Microbial biotechnology meets ecological theory: a unified framework to design industrial microbial consortia

Abstract

Microbial biotechnology and ecological theory are increasingly converging, driven by the recognition that microbial function emerges from community interactions and environmental context. Traditional reductionist approaches, focused on single strains, often fail to capture the complexity and emergent behaviors of multispecies systems underpinning bioproduction, bioremediation, and food fermentation. This review presents a framework for integrating ecological principles into microbial biotechnology to enable the rational design of industrial consortia. Here, I argue that industrial microbiomes provide an ideal intermediate between simplified laboratory models and natural ecosystems, being tractable, moderately complex, and ecologically realistic. Understanding context dependence, ecosystem-informed design, and evolutionary dynamics is essential for engineering stable, resilient, and high-performing communities. Using agri-food microbiomes as examples, this review illustrates how ecological and evolutionary concepts can guide the design of synthetic consortia with predictable functionality. By combining ecological theory with engineering strategies, microbial biotechnology can move from descriptive empiricism toward predictive, theory-driven design.

Introduction

Microbial biotechnology and ecological theory have historically occupied different conceptual worlds. Biotechnology, rooted in molecular biology and genetics, advanced through reductionist strategies, isolating individual components and reassembling them into functional systems. Ecology, in contrast, is characterized by systems thinking, focusing on interactions, environmental context, and emergent properties across multiple scales (Levin 1992).

The recent transition in microbial biotechnology from single-strain processes to multispecies consortia has exposed the limitations of maintaining disciplinary boundaries (Shong et al. 2012). In complex environments, outcomes are often emergent and cannot be fully predicted using strain-centric approaches (Widder et al. 2016). Whether in bioproduction, bioremediation, or gut health, the success of microbial consortia depends on our ability to anticipate and shape collective behaviors; those that arise not from individual genomes alone, but from interactions among community members and their environment.

However, our current understanding of microbial species interactions mostly comes from two ends of the complexity spectrum, either observational studies of highly diverse microbiomes (in soils, oceans, or host-associated ecosystems) or controlled experiments in overly simplified lab systems. But there’s an underexplored middle ground. Our group, along with others, have shown that microbial-based industrial processes can serve as ideal model systems for studying microbial interactions because: (i) they exhibit a moderate level of complexity, bridging the gap between simplified laboratory models and the intricacy of natural ecosystems; (ii) they are highly tractable, as most industrial microbes can now be fully manipulated at the genomic level; and (iii) they represent ecologically realistic systems, reflecting long-standing microbial coexistence of functionally diverse species (Belda et al. 2025a; Daims et al., 2006; De Roy et al., 2014; Wolfe and Dutton 2015). Viewed this way, industrial settings offer a neutral playing field, where ecology and biotechnology can meet on equal terms, and ultimately inform one another toward the rational design and engineering of microbiomes. In this work, we use the term industrial microbiomes to refer to microbial communities that operate in human-managed environments, such as agricultural soils, bioreactors, food-processing facilities, and fermented products, where community composition and function directly impact process performance and product quality (Fig. 1).

Fig. 1.

Schematic illustration of the unified eco-evolutionary framework proposed in Sect. 3, highlighting how ecological assembly rules, community complexity, and evolutionary dynamics jointly inform the rational design of industrial microbial consortia

Microbial communities are more than the sum of their members. In natural microbiomes, emergent interactions, such as cross-feeding, can allow some species to persist in environments where they would not survive alone, helping to maintain community functional potential (Goldford et al. 2018). Beyond individual survival, these communities often display functional resilience, maintaining stable community-level processes despite compositional changes (Bissett et al. 2013; Fernández et al. 1999). This robustness reflects the redundancy and complementarity of microbial functions, as well as the dynamic reconfiguration of species interactions through ecological selection, niche partitioning, and feedbacks that shape community organization over time (Strous and Sharp 2018). While these properties confer remarkable ecological versatility and stability, the inherent complexity and context-dependence of natural microbiomes make their performance difficult to standardize or predict, limiting their direct translation to biotechnological applications. Thus, a central goal in microbial biotechnology is to capture the metabolic versatility and ecological robustness of natural microbiomes within synthetic consortia that serve as simplified and predictable versions of them.

Ecological theory provides a valuable framework to address this challenge, but its integration into microbial biotechnology remains limited (Prosser et al. 2007). Incorporating ecological theory is essential for transforming microbial ecology into a predictive, hypothesis-driven discipline and for advancing biotechnology beyond descriptive empiricism. To move in that direction, complexity should be seen not as a problem to overcome but as a property that can be used to design more stable and productive microbial consortia (Johns et al. 2016). This is a core principle in synthetic ecology, an emerging field exploring the intersection of community ecology and synthetic biology to provide theoretical and methodological tools needed for engineering microbial consortia (San Roman et al., 2025).

This review traces the convergence between microbial biotechnology and ecological theory, proposing a unified framework for improving microbial-based industrial processes through the integration of ecological and engineering principles. Its main aim is to engage researchers in microbial biotechnology by providing an accessible introduction to the power of ecological theory, demonstrating how it can move the field beyond descriptive empiricism toward predictive and theory-driven design of microbial consortia.

Integrating ecological context into the design of microbial systems

Context dependence is a central concept in ecology, explaining how the performance of organisms is shaped by their biotic and abiotic surroundings (Chase 2003). This concept challenges the conventional engineering view in biotechnology, where microbes are often treated as plug-and-play units. Many properties of microbial populations and communities are emergent and non-additive (Yang 2021), arising from interactions among species (Foster and Bell 2012) and shaped by environmental factors such as spatial structure and nutrient availability (Pacheco et al. 2021; Estrela and Brown 2018). In this context, emergent traits are population- or community-level properties that cannot be predicted from the characteristics of individual species alone. Thus, while individual microbial traits are generally fixed, the collective outcomes of populations and communities are modulable and context-dependent, reflecting complex and dynamic ecological relationships.

Ecological theory provides a powerful framework to understand these dynamics by focusing on mechanisms that determine how microbial communities assemble, persist, and function (Bittleston et al. 2020; Burke et al. 2011; Friedman et al. 2017; Golford et al., 2018; Lee et al. 2023; Nemergut et al. 2013). Concepts like niche construction and environmental filtering explain how microbes both shape and are shaped by their habitats, helping us understand why the same strain may behave differently across systems (Thakur and Wright 2017). Similarly, the idea of priority effects, where early colonizers influence the trajectory of community development, underscores why inoculation strategies and initial conditions can have lasting effects on industrial performance of microbiomes (Boyle et al. 2021; Debray et al. 2022). These concepts have a long-standing history of research in traditional ecology and thus, there is a strong theoretical support that enables the formulation of hypotheses to test in microbial systems, advancing biotechnology in a more reproducible and meaningful way. For instance, San Roman and Wagner (2018), using genome-scale models, illustrate how microbial metabolism can generate new ecological niches via cross-feeding. This mechanism, where one species consumes a substrate and excretes a secondary metabolite utilized by others, can be engineered to enhance the productivity of industrial consortia (Senne de Oliveira Lino et al. 2021).

An increasing body of research demonstrates that the initial taxonomic composition of microbial communities can inform their subsequent population dynamics, influencing how species abundances change over time (Bittleston et al. 2020). At the same time, community composition often correlates with functional outcomes, even as some taxa fluctuate or are replaced. This apparent decoupling between taxonomic and functional dynamics is frequently attributed to functional redundancy, where multiple species can perform similar ecological roles (Galand et al. 2018). These insights have motivated efforts to understand and improve the predictability of microbiome dynamics and function, a goal with important implications for both ecological understanding and industrial applications (Averill et al. 2021; Gopalakrishnappa et al. 2022; Moran and Tikhonov 2024).

Although forecasting community-level behavior remains challenging, the dynamics of individual microbes or pairs of interacting species are often more tractable and, in some cases, can be inferred from genomic information alone (Silva-Andrade et al. 2024). This accessibility has made microbial systems uniquely valuable for dissecting interaction mechanisms with a level of precision rarely achievable in macroecology. Leveraging these insights, researchers have developed increasingly accurate predictions of community outcomes, first through pairwise interaction models and more recently by incorporating higher-order interactions (Ishizawa et al. 2024). However, the combinatorial explosion of possible community configurations, particularly in industrial settings with dozens of candidate species, renders exhaustive experimental testing impractical. To address this challenge, recent studies have applied advanced mathematical frameworks (Arya et al. 2023) and approaches inspired by genetic epistasis, enabling systematic exploration of complex community–function landscapes (Diaz-Colunga et al. 2024).

However, leveraging such interactions first requires knowledge of who is present in the system. Environmental variables play a decisive role in shaping the composition and structure of microbial communities, as clearly shown in natural ecosystems (Martiny et al. 2006). This principle extends directly to engineered and anthropogenic environments, including agricultural soils (Haj-Amor et al. 2022; Zhang et al. 2024), wastewater treatment bioreactors (de Celis et al. 2022; Hannaford et al. 2023), and contaminated sites undergoing bioremediation (Head et al. 2006; Van Hamme et al. 2003). Manipulating the environment is thus a powerful lever for engineering both the composition and function of microbial communities. The framework developed by Sanchez et al. (2024), for instance, enables rational design of culture conditions (i.e. combinations of nutrients and environmental factors) to improve the performance of industrial microbes.

A unified framework for designing industrial microbial consortia

To advance toward a generalizable and theory-informed framework for industrial microbiome engineering, I propose a three-part approach that combines ecological assembly rules, simplified models of complexity, and strategies that promote evolutionary resilience.

Ecological principles guiding community assembly and function

In both natural and engineered environments, microbial communities emerge through a combination of environmental filtering, resource competition, and historical contingency (Fukami 2015; Nemergut et al. 2013). Abiotic conditions such as pH, temperature, or oxygen levels, set the first filter by selecting for organisms physiologically capable of thriving under given conditions. In practice, this means that environmental conditions are one of our strongest design tools. Adjusting nutrients or temperature can select for functional guilds that perform key tasks. This concept has long been exploited in systems like anaerobic digesters or nitrification bioreactors, where operating parameters determine which functional guilds persist (de Celis et al. 2022; Xu et al. 2021). Within the subset of microbes able to establish, coexistence is often achieved through metabolic specialization and niche differentiation (Nguyen et al. 2025; Oakley et al. 2012). For example, lignocellulose-degrading consortia commonly stabilize through the cooperation of cellulolytic, fermentative, and methanogenic populations, each occupying distinct metabolic roles (Basak et al. 2022; Narihiro et al. 2015; Wang et al. 2023). In these systems, competition is reduced by cross-feeding interactions, where by-products of one organism become resources for another. In fact, this mechanism can be intentionally engineered to enhance bioproduction (San Román and Wagner, 2018; Senne de Oliveira Lino et al. 2021). Historical effects also play a significant role. The order and timing of species arrival (the so-called priority effects) can also determine long-term community trajectories (Debray et al. 2022; Svoboda et al. 2018). Early introduction of key degraders or syntrophic partners can stabilize function, while delayed inoculation may lead to dominance by less productive competitors (Sierocinski et al. 2017). Interestingly, phylogenetic relatedness has been linked to the strength and direction of these effects, suggesting that evolutionary relationships may help predict compatibility and functional stability (Peay et al. 2012).

Managing and harnessing microbial community complexity

Designing microbial consortia inevitably means working with the complexity. But the real challenge isn’t to make systems simpler, but to find the tools and concepts that let us predict and guide their emergent behaviors (Sanchez et al. 2021; van den Berg et al. 2022). Recent ecological work suggests that this can be achieved by shifting focus from taxonomic detail to community-level functional organization. For example, Lee et al. (2025) showed that soil microbiome responses to environmental change can be captured by a small number of functional regimes, effectively collapsing high-dimensional taxonomic variation into low-dimensional functional descriptors. Although developed in natural systems, this functional abstraction provides a powerful conceptual blueprint for microbiome engineering, where community design may be guided by quantitative functional states rather than exhaustive taxonomic specification.

Traditional synthetic biology has approached microbial community design from a bottom-up perspective, assembling systems from well-characterized components with known individual traits (Purnick and Weiss 2009). This approach has delivered significant progress, from lignocellulose bioconversion (Lin 2022) and pathogen biocontrol (Hao et al. 2022) to the production of fine chemicals and complex metabolites (Sgobba and Wendisch 2020; Vortmann et al. 2021). More recently, rationally designed yeast consortia have provided a first industrial demonstration that complex microbial communities can be rendered predictable with sufficient ecological characterization (Ruiz et al. 2023). Specifically, by quantifying how the functional contribution of each species to the fermentative potential of wine yeast consortia depends on the functional state of the background community —an ecological analog of the global epistasis framework formalized by Diaz-Colunga and colleagues (2024)— this work enabled accurate predictions of the performance of newly assembled consortia and illustrates the translational potential of this theoretical framework. However, bottom-up strategies frequently fail to account for emergent properties resulting from nonlinear interspecies interactions, which play a crucial role in shaping the performance of multispecies consortia. Contrary, in top-down strategies complex inocula (i.e. natural microbial communities) are subjected to selection regimes based on desired functions allowing communities to evolve toward stable configurations that optimize performance (Goldford et al. 2018; Swenson et al. 2000). Recent studies demonstrate how such evolutionary enrichment can yield industrially relevant outcomes, such as enhanced pollutants biodegradation or complex substrates consumption (Arias-Sanchez et al., 2024; Wright et al. 2019; reviewed by Thomas et al. 2024; Yu et al. 2023). These evolved consortia often outperform both their original communities and their bottom-up engineered counterparts, reflecting the adaptive potential of microbial ecosystems under selection. In reality, the most promising path may lie between these two extremes. A hybrid, iterative approach can combine rational design with evolutionary refinement, starting from an ecologically informed community structure, then allowing adaptive processes and empirical testing to fine-tune the system (San León and Nogales 2022; Zhang and Reed 2014; Konstantinidis et al. 2021).

Within this broader framework, modularity emerges as an alternative strategy to harness complexity. It allows large communities to be decomposed into functional units, or guilds, responsible for distinct metabolic or ecological roles (Zomorrodi and Segrè 2016). This decomposition provides a manageable level of organization at which to study, model, and optimize community function. In practice, modular architectures have enabled stable coupling of cellulose-degrading and methanogenic guilds in anaerobic digesters (Sampara et al. 2024) or predictable substrate utilization in particle-associated microbial systems (Enke et al. 2019). Modularity also confers adaptability under fluctuating conditions, as modules can reconfigure without compromising overall community performance (Parter et al. 2007; Garcia and Trinh 2019; Chen et al. 2024). Computational approaches like community flux balance analysis (cFBA) are further advancing this idea, enabling the simulation and optimization of inter-module interactions under defined environmental constraints (Khandelwal et al. 2013; Dukovski et al. 2021).

Integrating evolutionary dynamics for long-term stability and adaptability

However, even well-designed microbial communities are not static, but it is necessary to account for their evolutionary dynamics. Over time, micro-evolutionary events can introduce stochasticity, as certain lineages may acquire selective advantages that shift species abundances and community function. While this evolutionary drift is sometimes viewed as a source of instability, it can also foster robustness and adaptability (Alekseeva et al. 2021). Studying how evolution shapes interactions between coexisting taxa can deepen our ecological understanding and point to new strategies for shaping microbial ecosystems (Sanchez et al. 2021). Propagating multispecies consortia under controlled selection regimes over hundreds of generations can drive stable and reproducible eco-evolutionary trajectories, leading to improved community-level performance (Chang et al. 2021; Goyal et al. 2022). Synthetic biology now offers tools to guide these processes. Quorum-sensing–based circuits and feedback systems can regulate cell density or metabolite accumulation, preventing the dominance of “cheater” lineages and maintaining functional balance (Sanchez and Gore 2013; Scott et al. 2017; Zand et al. 2025). More advanced versions, such as quorum-regulated lysis or dynamic feedback loops, allow autonomous control of population ratios in changing environments (Stephens and Bentley 2020; Huang et al. 2024).

In summary, the integration of ecological assembly rules, iterative engineering strategies, and evolutionary control provides a coherent framework for designing industrial microbial consortia, where environmental filters determine who can coexist, ecological design defines how to organize communities, and evolutionary feedbacks shape how they persist and adapt over time.

Understanding ecological interactions to design industrial microbial consortia: a case study in agri-food microbiomes

To illustrate how the three-part framework proposed in the previous section can be operationalized in practice, this section examines agri-food microbiomes as a representative case study. These systems span a continuum from agricultural soils to food-processing environments and fermented products, providing experimentally tractable examples of (i) ecological assembly rules, (ii) strategies to manage and simplify microbial community complexity, and (iii) eco-evolutionary dynamics that shape long-term stability and function. Organizing agri-food microbiome research through this lens highlights how ecological theory can directly inform industrial microbiome design.

Ecological assembly rules in agri-food microbiomes

To establish industrial systems as effective experimental platforms, and ultimately apply ecological principles for the optimal design of microbial consortia, it is essential to characterize diversity patterns across environmental gradients and operational conditions, while simultaneously monitoring community dynamics over time. High-throughput sequencing has greatly expanded our understanding of microbial composition in ecosystems of industrial relevance, ranging from agricultural soils to food processing facilities (Auchtung et al. 2025; Singh et al. 2020). Combined with systematic measurements of environmental parameters, these studies have begun to reveal how context-dependent factors shape community structure and function, providing a mechanistic basis for defining the ecological drivers of industrial microbiome performance.

Agricultural soils constitute an especially compelling system for understanding microbiome complexity before attempting to engineer it. As previously discussed, industrial microbiomes tend to be less diverse than their natural counterparts. Peng et al. (2024) confirmed this trend, analyzing thousands of soil samples across natural ecosystems (forests, grasslands, wetlands) and agricultural lands. Their study revealed that agricultural conversion leads to both taxonomic and functional homogenization of soil bacterial communities. Importantly, genes associated with nitrogen fixation, phosphorus mineralization, and nutrient transport were significantly depleted in cropland soils. In response, the crop microbiome rewilding approach advocates reintroducing beneficial microbes derived from the wild relatives of domesticated crops (Raaijmakers and Kiers 2022). As an important first step, the main microbiological and ecological characteristics of the microbiomes associated with the wild progenitors of several major crops have already been described (de Celis et al., 2024a; Fernández-Alonso et al. 2025). However, despite these advances, most microbial inoculants fail to persist after introduction, highlighting the need for predictive ecological models that identify the biotic and abiotic determinants (i.e. assembly constraints and historical contingency) of successful establishment and long-term functionality (Cordovez et al. 2019).

From the perspective of the framework proposed in Sect. 3, these failures emphasize that successful inoculation requires not only functional traits, but also ecological compatibility with resident communities. In this context, invasion ecology offers a powerful conceptual lens for understanding both the spread of phytopathogens and the establishment dynamics of beneficial microbial inoculants. The same principles that govern species invasions (i.e. propagule pressure, niche preemption, and priority effects) also regulate microbial colonization in agroecosystems (Mallon et al. 2015; Toju et al. 2018). Pathogens often exploit disturbed or low-diversity communities where ecological niches remain unoccupied, facilitating their establishment and spread (van Elsas et al. 2012), while successful inoculants exhibit the traits of effective invaders: ecological compatibility (low niche overlap with resident communities), competitive superiority (high growth rate and stress tolerance, and resistance to antagonism) and wide environmental tolerance (Mawarda et al. 2020). Recent work further refines this view by demonstrating that community diversity itself can limit invasion success through nutrient niche occupation. Specifically, Spragge et al. (2023) showed that diverse microbiomes suppress pathogen establishment by blocking access to limiting nutrients, even in the absence of direct antagonistic interactions. Conversely, microbiome composition can also be reshaped through ecological competition. Bakkeren et al. (2025) showed that targeted strain displacement can be achieved by introducing competitors that exploit overlapping nutrient niches more efficiently, leading to predictable replacement of resident strains. These results suggest that competitive interactions and resource overlap can be leveraged as practical design variables for steering microbiome composition in agri-food contexts. Thus, reframing microbial inoculation as an ecological invasion clarifies why identical strains may succeed in one soil yet fail in another, emphasizing the need to integrate community context as a key design variable.

A complementary research direction focuses on identifying core microbiome members. Core taxa often reflect long-term adaptation to local environmental conditions, mediated by plant–microbe interactions, and are essential for maintaining system functionality. Their identification may inform the design of microbial inoculants aimed at restoring degraded soils or enhancing productivity in low-performance agricultural systems (Toju et al. 2018). In particular, numerous studies have leveraged this approach to pinpoint functionally significant taxa involved in nutrient cycling (Jiao et al. 2019), plant growth promotion (Zhou et al. 2024), and community resilience (Du et al. 2025). Nonetheless, translating core microbiome identification into successful inoculant deployment remains challenging. As mentioned before, establishment and persistence depend on evolutionary and ecological compatibility, particularly the capacity of introduced microbes to integrate into resident communities and access limiting resources without competitive exclusion (Delgado-Baquerizo et al. 2025). In this context, Zhou et al. (2024) demonstrated that synthetic communities assembled from native core microorganisms show greater potential to enhance plant yield than those composed of non-core or non-native taxa. This underscores the importance of designing inoculants that align with the ecological features of the host microbiome rather than relying solely on isolated functional traits.

Managing microbial community complexity across the agri-food chain

Building on an understanding of assembly rules, the second component of the framework concerns how microbial community complexity can be managed without eliminating it. In agri-food systems, this challenge is particularly evident, as microbial communities extend from soils and plant tissues to post-harvest and processing environments. Recent studies have applied top-down and bottom-up approaches to optimize agricultural soil microbiomes (Northen et al. 2024). Subjecting native microbiomes to selective enrichment under controlled environmental pressures, top-down approaches have yielded configurations that enhance nutrient cycling, pollutant degradation, or plant growth (Panke-Buisse et al. 2015; Zegeye et al. 2019). In contrast, researchers have used bottom-up strategies to reconstruct defined consortia from well-characterized isolates, replicating specific rhizosphere functions such as nitrogen fixation, pathogen suppression, and plant growth promotion (Gastélum et al. 2025; Herrera Paredes et al. 2018; Niu et al. 2017; Wang et al. 2024).

Beyond microbial manipulation, environmental modulation through agricultural management practices (i.e., soil tillage, irrigation regimes, fertilization, and pesticide use) represents another effective way to shape microbiome composition. Substantial evidence indicates that these practices induce consistent shifts in soil and plant-associated microbiomes (Hartman et al. 2018; Hartmann et al. 2015; McDaniel et al. 2014) though the outcomes are often non-targeted. It remains unclear whether unspecific increases in microbial diversity inherently translate into functional benefits for the agricultural system, resulting in increased crops yield. The framework proposed by Delgado-Baquerizo et al. (2025) remains a seminal reference for researchers seeking to apply ecological and evolutionary principles in the rational design of synthetic consortia for soil microbiome engineering.

Importantly, microbial community complexity is not reset at harvest but propagates along the agri-food chain, creating ecological legacies that shape downstream systems.

Eco-evolutionary dynamics and ecological legacies in fermented food systems

An ecological continuum exists between the field and the food chain, extending beyond crop harvest, as the microbiomes established in agricultural soils and plant tissues exert a lasting influence on post-harvest and processing environments. Microbes originating from the rhizosphere, phyllosphere, and endosphere often persist through harvesting, storage, and transport, forming the initial inoculum that shapes the microbial succession on fruits, vegetables, and other plant-derived products (Leff and Fierer 2013; Zhang et al. 2021). Thus, pre-harvest conditions, including soil management, irrigation, and fertilization, indirectly influence the composition and functional potential of food-associated microbiomes (Berg et al. 2025; Rillig et al. 2018). Recognizing this ecological linkage between crop-associated and food-associated microbiomes is essential for understanding how pre-harvest conditions set the stage for microbial dynamics during storage and processing (Zhang et al. 2025).

The concept of microbial terroir captures this ecological inheritance, describing how region-specific environmental conditions and native microbiomes influence fermentation dynamics and product characteristics. This term was coined in the context of wine fermentation, where vineyard-specific microbiomes were shown to influence fermentation dynamics and final aromatic profiles (Belda et al. 2017; Gilbert et al. 2014), but now, it has expanded to encompass other fermentation systems such as cocoa, coffee, and tea (Johnston-Monje et al. 2023; Soares et al. 2025). These findings underscore that food quality and identity are, at least in part, the result of ecological interactions initiated in the field and maintained through harvest, storage, and processing. Post-harvest and food-processing environments can thus be conceptualized as ecological filters acting upon microbial legacies inherited from cultivation. During storage and transformation, selective pressures, such as temperature, humidity, nutrient gradients, and sanitation practices, drive microbial succession and adaptation. In a recent review, we identified the main eco-evolutionary forces driving the performance of wine fermentations (Belda et al. 2025b), arguing that only through ecological theory can we meaningfully advance the modeling and engineering of complex industrial microbiomes. In parallel, we also highlight that ecology and evolution not only enrich food microbiome research but also benefit reciprocally from it, as fermented foods provide experimentally tractable systems for studying community-level eco-evolutionary dynamics (Belda et al. 2025a; Wolfe and Dutton 2015).

Among such systems, cheese rind communities have become emblematic model microbiomes for dissecting ecological and evolutionary principles (Wolfe et al. 2014). These simplified yet functionally representative ecosystems permit experimental deconstruction and synthetic reconstruction to test eco-evolutionary hypotheses, revealing the mechanisms underpinning community assembly, interaction networks, and emergent functionality (Cosetta and Wolfe 2020). Bonham et al. (2017) demonstrated extensive horizontal gene transfer among cheese-associated bacteria, highlighting its role in rapid adaptation and niche differentiation. Morin et al. (2018) further showed that genetic requirements for microbial growth and interaction vary with community complexity, revealing how higher-order interactions emerge as systems become more intricate, and Melkonian et al. (2023) reported that cooperative and competitive interactions between Streptococcus thermophilus and Lactococcus strains directly shape community stability and flavor compound production. Similar insights from kefir fermentation highlight the importance of spatiotemporal niche partitioning and metabolic cooperation, whereby early colonizers modify the environment to facilitate the sequential establishment of later species, sustaining long-term coexistence and functional stability (Blasche et al. 2021). More recently, and applied to cocoa fermentation, Gopaulchan et al. (2025) demonstrated that interactions among bacteria and fungi, mediated by abiotic factors such as pH and temperature, govern fine chocolate flavor development. In this work, a defined microbial consortium was sufficient to reproducibly generate premium chocolate characteristics, exemplifying how microbial interactions determine product traits. Our own work on wine fermentations further illustrates that the performance of complex yeast consortia can be predicted by characterizing the ecological effects of individual strains within community contexts, thereby enabling the rational design of optimized consortia for industrial applications (Ruiz et al. 2023). At the molecular level, we have also identified orthologous genes responsible for species-specific contributions of different yeasts to wine flavour (de Celis et al. 2024b). Finally, fermented foods provide unique opportunities to study rapid evolutionary processes across microbial taxa, from bacteria to yeasts and filamentous fungi. As reviewed by Steensels et al. (2019), domestication has driven the emergence of industrially relevant traits through genomic changes ranging from single-nucleotide polymorphisms and copy-number variations to structural rearrangements, horizontal gene transfer, and interspecific hybridization.

Conclusion

Integrating microbial biotechnology with ecological theory provides a powerful, unified framework for the rational design of industrial microbial consortia. By recognizing that microbial function emerges from context-dependent interactions and evolutionary dynamics, we move beyond reductionist, strain-centric approaches toward systems capable of predictable and resilient performance. Industrial microbiomes, from soil to fermentation environments, offer tractable and ecologically realistic model systems, bridging the gap between laboratory simplicity and natural complexity. Harnessing ecological principles combined with adaptive evolutionary strategies, enables the engineering of microbial communities that are both robust and functionally optimized. Industrial microbiomes, especially fermented foods, exemplify how controlled microbial consortia can serve as experimental platforms to test fundamental questions in ecology and evolution while simultaneously guiding biotechnological applications.

Despite substantial progress and the adoption of ecological concepts in agri-food microbiome research, several field-specific limitations continue to constrain rational microbiome design. Many studies remain largely descriptive or correlative, with limited mechanistic resolution of species interactions and their quantitative contribution to function. Functional outcomes are often highly context-dependent, varying across batches, facilities, and environmental conditions, which hampers reproducibility and transferability. In addition, long-term evolutionary processes, including strain adaptation and the emergence of destabilizing phenotypes, are rarely incorporated into design frameworks despite their relevance in repeated or continuous industrial processes. Addressing these challenges will require tighter integration of ecological theory, longitudinal experimentation, and quantitative modeling under industrially realistic conditions. Future work should prioritize developing quantitative frameworks linking ecological interactions to industrial performance metrics, enabling truly predictive and scalable microbiome design.

Author contributions

I.B. conceived and wrote the manuscript.

Funding

Research in Ignacio Belda’s laboratory is supported by the Spanish National Research Agency (MICIU/AEI/10.13039/501100011033) and co-funded by the European Regional Development Fund (ERDF/EU). Current projects include grant PID2019-105834GA-I00 (Wineteractions) and grant PID2022-138343NB-I00 (INDUSYNCON).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.