Marine Ecosystems as Complex Adaptive Systems: Emergent Patterns, Critical Transitions, and Public Goods

Siderophore-mediated iron partition promotes dynamical coexistence between cooperators and cheaters

Microbes shape their habitats by consuming resources and producing a diverse array of chemicals that can serve as public goods. Despite the risk of exploitation by cheaters, genes encoding sharable molecules like siderophores are widely found in nature, prompting investigations into the mechanisms that allow producers to resist invasion by cheaters. In this work, we presented the chemostat-typed "resource partition model" to demonstrate that dividing the iron resource between private and public siderophores can promote stable or dynamic coexistence between producers and cheaters in a well-mixed environment. Moreover, our analysis shows that when microbes not only consume but also produce resources, chemical innovation leads to stability criteria that differ from those of classical consumer resource models, resulting in more complex dynamics. Our work sheds light on the role of chemical innovations in microbial communities and the potential for resource partition to facilitate dynamical coexistence between cooperative and cheating organisms.

Instability of oscillations in the Rosenzweig-MacArthur model of one consumer and two resources

The system of two resources _R and _R and one consumer C is investigated within the Rosenzweig-MacArthur model with a Holling type II functional response. The rates of consumption of particular resources are normalized as to keep their sum constant. Dynamic switching is introduced as to increase the variable C in a process of finite speed. The space of parameters where both resources coexist is explored numerically. The results indicate that oscillations of C and mutually synchronized _R, which appear equal for the rates of consumption, are destabilized when these rates are modified. Then, the system is driven to one of fixed points or to a limit cycle with a much smaller amplitude. As a consequence of symmetry between the resources, the consumer cannot change the preferred resource once it is chosen.

Microbes and Climate Change: a Research Prospectus for the Future

Climate change is the most serious challenge facing humanity. Microbes produce and consume three major greenhouse gases-carbon dioxide, methane, and nitrous oxide-and some microbes cause human, animal, and plant diseases that can be exacerbated by climate change. Hence, microbial research is needed to help ameliorate the warming trajectory and cascading effects resulting from heat, drought, and severe storms. We present a brief summary of what is known about microbial responses to climate change in three major ecosystems: terrestrial, ocean, and urban. We also offer suggestions for new research directions to reduce microbial greenhouse gases and mitigate the pathogenic impacts of microbes. These include performing more controlled studies on the climate impact on microbial processes, system interdependencies, and responses to human interventions, using microbes and their carbon and nitrogen transformations for useful stable products, improving microbial process data for climate models, and taking the One Health approach to study microbes and climate change.

Ecosystem Capacity for Microbial Biodegradation of Munitions Compounds and Phenanthrene in Three Coastal Waterways in North Carolina, United States

Munitions compounds (i.e., 2,4,6-trinitrotoluene (TNT), octahy-dro-1,3,5,7-tetranitro-1,3,5,7-tetrazocin (HMX), and hexadydro-1,3,5-trinitro-1,3,5-triazin (RDX), also called energetics) were originally believed to be recalcitrant to microbial biodegradation based on historical groundwater chemical attenuation data and laboratory culture work. More recently, it has been established that natural bacterial assemblages in coastal waters and sediment can rapidly metabolize these organic nitrogen sources and even incorporate their carbon and nitrogen into bacterial biomass. Here, we report on the capacity of natural microbial assemblages in three coastal North Carolina (United States) estuaries to metabolize energetics and phenanthrene (PHE), a proxy for terrestrial aromatic compounds. Microbial assemblages generally had the highest ecosystem capacity (mass of the compound mineralized per average estuarine residence time) for HMX (21-5463 kg) > RDX (1.4-5821 kg) ≫ PHE (0.29-660 kg) > TNT (0.25-451 kg). Increasing antecedent precipitation tended to decrease the ecosystem capacity to mineralize TNT in the Newport River Estuary, and PHE and TNT mineralization were often highest with increasing salinity. There was some evidence from the New River Estuary that increased N-demand (due to a phytoplankton bloom) is associated with increased energetic mineralization rates. Using this type of analysis to determine the ecosystem capacity to metabolize energetics can explain why these compounds are rarely detected in seawater and marine sediment, despite the known presence of unexploded ordnance or recent use in military training exercises. Overall, measuring the ecosystem capacity may help predict the effects of climate change (warming and altered precipitation patterns) and other perturbations on exotic compound fate and transport within ecosystems and provide critical information for managers and decision-makers to develop management strategies based on these changes.

Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of Escherichia coli Diauxic Growth

Microbes have adapted to greatly variable environments in order to survive both short-term perturbations and permanent changes. A classical and yet still actively studied example of adaptation to dynamic environments is the diauxic shift of Escherichia coli, in which cells grow on glucose until its exhaustion and then transition to using previously secreted acetate. Here we tested different hypotheses concerning the nature of this transition by using dynamic metabolic modeling. To reach this goal, we developed an open source modeling framework integrating dynamic models (ordinary differential equation systems) with structural models (metabolic networks) which can take into account the behavior of multiple subpopulations and smooth flux transitions between time points. We used this framework to model the diauxic shift, first with a single E. coli model whose metabolic state represents the overall population average and then with a community of two subpopulations, each growing exclusively on one carbon source (glucose or acetate). After introduction of an environment-dependent transition function that determined the balance between subpopulations, our model generated predictions that are in strong agreement with published data. Our results thus support recent experimental evidence that diauxie, rather than a coordinated metabolic shift, would be the emergent pattern of individual cells differentiating for optimal growth on different substrates. This work offers a new perspective on the use of dynamic metabolic modeling to investigate population heterogeneity dynamics. The proposed approach can easily be applied to other biological systems composed of metabolically distinct, interconverting subpopulations and could be extended to include single-cell-level stochasticity. IMPORTANCE Escherichia coli diauxie is a fundamental example of metabolic adaptation, a phenomenon that is not yet completely understood. Further insight into this process can be achieved by integrating experimental and computational modeling methods. We present a dynamic metabolic modeling approach that captures diauxie as an emergent property of subpopulation dynamics in E. coli monocultures. Without fine-tuning the parameters of the E. coli core metabolic model, we achieved good agreement with published data. Our results suggest that single-organism metabolic models can only approximate the average metabolic state of a population, therefore offering a new perspective on the use of such modeling approaches. The open source modeling framework that we provide can be applied to model general subpopulation systems in more-complex environments and can be extended to include single-cell-level stochasticity.

Review and perspective on mathematical modeling of microbial ecosystems

Understanding microbial ecosystems means unlocking the path toward a deeper knowledge of the fundamental mechanisms of life. Engineered microbial communities are also extremely relevant to tackling some of today's grand societal challenges. Advanced meta-omics experimental techniques provide crucial insights into microbial communities, but have been so far mostly used for descriptive, exploratory approaches to answer the initial 'who is there?'

QUESTION

An ecosystem is a complex network of dynamic spatio-temporal interactions among organisms as well as between organisms and the environment. Mathematical models with their abstraction capability are essential to capture the underlying phenomena and connect the different scales at which these systems act. Differential equation models and constraint-based stoichiometric models are deterministic approaches that can successfully provide a macroscopic description of the outcome from microscopic behaviors. In this mini-review, we present classical and recent applications of these modeling methods and illustrate the potential of their integration. Indeed, approaches that can capture multiple scales are needed in order to understand emergent patterns in ecosystems and their dynamics regulated by different spatio-temporal phenomena. We finally discuss promising examples of methods proposing the integration of differential equations with constraint-based stoichiometric models and argue that more work is needed in this direction.

The Temperature Dependence of Phytoplankton Stoichiometry: Investigating the Roles of Species Sorting and Local Adaptation

The elemental composition of phytoplankton (C:N:P stoichiometry) is a critical factor regulating nutrient cycling, primary production and energy transfer through planktonic food webs. Our understanding of the multiple direct and indirect mechanisms through which temperature controls phytoplankton stoichiometry is however incomplete, increasing uncertainty in the impacts of global warming on the biogeochemical functioning of aquatic ecosystems. Here, we use a decade-long warming experiment in outdoor freshwater ponds to investigate how temperature-driven turnover in species composition and shifts in stoichiometric traits within species through local thermal adaptation contribute to the effects of warming on seston stoichiometry. We found that experimental warming increased seston C:P and N:P ratios, while the C:N ratio was unaffected by warming. Temperature was also the dominant driver of seasonal variation in seston stoichiometry, correlating positively with both C:P and N:P ratios. The taxonomic composition of the phytoplankton community differed substantially between the warmed and ambient treatments indicating that warming resulted in differential sorting of species from the regional pool. Furthermore, taxonomic composition also changed markedly over the year within each of the warmed and ambient treatments, highlighting substantial temporal turnover in species. To investigate whether local adaptation also played an important role in shaping the effects of warming on seston stoichiometry, we isolated multiple strains of the cosmopolitan alga, Chlamydomonas reinhardtii from across the warmed and ambient mesocosms. We found that warmed isolates had higher C:P and N:P ratios, shifts that were comparable in direction and magnitude to the effects of warming on seston stoichiometry. Our results suggest that both species sorting and local adaptation are likely to play important roles in shaping the effects of warming on bulk phytoplankton stoichiometry and indicate that major shifts in aquatic biogeochemistry should be expected in a warmer world.