico* "host prediction" results.

## 10. RNA viruses - understudied or overlooked in soils?

Compared to DNA viruses, our knowledge of RNA viruses in soils has only recently begun to catch up. Mining of metatranscriptomes from various soil types has greatly expanded our understanding of the impacts on RNA virus community structure from temporal dynamics (Starr et al., 2019; Muscatt et al., 2022), soil type and land management (Hillary et al., 2022), moisture content (Wu et al., 2021a), and soil nutrient properties (Chen et al., 2022; Sadiq et al., 2024). Global meta-analyses have provided insights into RNA virus diversity from various habitats (Edgar et al., 2022; Neri et al., 2022), allowing researchers to better understand their spatial distributions. Despite these seminal studies, many questions remain, such as how edaphic factors influence RNA virus diversity, distribution, and abundance, and the roles of RNA viruses in nutrient cycling.

To address these ecological questions, the extensive methodological challenges of studying RNA viruses must also be addressed. Many field and wet lab techniques are optimized for dsDNA bacteriophage viromics or microbial metatranscriptomics. RNA viruses exploit various biological strategies rarely encountered in DNA bacteriophages, e.g., dsRNA genomes, and obligate intracellular lifestyles that can potentially influence their detection in both bulk metatranscriptomes (Urayama et al., 2016) and size-fractionated RNA viromes (Hillary et al., 2022).

RNA virus genome detection often relies on identifying the *RNA-dependent RNA polymerase* (RdRp) gene, while RNA viral genomes can be carried on multiple segments, including dividing the RdRp gene itself

(Sutela et al., 2020; Chiba et al., 2021). Unfortunately, most general-purpose virus detection tools (e.g., GeNomad (Camargo et al., 2023) or VirSorter2 (Guo et al., 2021)) assume that each contig represents an entire genome, and even specialist RNA virus identification tools (e.g., NeoRdRp (Sakaguchi et al., 2024), RdRp-scan (Charon et al., 2022), and Palmscan (Babaian and Edgar, 2022)) assume that a single input protein represents a complete RdRp and are unable to associate multiple segments to form complete genomes.

Further challenges are encountered in host prediction. DNA viruses frequently leave genetic "footprints" in their hosts' DNA genomes, which can be as short as single CRISPR spacers (<100 bp) from past infections and as large as complete viral genomes integrated into the host chromosome. In contrast, only a small number of CRISPR systems are known to be able to acquire spacers from RNA (Silas et al., 2017), and the integration of non-retroviral RNA viruses into their hosts' genome remains an understudied phenomenon (Kondo et al., 2015; Veglia et al., 2023). Novel RNA viruses of multicellular organisms confound our ability to accurately predict an RNA virus's host, as the virus may replicate within an organism detectable in the same sequencing sample or may rely on a vector organism to transport it into another organism absent in the same sequencing sample, which may be the actual host (i.e., where the virus eventually propagates).

RNA virus genome analysis also presents unique challenges, as many genome annotators (tools for identifying functional and structural genes) were constructed with DNA entities in mind and can underperform with RNA viruses (González-Tortuero et al., 2021). These difficulties may stem from the higher prevalence of polyproteins (continuous ORFs coding for multiple different protein domains), relatively shorter genome sizes of many RNA viruses, and more common use of non-standard genetic codes (e.g., most mitoviruses and many fungal (mycoviruses) and protist viruses). Further obstructing the issue is the prevalence of short, overlapping genes (encoded in the same loci but on different reading frames, e.g., the lysis protein of many leviviruses). While these hindrances can be overcome with six-frame translation approaches instead of traditional open reading frame (ORF) prediction, six-frame searches are more computationally expensive and have higher false-positive rates. Another consideration is that folding of the genomic RNA molecule onto itself can be implicated in controlling key aspects of protein translation (e.g., internal ribosomal entry sites (IRES) (Martinez-Salas et al., 2017)) or the life cycle (e.g., packaging signals facilitating genome encapsidation in protocapsids (Ye et al., 2021)).

Many challenges faced by soil RNA viral ecology can be expanded to the larger field (Charon et al., 2022), and to address these issues, we foresee a requirement for further customization of field and laboratory techniques that inform soil microbial ecology studies aiming to understand specific knowledge gaps in soil RNA virology. Also, the development of bioinformatics suites dedicated to RNA viruses and/or aware of the many complicating factors of RNA virus biology are needed, such as for detecting divided RdRps and linking different segments of the same viral genome, a particularly problematic task in highly diverse and complex environments such as soils. Lastly, centralized resources for the scientific community, e.g., databases of RdRp profiles and RNA viral sequences in a standardized format, simplifying interoperability, are urgently needed.

## 11. Summary and outlook

Now, at least two decades since soil viruses were recognized as important and largely unknown contributors to soil microbial ecology and biogeochemical cycling (Ashelford et al., 2000, 2003; Williamson et al., 2005; Kimura et al., 2008), laboratory and bioinformatic methods have advanced sufficiently that we are poised to answer some of the most pressing questions in the field (Table 1). For example, viromics seems to capture most dsDNA viral diversity, and promising new approaches to identify viral RdRps in RNA sequencing data suggest that we can start to confidently determine who's where, when. However, early
<table><caption>**Table 1**
Summary of questions and research needed.</caption><thead><tr><td>Question</td><td>Recent findings</td><td>Unknown aspects</td><td>Relevant methods for future research</td></tr></thead><tbody><tr><td>How abundant are viruses in soils?</td><td><ul><li>Correlation between viral particle and bacterial counts</li><li>Viral decay rates may be slower in soils than aquatic habitats</li></ul></td><td><ul><li>Proportion of missed viruses due to methodology biases</li><li>Proportion of viral abundance that represents infectious particles</li><li>Mechanisms of viral particles encountering hosts</li><li>Viral production and decay rates</li><li>Soil viral successional patterns across a range of spatiotemporal scales</li><li>Reason for disconnect between virus and host communities</li><li>How to measure viral impacts on ecosystem processes considering dominant spatiotemporal effects on viral communities</li><li>Abundance of lysogeny genes across a wide range of soils</li><li>Relative proportion of viral life strategies under different conditions</li><li>Occurrence and frequency of pseudolysogeny</li></ul></td><td><ul><li>Optimized viral extraction combined with microscopy and sequence-based approaches</li><li>Microcosm experiments and model virus-host isolates</li><li>Expand field sampling efforts across a range of spatiotemporal scales</li><li>Paired viromes, metatranscriptomes and stable isotope probing (SIP) on same soil samples</li><li>Soil microcosm experiments incorporating a range of temporal scales</li><li>Identify viral life cycles using hallmarks within metagenomes, host genomes, viromes and metatranscriptomes (e.g., look for lysogeny genes, integration into host genomes, presence in the free viral particle fraction)</li><li>Empirical and modeling-based estimates</li><li>SIP</li></ul></td></tr><tr><td>Why do soil viral communities turnover so rapidly over space and time?</td><td><ul><li>Soil viral communities shift over time and differ over space</li><li>Stronger distance-decay relationships for viruses vs. prokaryote hosts</li></ul></td><td><ul><li>Impact of viral lysis on rate of C-cycling</li><li>Influence of auxiliary metabolic genes (AMGs)</li><li>Contribution of viral particles to soil necromass</li><li>Viral impact on microbially-mediated soil processes</li><li>Cultivation of viruses with appropriate host range</li><li>Coevolution dynamics and fitness costs for hosts and viruses</li><li>The function of most predicted soil virus proteins</li></ul></td><td><ul><li>Combining isotope tracing and ‘omics data with quantitative biogeochemical modeling</li><li>Functional confirmation of relevant AMGs</li><li>Cultivation of soil viruses of targeted microbial functional groups</li><li>In vitro culture and soil microcosm experiments with phage cocktails</li><li>Development of model soil virus-host systems</li><li>Large language models (LLMs)</li><li>Clustering of proteins with known functions to unknowns</li><li>In vitro experiments to validate predictions</li><li>Cultivation of soil viruses</li><li>Hi-C, single-cell genomics, targeted single-cell sequencing of marker genes</li></ul></td></tr><tr><td>How prevalent are different viral life cycles (lysis, lysogeny, others) in soils?</td><td><ul><li>Relatively low abundance of lysogeny genes in soil viromes</li></ul></td><td><ul><li>Many viruses lack predicted hosts</li><li>Predictions limited to a putative host at high taxonomic levels vs specific population/strain</li><li>Edaphic factors that influence RNA virus diversity, distribution, and abundance</li><li>Roles of RNA viruses in nutrient cycling</li><li>Methodological biases in RNA detection approaches</li><li>The hosts of RNA viruses, especially for eukaryotes</li></ul></td><td><ul><li>Customization of field and laboratory techniques for RNA virus studies</li><li>Development of bioinformatic tools for RNA viruses</li><li>Standardizing databases of RNA-dependent RNA polymerase (RdRp) profiles and RNA viral sequences</li></ul></td></tr><tr><td>What does viral activity mean in the context of soils?</td><td><ul><li>New viral genomes and particles are being produced in soil, especially after wetting</li><li>Viral turnover rates can be significantly greater than that of hosts</li><li>Viral lysis can cause considerable microbial death following an induction event</li></ul></td><td><ul><li>Impact of viral lysis on rate of C-cycling</li><li>Influence of auxiliary metabolic genes (AMGs)</li><li>Contribution of viral particles to soil necromass</li><li>Viral impact on microbially-mediated soil processes</li><li>Cultivation of viruses with appropriate host range</li><li>Coevolution dynamics and fitness costs for hosts and viruses</li><li>The function of most predicted soil virus proteins</li></ul></td><td><ul><li>Combining isotope tracing and ‘omics data with quantitative biogeochemical modeling</li><li>Functional confirmation of relevant AMGs</li><li>Cultivation of soil viruses of targeted microbial functional groups</li><li>In vitro culture and soil microcosm experiments with phage cocktails</li><li>Development of model soil virus-host systems</li><li>Large language models (LLMs)</li><li>Clustering of proteins with known functions to unknowns</li><li>In vitro experiments to validate predictions</li><li>Cultivation of soil viruses</li><li>Hi-C, single-cell genomics, targeted single-cell sequencing of marker genes</li></ul></td></tr><tr><td>What effect does the viral shunt have on the soil carbon cycle?</td><td><ul><li>Augmenting virus loads in soil modulate C and N fluxes</li></ul></td><td><ul><li>Many viruses lack predicted hosts</li><li>Predictions limited to a putative host at high taxonomic levels vs specific population/strain</li><li>Edaphic factors that influence RNA virus diversity, distribution, and abundance</li><li>Roles of RNA viruses in nutrient cycling</li><li>Methodological biases in RNA detection approaches</li><li>The hosts of RNA viruses, especially for eukaryotes</li></ul></td><td><ul><li>Customization of field and laboratory techniques for RNA virus studies</li><li>Development of bioinformatic tools for RNA viruses</li><li>Standardizing databases of RNA-dependent RNA polymerase (RdRp) profiles and RNA viral sequences</li></ul></td></tr><tr><td>Can viruses be exploited for controlling microbially-mediated soil processes?</td><td><ul><li>Workflows for identifying virus genomes, prediction of viral proteins and potential impacts on host metabolism and biogeochemistry</li><li>In silico viral host prediction improved via integrating several approaches</li></ul></td><td><ul><li>Many viruses lack predicted hosts</li><li>Predictions limited to a putative host at high taxonomic levels vs specific population/strain</li><li>Edaphic factors that influence RNA virus diversity, distribution, and abundance</li><li>Roles of RNA viruses in nutrient cycling</li><li>Methodological biases in RNA detection approaches</li><li>The hosts of RNA viruses, especially for eukaryotes</li></ul></td><td><ul><li>Customization of field and laboratory techniques for RNA virus studies</li><li>Development of bioinformatic tools for RNA viruses</li><li>Standardizing databases of RNA-dependent RNA polymerase (RdRp) profiles and RNA viral sequences</li></ul></td></tr><tr><td>How can we decode viral genomes, proteins, and metabolic impacts in soils?</td><td><ul><li>Workflows for identifying virus genomes, prediction of viral proteins and potential impacts on host metabolism and biogeochemistry</li><li>In silico viral host prediction improved via integrating several approaches</li></ul></td><td><ul><li>Many viruses lack predicted hosts</li><li>Predictions limited to a putative host at high taxonomic levels vs specific population/strain</li><li>Edaphic factors that influence RNA virus diversity, distribution, and abundance</li><li>Roles of RNA viruses in nutrient cycling</li><li>Methodological biases in RNA detection approaches</li><li>The hosts of RNA viruses, especially for eukaryotes</li></ul></td><td><ul><li>Customization of field and laboratory techniques for RNA virus studies</li><li>Development of bioinformatic tools for RNA viruses</li><li>Standardizing databases of RNA-dependent RNA polymerase (RdRp) profiles and RNA viral sequences</li></ul></td></tr><tr><td>What does “host” mean in the context of soil viromics?</td><td><ul><li>RNA viruses are abundant and diverse in some soils</li></ul></td><td><ul><li>Many viruses lack predicted hosts</li><li>Predictions limited to a putative host at high taxonomic levels vs specific population/strain</li><li>Edaphic factors that influence RNA virus diversity, distribution, and abundance</li><li>Roles of RNA viruses in nutrient cycling</li><li>Methodological biases in RNA detection approaches</li><li>The hosts of RNA viruses, especially for eukaryotes</li></ul></td><td><ul><li>Customization of field and laboratory techniques for RNA virus studies</li><li>Development of bioinformatic tools for RNA viruses</li><li>Standardizing databases of RNA-dependent RNA polymerase (RdRp) profiles and RNA viral sequences</li></ul></td></tr><tr><td>RNA viruses - understudied or overlooked in soils?</td><td><ul><li>RNA viruses are abundant and diverse in some soils</li></ul></td><td><ul><li>Many viruses lack predicted hosts</li><li>Predictions limited to a putative host at high taxonomic levels vs specific population/strain</li><li>Edaphic factors that influence RNA virus diversity, distribution, and abundance</li><li>Roles of RNA viruses in nutrient cycling</li><li>Methodological biases in RNA detection approaches</li><li>The hosts of RNA viruses, especially for eukaryotes</li></ul></td><td><ul><li>Customization of field and laboratory techniques for RNA virus studies</li><li>Development of bioinformatic tools for RNA viruses</li><li>Standardizing databases of RNA-dependent RNA polymerase (RdRp) profiles and RNA viral sequences</li></ul></td></tr></tbody></table>

work in this area suggests that viral communities — and their activities and abundances — differ so substantially over time and space that such surveys would benefit from focus, for example, targeting specific habitats over space (e.g., at local scales from centimeters to field-scale habitat boundaries) and/or specific locations over time (e.g., small plots in the same field areas weekly-to-monthly for a few years) to identify common patterns before expecting to holistically characterize the global soil virosphere. Importantly, since viromics has thus far proven to be far better than total metagenomics for capturing dsDNA soil viral diversity (at least for viruses captured in the virome fraction), we are in favor of generating additional viromic datasets via such targeted experimental designs, rather than simply leveraging existing metagenomic datasets collected for other purposes. Of course, the most interesting questions extend beyond these surveys to explain why certain viruses are in some places at some times and not others and what impacts

viruses have on their hosts and environments. These are the difficult questions to answer, even with current methods. Here we have summarized the state of the field in light of the most pressing questions, highlighted new results and methods, and offered suggestions for moving the field forward, within the constraints of current methodological limitations.

Without intending to temper the recent excitement over soil viruses, we must also remember that we have a responsibility to avoid getting too far ahead of ourselves before the methods and datasets have had time to catch up. There are pros and cons to being at this exciting new stage — on the one hand, new methods are opening up new lines of research, but on the other, we are just beginning to understand the strengths and limitations of these methods in the context of soil viral ecology. We have relatively few virus-host representatives from soil in pure culture, which invites exciting new efforts (both high- and low-
throughput) to build culture collections and conduct experiments, but this also means that we lack a ground truth for bioinformatic predictions. New bioinformatics tools, some tailored specifically to viruses (e.g., iPHoP (Roux et al., 2023), VIBRANT (Kieft et al., 2020), geNomad (Camargo et al., 2023)) and others adapted from other use cases (e.g., LLMs), are enabling cutting-edge investigations at levels of complexity that were difficult to imagine even a few years ago, but we still lack the empirical data to train these tools on soil viruses specifically. Basic questions from 'is this really a virus' to 'what hosts can this virus infect' to 'what life strategies is this virus capable of, and what stage is it in currently' to 'is this an auxiliary metabolic gene, and what function might it perform' can be estimated or predicted bioinformatically, but for the most part, we cannot know any of these for certain based on bioinformatic predictions alone. Given these limitations, we must be extra careful not to overinterpret results or misuse data collected for other purposes.

But of course, the future is bright. Multiple approaches for estimating viral particle abundances (in an absolute or relative sense), including viromic DNA yields, suggest that we may soon be able to estimate bulk viral production and decay rates at the community scale in different soils. We are ready to more thoroughly consider host physiological states and leverage measures of viral activity (e.g., via SIP and/or metatranscriptomics) when assessing the likelihood of successful viral infections, and, ultimately, for predicting the downstream ecological and evolutionary consequences of different viral replication strategies. We can lean on ever-more reliable “host” prediction tools to piece together virus-host dynamics and potential viral influences on host community composition and metabolism (with the caveat that bioinformatic virus-host linkages may not always indicate successful infection). Similarly, machine learning tools and LLMs offer the potential to make headway on viral protein dark matter. The field is poised to leverage increasingly comprehensive analytical tools for identifying RNA viruses to address their contributions to soil ecology and biogeochemistry. Probably most importantly, using multiple approaches in combination across scales from microscopic to microcosmic to the field to the globe, we can begin to tease apart the ways in which individual infections advance to local chain-reaction ‘epidemics’ that interconnect with community-scale evolutionary dynamics to reveal how soil viruses drive and respond to environmental change.

### CRediT authorship contribution statement

**Christina Hazard:** Writing – review & editing; Writing – original draft, Conceptualization. **Karthik Anantharaman:** Writing – original draft. **Luke S. Hillary:** Writing – original draft. **Uri Neri:** Writing – original draft. **Simon Roux:** Writing – original draft. **Gareth Trubl:** Writing – original draft. **Kurt Williamson:** Writing – original draft. **Jennifer Pett-Ridge:** Writing – review & editing; Writing – original draft. **Graeme W. Nicol:** Writing – review & editing, Writing – original draft. **Joanne B. Emerson:** Writing – review & editing, Writing – original draft.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

CH and GWN's contribution to this work was funded by the Agence Nationale de la Recherche grant 'CONSERVE', award number ANR-22-CE02-0006-01 (grant to CH and GWN). JBE's contribution to this work was funded by the U.S. Department of Energy (DOE), Office of Biological and Environmental Research (BER), award number DE-SC0021198 (grant to JBE). LSH's contribution to this work was

funded by the U.S. DOE-BER, Genomic Science Program, award number DE-SC0023127 (subaward to JBE, grant PI Sydney Glassman). Work at Lawrence Livermore National Laboratory (GT, JP-R) was supported by the U.S. DOE-BER, Genomic Science Program "Microbes Persist" Scientific Focus Area (#SCW1632) and conducted under the auspices of the U.S. DOE under Contract DE-AC52-07NA27344. Work conducted by the U.S. DOE Joint Genome Institute (https://ror.org/04xm1d337) (SR, UN), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. DOE operated under Contract DE-AC02-05CH11231.

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