This classification follows from a stability analysis of the susceptible free and resistance-free equilibria. Since we are working in discrete time, an equilibrium is stable if the magnitude of the largest Eigenvalue of the Jacobian matrix evaluated at the equilibrium is less than unity. If neither equilibrium is stable then both susceptible and resistant plants are able to invade a population consisting almost exclusively of the other when rare, and so the genotypes are predicted to coexist. If only the susceptible-free equilibrium is stable, then resistance dominates. If only the resistance-free equilibrium is stable, then susceptibility dominates. But if both equilibria are stable, then the long term outcome depends on the initial densities of each genotype. Extensive numerical simulations of the model were performed to verify that local stability analyses could be used to infer the long-term outcome for all initial conditions. In particular we tested 10,000 combinations of parameters and initial conditions . In all cases the outcome after 10,000 generations of the model matched that predicted by the stability analysis described above. We also performed a number of individual tests for pairs of sets of parameters chosen to cross stability boundaries: the stability analysis predicted behaviour in full simulations of the model in the large number of cases we tested.CMV-Fny accumulates to a higher titer than CMVΔ2b in systemically-infected tomato leaves. Semiquantitative reverse transcription-polymerase chain reaction analysis of viral RNA accumulation leaves of tomato plants systemically infected with CMV-Fny or CMVΔ2b. CMV RNA accumulation was determined by RT-PCR after 30 cycles of PCR and compared to the levels of the elongation factor 1 alpha transcript .

The CMV-specific PCR products from CMV-infected leaves accumulated to higher levels than those from CMVΔ2b infected leaves. RT-quantitative PCR of CMV accumulation relative to CMVΔ2b. Graph shows the mean accumulation of viral RNA in systemically-infected tissues of plants inoculated with CMV-Fny or CMVΔ2b at 10 and 18 dpi. Mean accumulation of virus-specific PCR products is shown for CMV and CMVΔ2b and error bars represent standard errors around the mean for n = 4 samples for CMVΔ2b at 10dpi and n = 3 and 2, respectively, for CMV at 10 and 18dpi. The housekeeping transcript control was EF1α and levels are shown relative to CMVΔ2b, which is designated as ‘1’. . Pollen yield from mock-inoculated and virus infected flowers is similar. Fully open flowers from 12 mock-inoculated and nine CMV-PV0187-infected plants were excised into microfuge tubes containing 300μl of water and vortexed for 40 seconds. Using a microscope, plastic pot manufacturers released pollen grains were counted in technical triplicates using a cell-counting chamber. The mean number of pollen grains released by flowers is shown. Error bars indicate standard error around the mean. The viability of pollen from mock-inoculated and CMV-infected flowers is similar. Pollen was harvested into microfuge tubes from flowers by manual buzzing with an electrical toothbrush and stained with fluorescein diacetate. Data are from nine mock-inoculated and nine CMV-PV0187 infected plants. Esterase activity in viable pollen grains releases fluorescein that fluoresces under blue light. The percentage of pollen grains fluorescing is indicated with error bars indicating standard error around the mean. Typical microscopic fields of view for pollen grains extracted from flowers of mock-inoculated and CMV-PV0187-infected plants viewed under blue light and bright field with an epi-fluorescent microscope connected to a digital camera . Upper panels were viewed with blue light illumination under bright field optics enabling viable and non-viable pollen grains to be counted. Lower panels show pollen grains viewed with epi-fluorescent optics only. Scale bar = 100μm.The three genomic RNAs of CMV-PV0187 were sequenced.

The RNA sequences were compared to those of CMV-Fny and other CMV strains and isolates. Phylogenetic analysis using the RNA sequences of CMV-PV0187 RNAs 1, 2, and 3, with corresponding sequences of other CMV strains and isolates. Phylogenetic analysis using the neighbour-joining method under the Kimura-2 parameter was conducted in MEGA software . The bootstrap consensus tree was carried out with 1000 replications. Panels show the phylogenetic analysis of RNAs1, 2 and 3. The CMV-PV0187 sequence data used in this analysis is available at NCBIunder GenBank accession numbers KP165580, KP165581 and KP165582 corresponding to RNA1, RNA2, and RNA3, respectively. PV0187-CMV groups closely with CMV-Fny , with which it has an overall 99% RNA sequence identity. The predicted 110 residue amino acid sequences of the 2b proteins of CMV-Fny and CMV-PV0187 are identical. The amino acid sequences are a virtual translation of the 2b open reading frames of the two CMV strains. The numbers 60, 61, and 110 indicate amino acid residue positions.The growth and morphology of leaves, flowers and fruit were compared between tomato plants that had been mock-inoculated or infected with CMV-PV0187. Plants or plant organs were photographed and typical images are shown in panels A-E. Tomato plants inoculated with CMV-PV0187 at the seedling stage show marked stunting compared to mock-inoculated plants . Mature, expanded leaves of infected and mock-inoculated plants. Young, upper leaves of infected and mock-inoculated plants. Flowers from mock-inoculated and CMV-PV0187 infected plants are similar in appearance and show no gross differences in morphology. Tomato fruits from mock-inoculated plants are larger than those from CMV-PV0187 infected plants. Scale bars = 3 cm.Growth rate of resistant mutants in the vicinity of the equilibrium at which only susceptible plants are present. The panel shows a series of full two-way sensitivity analyses of the model, showing effects on the growth rate of rare mutant resistant plants in the vicinity of the equilibrium at which only susceptible plants are present, caused by independently changing pairs of parameters . All pair-wise combinations of two parameters are shown: dots on each axis show default values of each parameter. In all cases, the magnitude of the largest Eigenvalue of the Jacobian matrix at the model equilibrium–which is equivalent to the initial discrete time rate of exponential growth over successive seasons of rare mutant resistant plants -is shown by color. Note that Fig 8 in the main text characterises long-term evolutionary outcomes by distinguishing regions in which growth rates of each type of mutant are larger than or smaller than one, and so in which the equilibria can be invaded : these results therefore provide additional numerical detail in support of that figure. Growth rate of susceptible mutant plants in the vicinity of the equilibrium at which only homozygous resistant plants are present .Design of free choice bee-pollination experiment. A large flight arena was constructed out of nylon netting with three zipped doors to allow full access. Within this flight arena a bumblebee colony was attached by a tube to a small flight arena containing a microtiter plate filled with 30% sucrose to allow the bumblebees to feed freely. Sliding gates on the side of the small arena permitted one bee to be released into the larger arena containing three mock-inoculated and three cucumber mosaic virus -infected flowering tomato plants. Cartoon demonstrating the arrangement of mock-inoculated and CMV-infected plants within the larger flight arena. The plant microbiota, defined here as the community of bacteria, fungi, archaea, viruses, and other microscopic organisms that live on or in plant tissues , confer many services as well as disservices to their hosts, including disease development and defense , protection against herbivory , tolerance of abiotic stress , and aid in nutrient uptake . These microbial communities associate with all plant tissues , including seeds . Seeds play a major role in plant communities as agents of dispersal, genetic diversity,and regeneration , and they have significant economic and social value through agriculture . Seeds also are a major bottleneck in natural plant populations, as they face heightened mortality from abiotic stressors, pests, pathogens, and predators . As the initial source of inoculum in a plant’s life cycle, seed microbes are can be transmitted across plant generations and have lifelong impacts .

Consequently, understanding how seeds acquire and interact with their microbiota, for example, via priority effects or according to the Primary Symbiont Hypothesis , has implications for improving seed health, seedling establishment, and plant community structure. Previous work on seed microbiota has primarily taken a pattern-based approach to studying assembly processes . Such an approach uses culturing and/or next-generation sequencing to compare, contrast, and correlate patterns in microbial community composition, diversity, and species co-occurrences. Typically, however, these community data provide limited insights into processes such as dispersal, microbe-plant interactions, and microbemicrobe interactions. Given that seed microbial communities are highly variable across individual plants, plant species, and locations , such pattern-based data cannot always be used to predict assembly outcomes. Moreover, such studies often consider how these assembly processes occur at a single spatial scale . We hypothesize that a mechanistic, black plastic plant pots wholesale multi-scale approach would provide a more complete understanding of how microbial communities assemble in seeds, with the field of meta community ecology providing a theoretical framework for such an approach. Metacommunity theory accounts for the interaction between ecological processes and habitat heterogeneity across spatiotemporal scales to impact community patterns . This emphasis on multiple scales and heterogeneity can help explain the main drivers of community assembly and patterns of biodiversity and co-occurrence . Plant-associated microbial communities vary widely across environmental gradients and host genetics from the levels of tissues to populations . As such, treating individual plants as heterogeneous habitats for microorganisms that are embedded in a larger, heterogeneous landscape of multiple plants representing different species provides a new approach to observing, testing, and modeling drivers of microbial community variation . However, the study of microbiota through a meta community lens is still relatively new, both for animals and plants , and the plant seed represents a relatively understudied microbiome in this context. In this review, we address how mechanisms of seed microbial community assembly have been studied at different spatial micro-, meso-, and macro-scales , and advocate for a meta community-based approach to seed microbiology in future work. For this review, we use the definition of community assembly from Fukami : “the construction and maintenance of local communities through sequential, repeated immigration of species from the regional species pool.” Additionally, most studies that we cover in our review will be focused on fungi and bacteria . We acknowledge that archaea, viruses, and protists are frequent members of plant-associated microbial communities , many plant viruses are seed transmitted , and viruses can play a major role in the diversity and function of soil microbial communities . However, the ecological roles of these microbes in plant microbial communities, including those of seeds, are still largely unknown. As such, we cannot speak on their contributions to seed microbiota assembly here and recommend new research on these microbes in seeds. We will first summarize the modes of microbial acquisition into seeds, and how meta community ecology frames this assembly process. We then discuss studies of seed microbiome assembly which examine the processes of filtering, species interactions, dispersal, and ecological drift. We specifically highlight studies that address assembly processes during seed development and maturation, as these stages are understudied compared to seed dormancy and germination, and they are likely the source of microbes that persist between plant generations . Lastly, we suggest future lines of research to gain a more mechanistic, scale-explicit understanding of seed microbiome assembly.Plant seeds are generally composed of three tissues: a seed coat which provides physical protection , an embryo which is the precursor to the seedling and is made up of an immature root, a stem, and one or more embryonic leaves , and an endosperm which typically consists of carbohydrates and proteins and provides nutrition for the embryo during germination and growth before photosynthesis can occur . Seed development involves three stages . Following fertilization by pollen, the egg cells divide and differentiate into the embryo and endosperm tissues, in a process called histodifferentiation . Next, the cells expand and mature with reduced division, and seed mass increases during this filling stage, as nutrient reserves are deposited into the endosperm . After this, nutrient accumulation declines, and the seed goes into maturation drying and loses about 10%–15% moisture content before it is ready to be dispersed .During seed development, microbes may enter the seed tissues via three distinct routes of transmission: vertical, floral, and horizontal . Vertical transmission involves microbes traveling from other organs of the mother plant to the developing embryo.