Environmental heterogeneity and the evolution of plant-virus interactions: Viruses in wild pepper populations

Accepted Manuscript Title: Environmental heterogeneity and the evolution of plant-virus interactions: Viruses in wild pepper populations Authors: Aurora Fraile, Michael J. McLeish, Israel Pag´an, Pablo Gonz´alez-Jara, Daniel Pi˜nero, Fernando Garc´ıa-Arenal PII: DOI: Reference:

S0168-1702(16)30793-6 http://dx.doi.org/doi:10.1016/j.virusres.2017.05.015 VIRUS 97143

To appear in:

Virus Research

Received date: Revised date: Accepted date:

1-12-2016 18-5-2017 19-5-2017

Please cite this article as: Fraile, Aurora, McLeish, Michael J., Pag´an, Israel, Gonz´alezJara, Pablo, Pi˜nero, Daniel, Garc´ıa-Arenal, Fernando, Environmental heterogeneity and the evolution of plant-virus interactions: Viruses in wild pepper populations.Virus Research http://dx.doi.org/10.1016/j.virusres.2017.05.015 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Environmental heterogeneity and the evolution of plant-virus interactions: Viruses in wild pepper populations Aurora Frailea, Michael J. McLeisha, Israel Pagána, Pablo González-Jaraa,c, Daniel Piñerob, Fernando García-Arenala* a

Centro de Biotecnología y Genómica de Plantas UPM-INIA and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, Spain b Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, México D.F. México. c Present adress: Servicio de Animalario. Centro de Investigaciones Biológicas, C.S.I.C., Madrid, Spain. *Corresponding autor at: Centro de Biotecnología y Genómica de Plantas UPM-INIA, Campus de Montegancedo, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, Spain. Email: [email protected]

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Highlights 

Human management of wild pepper populations results in increased disease risk

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The impact of ecosystem simplification on infection is virus-dependent

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Ecological niche was a predictor of virus prevalence.

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Virus and host phylogenetic congruence occurs only for wild populations

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Virus infection of chiltepin in wild populations negatively impacts host fitness

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Abstract Understanding host-pathogen interactions requires analyses to address the multiplicity of scales in heterogeneous landscapes. Anthropogenic influence on plant communities, especially cultivation, is a major cause of environmental heterogeneity. We have approached the analysis of how environmental heterogeneity determines plant-virus interactions by studying virus infection in a wild plant currently undergoing incipient domestication, the wild pepper or chiltepin, across its geographical range in Mexico. We have shown previously that anthropogenic disturbance is associated with higher infection and disease risk, and with disrupted patterns of host and virus genetic spatial structure. We now show that anthropogenic factors, species richness, host genetic diversity and density in communities supporting chiltepin differentially affect infection risk according to the virus analysed. We also show that in addition to these factors, a broad range of abiotic and biotic variables meaningful to continental scales, have an important role on the risk of infection depending on the virus. Last, we show that natural virus infection of chiltepin plants in wild communities results in decreased survival and fecundity, hence negatively affecting fitness. This important finding paves the way for future studies on plant-virus co-evolution. Keywords: Biodiversity, infection risk, disease risk, plant-virus co-evolution, virulence

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1. Introduction For a long time, plant pathologists have had the perception that infectious diseases of plants were more severe in crops than in wild plants, which was attributed to ecological differences between crops and wild ecosystems. Specifically, three factors were associated with the high impact of pathogens in crops: i) the reduced species diversity, ii) the higher host density of crop fields compared to wild ecosystems, and iii) the reduced genetic diversity of crops relative to their wild ancestors or relatives (Burdon and Chilvers, 1982; Day, 1978). Biodiversity loss, land use changes and agriculture have been identified as factors in pathogen emergence (Jones et al., 2008; Rogers and Randoph, 2003; Woolhouse and Gowtage-Sequeria, 2005). More recently, concerns about biodiversity loss and emergence and re-emergence of human and animal pathogens have prompted theoretical developments linking biodiversity and disease risk (Keessing et al., 2006; Keessing et al., 2010). Because host-parasite interactions occur across different, nested levels of organisation, understanding the emergence and evolution of infectious diseases requires analyses at the landscape scale (Biek and Real, 2010; Mideo et al., 2008). Spatial heterogeneity of ecological factors, both biotic and abiotic, across the landscape, may modulate the ecology and genetic structure of host populations, thus affecting the patterns of infection, and the genetic structure of pathogens (Archie et al., 2009; Biek and Real, 2010; Meentemeyer et al., 2012). Human management of plant populations is an important source of heterogeneity. Cultivation is intrinsically linked to a decrease in habitat diversity and increase in host plant density (Stukenbrock and McDonald, 2008), often with a loss of plant genetic diversity and its spatial structure (Gross and Olsen, 2010; Tang et al., 2010). Therefore, a reshaped host ecology resulting from human management should affect pathogen infection risk. However, support for this prediction is often circumstantial and mostly derives from the study of foliar fungal diseases (Stukenbrock and McDonald, 2008). Despite viruses being the major group of emerging plant pathogens (Anderson et al., 2004), empirical studies in wild ecosystems on the relationship among landscape heterogeneity, infection dynamics, and evolution are few and data are sparse (but see Borer et al., 2010; Cooper and Jones, 2006; Jones et al., 2008; Malmstrom et al., 2005; Power et al., 2011; Roossinck et al., 2010; Roossinck, 2012). We approached the study of the relationship between landscape heterogeneity and virus infection dynamics and evolution by focusing on a wild plant currently undergoing incipient domestication. The wild pepper or chiltepin, Capsicum annuum var. glabriusculum (Dunal) Heiser and Pickersgill, is considered the ancestor of the cultivated chili and bell pepper, C. annuum var. annuum (Pickersgill, 1997), an important crop worldwide. Chiltepin is a deciduous, perennial bush that may live for 5-8 years, which vegetates and reproduces during the rainy season. It is distributed from Colombia to the Southwestern United States. In Mexico it is found in a variety of biogeographic provinces and habitats, from dense evergreen forests in the Yucatan peninsula and 4

the Gulf of Mexico, to deciduous xeric woodlands in the central plateau, or the Sonoran desert where it is commonly associated with nurse trees (González-Jara et al., 2011; Tewksbury et al., 1999). The pungent red fruits of chiltepin are consumed by birds, which disperse the seeds (Tewksbury et al., 1999). Humans interfere with chiltepin ecology in a variety of ways because they also value chiltepin fruits. The least invasive form of disturbance is fruit harvesting from wild populations, a common practice in central and northern Mexico (Votava et al., 2002). Another moderate form of disturbance involves tolerating or tending of naturally dispersed plants within anthropic habitats such as pastures or living fences, which we call “let-standing” plants (sensu Casas et al., 2007). In the past 25 years, chiltepin cultivation has started in home gardens or as monocultures in small traditional fields, possibly as a result of growing demand and recession of their wild populations (Rodríguez del Bosque et al., 2002). In monoculture, chiltepin is managed as an annual crop derived from seeds gathered from the previous one, and represents the most intense form of disturbance. Cultivated chiltepin does not show obvious phenotypic differences with wild populations (González-Jara et al., 2011), and does not present any of the major traits of pepper domestication syndrome. These traits include enlarged, pendulous, non-deciduous fruits of different colours and pungency, flower morphology favouring selfing, and synchronised high germination rates (Paran and van der Knaap, 2007). Thus, the diversity of ecosystems in which wild chiltepin populations grow, and the three intensities of human disturbance, offers a unique opportunity to analyse the relationship between landscape heterogeneity and plant-virus interactions. In this article we summarise previous findings on the relationship between human management and virus infection patterns and evolution, we present new analyses based on field data relating infection risk to landscape heterogeneity, and analyse the impact of viral diseases on the fitness of this wild plant.

2. The relationship between habitat plant biodiversity and infection risk is virus-dependent Between 2007 and 2009 we monitored 27 chiltepin populations at different locations over the species range in Mexico representing three different levels of human management: eleven wild populations; six populations of let-standing plants, and ten cultivated populations. The locations of these populations are indicated in Figure 1. Most Wild populations were on dry deciduous forests, except for those in Costa del Pacífico Sur and Sierra madre Oreintal Biogeographic provinces, which grow in evergreen forests (HUA-w, and TLA-w, repectively in Fig. 1). Let standing populations (LS in Fig. 1) occupy leaving fences or pasture lands, and cultivated populations (C in Fig. 1) grow either in home gardens or small monoculture plots A detailed description of each population is provided by González-Jara et al. (2011). At each location, we censused: 1) the chiltepin population and the status of each plant: asymptomatic or showing symptoms commonly 5

related to virus infection (i.e., mosaic, leaf curl, leaf lamina reduction, and/or stunting). Populations largely differed in size, so that the number of evaluated plants varied accordingly, between 9 and 156 plants/population with an average over the three surveyed years of 26 plants/population (Pagán et al., 2012); 2) the density of chiltepin plants (plants/m2); and 3) the non-herbaceous vegetation recorded as the number of individuals of each bushy and arboreal species in the same area of the chiltepin population to estimate species richness. Floristic inventories were raised along 3 m wide and 250 m long transects that included the chiltepin population, so that the area monitored was the same for each site. At each site and visit, symptomatic and asymptomatic plants were sampled, and analysed in the laboratory for infection by Chiltepin yellow mosaic virus (ChYMV), a tymovirus described from chiltepin plants showing mosaic symptoms (Pagán et al., 2010), Cucumber mosaic virus (CMV, Cucumovirus), Pepper golden mosaic virus (PepGMV, Begomovirus), Pepper huasteco yellow vein virus (PHYVV, Begomovirus), and species of the genus Potyvirus. Plants were also genotyped using nine polymorphic microsatellite markers, and the genetic diversity estimated for each chiltepin population (González-Jara et al., 2011). Data on the incidence of the above viruses, and on the genetic structure of the host population, have been reported previously (González-Jara et al. 2011; Pagán et al. 2012; Rodelo-Urrego et al. 2012; Poulicard et al., 2016). ChYMV was found only at populations near Tula (Tamaulipas) in the central plateau (TUL in Figure 1), with high prevalence (~43%). Thus, it was excluded from further analysis. CMV, PepGMV, PHYVV and potyviruses were found in all surveyed populations, with prevalences of 6.5%, 13.4%, 11.1%, and 15.4%, respectively (Pagán et al., 2012; Poulicard et al., 2016; RodeloUrrego et al., 2013). Infection by any of these groups of viruses explained about 90% of observed symptoms, and begomovirus infection accounted for 65% of symptomatic plants. The relationship between habitat traits and begomovirus and CMV incidence has been analysed previously (Pagán et al., 2012; Rodelo-Urrego et al., 2013), and here we also present new analyses for potyviruses. Generalized lineal mixed model (GLMM) analyses indicated that the level of human management of the plant population did not affect CMV or potyviruses prevalence (w(2,49)=3.501, P=0.174). On the other hand, PepGMV and PHYVV prevalence was higher in cultivated than in let-standing and wild populations (w(2,49)>14.99, P<1x10-4), PepGMV and PHYVV prevalence was similar in letstanding and wild populations (P=0.760) (Figure 2). Accordingly, prevalence of symptomatic plants was higher in cultivated than in let-standing and wild populations (w(2,49)=7.619, P<1x10-4). We also analysed the relationship between the level of human management and diversity (as both plant species richness and chiltepin genetic diversity) and plant density. Species richness and host genetic diversity were significantly higher in wild than in cultivated populations, with intermediate values in let-standing populations (w(2,24)>2.390, P<0.045), and the opposite trend was observed for plant density (w(2,24)>4.995, P<0.015). Principal Components Analysis (PCA) indicated that changes in the percentage of variance contributed by each of the three ecological factors differed across the levels of human management. In wild populations, the first three PCs explained 95.7% 6

of the total variance in the chiltepin populations. Species richness was highly associated with PC1, plant density with PC2, and host genetic diversity with PC3 (squared loadings>81.9%). PCAs considering either let-standing or cultivated populations separately yielded similar percentages of the total variance in the first three PCs. However, contributions of variables to the loadings in each PC differed: host genetic diversity was now associated with PC1, species richness with PC2, and plant density with PC3 (squared loadings>82.7%). Thus, human management of the chiltepin populations had an impact on host ecology as well as on virus epidemiology. We further explored whether the observed changes in species richness, host plant genetic diversity and host plant density were associated with potyvirus, PepGMV, PHYVV and CMV prevalence in chiltepin populations. To do so, we performed bivariate correlation analyses using year-specific values of the above traits for each monitored chiltepin population. We considered linear and non-linear models, and we selected the best-fitted model using the Akaike´s Information Criterion. When all populations were considered together (n=50), a negative association between species richness and PepGMV and PHYVV prevalence was observed (R2>0.198; P<0.030), whereas potyviruses prevalence was positively associated with plant density (R2>0.201; P<0.024). In wild populations (n=20), PepGMV and PHYVV prevalence was negatively associated with species richness (R2>0.229; P<0.021), and positively associated with host plant density (R2>0.437; P<0.050). Potyviruses prevalence was also positively associated with plant density (R2>0.275; P<0.048). None of the three ecological factors had a significant relationship with CMV prevalence. In let-standing populations (n=11), PepGMV prevalence was negatively associated with species richness (R2=0.625; P=0.050), and PHYYV prevalence was negative associated with host genetic diversity (R2=0.554; P=0.050). In addition, a negative correlation between species richness and CMV prevalence was found (R2=0.458; P=0.041), and plant density showed a positive association with CMV and potyviruses prevalence (R2>0.311; P<0.050). Finally, in cultivated populations (n=19) none of the ecological factors analysed was associated with PepGMV or PHYVV prevalence, whereas plant density explained 28.5% and 34% of the variance in CMV and potyviruses prevalence, respectively (P<0.022). It should be noted that the association between the prevalence of symptomatic plants and each of the three ecological factors analysed followed the same patterns observed for PepGMV and PHYVV prevalence, again indicating that begomovirus infection risk and disease risk was largely equivalent in chiltepin populations. Our results show that environmental heterogeneity and biodiversity loss associated with human management of habitats, among other factors are correlated with infection and disease risk. This heterogeneity across habitats explains between 20% and 62% of the variation in virus incidence. Hypotheses relating biodiversity to disease risk have been forwarded that predict both and increase or a decrease of risk with decreasing biodiversity, i.e., amplification or dilution effects (Keesing et al., 2006; Ostfeld and Keesing, 2000). Our results significantly contribute to clarify this relationship, showing that for the same host species the effects of plant diversity loss on infection risk depend on the specific virus considered. Obviously, other environmental traits not considered 7

here may also affect virus incidence. Nevertheless, our results underline the relevance of different ecological factors in determining infection/disease risk in host plant populations under different levels of anthropic influence.

3. Ecological modelling of infection risk Geographic scale dependencies are also important in the interpretation of disease risk (EstradaPeña et al., 2014; Peterson et al., 2004). Correlations between variables, as in the previous section, might be context dependent because wide geographic areas are subject to scale, taxonomic, and ecological variation (Thompson, 1999; Stevens and Tello, 2014). So, it is important to consider scale effects on environmental heterogeneity in evaluating general explanations for disease risk (Katayama et al., 2014). In these section we present new analyses of the data reviewed in the previous one aimed at testing the hypotheses that: 1) chiltepin populations distributed across Mexico are ecologically distinct; and 2) environmental factors contribute to geographic variation in virus prevalence. For this, outlying mean index (OMI) analysis (Dolédec et al., 2000) was used to define ecological dimensions for each C. annuum glabriusculum presence record. Unlike the Maximum Entropy (MAXENT) approach, the characterisation of a population’s abiotic environmental characteristics (habitat) using OMI analyses does not rely on the assumption of niche conservatism. The OMI analysis is an extension of a principal components analysis, used to estimate deviations in mean habitat requirements, from the mean conditions of the entire sampling area. The aim of the OMI analysis is to identify habitat distinctions between populations, not predict species distributions based on the assumptions of species conservatism in abiotic conditions. Abiotic variables that are believed to influence the occupancy of a species were used to characterise habitats occupied by chiltepin populations. A variation of the OMI approach, called canonical OMI (canOMI; Calenge, 2006) was used to correct for possible distortions in the OMI analysis caused by disproportionately large variances in the presence data. For details on the approach see Supplemental Methods. Our presence data clearly shows a bias towards coastal localities (Fig. S2) and possibly indicates sampling bias in this region. Support indicated by the AUC scores for the distribution models (0.53-0.68), indicated the probability of predicted distributions was weak. However, the purpose of these models was to select variables that might best differentiate populations. The top ranked variables consistently produced in consecutive MAXENT models were considered the optimum variables for use in the canOMI analysis. Five climate layers were selected from the best MAXENT model comprising precipitation and temperature variables, including annual mean precipitation, precipitation driest quarter, mean diurnal range, isothermality, and mean temperature wettest quarter (for detailed explanations see: http://www.worldclim.org/). These variables were incorporated with land use, edaphic (soil type, 8

texture, physical phase, chemical phase), solar radiation, and topological factors into the canOMI analysis that does not rely on the assumption that particular variables are conserved across the range. The results showed that geographic distributions of the chiltepin populations and their ecological characteristics were not always coincidental (Fig. 3 and Fig. S2). The canOMI model generated two primary clusters central in ‘ecological space’ compared to several outlying populations. The outlying populations of HUA (Wild), DZI (Wild), and TLA (Wild) were geographically distinct (Fig. 3). Conversely, the BER (Wild) and HUJ (Wild) populations shared similar abiotic environmental characteristics, but not geographic proximity. Other instances include the MAZ-MAU and PVE populations. This cluster included Wild, Let-standing, and Cultivated populations. Presence data sourced from the literature (LIT) (Fig. S2) indicated a relatively typical ecological space for chiltepin. The sensitivity control locality (CHER) clustered within the same ecological space as the adjacent HER site. The control location showed some deviation from the study site 8 km away and apparent sensitivity to habitat differences at least at this distance. The ecologically distinct areas that Wild populations occupied indicated a range of conditions under which they and adjacent cultivated populations grow (Fig. 3), the extent of their ecological niche, and niche differences between populations (Araújo et al. 2011). That is, wild and cultivated populations shared similar conditions, but also grew under distinct ecological conditions. Geographic or ecological distinctiveness might result in local community variation among chiltepin populations, especially when dispersal between them is rare, and in local adaptation of host and viruses (Seabloom et al. 2015). Geographic distance will therefore have a negative relationship with virulence (e.g. Ebert 1994) and a positive relationship with host relatedness, which might result in differences among populations in the evolution of host resistance mechanisms (Antonovics et al. 2013). Next, we evaluated the contribution of the variables used in the canOMI, habitat category (Wild, Cultivated, Let-standing), species diversity, and chiltepin density to prevalence response models. For this, Markov chain Monte Carlo generalised linear mixed models (MCMCglmm) were used to assess variance contributions to prevalence responses of PepGMV and PHYVV (clumped together under “begomovirus”) and CMV, which in the previous section had been shown to show different responses to biodiversity loss. For details on procedures see Supplemental Methods. The MCMCglmm analyses indicated different combinations of predictor variables influenced prevalence depending on the virus species. Genetic relatedness among host populations was more important in structuring variance components in the begomovirus models (Table 1). The null MCMCglmms showed that the influence of host genetic relatedness on begomovirus prevalence was greater than that for CMV (Table 1). The inclusion of ecological variables improved the deviance information criterion (DIC; Spiegelhalter et al., 2002) in the begomovirus models. The MCMCglmms that included all the predictor variables indicate the contribution of ecological factors to begomovirus prevalence was greater than that of CMV prevalence (Table 2). The results show 9

that chiltepin density had a stronger influence on CMV prevalence compared to begomovirus. The Wild category had a negative relationship with begomovirus (posterior mean = -0.524; pMCMC = 0.446) and CMV (posterior mean = -0.734; pMCMC = 0.304) prevalence and weak predictive value in both models compared to the abiotic environmental and density factors (Table 2). Together, these new analyses were consistent with previous findings (Pagán et al., 2012) and with the new analyses presented in the previous section of this article. The findings highlight the importance of community diversity in infection prevalence (Keesing et al., 2006). Defining ecological characteristics in terms of geographic and climatic variation at large spatial scales provided a perspective for generalising about disease risk. As biodiversity results from multidimensional factors across geographic gradients, hypotheses that explain disease risk that rely solely on estimates of species richness and abundance, ignore other important dimensions of ecological organisation at the metapopulation level (e.g. Tack and Laine, 2014). Moreover, the influence that ecological factors have on virus prevalence was virus-dependent. A geographically broad perspective showed that anthropogenic factors were important, but the ecological conditions each population occupied represented a valuable predictor of Begomovirus prevalence.

4. Environmental heterogeneity and virus evolution In the previous sections we showed that chiltepin population heterogeneity due to human management affects PepGMV, PHYVV, and CMV infection risk and disease risk. Such effects may result in changes to genetic and demographic processes in the virus population, affecting its evolution (Archie et al., 2009; Grenfell et al., 2004; Pybus and Rambaut, 2009). Changes in host ecology and disease epidemiology due to human management should also result in genetic changes in the host population (Stukenbrock and McDonald, 2008; González-Jara et al., 2011). Thus, co-existence of hosts and parasites across a range of ecological conditions may lead to codivergence (Nieberding and Olivieri, 2007; Thompson, 2005), understanding co-divergence as the concomitant co-speciation of host and viral lineages (Charleston and Perkins, 2006). This is a subject that has received considerable attention, leading to a number of reports of host-virus codivergence (e.g. Bernard, 1994; Gibbs et al., 2010; Pavesi, 2005; Torres-Pérez et al., 2011). However, evidence on how landscape heterogeneity, in particular due to anthropogenic factors, influences plant-virus interactions potentially resulting in co-divergence, is limited. We have addressed this question focusing on the two most prevalent viruses in chiltepin populations, PepGMV and PHYVV, and here we summarise the most significant of our previous results (Rodelo-Urrego et al., 2013). We determined the nucleotide sequence of the coat protein (CP) gene of 136 PepGMV isolates from 15 chiltepin populations, and 112 PHYVV isolates from 16 chiltepin populations, in both cases including wild, let-standing, and cultivated populations. Using this data set, we first analysed the spatial genetics of both viruses (Rodelo-Urrego et al., 2013). 10

For both PepGMV and PHYVV, the within-population nucleotide diversity varied largely among populations (PepGMV: from π 0.0403±0.0058 to 0.0013±0.0008, and PHYVV: from π 0.0990±0.0082 to 0.0042±0.0017), but on average PHYVV was 1.6 times more diverse than PepGMV (0.0602±0.0043 vs. 0.0375±0.0038, respectively). Analyses of Molecular Variance (AMOVA) showed that the population diversity of both viruses was structured according to chiltepin population (P<1x10-3), with similar NST values (0.306±0.019 for PepGMV, 0.304±0.015 for PHYVV). By contrast, AMOVA showed that PepGMV and PHYVV population diversity was not structured according to the level of human management (PepGMV π: 0.0322±0.0038 to 0.0396±0.0040 and PHYVV π: 0.0558±0.0052 to 0.0587±0.0044; P>0.345). Mantel tests showed that, for both viruses, genetic distance was positively correlated with geographic distance (r2>0.216; P=0.001), which also largely held when cultivated populations, or wild plus let-standing populations were analysed separately (r2>0.151; P<0.066). In summary, PepGMV and PHYVV genetic diversity was strongly structured according to the host population, but not according to the level of human management. Barriers to virus short-distance dispersion could explain the spatial pattern observed at the host population scale. Indeed, this scale of spatial differentiation is compatible with migration flights of the whitefly Bemisia tabaci, the PepGMV and PHYVV vector, being of only a few kilometres (Byrne, 1999). These spatial patterns were associated with rapid evolutionary rates (mean of 2.5-4.1x10-3 nucleotide substitutions/site/year), and recent divergence times (Time to the Most Recent Common Ancestor: 62-14 years before present) of PepGMV and PHYVV populations, which are compatible with a recent introduction of both viruses into chiltepin populations (Rodelo-Urrego et al., 2013). The spatial pattern of genetic differentiation and dispersion of PepGMV and PHYVV was highly similar to that reported for the same chiltepin populations (González-Jara et al., 2011), indicating that common ecological factors were shaping the genetic structure of both host and viruses, and/or that the genetic structure of the host is a major determinant of the genetic structure of the viruses. Both processes could lead to the congruence of the genealogies of host and virus populations, which may be indicative of plant-virus co-divergence. Thus, we tested the extent of congruence between the chiltepin and the PepGMV and PHYVV phylogenies considering: (i) all host populations; and (ii) only wild and let-standing populations, as cultivated populations disrupt the spatial structure of the host (González-Jara et al., 2011). ParaFit tree reconciliation tests (Legendre et al., 2002), which considers both tree topologies and branch lengths, found no significant association between the phylogenies of the host and of each virus phylogeny when all populations were considered (P>0.071), but a significant association was observed when cultivated populations were excluded from the analysis (P<0.050) (Figure 4). Congruence between host and parasite phylogenies has often been taken as evidence of co-divergence, although the mode of parasite transmission and the relative evolutionary rates of host and parasite, among other factors, may affect this conclusion (Nieberding and Olivieri, 2007). Indeed, phylogenetic congruence is a topological phenomenon that has many explanations, one of which is co11

divergence (Jackson and Charleston, 2004). The short-term nucleotide substitution rates of begomoviruses are several orders of magnitude higher than the mean neutral substitution rate for 28 angiosperm species (3.4x10-9, Kay et al., 2006). Moreover, the recent diversification of the PepGMV and PHYVV populations, and the evidence that the demographic history of chiltepin has occurred within a much larger time frame (several thousand years, González-Jara et al., 2011) than that of PepGMV and PHYVV (a few decades), support different temporal scales of host and viruses evolution. All this evidence strongly argues against a hypothesis of chiltepin-begomovirus co-divergence. Rather, it could be hypothesized that barriers to gene flow will be similarly scaled for host(s) and viruses, as the dispersal range of the virus vectors is similar or smaller than the foraging ranges of host pollinators and dispersal agents (Byrne, 1999; Carlo et al., 2009; Greenleaf et al., 2007). Thus, local ecological differences would explain the spatial congruence of virus and hosts phylogenies. In summary, our results indicate that environmental heterogeneity, including human management

of

host

populations,

affects

the

genetic

structure

of

chiltepin-infecting

begomoviruses. Similar spatial genetic structures for host and viruses result in congruence between their phylogenies, which only appear to be due to spatially driven co-divergence. Therefore, our results highlight the need to consider ecological heterogeneity for understanding plant virus evolution and host-parasite co-divergence.

5. Effect of virus infection on plant survival and fecundity For a complete view of the relationship between environmental heterogeneity and plant-virus interactions it is necessary to understand the effects of virus infection on plant fitness (Alexander et al., 2014). Virus infection resulting in disease expression is assumed to be virulent, i.e., to reduce the plant fitness, but studies relating virus infection and plant fitness are not abundant (Fraile and García-Arenal, 2010). These studies are particularly necessary for wild plants in natural communities, with the few published analyses focussing on experiments of controlled infection of plants in environments assumed to approach their natural condition (Friess and Maillet, 1997; Malmstrom et al., 2005; Maskell et al., 1999). These results may differ from analyses of naturally infected plants in wild communities, as recent data indicate that the outcome, positive, neutral or negative, of plant virus interactions is environment dependent (Fraile and García-Arenal, 2016; Hily et al., 2016; Xu et al., 2008). Field data from wild chiltepin populations allows evaluating the effects of non-manipulative virus infection of chiltepin plants in their wild communities on two components of their fitness: survival, and fecundity. Here, we present new data and analyses addressing this subject. The fraction of plants showing symptoms of virus infection varied according to year. When only the wild populations were considered (re-elaborated from Pagán et al., 2012), the percentage 12

of symptomatic plants was of 16.5, 12.1, and 5.0 for 2007, 2008, and 2009 respectively, suggesting that few plants became newly diseased during the surveyed period. Local people involved in chiltepin fruit gathering from wild populations, who helped us locate them, were aware of symptoms of virus infection, and associated them with a lower production of fruits and a decrease of the plant life span, estimating that plants died about two years after developing symptoms. To analyse the effect of disease on plant survival, the number of symptomatic and asymptomatic plants of different ages was determined. Plant age was classed in the field according to the number of ramification orders, since each year lateral buds develop into new branches when plants resume vegetation after the dry season. Plants were divided into four age classes, i.e., class 1 included plants that had germinated the year the survey was conducted (<1 year), class 2 included those of 1-2 years, class 3 included those of 3-4 years and class 4, those of more than 4 years. These categories avoided having age classes with very few plants. A contingency analysis of the data for the 2007 survey (Figure 5A) showed that the distribution of symptomatic and asymptomatic plants was not homogeneous over age classes ( 2=23.477, P<1x10-3). The contingency analysis also showed that the transition from class 2 to 3 was accompanied by a significant increase in the proportion of symptomatic plants (from 10.5 to 30.4;

2=10.765, P<1x10-3), while the transition from class 3 to 4 was accompanied by a significant decrease of the fraction of symptomatic plants (from 30.4 to 5.3; 2=5.170, P=0.037). These data strongly suggest that symptomatic plants have a reduced life span, and are compatible with a survival of about two years after development of symptoms. Interestingly, the distribution of plants infected by any of the analysed viruses (see below) and of non-infected plants (Figure 5B) was homogeneous over age classes (2= 4.383, P= 0.211), indicating that the probability of infection does not depend on plant age. Comparison of the data in Figure 5A and 5B suggests that the incubation period is about one year, and that it is the status diseased or non-diseased rather than infected or non-infected that associates with an effect on plant survival. Non-homogeneity of the distribution of symptomatic and asymptomatic plants over age classes was maintained for 2008 and 2009, but significance was lower and the transitions from one age class to the other were not significant. This might be due to the low number of symptomatic plants in these two years (data not shown). Importantly, 2007 data for the Bernal (Queretaro) population in the central plateau (Table 3), which was the largest monitored wild population, indicated also a significantly higher than expected from random decrease of symptomatic plants in the transition from age class 3 to age class 4 (2= 5.857, P= 0.017), which was marginal for 2008 (2= 3.928, P= 0.064), thus being consistent with data for the total of wild populations. To analyse the potential effects of viral diseases on host fecundity during a survey conducted in 2010, the number of branches, flowers, and fruits of each chiltepin plant at each of seven wild populations were determined. Even if all flowers did not develop into fruits, we used the number of flowers and fruits per plant as a proxy for plant fecundity. Plant fecundity increased with age, but 13

the relationship between age and fecundity varied according to population. This is shown, for instance, by the comparison of plant fecundity in the Bernal and Tula populations (BER and TUL in Figure 6), which are both located about 100 km distant in the same biogeographical province in the central plateau (Figure 1), and differed in plant fecundity across age classes. Fecundity of Tula plants was lower than fecundity of Bernal plants after age class 2 (Figure 6). In addition, the number of seeds per fruit was analysed in ten randomly chosen fruits collected in the field from each of the 10 populations evaluated. The number of seeds per fruit (11.07±0.44) did not depend on population (w(9,91)=9.38, P=0.402 in a GLMM). When the fecundity of symptomatic and asymptomatic plants was compared, it was found that symptom expression was associated with a decrease of fecundity for plants in age classes 3 and 4, but not for younger plants. Mean fecundity of symptomatic plants older than 3 years decreased by approximately 50 % (51.65±17.70 for class 3, 52.65±9.25 for class 4), although it varied largely from 0 to 90% among individual plants, regardless of population. Data did not allow to evaluate the correlation of these findings with the type of virus infection. All these results taken together indicate that disease due to virus infection is associated with a negative effect on two major components of the wild pepper fitness, survival and fecundity. Interestingly, the fraction of infected plants showing symptoms was significantly higher in cultivated than in wild populations (~ 67% and 44%, respectively 2=34.79, P<1x10-4). The expression of symptoms by infected plants differed according to habitat for each virus. For begomoviruses it was of 61 and 35%, and for potyviruses of 45 and 10%, in cultivated and wild populations, respectively (Pagán et al., 2012; Poulicard et al., 2016), which indicates that virulence was lower in wild populations, thus depending on environmental conditions, particularly for potyviruses. Thus, the effect of infection on the host plant might also be dependent on the level of human management. This possibility, however, should be envisioned with caution, as the negative effects of infection on plant survival and fecundity may be alleviated in conditions of higher resource availability to the plant (Malmstrom et al., 2006; see also Alexander et al., 2014), as in the cultivated populations as compared to the wild ones, which could compensate for the more frequent expression of symptoms.

6. Conclusions In this work, we present results that expand our understanding how landscape heterogeneity and scale multiplicity affects plant virus interactions. Over the years we have studied a unique biological system, the wild ancestor of domesticated pepper, known in some regions of Mexico as chiltepin, and some of the viruses that infect this plant in natural communities (González-Jara et al., 2011; Pagán et al., 2012; Poulicard et al., 2016; Rodelo-Urrego et al., 2012; Rodelo-Urrego et al., 2015). What makes this system unique is that chiltepin is found over a large 14

geographic area in Mexico, growing in a variety of habitats in different regions, and that its populations are found under different anthropogenic influences including cultivation and incipient domestication. Human management of chiltepin populations is associated with a reduction of habitat biodiversity and host species genetic diversity and an increase of host plant density. We show that these three factors have different impacts on disease and infection risk according to the level of human management and according to the specific virus species or genus considered. Most significantly, plant diversity-infection risk relationships are virus-dependent. Moreover, we defined the chiltepin ecological niche by considering a broad range of abiotic and biotic variables at the continental scale. We then evaluated the contribution of these factors to virus prevalence, jointly with the previously considered habitat category, species diversity, and host plant density. Analyses from this broad perspective showed that the ecological conditions at each population were valuable predictors of the risk of infection by the most prevalent viruses, two species of the genus Begomovirus. Genetic analyses of these two viruses showed that, despite their recent invasion of the chiltepin populations, their genetic variation had a strong spatial structure apparently conditioned by a suite of abiotic and biotic variables and host ecology, but not by the level of human management. The genetic structure of both host and virus populations were similar, suggesting that ecology shapes them in a similar way, and that cultivation disrupts the spatial structure of host and virus populations and their phylogenetic congruence. We also show that the relationship between infection and disease was different according to level of human management, and that in wild populations virus infection resulted in the decrease of two important components of the host fitness, survival and fecundity. How the impact of virus infection in individual plants impacts the demography of the host plant populations is a relevant subject that awaits further research. However, the negative effects of viruses on chiltepin fitness is a prerequisite for, and may result in, plant-virus coevolution (Woolhouse et al., 2002). Recent work has analysed the evolution of recessive resistance to potyviruses in chiltepin populations, and how human management affects such evolution (Poulicard et al., 2016). Analyses of the evolution of resistance factors in chiltepin, and of its dependency on ecological factors, will undoubtedly contribute to a better understanding of plant-virus coevolution in heterogeneous landscapes.

Acknowledgements This work was in part funded by a grant from Fundación BBVA, Spain (‘‘Impacto de los patógenos en la conservación de especies amenazadas: aplicación a las poblaciones silvestres de chiltepín en México’’, BIOCON05/101) and grant BFU2015-60418-R, Plan Estatal de I+D+I, Spain.

15

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Figure Captions

Figure 1.

Geographic location of chiltepin populations. Map shows the location of populations from wild (W, green), let standing (LS, red), and cultivated (C, blue) chiltepin populations within biogeographical provinces (grey scale) in Mexico. Each chiltepin population is represented by a three-letter code. Modified from Pagán et al. 2012.

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Figure 2. Prevalence of virus symptoms and infection in chiltepin populations. Average prevalence (%) of Pepper golden mosaic virus (PepGMV, Dark blue), Pepper huasteco yellow vein virus (PHYVV, light blue), Cucumber mosaic virus (CMV, red), Potyvirus species (green) and symptomatic plants (grey) in chiltepin populations under three levels of human management (wild, let-standing and cultivated).

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Figure 3. Ecological niche similarity among chiltepin populations conducted with canonical OMI analysis. Vectors for variable scores are indicated with grey arrows and boxes showing the variables used in the model. Colour boxes indicate Wild (orange), Let-standing (blue) and Cultivated (green) populations. The black-filled box indicates presence data from the literature (LIT) and the sensitivity control (CHER), a site with relatively dense vegetation compared to the cultivated site HER, 8 km away.

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Figure 4. Congruence between chiltepin and PepGMV phylogenies. Phylogenies of chiltepin and PepGMV, considering all populations and only wild+let-standing populations are compared. Green lines denote congruent branches and red lines denote non-congruent branches. The number of congruent branches over the total number of branches compared is indicated at the bottom left side of each pair of trees. Modified from Rodelo-Urrego et al. (2013).

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Figure 5. Age class distribution of diseased (A) or virus-infected (B) plants. Data are for all populations sampled at year 2007. Diseased plants are recognised by symptoms of virus infection. Virus infection was monitored for ChYMV, CMV, PepGMV, PHYVV and potyviruses. Age classes are defined in the main text.

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Figure 6. Fecundity of chiltepin plants according to age. Fecundity was estimated as the total number of flowers and fruits per plant. Data are for asymptomatic plants of wild chiltepin populations at Bernal (red squares) and Tula (blue lozenges). Age classes are defined in the main text.

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Tables Table 1. Variance structures of fixed and random effects modeled for Begomovirus and Cucumber mosaic virus (CMV) prevalence responses. Variance estimates were generated using Markov chain Monte Carlo generalised linear mixed models. Phylogeny Fixed effects var.G l-95% u-95% var.R l-95% u-95% CI CI CI CI Begomovirus prevalence response prev ~ 1 0.480 0.002 1.681 0.867 0.228 1.603 prev ~ habitat 0.250 0.002 1.016 0.891 0.337 1.580 prev ~ habitat+H 0.271 0.001 1.184 0.936 0.236 1.704 prev ~ habitat+H+density 0.290 0.002 1.238 0.996 0.278 1.856 prev ~ habitat+H+density+CA1 0.372 0.002 1.543 0.972 0.136 1.856 prev ~ 0.388 0.001 1.667 0.904 0.005 1.652 habitat+H+density+CA1+CA2 CMV prevalence response prev ~ 1 0.246 0.002 0.845 0.966 0.414 1.659 prev ~ habitat 0.253 0.002 0.961 0.964 0.396 1.704 prev ~ habitat+H 0.302 0.003 1.177 0.994 0.338 1.765 prev ~ habitat+H+density 0.328 0.003 1.182 0.877 0.311 1.677 prev ~ habitat+H+density+CA1 0.331 0.002 1.261 0.927 0.289 1.727 prev ~ 0.366 0.002 1.380 0.966 0.272 1.850 habitat+H+density+CA1+CA2 Var.R = residual variance component of fixed effect model. Var.G = random variance component of phylogeny effect. I-95% & u-95% CI = lower and upper 95% credible intervals for highest probability density (HPD). DIC = deviance information criterion. Habitat = Wild, Cultivated, or Let-standing categories. Density = plants/m2 of shrub and tree vegetation types. H = Tsallis diversity index. CA1 = first (decomposed) canonical OMI axis; ecological niche. CA2 = second (decomposed) canonical OMI axis; ecological niche.

27

DIC

70.66 72.39 73.47 75.56 75.45 73.76

73.16 74.45 75.76 73.52 75.41 76.58

Table 2. Fixed effect variable Markov chain Monte Carlo (MCMC) probabilities for Begomovirus (Beg.) and Cucumber mosaic virus (CMV). Bold posterior MCMC values indicate the factors with population means most different from zero. Fixed effect Beg. CMV pMCMC pMCMC (Intercept) 0.442 0.928 Let-standing 0.957 0.911 Wild 0.457 0.308 Tsallis diversity 0.668 0.638 0.108 Density 0.845 CA1 ecological niche 0.499 0.621 CA2 ecological niche 0.143 0.622 The posterior probability pMCMC associated with each variable indicates departures from zero and is not a conventional p-value. For instance, low values indicate that the population mean is more likely to be different from zero. CA: Canomical OMI axis.

28

Table 3. Percentage of plants showing symptoms of virus infection in the Bernal population according to age. 2007 2008 2009 BERNAL % % % N N N age class symptomatic

1 2 3 4

4 22 50 9

0 22.7 34.0 0

symptomatic

36 11 43 19

0 9.1 34.9 11.8

symptomatic

68 32 26 24

0 0 0 16.62

TOTAL 85 25.9 109 16.5 150 2.7 N: Number of plants rated as symptomatic or asymptomatic in field surveys in years 2997, 2008 and 2009.

29