Discussion
Here, we use community modelling to uncover determinants of wild plant
virus diversity, a vastly unexplored component of biodiversity
(Roossinck 2011). We show that virus communities exhibit a clear nested
structure with abundant co-occurrences, both pairwise and higher-order.
The observed coexistence patterns are mediated by host plant
characteristics, abiotic environment and spatial structure, as well as
associations among the viruses. Many present-day threats posed by
infectious diseases involve interactions that are manifested across
nested scales of biological organisation (Johnson et al. 2015),
and our findings shed light on how plant virus coexistence is maintained
at different spatial scales.
The simplest within-host viral communities detected in our data
consisted of single infections, but nearly half (46%) of the infected
plants hosted multiple infections. Single infections were typically
observed only for the few most common viruses, and half of all the
viruses never occurred as single infections. Our findings are in line
with other studies that have found high levels of coinfection (Al
Rwahnih et al. 2009; Rey et al. 2012; Tugume et al.2016). However, our approach enabled the detection of virus communities
in a substantial number of host individuals, with capacity to detect
viromes consisting of up to 24 distinct taxa. This suggests that virus
communities are highly variable within and among hosts.
From the difference between species-area and coexistence curves, we see
that a larger number of hosts is needed to maintain the coexistence
patterns that could be derived from the overall virus richness. Indeed,
increasing environmental heterogeneity and varying responses of viruses
to this heterogeneity promote coexistence by reducing competition, as
predicted by the classic species sorting paradigm, which has been shown
to be influential for microbial communities (see e.g. Székely &
Langenheder 2014 and refs. within). Combined with the weakening effects
of demographic stochasticity and the homogenising effects of dispersal,
these mechanisms lead to more stable coexistence at increasing spatial
scales (Hart et al. 2017; Levine & Hart 2020).
We found that the conditional network models (CRFs), outperformed the
model incorporating only associations between viruses (MRF) in
explaining virus community structure. The differences between the CRF
model variants were less pronounced, as seen from their equal model fit
and performance. Hence, we conclude that there is environmental
variation and processes operating at different spatial scales that
influence the wild virus community coexistence structure both directly
and indirectly. The environmental characteristics and the spatial
variables explain community structure almost interchangeably.
Previously, e.g. within-host diversity of pathogens has been shown to
increase with latitude, while pathogen turnover follows an opposite
trajectory, suggesting limited transmission in lower latitudes (Seabloomet al. 2010).
Our analysis revealed spatial variables to influence associations
between viruses, resulting in indirect effects on their co-occurrence.
These viruses are vector-dispersed, and most likely dispersal-limited at
larger spatial scales (Pleydell et al. 2018), leading to less
homogenisation at these scales. However, at the scale of a host
populations, variation among host genotypes (Sallinen et al.2020) and demographic stochasticity (through e.g. herbivory, as seen
from the indirect effects of herbivore damage on the associations
between viruses) are expected to be important, as well as abiotic
interactions mediating coexistence (Kozanitas et al. 2017).
After accounting for the effects of the host and habitat
characteristics, as well as spatial structure on virus distributions,
non-random, significant positive associations between viruses remain.
Although these associations are based on purely observational
co-occurrence patterns, given the spatial scale of our sampling and our
method for detection that targets the host’s defence response (Kreuzeet al. 2009), we consider these indicative of biotic interactions
between the viruses within a host plant (Wintermantel et al.2008). The interactions among coinfecting viruses may involve positive
effects on replication (Pruss et al. 1997; Taiwo et al.2007), or even obligate dependencies as some viruses require their
specific helper virus in order to complete their lifecycle (DaPalmaet al. 2010). Viruses may also suppress host immunity allowing
subsequent infections to escape recognition by host immunity
(González-Jara et al. 2005). As expected, the inclusion of
explanatory variables reduced the number of direct associations between
viruses identified by the models, suggesting that shared environmental
responses play an important role in the assembly of the communities (see
e.g. Leathwick et al. 2006; Ovaskainen & Soininen 2011). After
accounting for host- or habitat-related or spatial variables, support
for several direct associations (e.g. Tombusviridae-Alphaflexiviridae,
Betaflexiviridae- Potyviridae and Tombusviridae-Betaflexiviridae) was no
longer detected.
Importantly, we found that the inclusion of the additional conditioning
variables changed the association networks especially through their
indirect effects. For example, we found a significant indirect effect of
the coarsest-scale spatial variable (MEM1) on the association between
Fimoviridae and Alphaflexiviridae, while the direct effect of this
spatial variable was not significant. Hence, the occurrence probability
of Alphaflexiviridae is jointly affected by the occurrence of
Fimoviridae as well as spatial structure. Due to symmetry, Fimoviridae
is similarly affected jointly by the occurrence of Alphaflexiviridae and
spatial structure. These indirect significant effects were more frequent
(in total 32 effects) in comparison to direct effects (in total 10). The
indirect links can be indicative of either biotic interactions between
viruses only manifesting under certain environmental conditions, or
these environmental conditions having an effect only in interaction with
the other virus (Kozanitas et al. 2017). Such indirect effects
have traditionally been challenging to detect, yet not accounting for
them can lead to over- or underestimation of signals of biotic
interactions in co-occurrence data (Blanchet et al. 2020).
Although our virus community data set along with its environmental
explanatory variables is extensive and of high quality, our results are
limited by our sample size and its effects on the parameter estimation
of our modelling method. We use regularisation to avoid overfitting, but
we note that the estimated parameters could in theory change with more
data included. However, as seen from the species-area and
coexistence-area curves, our sampling effort captures the detected virus
diversity already before all the samples have been included, indicating
a promising sample size.
Our results demonstrate that natural plant virus communities are
characterised by single infections of few, dominating virus taxa as well
as diverse, non-random coinfections. Virus diversity can be explained by
coexistence-promoting mechanisms, some of which we could tease apart
with our modelling. We show that host and habitat characteristics, as
well as spatial structure, resolve some of the observed co-occurrence
patterns, to some degree interchangeably. Importantly, we find that some
virus-virus associations are mediated by either host or habitat
characteristics, or the spatial structure of the host populations.
However, a substantial part of the explained virus co-occurrence
patterns can be attributed to positive, direct associations among the
viruses. Moreover, we show that additional conditioning variables
changed virus association networks especially through their indirect
effects. Thus, our study contributes to increasing understanding on how
plant virus coexistence and thus biodiversity is maintained at different
spatial scales. Our results highlight a previously underestimated
mechanism of how human-driven environmental change can influence disease
risks by changing biotic associations between viruses that are
conditional on their environment.