# Determinants of host species range in plant viruses

Benoît Moury, Frédéric Fabre, Eugénie Hébrard, Rémy Froissart

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Benoît Moury, Frédéric Fabre, Eugénie Hébrard, Rémy Froissart. Determinants of host species range in plant viruses. Journal of General Virology, 2017, 98 (4), pp.862-873. 10.1099/jgv.0.000742. hal-01522298

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# Determinants of host species range in plant viruses

Moury, B. (1), Fabre, F. (2), Hébrard, E. (3), Froissart, R. (4,5)

(1) Pathologie Végétale, INRA, 84140 Montfavet, France

(2) UMR 1065, Santé et Agroécologie du Vignoble, INRA, Bordeaux Sciences Agro, Institut des Sciences de la Vigne et du Vin, F-33883 Villenave d'Ornon, France

(3) UMR186, IRD-Cirad-UM, Laboratory "Interactions Plantes Microorganismes Environnement", Montpellier France

(4) UMR385, INRA-CIRAD-SupAgro, Laboratory «Biologie des Interactions plantes-parasites», Campus international de Baillarguet, F-34398 Montpellier, France

(5) UMR5290, CNRS-IRD-UM1-UM2, Laboratory «Maladies Infectieuses & Vecteurs : Ecologie, Génétique, Evolution & Contrôle», Montpellier France

**Running title:** Determinants of host species range in plant viruses

**Corresponding author:** B. Moury, E-mail: benoit.moury@inra.fr

**Keywords:** plant virus, host range, genome segmentation, vertical transmission, vector, bipartite network

**Non-standard abbreviations:** HRB: host range breadth, absHRB: absolute host range breadth, relHRB: relative host range breadth.
Abstract

Prediction of pathogen emergence is an important field of research, both in human health and agronomy. Most studies of pathogen emergence have focused on involved ecological or anthropic factors rather than on the role of intrinsic pathogen properties. The capacity of pathogens to infect a large set of host species, i.e. to possess a large host range breadth (HRB), is tightly linked to their emergence propensity. Using an extensive plant virus database, we found that four traits related to virus genome or transmission properties were strongly and robustly linked to virus HRB. Broader host ranges were observed for viruses with single-stranded genomes, those with three genome segments and nematode-transmitted viruses. Also, two contrasted groups of seed-transmitted viruses were evidenced. Those with a single-stranded genome had larger HRB than non-seed-transmitted viruses, whereas those with a double-stranded genome (almost exclusively RNA) had an extremely small HRB.

From the plant side, the family taxonomic rank appeared as a critical threshold for virus host range, with a highly significant increase of barriers to infection between plant families.

Accordingly, the plant-virus infectivity matrix shows a dual structure pattern: a modular pattern mainly due to viruses specialized to infect plants of a given family and a nested pattern due to generalist viruses. These results contribute to a better prediction of virus host jumps and emergence risks.
Introduction

Pathogen emergence is the process by which the causative agent of a disease increases in incidence following its appearance in a new, or in a previously existing host population [1,2]. In the case of animal and human diseases, particular attention has been paid to pathogen “host jumps”, where a pathogen infecting a reservoir host generates epidemics and disease in a new species. Host jumps involve three major steps: (i) encounter of the new host, (ii) infection and (iii) propagation in that host population [3]. Most studies of pathogen emergence have focused on involved ecological and anthropic factors, such as increase in human population, modification of land use, introduction of pathogens in new areas through global travel or trade, or expansion of the geographical range of vectors. These factors are mostly influential on the above-mentioned steps (i) and (iii), and their effects are difficult to anticipate because they are influenced by multiple environmental variables and human activities. In contrast, step (ii) of host jump, i.e. infection of the new host, is determined to a large extent by intrinsic host and pathogen properties. As a consequence, identifying which pathogen properties determine host infection would help to compare risks of emergence among different pathogens.

Infectivity of a pathogen in a given species can be difficult to evaluate, especially for animal or human pathogens, for which controlled inoculation by a new pathogen may not be technically or ethically feasible. In contrast, determination of the species host range of plant parasites has been used for decades as a taxonomic criterion and extensive data on the “potential” host species range of plant parasites (as opposed to their “realized” host range) have been obtained by controlled inoculation experiments, especially for viruses [4]. However, evolution of plant virus host range is poorly known (but see [5-9]).

Notwithstanding, host species jumps are certainly not infrequent among plant viruses, given the extreme contrasts of their host range breadth (HRB), from a single species to more than
1000, and the incongruence between the phylogeny of most plant viruses and that of their host species (but see [10,11]). HRB was found to be a major determinant of bacterial or viral emergence in humans [12]. Also, host range expansion is an important cause of emergence for plant pathogens and especially plant viruses [13,14]. In order to improve our knowledge of the processes of plant virus emergence, we analyzed an extensive virus host range database (VIDE database; [15]) to answer the following questions:

-Do certain types of viruses possess the capacity to infect a broader range of plant species than others?

-Are viruses likely to jump to plant species belonging to distant taxa?

-What are the general patterns of plant-virus interactions at the species rank?

# Results

## Relationships between virus properties and HRB

In order to identify determinants of plant virus HRB, we analyzed its relationships with putative explanatory variables corresponding to virus genome or transmission properties. For each virus species in the database, two HRB estimates were obtained: absolute HRB (absHRB), the total number of plant species listed as hosts in the database, and relative HRB (relHRB), corresponding to absHRB divided by the total number of assayed plant species, whether hosts or non-hosts. In addition, different datasets were analyzed because of potential precision or accuracy issues associated with differences in (i) the total number of assayed plant species and/or (ii) their distribution within plant taxonomy between virus species (i.e. the fact that the assayed plant species are not equally evenly distributed across plant genera or
families for all viruses). To tackle the first issue, we analyzed a restricted dataset comprising the 293 virus species for which ≥15 plant species were assayed in addition to the entire dataset comprising 480 virus species. Indeed, the 293-virus dataset provides a better precision of HRB estimates (Method S1). To tackle the second issue, we analyzed additional restricted datasets for which a minimal diversity of plant genera or families, as assessed by Hill numbers of order 2 (Hill<sub>gen.</sub> and Hill<sub>fam.</sub>, respectively) [16] has been assayed (Table S1 and Methods section).

We analyzed the correlations between the explanatory variables using Multiple Correspondence Analyses (MCAs) (Fig. S1 available in the online Supplementary Material). The two main axes of the MCAs accounted for 23.0% and 22.3% of the total variation for the entire (480 species) and restricted (293 species) datasets, respectively, and revealed only limited redundancy between the explanatory variables. The following two axes (orders 3 and 4) explained 16% and 17% of the total variation for these two datasets, respectively. Moreover, absHRB and relHRB used as illustrative variables were poorly linked to the two main axes, suggesting that they are not redundant with the explanatory variables.

The absHRB of viruses was highly significantly linked to four different viral properties, their genome nature, their number of genome segments, their mode of vertical transmission and their vector type (variables GEN, SEG, VER and VEC, respectively) and, to a lower extent, to their abiotic horizontal transmission mode (Table 1). Viruses with a single-stranded (ss) genome (either composed of RNA or DNA) had a broader host range (16.7 and 12.6 plant species on average, respectively) than viruses with a double-stranded (ds) genome (3.6 and 3.9 species on average for dsRNA and dsDNA viruses, respectively). In contrast, there was no significant difference in the absHRB of positive- and negative-sense (or ambisense) RNA viruses (P-value=0.097; Kruskal-Wallis test). Viruses with three genome
segments had a significantly broader host range (28.3 species on average) than other groups (10.5 to 15.7 species on average). Concerning vertical transmission, seed-transmitted viruses had a broader host range (20.2 species on average) than viruses with no vertical transmission or viruses transmitted through vegetative propagation (12.7 to 13.5 species on average). Both viruses transmitted vertically through contamination of the seed coat or of the seed embryo had larger host ranges than other virus groups and no significant difference was observed between these two groups of seed-transmitted viruses (data not shown). Concerning biotic horizontal transmission, nematode-borne viruses had a broader host range (33.5 species on average) than other types of viruses, whereas little absHRB differences were observed among viruses corresponding to other vector types or with no known vector (from 10.9 to 15.1 species on average). Thrips-transmitted viruses had broad host ranges (38.3 species on average) but did not depart significantly from other virus groups maybe because of their underrepresentation in the dataset (six virus species only). In addition, viruses transmitted both vertically through seeds and horizontally through biological vectors had a larger host range than viruses transmitted exclusively through seeds, exclusively through vectors, or neither through seeds nor vectors (data not shown; [17]). Concerning abiotic horizontal transmission, viruses transmitted by contact between plants had a slightly broader host range than viruses with no horizontal abiotic transmission (16.5 vs. 14.4 species, respectively).

The robustness of the links between these virus traits and HRB was examined with restricted virus sets limiting potential precision or accuracy issues (Method S1 and Table S1). For all these restricted datasets (13/13), the links between the two HRB estimates (absHRB and relHRB) and the explanatory variables SEG, VER and VEC were similar to those observed for the entire dataset, meaning that viruses with three genome segments had a larger HRB than viruses with a single genome segment, seed-transmitted viruses had a larger HRB
than viruses with no vertical transmission and nematode-borne viruses had a larger HRB than arthropod-borne viruses (Table S1). Two other explanatory variables, genome type and kind of vector transmission, showed less robust effects. Only 3/13 of these datasets revealed significant differences among virus groups differing by their genome nature, viruses with a ssRNA genome having a significantly larger HRB than at least one of the virus groups with a ds genome. In six additional datasets, an overall effect of virus genome nature on HRB was detected with the Kruskal-Wallis multiple test but no differences between virus groups were detected. However, the trend was similar and viruses with a ssRNA genome had a larger HRB than viruses with a ds genome. This lack of robustness was probably due to the underrepresentation of viruses with dsRNA or dsDNA genomes in many of these datasets (four viruses or less in these two groups) and consequently a lack of statistical power, i.e. a poor capacity to detect true differences between virus groups. For the kind of vector transmission, only datasets comprising viruses for which ≥15 plant species were assayed revealed significant differences in HRB, with non-vectored viruses having a significantly larger HRB than viruses transmitted by vectors in a non-circulative manner.

We computed conditional inference regression trees to synthesize the effect of the four main significantly explanatory variables (GEN, SEG, VER and VEC) on HRB. Regression trees take into account the interactions between explanatory variables and indicate which combinations of variables and variable levels correspond to higher or lower HRB. With the 480-virus dataset, the first dichotomy in the regression tree for absHRB was linked to the VEC variable and separated nematode-borne viruses from others (Fig. 1a). The second and third dichotomies were linked to the SEG and GEN variables, respectively. More distal nodes in the tree were linked to the VER variable and, again, to the SEG variable. With the 293-virus dataset, a similar and simpler regression tree was obtained for relHRB (Fig 1b) or
absHRB (data not shown). The nodes of the tree were linked to the VEC and SEG variables.

Overall, the regression trees strengthen the importance of the nature of virus vectors and of genome segmentation to explain virus HRB and emphasize which combinations between these biological properties correspond to a higher or lower HRB.

Differences in HRB were also compared between the most represented virus genera (Table S2). *Nepovirus* was the only genus revealing a significantly broader host range than other genera (*Begomovirus*, *Carlavirus*, *Potyvirus* and *Tymovirus*). Nepoviruses are nematode-borne and seed-transmitted, two traits that were linked to large host ranges in the previous analyses (Table 1; Table S1). In the 480-virus dataset, the only additional effect detected was the smaller HRB of alphacryptoviruses (1.2 host species on average), a group of seed-transmitted dsRNA viruses, compared to most other virus genera (data not shown).

A model was built to predict the absHRB class of a virus (class 1: ≤5 host species; class 2: 6 to 15 host species; class 3: >15 host species) from the four variables GEN, SEG, VER and VEC (Method S2). The overall rate of correct assignments to absHRB classes by the model was 0.48 compared to a rate of 0.33 obtained with a random classifier choosing the absHRB class randomly. A detailed analysis of the predictive performance of the model is provided in Method S2. Additional models aiming to predict the diversity of host genera or families are also presented in Method S2.

## Plant determinants of virus host range

We examined at which plant taxonomic rank the barriers to infection by viruses are more widespread and/or stronger. Four different Hill numbers [16] of order q=0 to q=3, integrating plant taxon richness and abundance were calculated for the hosts of each virus species and at
different plant taxonomic ranks (Fig. 2). Depending on q, the Hill numbers place more weight on host richness or distribution evenness. However, whatever q, the ranking of the Hill numbers and the significant differences between taxonomic levels were remarkably similar. A sharp decrease of host diversity was observed between the genus and the family levels. Then, no significant difference of diversity was observed between the family and the order levels and small but significant host diversity reduction was observed at higher taxonomic ranks.

These results indicate moderate barriers to infection at the within-family rank, each virus species being generally infectious in several plant genera, and much more frequent and/or stronger barriers to infection at the between-family rank. By comparison, only few additional barriers to infection occur at higher plant taxonomic ranks. Consequently, the plant family appears as a key taxonomic threshold for plant virus host range.

## Structure of the plant-virus infectivity matrix

From the entire virus species – plant species infectivity matrix obtained from the database, a smaller matrix (37 viruses × 28 plants) containing relatively few (<10%) missing host/non-host data was extracted and analyzed for nested or modular structural patterns, two important properties that correspond to two contrasted models of host range evolution [18]. This matrix was shown to be highly significantly nested (*P*-value <0.001) (Fig. 3a), whatever the algorithm used, the way to simulate the status of the missing data and the null model used for significance assessment (Table 2).

Concerning modularity analyses, the edge-betweenness algorithm failed to detect any module in the matrix. For the other two algorithms (spinglass.community and leading.eigenvector.community), modularity was significant in most simulation cases
(0.006<P-value <0.078; Table 2). In only one of eight combinations of modularity algorithm,
way of simulating missing data and null model was the significance slightly above 0.05. The
maximal modularity (0.14 for spinglass.community algorithm) was obtained when plant and
virus species were distributed into three modules (Fig. 3b). There was no obvious property
shared by viruses belonging to the same module but a strong association between modules
and plant botanical families. Module 1 contained a majority of plants (7 of 11 species) of the
*Fabaceae*, whereas modules 2 and 3 comprised almost exclusively plants of the
*Amaranthaceae* (7 of 8) and *Solanaceae* (8 of 9), respectively (Fig. 3b).

These patterns reflect two groups of plant viruses. On one hand, “generalist” viruses
with broad host ranges were identified within module 1 and tended to possess the capacity to
infect plants of any module. They contributed mainly to the nested pattern of the matrix. On
the other hand, “specialist” viruses belonging to modules 2 and 3 were mostly able to infect
plants belonging to their own module and ensured the modular pattern of the matrix (Fig. 3b).

Discussion

A major asset of the VIDE database is the compilation of experimental, rather than
only natural, host range data. This allowed identifying properties linked to the virus intrinsic
capacity to infect a set of plants, irrespective of the exposure of these plants to the virus in
natural conditions. Consequently, our analyses can be useful to estimate the risks of future
emergence in new plant species for a virus possessing a given set of traits if plant exposure
conditions were to change. As for any large dataset, there are a number of pitfalls to avoid for
analyses and interpretation of the results. These pitfalls can be related to (i) the exhaustiveness
of the database and to (ii) the precision and (iii) accuracy of HRB estimates obtained from the database.

The database is based on the literature available in 1996 and has not been updated since. It includes host range data for 480 virus species, which represents 53% of the total number (900) of plant virus species accepted in the latest ICTV report [19-20]. The 19 virus families accepted to date are represented by at least 40% of their species members, except Geminiviridae (29 species of 196), Bunyaviridae (2 of 8), Ophioviridae (0 of 6) and Endornaviridae (0 of 4). The major quantitative difference between the viruses in the database and the present taxonomy is the huge increase of species in the genus *Begomovirus* (family Geminiviridae), for the majority of which few HRB data are available. If we withdraw the 167 begomovirus species absent from the database, our dataset represents 66% of the total virus species. Twelve virus genera which include few species (2.3 on average) are not represented in the dataset, of a total of 85 genera. Concerning the plant species used to estimate HRB, it should be emphasized that virologists use more and more genome sequence data to characterize virus species to the detriment of host range data. In summary, the database is quite exhaustive and the missing virus species are quite evenly distributed across genera and families. However, care should be taken about the interpretation of our results for some virus taxa when few HRB data are available due to the peculiarities of the virus biology (see below).

Concerning the precision of HRB estimates, a limiting factor is the number of assayed plant species which varies between viruses and can be small for some of them. To minimize the impact of this source of imprecision, we defined a reduced set of 293 plant viruses for which the absHRB and relHRB were fairly well correlated, indicating that representative sets of plants were used to characterize virus HRB (Method S1). Then, we analyzed separately the
absHRB in the 480- and 293-virus datasets and the relHRB in the 293-virus dataset (Table S1). Importantly, most relationships between virus HRB and biological properties were highly significant for the three HRB estimates. The robustness of these results minimizes the risk that they would have been affected by imprecisions in HRB estimation.

The unequal distribution of the assayed plant species across plant taxa may affect the accuracy of HRB estimation. Consequently, we analyzed the links between virus HRB and genome or transmission properties using datasets with increasing requirements in terms of diversity and distribution of assayed plant species among plant genera and families (i.e. with increasing thresholds for Hill<sub>gen.</sub> and Hill<sub>fam.</sub>, respectively) (Table S1). For all these virus subsets, results were similar to those obtained with the entire dataset for the SEG, VER and VEC variables. For the GEN variable, results were also similar when there was a sufficient number of virus species in the different groups of genome nature. Again, this strengthens the robustness of our results.

Importantly, the relationships observed between viral traits and HRB were not due to particular virus taxa with extreme HRB values but, rather, were shared throughout the virus diversity. *Nepovirus* was the only virus genus fairly well represented in the database (≥12 member species) that exhibited a significantly larger HRB (Table S2). After removing nepoviruses from the 480- or 293-virus datasets, the four variables GEN, SEG, VER and VEC were still significantly linked to virus HRB (data not shown).

A strong link between plant taxonomy and virus host range was evidenced in our study and seems to be general for plant parasites [21]. Overall, there were relatively few barriers to virus infection at the plant within-genus rank but frequent infection barriers between plant
families (Fig. 2). The plant family rank can therefore be considered as a critical taxonomic threshold that often limits virus host range. This was confirmed, on a smaller dataset, by the analysis of the plant-virus infectivity matrix (Fig. 3). The matrix was significantly modular. Each detected module was strongly associated with a plant family but not with a particular virus taxon. In tree-fungus interactions, the detected modules were also linked to host taxa but not to fungal taxa [22]. Consequently, plant taxonomy may be used as a first predictor of virus host jumps. However, the plant-virus matrix presented also a significantly nested pattern, which was mostly due to “generalist” viruses that were able to infect plants belonging to any of the three modules (Fig. 3). The apparent dichotomy between specialist and generalist viruses suggests that some viral traits (including potentially those revealed in our study) and/or selective forces have determined contrasted host range breadth among plant viruses.

We identified several virus traits robustly and strongly linked to their HRB: genome nature and segmentation, occurrence and mode of vertical transmission and vector type (Table 1 and Table S1). At this stage, we can only speculate as to whether these four virus traits are determinants of virus HRB or if they are linked with HRB for other reasons. Viruses with a ss genome had broader host ranges than viruses with a ds genome, with a 3- to 4-fold difference between these two groups (Table 1). In accordance, Woolhouse *et al.* [1] observed that ssRNA viruses are the animal pathogens most prone to emerging via host jumps. One possible explanation is that viruses with a ss genome tend to have higher mutation and evolution rates than those with a ds genome [23], perhaps a consequence of the higher instability of ss nucleic acids [24]. However, this putative mechanism does not explain why ssDNA viruses have a broader plant host range than dsRNA viruses, since they share similar evolution rates [23].

The segmentation of virus genomes was also strongly linked to HRB. Several hypotheses can be raised to explain the increase of HRB for viruses with two, and especially
with three genome segments (Table 1). It is noteworthy that this was not due to a number of
viruses with three genome segments that possess extremely large host ranges, especially
*Cucumber mosaic virus, Tomato spotted wilt virus and Alfalfa mosaic virus*. Indeed, the
Kruskal-Wallis test used is based on ranks and therefore not artificially influenced by extreme
HRB values. Moreover, the SEG variable remained significantly linked to HRB after
withdrawing these three viruses from the datasets (*P*-values=1.2×10⁻³ to 3.7×10⁻³).

Hypotheses regarding the advantage of virus genome segmentation are longstanding [25]. It
was proposed that segmentation increases genome stability, replication fidelity and/or
replication rapidity [26-28]. Genome segmentation could also allow better regulation of gene
expression [29] and, on a longer term, could favor genome exchanges through reassortment
between virus isolates, which can be advantageous for virus adaptation in a changing
environment (the so-called “advantage of sex”; [30]). On the opposite, the low within-host
effective population size of plant viruses may be the main limitation of genome segmentation.
Only a small number of virus particles contribute to the infection of an individual plant cell
and hence the multiplicity of infection (MOI) is now considered to be low, though variable
over time, for plant viruses [31-32]. A consequence of virus genome segmentation is that the
minimum MOI required for infection would increase very rapidly as the number of genome
segments increases. This suggests an optimum number of genome segments (3 or 4), for
which the advantages of genome segmentation are not counterbalanced by the necessity of
higher MOIs [33]. Interestingly, the observation that viruses with >3 genome segments have
narrower host ranges, on average, lends support to this hypothesis (Table 1).

The larger host range of seed-transmitted viruses (Table 1; [17]) seems counter-
intuitive, since seed transmission through the embryo is often associated with host
specialization. For example, dsRNA plant viruses of family Endornaviridae and of genera
*Alphacryptovirus* and *Betacryptovirus* (family Partitiviridae) are transmitted by ovule and by pollen to the seed and their host range is restricted to *ca*. one plant species because of the lack of horizontal spread, mechanical transmission and even graft transmission [19]. The 480-virus dataset contained only 22 members of the family Partitiviridae and no member of the family Endornaviridae, which may have led to a general overestimation of the average HRB of seed-transmitted viruses. An ultimate cause of the broader host range of some groups of seed-transmitted viruses could be linked to the probability of infecting new, healthy plants. A trade-off has been observed between the efficiency of vertical seed transmission and horizontal transmission in some plant viruses [34]. As a consequence, seed transmission could decrease the probability of infecting new plants through horizontal transmission. This negative effect could be compensated by a broader host range, offering more opportunities for virus dissemination. This evolutionary trend would only be of interest for viruses with mixed (i.e. both vertical and horizontal) transmission modes. Confirming this hypothesis, plant viruses transmitted both through seeds and by vectors have a broader host range than viruses with no vector transmission or with exclusive vector transmission (data not shown and [17]). An alternative hypothesis, with a causal link in the opposite direction, could be that viruses with a broader host range have more opportunities of being seed-transmitted in at least one of their host species. Indeed, many plant viruses are transmitted through seeds only in a small subset of their host species. Overall, we postulate that two different groups of seed-transmitted viruses should be distinguished: Those that are also transmitted horizontally by vectors exhibit large host ranges and those that are exclusively transmitted vertically show an extremely narrow host range.

Finally, the host range of nematode-transmitted viruses was about twice as large as that of viruses transmitted by arthropods, fungi or without vectors (Table 1; [17]). Again, an
explanation for the broader host range of nematode-transmitted viruses could be linked to the
probability of infecting new plants. Because of the poor migration capacity of their vectors
(only a few centimetres per year in uncultivated woodland habitats; [35]), nematode-borne
viruses could have evolved broader host ranges to increase their chance of infecting new
plants. The same tendency was not observed for other soil-borne viruses, such as those
transmitted by fungi (Table 1), which may be due to the fact that fungal vectors may have a
higher dissemination capacity than nematodes and/or that nematode-borne viruses are more
prone to becoming extinct after the death of their host.

Methods

Plant virus database

Plant virus host range data were obtained from the VIDE (Virus Identification Data
Exchange) database [15], which includes a list of host and non-host plant species for each
virus species. Non-hosts have been determined by controlled laboratory inoculation
experiments, whereas hosts include both naturally and experimentally-infected species. Local-
lesion hosts, in which the virus multiplies and moves from cell to cell in the inoculated organs
to some extent, are considered as hosts in the database. This is justified by the facts that (i)
local lesions result from hypersensitive reactions triggered by gene-for-gene interactions
between the plant and the virus (e.g. [36,37]), (ii) mutations in the plant resistance gene and/or
in the virus avirulence gene can allow full systemic infection [38-40] and (iii) environmental
conditions can abolish the expression of the resistance in local-lesion plants and lead to full
susceptibility [41]. Plant species included both in the host and non-host lists for a given virus
species were considered as hosts. Indeed, these differences were due to the choice of plant and
virus genotypes in different studies and probably the result of intraspecific variability
affecting host resistance and/or virus pathogenicity. The list of host and non-host plant species
for each virus species was copied from the database to Excel in April 2009 and then formatted
using the R software version 3.0.2 [42].

Among the virus species described in the database, we kept only those considered as
definitive or tentative species in the latest ICTV (International Committee on Taxonomy of
Viruses) report [19]. Virus taxonomy at the genus or higher ranks was as proposed by ICTV.
Biological properties of viruses were obtained from Brunt *et al*. [15] and King *et al*. [19].

Estimating host range breadth of plant viruses and tackling potential biases

A total of 480 viral species contained host range data in the database. The most obvious
estimate of virus HRB is the absHRB. However, this estimate can be affected by precision
and accuracy issues. First, the total number of assayed plant species varies greatly between
virus species, which affects the precision of HRB estimate. For example, absHRB usually
underestimates the HRB of viruses for which experimental inoculation is difficult to
implement (no artificial inoculation method, vector unknown, vector difficult to raise, no
horizontal transmission). To tackle this issue, we analyzed the relHRB. To ensure a
satisfactory precision for relHRB, we restricted the dataset to the 293 virus species for which
a minimal number of n=15 plant species were assayed (see Method S1 for justification; Table
S1). Second, the distribution of assayed plant species within plant taxonomy also varies
greatly between virus species, which can be a source of bias. For example, if a large number
of plant species of the same genus as the one where the virus was initially isolated are
assayed, this may artificially increase the HRB estimate because there are relatively fewer
infection barriers at this plant taxonomic rank. To tackle this bias source, we restricted the dataset to virus species for which a minimal diversity of plant genera or families was assayed, as assessed by Hill's diversity. For each virus species, the diversity of assayed plant species was calculated at different plant taxonomic levels (genus, family...) with the Hill numbers [16], of increasing use in ecology [43]. Hill [16] integrated taxon richness and abundance into a class of diversity measures (Hill numbers) defined for $q \neq 1$ as:

$$q_D = \left( \sum_{i=1}^{S} p_i^q \right)^{1/(1-q)} \quad (1),$$

in which S is the number of taxa in the sample, and the i^th^ taxon (i = 1, 2, ..., S) has relative abundance p_i. The parameter q determines the sensitivity of the measure to the relative frequencies. For q = 0, the abundances of individual taxa do not contribute to the sum in equation (1), so that $^0D$ corresponds to taxon richness. For q=1, equation (1) is undefined, but its limit as q tends to 1 is the exponential of the Shannon index:

$$^1D = \exp(-\sum_{i=1}^{S} p_i \times \log p_i) \quad (2).$$

The variable $^1D$ weighs species in proportion to their frequency. When q = 2, equation (1) yields Simpson diversity, which places more weight on the frequencies of abundant taxa and discounts rare taxa. Usually, a characterization of the taxon diversity of a sample with S taxa and relative abundances (p_1, p_2, ..., p_S) is conveyed by a diversity profile (a plot of $^qD$ vs. q from q = 0 to q = 3). In the analysis of assayed plant species, we found that the Hill numbers of order q=0 to q=3 were highly correlated (r>0.90) at the genus or at the family level, but less correlated between the genus and family levels (data not shown). Consequently, we chose different minimal thresholds of $^2D$ calculated at the genus and at the family levels (Hillgen. and Hillfam) to analyze the robustness of our results (Table S1).
Relationships between host range breadth and biological properties of plant viruses

To unravel virus properties explaining differences in HRB, the response variables absHRB and relHRB were compared among groups of viruses sharing the same biological or transmission traits (putative explanatory categorical variables). These traits represent major life history traits or genome properties of plant viruses:

- The genome nature, categorized as ss or ds RNA or DNA (variable 'GEN');

- the number of genome segments, viruses with >3 genome segments being grouped into the same category because they were underrepresented in the database (20 species) (variable 'SEG');

- the occurrence and mode of vertical transmission, categorized as (i) seed transmission and (ii) no vertical transmission (variable 'VER');

- the occurrence and mode of abiotic horizontal transmission, categorized as (i) graft transmission (when some host plants are usually multiplied by grafting) but no substrate, tool or contact transmission, (ii) substrate (i.e. soil or irrigation water) transmission but no tool or contact transmission, (iii) tool transmission but no contact transmission, (iv) contact transmission between plants and (v) no abiotic horizontal transmission known;

- the vector type, categorized as (i) aphid, (ii) whitefly, (iii) other Hemiptera, (iv) Coleoptera, (v) thrips, (vi) mite, (vii) fungus or soil-borne protists (named collectively fungus for simplicity), (viii) nematode, and (ix) no vector known;

- the vector type defined as previously but with arthropods grouped into a single category (variable 'VEC');
- the mode of vector transmission, categorized as (i) non-vectored, (ii) non-circulative (non-
or semi-persistent), (iii) circulative (i.e. persistent but non-multiplicative) and (iv) circulative-
multiplicative.

Variables linked to nucleotide composition or within-species genetic diversity or evolutionary
constraints were not included because of the lack of data for a large set of viruses in the
database. Additional variables did not provide consistent results (number of vector species) or
are confounded with other variables (genome linearity or circularity) and are therefore not
presented.

All statistical analyses were performed with the R software version 3.0.2. First, we performed
MCAs using the package 'FactoMineR' to analyze the correlations and putative redundancy
between the explanatory variables. Then, an analysis of the links between HRB estimates and
each explanatory variable was performed. Since residues of linear models explaining absHRB
or relHRB with any of the explanatory variables departed significantly from a normal
distribution, we used non-parametric Kruskal-Wallis tests, using the package 'pgirmess', to
assess the significance of the link between HRB estimates and each explanatory variable and
to compare the HRB between virus categories. Additionally, regression trees were realized to
better explore the effect of the most significant explanatory variables on HRB. We used the
conditional inference trees method 'ctree' implemented in the package 'party' with a
minimum number of 15 viruses in each terminal "leaf" of the tree and default setting for other
parameters to describe the conditional distribution of absHRB and relHRB as a function of the
four categorical variables GEN, SEG, VER and VEC.

Relationships between virus host range and plant taxonomy
Different diversity indices were calculated to unravel the links between the host status of plant
species and their taxonomic proximity. For each virus species, the host diversity was
calculated as presented before with the Hill numbers of orders q=0 to q=3. The mean Hill
numbers of host plants for all virus species that we obtained for q = 0 to q = 3 were compared
among plant taxonomic ranks with Kruskal-Wallis tests.

Plant taxonomy was according to Brunt *et al.* [15] and Watson & Dallwitz [44,45] at the
species, genus and family ranks and according to Stevens [46] at higher ranks.

**Structure of the plant-virus infectivity matrix**

Infectivity matrices, where a set of parasite species or genotypes are confronted to a set of
host species or genotypes, contain binary data related to parasites’ host range (e.g. 1 for hosts
and 0 for non-hosts). Structural patterns of such matrices, notably their modularity and
nestedness, vary depending on host range evolution at the species or intraspecific levels
[18;47-49]. In a nested pattern, the host range of the more-specialized viruses is a subset of
the host range of less-specialized viruses, leading to a stair-shaped pattern for host cases. This
corresponds to a host range expansion pattern of virus evolution. In contrast, a modular
pattern, where each virus is specialized to infect one (or a small set of) host species,
corresponds to a host shift pattern of evolution. In that case, viruses become specialized on
new hosts at the cost of losing infectivity on older hosts.

Methods to estimate nestedness and modularity are described in detail in Weitz *et al.*
[18] and were computed using the ‘bipartite’ and ‘igraph’ packages, respectively. Because
these methods do not accept missing data (plant-virus combinations for which the host status
is unknown), the first step was to extract a sub-matrix from the database with a good
compromise between the number of plant and virus species and the amount of missing data.

For this, the lines and columns of the initial matrix were permuted to rank plant and virus
species by increasing numbers of missing data, evidencing a 37 virus species × 28 plant
species subset containing only 9.8% of missing data which was subsequently analyzed.

Attempts to analyze larger matrices were not successful because of too many missing data (data not shown). Then, we simulated the host/non-host status for missing data. Two approaches were followed: (i) the host/non-host status of missing data was set as proportional to the total amounts of host and non-host cases in the rest of the infectivity matrix (Bernoulli model) and (ii) each plant-virus combination missing in the matrix was assigned a probability of being a host case which was equal to the mean of the frequencies of host cases in the same column and in the same line of the initial matrix (probabilistic degree model). In both cases, 100 filled matrices were simulated. For statistical significance assessment, the nestedness and modularity of the infectivity matrices filled as described previously were compared to two different null models [18]: (i) the Bernoulli random null model, where the same total number of host cases as in the filled matrix was randomly distributed in matrices containing the same number of lines and columns as the filled matrix and (ii) the probabilistic degree null model, where each plant-virus combination was assigned a probability of corresponding to a host case which was equal to the mean of the frequencies of host cases in the same column and in the same line of the filled matrix. Each of the 100 filled matrices was compared to 1000 matrices generated under both null models.

Funding information
This work was supported by the MIE (Maladies Infectieuses Emergentes) interdisciplinary programme of CNRS (Centre National de la Recherche Scientifique). The funders did not play any role in the study or in the preparation of the article or decision to publish.

**Acknowledgments**

Thanks to Véronique Decognet for efficient help in the management of the database, to Denis Fargette and Hervé Lecoq for the spark of this study and to Thierry Candresse, Mark Tepfer, Karine Berthier, Stéphane Blanc and Julien Papaïx for help in analyses and/or constructive comments on the manuscript.

**Conflict of interest**

The authors declare no conflict of interest.

**Ethical statement**

Not applicable
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<table><caption>**Table 1.** Mean host range breadth of groups of plant viruses sharing genome or transmission properties.</caption><thead><tr><th>Virus trait<br>[Variable]</th><th>Significance*</th><th>Virus group</th><th>absHRB</th><th>N†</th></tr></thead><tbody><tr><td rowspan="4">Genome type<br>[GEN]</td><td rowspan="4">&lt;10<sup>-15</sup>***</td><td>ssRNA</td><td>16.7 a‡</td><td>384</td></tr><tr><td>ssDNA</td><td>12.6 a</td><td>44</td></tr><tr><td>dsDNA</td><td>3.9 b</td><td>21</td></tr><tr><td>dsRNA</td><td>3.6 b</td><td>31</td></tr><tr><td rowspan="4">Genome segments<br>[SEG]</td><td rowspan="4">9.3×10<sup>-5</sup>***</td><td>3 segments</td><td>28.3 a</td><td>35</td></tr><tr><td>2 segments</td><td>15.7 b</td><td>109</td></tr><tr><td>1 segment</td><td>13.5 b</td><td>316</td></tr><tr><td>&gt;3 segments</td><td>10.5 b</td><td>20</td></tr><tr><td rowspan="2">Vertical transmission<br>[VER]</td><td rowspan="2">8.0×10<sup>-5</sup>***</td><td>seed transmission</td><td>20.2 a</td><td>140</td></tr><tr><td>none</td><td>12.8 b</td><td>340</td></tr><tr><td rowspan="5">Abiotic<br>horizontal<br>transmission</td><td rowspan="5">0.005**</td><td>substrate</td><td>22.0 ab</td><td>6</td></tr><tr><td>contact between<br>plants</td><td>16.5 a</td><td>61</td></tr><tr><td>graft</td><td>16.3 ab</td><td>80</td></tr><tr><td>none</td><td>14.4 b</td><td>319</td></tr><tr><td>tools</td><td>10.1 ab</td><td>14</td></tr><tr><td rowspan="10">Vector type</td><td rowspan="10">1.2×10<sup>-4</sup>***</td><td>thrips</td><td>38.3 ab</td><td>6</td></tr><tr><td>nematode</td><td>33.5 a</td><td>24</td></tr><tr><td>Coleoptera</td><td>15.1 ab</td><td>42</td></tr><tr><td>aphid</td><td>14.8 b</td><td>168</td></tr><tr><td>no vector</td><td>14.4 b</td><td>120</td></tr><tr><td>fungus</td><td>11.3 b</td><td>24</td></tr><tr><td>other Hemiptera</td><td>11.1 b</td><td>55</td></tr><tr><td>whitefly</td><td>10.9 b</td><td>35</td></tr><tr><td rowspan="4">Vector type<br>(arthropods<br>grouped)<br>[VEC]</td><td rowspan="4">1.3×10<sup>-4</sup>***</td><td>nematode</td><td>33.5 a</td><td>24</td></tr><tr><td>no vector</td><td>14.4 b</td><td>120</td></tr><tr><td>arthropod</td><td>14.0 b</td><td>312</td></tr><tr><td>fungus</td><td>11.3 b</td><td>24</td></tr><tr><td rowspan="3">Kind of<br>vector<br>transmission</td><td rowspan="3">0.87<sup>ns</sup></td><td>circulative and non-<br>multiplying</td><td>16.4 a</td><td>80</td></tr><tr><td>no vector<br>transmission</td><td>14.7 a</td><td>165</td></tr><tr><td>non circulative</td><td>14.7 a</td><td>207</td></tr></tbody></table>
*P*-values of Kruskal-Wallis significance tests are followed by ns, *, **, *** when tests are non-significant, p-value<0.05, p-value<0.01 and p-value<0.001, respectively.

†N: number of virus species in the group.

‡Mean values of absolute host range breadth (absHRB) for each virus group based on a dataset of 480 virus species. Virus groups sharing letters are not significantly different according to Kruskal-Wallis multiple comparisons (p-value>0.05).
<table><caption>**Table 2.** Statistical significance of nestedness and modularity in a 37 virus species × 28 plant species infectivity matrix.</caption><thead><tr><th>Analysis</th><th>Algorithm</th><th>100 B ×<br>1000 B*</th><th>100 B ×<br>1000 PD</th><th>100 PD<br>× 1000 B</th><th>100 PD ×<br>1000 PD</th></tr></thead><tbody><tr><td rowspan="3">Nestedness</td><td>binmatnest2<sup>†</sup></td><td>&lt;10<sup>-5</sup><sup>‡</sup></td><td>&lt;10<sup>-5</sup></td><td>&lt;10<sup>-5</sup></td><td>&lt;10<sup>-5</sup></td></tr><tr><td>NODF2<sup>†</sup></td><td>&lt;10<sup>-5</sup></td><td>&lt;10<sup>-5</sup></td><td>&lt;10<sup>-5</sup></td><td>&lt;10<sup>-5</sup></td></tr><tr><td>wine<sup>†</sup></td><td>&lt;10<sup>-5</sup></td><td>&lt;10<sup>-5</sup></td><td>&lt;10<sup>-5</sup></td><td>&lt;10<sup>-5</sup></td></tr><tr><td rowspan="2">Modularity</td><td>leading.eigenvector.community<sup>†</sup></td><td>0.078</td><td>0.049</td><td>0.037</td><td>0.023</td></tr><tr><td>spinglass.community<sup>†</sup></td><td>0.021</td><td>0.008</td><td>0.013</td><td>0.006</td></tr></tbody></table>

* Models used to fill missing data in the infectivity matrix (100 simulations) and to compare them to null hypotheses for statistical assessment (1000 simulations) [18]. B: Bernoulli model; PD: Probabilistic degree model.

†R functions used to estimate nestedness or modularity.

‡P-values corresponding to the frequency of null model simulations (over 10⁵) showing higher nestedness or modularity than the filled infectivity matrix.
**Fig. 1: Conditional inference regression trees modelling the host range breadth (HRB) of plant viruses with four explanatory variables representing major virus biological properties.** (a). Absolute host range breadth (absHRB) using a dataset of 480 virus species.

(b). Relative host range breadth (relHRB) using a dataset of 293 virus species. Explanatory variables were the genome nature of viruses (variable ‘GEN’; 4 categories: ssrna, ssdna, dsrna, dsdna; see Methods section), their number of genome segments (variable ‘SEG’, 4 categories: one, two, three, >3), the mode of vertical transmission (variable ‘VER’, 2 categories: seed, no) and the vector type (arthropods were grouped) (VEC). Trees should be interpreted by starting at the top, following each branch down from each node, to arrive to a terminal node. For each terminal node, boxplots of host range breadths are represented and the average host range breadths indicated. n: number of virus species in the group.
(a)

<figure><img src="image_2.png" /></figure>

(b)

<figure><img src="image_3.png" /></figure>
**Fig. 2: Comparison of the diversity of plant taxa in the host and/or non-host species groups of viruses.** Mean of Hill numbers of orders q=0 to q=3 corresponding to the diversity of host species averaged for all virus species at different plant taxonomic ranks. For each q order, means of Hill numbers were compared between plant taxonomic levels by a Kruskal-Wallis multiple test. Plant taxonomic levels sharing letters are not significantly different for a given q order. Taxonomic rank X was based on plant phylogeny and contains Eurosids I and II, Euasterids I and II, Commelinids, Asparagales, Liliales, Caryophyllales, Alismatales, Ranunculales, Proteales, Buxales, Cycadales, Dioscoreales, Polypodiales, Saxifragales, Vitales, Pinales, Charales, Cornales, Ericales, Geraniales and Myrtales [46].
<figure><img src="image_4.png" /></figure>
**Fig. 3: Evidence of nestedness (a) and modularity (b) in a 37 virus species × 28 plant species infectivity matrix.** The two matrices correspond to the same dataset after permutation of lines and columns. Black boxes correspond to hosts (infection) and white boxes to non-hosts (no infection). Gray boxes correspond to missing data. Modularity analyses allowed identifying three modules (delineated by gray lines), partially associated with three plant families.

Module 1 contained the following plant species: *Trifolium repens*, *Trifolium incarnatum*, *Solanum tuberosum*, *Lactuca sativa*, *Brassica campestris*, *Glycine max*, *Pisum sativum*, *Vicia faba*, *Vigna unguiculata*, *Cucumis sativus* and *Phaseolus vulgaris*, and the following virus species: BWYV (beet western yellows polerovirus), AMV (alfalfa mosaic alfamovirus), CMV (cucumber mosaic cucumovirus), TRSV (tobacco ringspot nepovirus), TSV (tobacco streak ilarvirus), SLRSV (strawberry latent ringspot sadwavirus), TRV (tobacco rattle tobravirus), BCTV (beet curly top curtovirus), PSV (peanut stunt cucumovirus), RCNMV (red clover necrotic mosaic dianthovirus), TBRV (tomato black ring nepovirus), SMV (soybean mosaic potyvirus) and CIYMV (clover yellow mosaic potexvirus).

Module 2 contained the following plant species: *Cucurbita pepo*, *Chenopodium album*, *Spinacia oleracea*, *Tetragonio tetragonoides*, *Beta vulgaris*, *Gomphrena globosa*, *Chenopodium amaranticolor* and *Chenopodium quinoa*, and the following virus species: CarMV (carnation mottle carmovirus), OkMV (okra mosaic tymovirus), CVMoV (carnation vein mottle potyvirus), ArMV (arabis mosaic nepovirus), HLV (heracleum latent vitivirus), TuMV (turnip mosaic potyvirus), BtMV (beet mosaic potyvirus), CymRSV (cymbidium ringspot tombusvirus), PFBV (pelargonium flower break carmovirus), TNV-A (tobacco necrosis A necrovirus), BYMV (bean yellow mosaic potyvirus), HVS (helenium carlavirus
S), PSbMV (pea seedborne mosaic potyvirus), SqMV (squash mosaic comovirus), BNYVV (beet necrotic yellow vein benyvirus) and CPMMV (cowpea mild mottle carlavirus).

Module 3 contained the following plant species: *Nicotiana tabacum*, *Nicotiana glutinosa*, *Nicotiana clevelandii*, *Lycopersicon esculentum*, *Petunia x hybrida*, *Zinnia elegans*, *Nicotiana rustica*, *Physalis floridana* and *Datura stramonium*, and the following virus species: TEV (tobacco etch potyvirus), CVB (chrysanthemum B carlavirus), SPMMV (sweet potato mild mottle ipomovirus), RMV (ribgrass mosaic tobamovirus), PopMV (poplar mosaic carlavirus), PTV (peru tomato mosaic potyvirus), PVMV (pepper veinal mottle potyvirus) and PhyMV (physalis mosaic tymovirus).
<figure><img src="image_5.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_6.png" /></figure>
Method S1. Determination of a virus subset with a good compromise between the
precision of host range breadth (HRB) estimates and number of remaining viruses.

In the VIDE database, the total number of assayed plant species (i.e. plant species for which the host or non-host status is known) varies greatly among virus species and can be small for some. Consequently, for those latter viruses the absolute host range breadth (absHRB), corresponding to the total number of reported host species is an imprecise estimate of HRB.

To take into account the total number of plant species assayed for each virus, we used the relative host range breadth (relHRB), i.e. the absHRB divided by the total number of assayed plant species. When the number of assayed plant species increases, so does the precision of HRB estimates, and absHRB and relHRB tend to be equivalent (see Figure below). The coefficient of correlation between absHRB and relHRB is therefore an indicator of the precision of HRB estimates. Accordingly, a global increase of ρ, the Spearman's coefficient of correlation between absHRB and relHRB, is observed as the total number of assayed plant species increases and corresponds to a decreasing number of viruses remaining in the dataset.

Note however that when the number of assayed plant species becomes high (>50), the number of remaining virus species is too low for a precise estimation of the coefficient of correlation between relHRB and absHRB. A minimal threshold of plant species number was noticeable (~15 plant species) beyond which the increase of ρ became smaller per additional plant species, corresponding to a change of slope of the linear regression between ρ and the number of remaining virus species in the dataset (see Figure below). Consequently, this threshold of 15 plant species corresponds to a good compromise between the precision of HRB estimation (ρ=0.74) and the number of virus species remaining in the dataset (293 species). Therefore, the relHRB was analyzed only for the restricted set of viruses (293 species) for which a minimum of 15 plant species was assayed (Table S1). The absHRB was analyzed for the 293-
virus dataset but also for more exhaustive datasets (Table 1; Suppl. Table S1). Globally, we expect more precision from the 293-virus dataset but more statistical power for more exhaustive datasets, especially for underrepresented virus groups (for example viruses with a double-stranded genome).

<figure><img src="image_7.png" /></figure>
Method S2.

Tentative models for the prediction of plant virus host range breadth

We defined an ordinal variable, Class_absHRB, ordering the 480 virus species in the database
into three equilibrated classes of increasing absHRB: class 1 (1 to 5 hosts; 145 virus species),
class 2 (6 to 15 hosts; 170 species), and class 3 (>15 hosts; 165 species). We then fitted and
analyzed the performance of a cumulative link model with a logit link explaining
Class_absHRB as a function of the four main viral properties found significant (VEC, SEG,
VER, GEN). The model only included the main effects of these four explanatory variables
without any interaction (most interaction parameters could not be estimated). The model was
fitted with the function "clm" of the package "ordinal" in the R software version 3.0.2. The
predictive performance of the model was evaluated by cross-validation tests. In these tests, we
randomly left out 10% of the virus species (48 species), fitted the model to the remaining
dataset (432 species) and determined the confusion matrix of the 48 left-out virus species for
the 3 classes of absHRB, i.e. a two dimension contingency table ("actual" and "predicted")
with the numbers of false positives, false negatives, true positives and true negatives. These
steps were iterated 500 times. The predictive performance of the model was summarized by
the average sensitivity, specificity, positive predictive value and negative predictive value for
each absHRB class. These indices were calculated with the 'confusionMatrix' function of the
'caret' package in the R version 3.0.2 software, using the approach "one level versus all". The
sensitivity is the probability that the model classifies in class cᵢ of absHRB (1 ≤ i ≤ 3) a virus
that belongs effectively to class cᵢ. The specificity is the probability that the model does not
classify in class cᵢ a virus that is effectively not in class cᵢ. The positive predictive value
(PPV) is the probability that a virus classified in class cᵢ by the model is indeed in class cᵢ (i.e.
true positive results of the model). The negative predictive value (NPV) is the probability that
a virus not classified in class cᵢ by the model is indeed not in class cᵢ (i.e. true negative results of the model). Over the 500 cross-validations realized, the overall accuracy (i.e. the overall rate of correct assignments to absHRB classes by the model) was 0.48. It should be compared to a no-information rate of 0.33 (proportion of correct assignments obtained by choosing a class randomly) or 0.35 (proportion of correct assignments obtained by choosing the most frequent class in the dataset). The detailed analysis for each absHRB class is provided in the table below. Focusing on the two extreme classes, the specificity of the model was high (0.92 and 0.79 for classes 1 and 3, respectively) but its sensitivity lower (0.36 and 0.47 for classes 1 and 3, respectively). Sensitivity and specificity measures can be combined with the prevalence of the three absHRB classes, i.e. their proportion in the cross-validation datasets, to estimate the positive predictive value (PPV) and negative predictive value (NPV) of the model (i.e. the proportions of true positive and true negative results). Accordingly, the probability that a virus classified in class 1 (or 3) by the model belongs truly to class 1 (or 3) is 0.65 (respectively 0.55). The probability that a virus not classified in class 1 (or 3) by the model does truly not belong to this class is 0.77 (respectively 0.74). These figures should be compared to the ones obtained with a baseline random classifier (i.e. a classifier choosing an absHRB class randomly), indicated in brackets in the table below.

<table><thead><tr><th rowspan="2">absHRB class</th><th rowspan="2">Prevalence</th><th colspan="4">Model performance index for host species</th></tr><tr><th>Sensitivity</th><th>Specificity</th><th>PPV</th><th>NPV</th></tr></thead><tbody><tr><td>Class1: 1 to 5 hosts</td><td>0.3</td><td>0.36 (0.33)</td><td>0.92 (0.67)</td><td>0.65 (0.3)</td><td>0.77 (0.7)</td></tr><tr><td>Class 2: 6 to 15 hosts</td><td>0.35</td><td>0.6 (0.33)</td><td>0.5 (0.67)</td><td>0.4 (0.35)</td><td>0.69 (0.65)</td></tr><tr><td>Class 3: >15 hosts</td><td>0.35</td><td>0.47 (0.33)</td><td>0.79 (0.67)</td><td>0.55 (0.35)</td><td>0.74 (0.65)</td></tr></tbody></table>
Next, we tested how the model performance changes if classes 2 and 3 are grouped. We defined a new binary ordinal variable (TwoClass_absHRB) with two classes of increasing absHRB: class 1 (1 to 5 hosts; 145 virus species) and class 2 (>5 hosts; 335 species). We then used the same analysis framework. The overall accuracy of this model was 0.75 that should be compared to a no-information rate of 0.5. The specificity of the model was high (0.95) but its sensitivity was low (0.3). The PPV and NPV were of 0.73 and 0.76, respectively. Finally we estimated the area under the Receiver Operating Characteristic curve associated to this binary predictor using the package pROC. This area (AUC) also measures the accuracy of a binary predictor. It ranges from 0.5 (no discrimination between the 2 classes of absHRB) to 1 (perfect discrimination). The AUC was estimated to 0.73 with a 95% confidence interval ranging from 0.68 to 0.78.

The same analyses were performed to predict the diversity of host genera and families (Hill numbers of order q=2, Hill<sub>gen</sub> and Hill<sub>fam</sub>, respectively; see Methods section). We defined the ordered variable Class_Hill<sub>gen</sub> with the following three equilibrated classes of increasing diversity of host genera: class 1 (Hill<sub>gen</sub> < 3.33; 156 virus species), class 2 (3.33 ≤ Hill<sub>gen</sub> ≤ 7.25; 160 species), and class 3 (Hill<sub>gen</sub> > 7.25; 164 species). Similarly we defined the ordered variable Class_Hill<sub>fam</sub> with the following three equilibrated classes of increasing diversity of host families: class 1 (Hill<sub>fam</sub> < 1.19; 159 virus species), class 2 (1.19 ≤ Hill<sub>fam</sub> ≤ 2.9; 158 species), and class 3 (Hill<sub>fam</sub> > 2.9; 163 species). The overall rate of correct assignments by the model was slightly lower (0.45) for Hill<sub>gen</sub> and slightly higher (0.53) for Hill<sub>fam</sub> than for absHRB (0.48). These values should be compared to a no-information rate of 0.33 (proportion of correct assignments obtained by choosing a class randomly). The detailed predictive performance are summarized in the tables below.
Model performance index for the diversity of host genera

<table><thead><tr><th>Hill<sub>gen</sub> class</th><th>Prevalence</th><th>Sensitivity</th><th>Specificity</th><th>PPV</th><th>NPV</th></tr></thead><tbody><tr><td>Class1: &lt; 3.33</td><td>0.32</td><td>0.36 (0.33)</td><td>0.86 (0.67)</td><td>0.55 (0.33)</td><td>0.74 (0.67)</td></tr><tr><td>Class 2: 3.33 to 7.25</td><td>0.33</td><td>0.55 (0.33)</td><td>0.51 (0.67)</td><td>0.36 (0.33)</td><td>0.69 (0.67)</td></tr><tr><td>Class 3: &gt; 7.25</td><td>0.34</td><td>0.46 (0.33)</td><td>0.82 (0.67)</td><td>0.57 (0.33)</td><td>0.75 (0.67)</td></tr></tbody></table>

Model performance index for the diversity of host families

<table><thead><tr><th>Hill<sub>fam</sub> class</th><th>Prevalence</th><th>Sensitivity</th><th>Specificity</th><th>PPV</th><th>NPV</th></tr></thead><tbody><tr><td>Class1: &lt; 1.19</td><td>0.33</td><td>0.45 (0.33)</td><td>0.91 (0.67)</td><td>0.71 (0.33)</td><td>0.77 (0.67)</td></tr><tr><td>Class 2: 1.19 to 2.9</td><td>0.32</td><td>0.59 (0.33)</td><td>0.54 (0.67)</td><td>0.38 (0.33)</td><td>0.73 (0.67)</td></tr><tr><td>Class 3: &gt; 2.9</td><td>0.34</td><td>0.56 (0.33)</td><td>0.86 (0.67)</td><td>0.67 (0.33)</td><td>0.79 (0.67)</td></tr></tbody></table>
<table><caption>**Table S1. Robustness of the links between host range breadth (HRB) estimates and five biological properties of virus species.** Different datasets were analyzed (columns) corresponding to different requirements in terms of numbers of assayed plant species (n) and/or in terms of diversity and distribution evenness of these species across plant genera or families (Hill<sub>gen.</sub> and Hill<sub>fam.</sub>, respectively, corresponding to the Hill number of order 2; see Methods section). As a consequence, the resulting datasets comprised varying numbers of virus species (N). The absolute HRB was analyzed for white columns. Absolute and relative HRBs were analyzed for gray columns and gave similar results, unless indicated. In that case, results for absolute and relative HRBs are on the left and on the right of the slash character, respectively.</caption><thead><tr><th></th><th>n≥15</th><th>Hill<sub>gen≥5</sub></th><th>Hill<sub>gen≥10</sub></th><th>Hill<sub>gen≥15</sub></th><th>Hill<sub>gen≥20</sub></th><th>Hill<sub>gen≥5</sub><br>and n≥15</th><th>Hill<sub>gen≥10</sub><br>and n≥15</th><th>Hill<sub>fam≥2</sub></th><th>Hill<sub>fam≥4</sub></th><th>Hill<sub>fam≥5</sub></th><th>Hill<sub>fam≥6</sub></th><th>Hill<sub>fam≥2</sub><br>and n≥15</th><th>Hill<sub>fam≥4</sub><br>and n≥15</th></tr></thead><tbody><tr><td>N</td><td>293</td><td>346</td><td>254</td><td>164</td><td>100</td><td>287</td><td>247</td><td>333</td><td>204</td><td>143</td><td>88</td><td>258</td><td>185</td></tr><tr><td>Genome type</td><td>*|NS*</td><td>✓</td><td>*</td><td>NS</td><td>NS</td><td>*|NS*</td><td>*|NS*</td><td>✓</td><td>✓</td><td>NS</td><td>NS</td><td>*|NS*</td><td>*</td></tr><tr><td>Genome segments</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Vertical transmission</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Vector type (arthropods grouped)</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Kind of vector transmission</td><td>✓</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>✓</td><td>✓</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS/✓</td><td>✓</td></tr></tbody></table>

<footnote>*NS: no significant effect (p-value>0.05) was detected with Kruskal-Wallis multiple test; ✓: a globally significant effect (p-value<0.05) was detected with Kruskal-Wallis multiple test and the different virus groups showed significant differences similar to those reported in Table 1; *: a globally significant effect (p-value<0.05) was detected with Kruskal-Wallis multiple test but no significant differences were observed between virus groups. However, the ordering of the different virus groups in terms of HRB was similar to that reported in Table 1.</footnote>
<table><caption>Table S2. Comparison of host range breadths of plant virus genera.</caption><thead><tr><th>Virus genus</th><th>absHRB<br>(293 viruses)</th><th>relHRB<br>(293 viruses)</th><th>N<sub>293</sub><sup>*</sup></th><th>absHRB<br>(480 viruses)</th><th>N<sub>480</sub><sup>*</sup></th></tr></thead><tbody><tr><td><i>Nepovirus</i></td><td>31.3 a<sup>†</sup></td><td>73% a</td><td>23</td><td>28.7 a</td><td>26</td></tr><tr><td><i>Ilarvirus</i></td><td>28.4 ab</td><td>71% ab</td><td>12</td><td>22.8 ab</td><td>16</td></tr><tr><td><i>Potexvirus</i></td><td>19.6 ab</td><td>56% abc</td><td>15</td><td>14.7 ab</td><td>23</td></tr><tr><td><i>Potyvirus</i></td><td>18.8 ab</td><td>43% c</td><td>60</td><td>15.5 b</td><td>79</td></tr><tr><td><i>Tymovirus</i></td><td>18.8 ab</td><td>41% c</td><td>16</td><td>17.0 ab</td><td>18</td></tr><tr><td><i>Comovirus</i></td><td>16.0 ab</td><td>54% abc</td><td>13</td><td>15.1 ab</td><td>14</td></tr><tr><td><i>Carlavirus</i></td><td>14.1 b</td><td>37% c</td><td>20</td><td>10.8 b</td><td>31</td></tr><tr><td><i>Begomovirus</i></td><td>12.3 b</td><td>42% bc</td><td>15</td><td>9.1 b</td><td>29</td></tr></tbody></table>

<footnote>*N<sub>293</sub> and N<sub>480</sub>: number of viruses of each genus in the 293- and 480-virus datasets, respectively.</footnote>

<footnote>†Mean values of absolute (absHRB) or relative (relHRB) host range breadth for different virus genera. Virus genera sharing letters are not significantly different according to Kruskal-Wallis multiple comparisons (p-value>0.05). Only virus genera containing at least 12 species in the 293-virus dataset were considered.</footnote>
**Fig. S1: Multiple Correspondence Analysis (MCA) of six plant virus traits used as explanatory variables for virus host range breadth.** Variables were projected on the first two axes of the MCA and the percentage of variance explained by each axis is indicated. (a). MCA using the whole virus dataset (480 species) with the absolute host range breadth (absHRB) variable included as illustrative variable. (b). MCA using the restricted virus dataset (293 species for which ≥15 plant species were assayed) with absHRB and relative host range breadth (relHRB) variables included as illustrative variables.
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>