Opportunities and Challenges of Understanding Community Assembly in Spontaneous Food Fermentation

Spontaneous fermentations that do not rely on backslopping or industrial starter cultures were especially important to the early development of society and are still practiced around the world today. While current literature on spontaneous fermentations is observational and descriptive, it is important to understand the underlying mechanism of microbial community assembly and how this correlates with changes observed in microbial succession, composition, interaction, and metabolite production. Spontaneous food and beverage fermentations are home to autochthonous bacteria and fungi that are naturally inoculated from raw materials, environment, and equipment. This review discusses the factors that play an important role in microbial community assembly, particularly focusing on commonly reported yeasts and bacteria isolated from spontaneously fermenting food and beverages, and how this affects the fermentation dynamics. A wide range of studies have been conducted in spontaneously fermented foods that highlight some of the mechanisms that are involved in microbial interactions, niche adaptation, and lifestyle of these microorganisms. Moreover, we will also highlight how controlled culture experiments provide greater insight into understanding microbial interactions, a modest attempt in decoding the complexity of spontaneous fermentations. Further research using specific in vitro microbial models to understand the role of core microbiota are needed to fill the knowledge gap that currently exists in understanding how the phenotypic and genotypic expression of these microorganisms aid in their successful adaptation and shape fermentation outcomes. Furthermore, there is still a vast opportunity to understand strain level implications on community assembly. Translating these findings will also help in improving other fermentation systems to help gain more control over the fermentation process and maintain consistent and superior product quality.

Species divergence under competition and shared predation

Species competing for resources also commonly share predators. While competition often drives divergence between species, the effects of shared predation are less understood. Theoretically, competing prey species could either diverge or evolve in the same direction under shared predation depending on the strength and symmetry of their interactions. We took an empirical approach to this question, comparing antipredator and trophic phenotypes between sympatric and allopatric populations of threespine stickleback and prickly sculpin fish that all live in the presence of a trout predator. We found divergence in antipredator traits between the species: in sympatry, antipredator adaptations were relatively increased in stickleback but decreased in sculpin. Shifts in feeding morphology, diet and habitat use were also divergent but driven primarily by stickleback evolution. Our results suggest that asymmetric ecological character displacement indirectly made stickleback more and sculpin less vulnerable to shared predation, driving divergence of antipredator traits between sympatric species.

A Single Interacting Species Leads to Widespread Parallel Evolution of the Stickleback Genome

Biotic interactions are potent, widespread causes of natural selection and divergent phenotypic evolution and can lead to genetic differentiation with gene flow among wild populations ("isolation by ecology") [1-4]. Biotic selection has been predicted to act on more genes than abiotic selection thereby driving greater adaptation [5]. However, difficulties in isolating the genome-wide effect of single biotic agents of selection have limited our ability to identify and quantify the number and type of genetic regions responding to biotic selection [6-9]. We identified geographically interspersed lakes in which threespine stickleback fish (Gasterosteus aculeatus) have repeatedly adapted to the presence or absence of a single member of the ecological community, prickly sculpin (Cottus asper), a fish that is both a competitor and a predator of the stickleback [10]. Whole-genome sequencing revealed that sculpin presence or absence accounted for the majority of genetic divergence among stickleback populations, more so than geography. The major axis of genomic variation within and between the two lake types was correlated with multiple traits, indicating parallel natural selection across a gradient of biotic environments. A large proportion of the genome-about 1.8%, encompassing more than 600 genes-differentiated stickleback from the two biotic environments. Divergence occurred in 141 discrete genomic clumps located mainly in regions of low recombination, suggesting that genes brought to lakes by the colonizing ancestral population often evolved together in linked blocks. Strong selection and a wealth of standing genetic variation explain how a single member of the biotic community can have such a rapid and profound evolutionary impact.

Maximum Individual Complexity is Indefinitely Scalable in Geb

Geb was the first artificial life system to be classified as exhibiting open-ended evolutionary dynamics according to Bedau and Packard's evolutionary activity measures and is the only one to have been classified as such according to the enhanced version of that classification scheme. Its evolution is driven by biotic selection, that is (approximately), by natural selection rather than artificial selection. Whether or not Geb can generate an indefinite increase in maximum individual complexity is evaluated here by scaling two parameters: world length (which bounds population size) and the maximum number of neurons per individual. Maximum individual complexity is found to be asymptotically bounded when scaling either parameter alone. However, maximum individual complexity is found to be indefinitely scalable, to the extent evaluated so far (with run times in years and billions of reproductions per run), when scaling both world length and the maximum number of neurons per individual together. Further, maximum individual complexity is shown to scale logarithmically with (the lower of) maximum population size and maximum number of neurons per individual. This raises interesting questions and lines of thought about the feasibility of achieving complex results within open-ended evolutionary systems and how to improve on this order of complexity growth.