Fungi compete for space and resources in Q. petraea seeds , and have negative interactions with bacteria in Populus trichocarpa seeds . Similar competitive exclusion has been observed in floral stigma communities , and in dormant seeds within the soil . However, seed microbes can also coexist via niche partitioning and other interactions. For example, TorresCortés et al. looked at how transmission of several bacterial pathogens impacted the composition of Raphanus sativus seed microbiomes. They found that these pathogens did not alter the composition of the seed microbiome, suggesting that differences in resource usage lead to coexistence between taxa . A more complete understanding of the types and outcomes of microbial species interactions prior to and during seed development is desirable.As with filtering, microbial dispersal to seeds occurs at multiple nested spatial scales, with different mechanisms at play for each spatial scale. For example, at the micro-scale, dispersal from floral stigmas to seeds can be impacted by variation in the level of protection or nutrients that are available to microbial colonizers, which is closely tied to stigma surface topography. The presence of pollen may also be important,dutch buckets as it has been shown that germinating pollen can enhance the flower-to-seed transmission of pathogens and that some bacteria can even induce pollen germination .

While there are no studies looking at the connection between floral topography and seed microbial transmission, experiments with flowers and leaves have demonstrated that bacterial dispersal is influenced by plant surface topography and surface water distribution. Conducting similar micro-scale inoculation experiments like these in flower to-seed systems will illuminate how microbes actually move. Seeing that microbes can be florally transmitted to seed, we need to consider studies on the dispersal of floral microbes to understand seed microbial communities at the meso-and macroscales. One major finding from floral studies is that microbes are dispersal limited at regional scales . For example, Belisle et al., 2012 found that yeast frequency in nectar communities of Mimulus aurantiacus was correlated with flower proximity, and they inferred that dispersal limitation was controlled by pollinator behavior. In a study on the floral microbiome across wildflower species of California, Vannette et al. observed that fungi were more dispersal limited between individual flowers and plant species than bacteria. Another major finding has been that pollinators can vector microbes between flowers and influence microbial community patterns. For example, Vannette and Fukami explored the variable effects of dispersal limitation on beta diversity in the nectar microbiome. Using a pollinator exclusion experiment, they found that increased dispersal by pollinators raised beta diversity and hypothesized that this increase was due to the stochasticity of dispersal timing which strengthens priority effects . A pollinator exclusion experiment in B. napus demonstrated that pollinators can also vector bacteria to seeds through flowers, impacting the local and regional diversity . These experiments indicate that dispersal may have unique effects on diversity in flower microbiome meta communities via arrival history.

However, all of these studies were performed only on the macro-scale, and they did not characterize dispersal traits. Furthermore, the association between dispersal patterns in floral microbial communities and those in seed communities has yet to be studied. Future experiments should explore if dispersal traits and arrival history consistently enhance beta diversity in flower and seed microbial communities among spatial scales .Ecological drift is defined as random fluctuations in species abundances over time, and can be driven by random birth, death, and migration events . Drift is particularly important when local communities are small and filtering is weak . This is key to note for seed microbial communities because they typically have low population sizes and low species richness . Random migration events may be particularly important for seed microbes, such as those vectored by rain or wind . However, while there is a lot of interest in drift and stochasticity in seed microbe research , drift as a process is difficult to study because it is hard to manipulate. Meta community ecologists have also generally found it difficult to get direct evidence of drift, with limited examples from experiments testing the coexistence of ecologically equivalent taxa . One alternative approach to direct observation used in plant microbiota studies is to fit community data to neutral models, where community members are assumed to be ecologically equivalent, and non-significant variation in community composition across samples is explained by neutral processes. Rezki et al. took this approach when studying the seed microbiota of R. sativus by fitting fungal and bacterial community data to a Sloan neutral model .

This model accounts for neutral birth, death, and immigration rates, and estimates immigration rates into communities based on species frequencies across samples . Immigration rates within the confidence interval of the predicted values imply that drift is structuring the community . Based on the model, they found that bacterial community assembly was driven primarily by drift, while fungal communities were driven more by dispersal . This study indicated that drift is important for some seed microbes, and more model-fitting studies or coexistence experiments are needed.At its core, meta community ecology emphasizes not only how the processes described above play out individually, but also how they interact with each other to produce emergent community patterns across scales. In plant microbiome research, the interaction between abiotic and host filters, also known as genotype-by-environment interactions, has been of growing interest because it provides a more holistic explanation for microbiome variation . Such an explanation can be applied to seed microbial communities, which may vary with seed nutrient profiles, osmotic stress, and water availability. However, as previously mentioned, GxE studies on plant microbiota face a scale problem where genotype and environment become synonymous at the micro-scale. Taking a plant trait-based approach to these studies may make the role of these effects more clear, and can connect micro-and macro-scales via host local adaptation. While not emphasized as much as GxE interactions, the interaction between dispersal and filtering is also important during seed microbiome assembly. At the micro-scale, variation in the plant surface landscape can create differences in dispersal limitation between taxa. Doan et al. demonstrated this interaction on synthetic leaf surfaces, finding that surface water acted as a conduit for bacterial dispersal. This effect may also be present in floral stigmas, which are highly heterogeneous landscapes . Indeed, in their work on transmission of the pathogen A. citrulli from watermelon flowers and fruit to seeds, Dutta et al. found that inoculum from the flower dispersed more frequently and ended up in deeper seed tissues than inoculum from the fruit. While these examples suggest that heterogeneity in the plant landscape impacts dispersal limitation to seeds,grow bucket more studies are needed. Dispersal also intersects with species interactions, most clearly through historical contingency or priority effects . In this phenomenon, the arrival order of community members dictates assembly outcomes, typically with an advantage to taxa that arrive first . Priority effects can occur either through niche preemption, where the first colonizers fill all available niches, or by niche modification, where the first colonizers alter the environment and its resulting niches . These effects are often cited as important in seed communities because they have few members . However, priority effect experiments in plant microbiota have typically been done in leaf and wood communities . As such, there is a need to understand the role of priority effects in seed communities. An exciting new approach for studying the multiple, interactive processes of dispersal, filtering, drift, and species interaction is with Joint Species Distribution Models , which extend single-species distributions to community-level dynamics . Leibold et al. used these models in tandem with variation partitioning to explain the internal structure of simulated meta communities. They found that this approach was a promising way to connect meta community pattern data to multiple assembly processes . In the seed microbiology literature, Fort et al. used JSDMs to infer how maternal filtering and abiotic filtering contributed to seed mycobiome variation in Q. petraea seeds . They found that fungal guild influenced which taxa varied with abiotic filters, with elevation selecting saprotrophs and seed specialists, and all taxon co-occurrences were positive associations .

While JSDMs were not used in a meta community context for this study, and they are limited in their omission of abundance data, these models provide an integrative approach for looking at seed microbiome assembly.Multiple tools exist for exploring and exposing the effects and interactions of filtering, species interactions, dispersal, and drift on microbial community assembly of individual seeds at multiple spatial scales. However, future work can do a better job of integrating and connecting meta community ecology models to traditional seed microbial ecology studies at micro-, meso-and macro-scales. One technical challenge of taking this approach pertains to the interrogation of microbial communities in individual seeds. Culture-based studies of individual seeds report low isolation frequencies, with most seeds containing zero or one microbial taxon . Additionally, most sequence-based studies to date pool seeds by fields or other groupings . As exceptions, Bergmann and Busby and Fort et al. sequenced fungi from individual tree seeds and found that sequencing depth was fairly high. However, the tree species in these studies produce large seeds; sequence-based detection of microbiota might be more difficult in small-seeded species . Additionally, it is often difficult or impossible to treat individual seeds as independent since experimental treatments or predictors are often applied at the fruit or plant level. To resolve these issues, future work could focus on species where seeds are fertilized independently , or one seed per fruit/plant could be sampled for large-seeded species. Alternatively, seeds could be pooled at the fruit or plant levels, since these are the levels where treatments are often applied and they sufficiently capture the variation in seed microbiota while still allowing for a meta community approach at the meso-and macro-scales. The appropriate level of pooling should be selected based on the transmission pathway of interest . Finally, seeds of large-fruited species could be pooled by parts of the fruit/pod for spatially explicit sampling at the meso-and micro-scales. These scale-explicit pooling approaches, along with the use of additional methods at the meso-and micro-scales , will allow for characterization of microbiota at or near the individual seed level while mitigating issues of low DNA amounts and cross-contamination. At the micro-scale, there are many opportunities to take a traits-based approach to host filtering of seed microbiota. These experiments could also take a microbial trait-based approach to host filtering and identify the genes required for successful transmission, which are still largely unknown . This could provide valuable insights into the genes required for transmission across the different pathways . Furthermore, metagenomic analyses across plants, populations, species, etc., could determine if these transmission-associated genes are common across meta communities. Such information could show if there is functional conservation across microbial communities, even if they are taxonomically variable. Finally, micro-scale experiments can also test how microbial community assembly is impacted by the interplay between deterministic and stochastic processes. In addition to these tests of microbial and plant trait impacts, experiments testing the role of dispersal in seed microbial community assembly among spatial scales should be conducted. At the micro-scale, experiments using synthetic microbial communities on stigmas with varying chemistry and topography can demonstrate how dispersal and selection occur between flowers and seeds, and what the role is of plant genetics and microbial adaptations. At the macro-scale, pollinator exclusion experiments similar to those in Prado et al. could be conducted across sites in natural landscapes. By using sites at varying distances and connectivity levels from each other, and analyzing both within- and among-site seed microbial community variation, one may obtain new information about how pollinators and patch connectivity impact multi-scale dispersal ability. These proposed studies would elucidate how dispersal contributes to meta community assembly among spatial scales. Along with these single-process studies, we envision studying the interactions between processes through both observational and experimental studies. As JSDMs continue to be refined to model nested and continuous meta communities, they will provide a way to analyze seed microbiome patterns and their associated assembly processes that is more sophisticated than previous modeling approaches. Additionally, priority effect experiments conducted at multiple points in the seed life cycle may reveal how historical contingencies impact seed microbiome assembly throughout the seed life cycle. Such experiments would also test the Primary Symbiont Hypothesis , which argues that seed communities are dominated by a single microbe with significant functional consequences for the plant. Finally, questions will need to be asked about seed microbiome assembly that go beyond just testing for spatial mechanisms.