Luciana Chavez Rodriguez

Increased human impacts on Earth systems are radically altering biogeochemical cycles. While long-term environmental observatories and Earth System Models (ESMs) provide valuable insights into the mechanisms of nutrient dynamics, their performance is limited at the fine spatial scales controlled by the functional diversity of plant and microbial communities. This gap in our understanding concerning the roles of microbial diversity and plant-microbial interactions in decomposition and nutrient dynamics extends across many global ecosystems. Recent advances in meta-omics technologies, including metagenomics, metatranscriptomics, metaproteomics, and metabolomics, offer a wide array of tools for assessing metabolic to genetic to evolutionary drivers of ecosystem functioning. Here, we explore the integration of meta-omics with traditional ecological approaches to examine responses to global environmental changes. We present case studies from diverse environments – soils, aquatic systems, clouds, and paleoarchives – demonstrating how meta-omics can unravel the roles of microbial diversity, metabolic pathways, and trait distributions critical to understanding greenhouse gas fluxes, nutrient cycling, and biogeochemistry. Although meta-omics is still beset with challenges including data heterogeneity arising from wide-ranging methods, omics-derived traits, kinetic parameters, and machine learning tools can be used to enhance ESM predictive capability. For example, emerging applications of meta-omics to ancient environmental DNA are extending our capacity to link historical patterns with future projections, offering a long-term perspective on ecosystem dynamics. This review highlights the potential of integrating omics with experimental manipulations alongside existing monitoring and modelling efforts to refine predictions of ecosystem responses to natural and anthropogenic-driven environmental changes. Because omics approaches cross a range of scientific domains, they could be used to foster collaboration and even integration within existing models, thus laying the foundation for informed conservation and ecosystem management strategies from local to global scales.

A major challenge in biogeochemistry is to reduce the uncertainty of projections made by soil carbon models. In the last two decades, carbon-use efficiency, the proportion of consumed carbon incorporated into microbial biomass, has become a central parameter to represent microbial control on carbon fluxes. For models that integrate other elements, like nitrogen, the adjustment of carbon-use efficiency in response to substrate stoichiometry has gained popularity as a mechanism to balance these fluxes, mostly due to its mathematical convenience. The reasoning behind this, is that microbes release the excess carbon as CO₂, a mechanism known as overflow respiration. This mechanism, however, causes a characteristic decrease of carbon-use efficiency when reaching nitrogen limitation. In this study we propose that the implementation of overflow respiration forces an unrealistic decrease of carbon-use efficiency for three reasons: 1) physiological mechanisms can minimize carbon excess, avoiding overflow; 2) carbon overflow has been reported in laboratory experiments mainly as dissolved organic carbon (organic acids), and not as CO₂; 3) functionally diverse microbial communities can exhibit higher-level dynamics, improving the recycling of nutrients and avoiding overflow. We use an individual-based microbial litter decomposition model to test the impact of these mechanisms on carbon-use efficiency. We found that physiological mechanisms such as flexible biomass stoichiometry can eliminate overflow, but nutrient allocation does not. When carbon overflow occurs as dissolved organic carbon, carbon-use efficiency increases under nitrogen limitation. Finally, a functionally diverse community can avoid carbon overflow, although carbon-use efficiency declines due to higher maintenance respiration. We demonstrate that the representation of overflow respiration in soil carbon models is more relevant than currently acknowledged. Redirecting carbon overflow to a dissolved organic carbon pool can lead to opposite trends in carbon losses. The soil carbon modeling community should thoroughly assess current implementations and explore more mechanistically grounded alternatives. This includes dissolved organic carbon pathways, more realistic microbial community representations, or other processes that better capture the complexity of microbial carbon use.

Soil microbial communities are rarely represented in soil models or with extreme simplifications due to their complexity. Acknowledging that temperature and moisture are the primary controls over microbial functional diversity, this research aims to determine the extent to which soil functional diversity can be predicted based on these factors. We used the aridity index (AI), as this easy-to-measure metric includes temperature and moisture. Following the YAS framework, a widely accepted trait-based approach to characterize soil microbial communities, we hypothesized that under an identical food source, the functional strategies employed by the community will go from high growth yield (Y) in humid areas to higher investment in stress tolerance (S) in arid areas. We also expected a trade-off between investment in S and Y, while relative investment in A (resource acquisition) should remain constant. We further hypothesized that AI is a decent predictor of the microbial investments into the Y, A, and S traits. We used the DEMENTpy model, an in silico simulator, to derive YAS investments for hypothetical soil microbial communities at five sites along an aridity gradient in Spain. We validated model simulations using mass loss from Rooibos tea samples from each site and employed a Dirichlet regression model to predict YAS investments, using AI. Contrary to the hypotheses, increasing aridity changes community investment from Y to A, with limited changes in S. The A strategy could be predicted considerably well based on AI, while Y and S could not. Together with further validation of our modeling results with experimental data, our findings lay the groundwork in deriving simple mathematical formulations that can be integrated into Earth system models, allowing for upscaling from genomes to Earth system processes.

Turnover of soil organic matter (SOM) by microbes is an important step in the soil carbon cycle. As microbes are living organisms that interact with their environment and one another, microbial communities are not static but can adapt to various conditions through changes in functional traits. Such adaptation of microbial functional traits can affect the fate of soil organic carbon. However, current microbial-explicit models commonly do not represent such eco-evolutionary dynamics, but treat microbes more akin to inanimate engines or chemical compartments. Eco-evolutionary optimization (EEO) approaches aim to abstract from the complexity of different ecological and evolutionary adaptation mechanisms by assuming that for given conditions, the microbial community might be dominated by those organisms with functional traits that would maximize fitness under these conditions. Different fitness proxies have been used in the literature – but a general framework for EEO approaches in SOM modeling is missing. Based on a review of previous studies, we suggest a classification of EEO approaches in SOM models based on the definition of microbial fitness and the time scale of optimization. Results from different EEO approaches differ systematically along the axes of our classification framework – however, they can also yield convergent qualitative patterns that match experimental observations. Taken together, our results show that EEO approaches have great potential for advancing SOM modeling. Yet, challenges remain – calling especially for further comparative studies and empirical validation of different approaches.

Soil microorganisms mediate carbon and nutrient fluxes in soils, and—as all organisms—are subject to eco‐evolutionary dynamics. Adaptation of soil microbial functionality to environmental conditions across space and time has consequences for biogeochemical fluxes that are often not explicitly considered in models describing soil organic matter (SOM) dynamics. Eco‐evolutionary optimization (EEO) tries to anticipate the outcome of eco‐evolutionary dynamics, and can inform on how microbial functional traits might adapt to environmental conditions based on the maximisation of different proxies of microbial fitness. While different approaches employ different fitness proxies, they all aim to increase realism and generality by grounding SOM models in eco‐evolutionary theory and introducing constraints on model parametrization. Despite this potential, challenges for widely applying EEO approaches to advance SOM models persist and open questions remain, primarily concerning implicit assumptions, convergence of predictions, and empirical validation of the different EEO approaches. In this Synthesis, we review EEO approaches that have been applied to SOM models and provide an instructive primer to EEO approaches. We then propose a general categorization, aiming to make their underlying assumptions explicit and give an outlook for future research directions.

Trait-based theory, coupled with mechanistic trait-based models, can be used to explore how changes in environmental conditions affect microbial community assembly and the impact on soil biogeochemistry. We used 27214 metagenome-assembled genomes (MAGs) and 5301 soil isolate genomes to investigate tradeoffs between life history strategies encoded in the genome. We used microTrait, a genome-based characterization toolset, to derive trait composition according to the YAS framework, which classifies microorganisms into high-yield (Y), resource-acquisition (A), and stress-tolerance (S) strategists. In contrast to the hypothesized three-way tradeoff, we observed a positive correlation between genome size and functional S and A traits and a negative correlation between genome size and the Y strategy measured as carbon use efficiency (CUE). Microbes with larger genomes favor investment in A and S at the cost of Y. Therefore, genome size, as a master trait, can enhance trait-based models by improving predictions of environmental impacts on soil microorganisms.

A major challenge in ecology is to understand how different species interact to determine ecosystem function, particularly in communities with large numbers of co‐occurring species. We use a trait‐based model of microbial litter decomposition to quantify how different taxa impact ecosystem function. Furthermore, we build a novel framework that highlights the interplay between taxon traits and environmental conditions, focusing on their combined influence on community interactions and ecosystem function. Our results suggest that the ecosystem impact of a taxon is driven by its resource acquisition traits and the community functional capacity, but that physiological stress amplifies the impact of both positive and negative interactions. Furthermore, net positive impacts on ecosystem function can arise even as microbes have negative pairwise interactions with other taxa. As communities shift in response to global climate change, our findings reveal the potential to predict the biogeochemical functioning of communities from taxon traits and interactions.

Deadwood is an important yet understudied carbon pool in tropical ecosystems. Deadwood degradation to CO₂ through decomposer (microbial, termite) activities is driven by wood moisture and temperature, which are in turn strongly influenced by local climate. Thus, climate data could be used to predict CO₂ fluxes from decaying wood. Given the increasing availability of gridded climate data, this link would allow for the rapid estimation of deadwood-related CO₂ fluxes from tropical ecosystems worldwide. In this study, we adapted a mechanistic fuel moisture model that uses weather variables (e.g., air temperature, precipitation, solar radiation) to simulate wood moisture and temperature along a rainfall gradient in Queensland, Australia. We then developed a Bayesian statistical relationship between wood moisture and temperature and CO₂ flux from pine (Pinus radiata) blocks and combined this relationship with our simulations to predict CO₂ fluxes from deadwood at 1 h temporal resolution. We compared our pine-based simulations to the moisture–CO₂ relationships from stems of native tree species deployed at the wettest and driest sites. Finally, we integrated fluxes over time to estimate the amount of carbon entering the atmosphere and compared these estimates to measured mass loss in pines and native stems. Our statistical model showed a positive relationship between CO₂ fluxes and wood moisture and temperature. Comparing cumulative CO₂ with wood mass loss, we observed that carbon from deadwood decomposition is mainly released as CO₂ regardless of the precipitation regime. At the dry savanna, only about 20 % of the wood mass loss was decomposed within 48 months, compared to almost 100 % at the wet rainforest, suggesting longer residence times of deadwood compared to wetter sites. However, the amount of carbon released in situ as CO₂ is lower when wood blocks are attacked by termites, especially at drier sites. These results highlight the important but understudied role of termites in the breakdown of deadwood in dry climates. Additionally, mass loss–flux relationships of decaying native stems deviated from those of pine blocks. Our results indicate that wood moisture and temperature are necessary but not sufficient for predicting CO₂ fluxes from deadwood degradation. Other factors, such as wood traits (wood quality, chemical composition, and stoichiometry) and biotic processes, should be considered in future modeling efforts.

Models estimating decomposition rates of dead wood across space and time are mainly based on studies carried out in temperate zones where microbes are dominant drivers of decomposition. However, most dead wood biomass is found in tropical ecosystems, where termites are also important wood consumers. Given the dependence of microbial decomposition on moisture with termite decomposition thought to be more resilient to dry conditions, the relative importance of these decomposition agents is expected to shift along gradients in precipitation that affect wood moisture. Here, we investigated the relative roles of microbes and termites in wood decomposition across precipitation gradients in space, time and with a simulated drought experiment in tropical Australia. We deployed mesh bags with non‐native pine wood blocks, allowing termite access to half the bags. Bags were collected every 6 months (end of wet and dry seasons) over a 4‐year period across five sites along a rainfall gradient (ranging from savanna to wet sclerophyll to rainforest) and within a simulated drought experiment at the wettest site. We expected microbial decomposition to proceed faster in wet conditions with greater relative influence of termites in dry conditions. Consistent with expectations, microbial‐mediated wood decomposition was slowest in dry savanna sites, dry seasons and simulated drought conditions. Wood blocks discovered by termites decomposed 16–36% faster than blocks undiscovered by termites regardless of precipitation levels. Concurrently, termites were 10 times more likely to discover wood in dry savanna compared with wet rainforest sites, compensating for slow microbial decomposition in savannas. For wood discovered by termites, seasonality and drought did not significantly affect decomposition rates. Taken together, we found that spatial and seasonal variation in precipitation is important in shaping wood decomposition rates as driven by termites and microbes, although these different gradients do not equally impact decomposition agents. As we better understand how climate change will affect precipitation regimes across the tropics, our results can improve predictions of how wood decomposition agents will shift with potential for altering carbon fluxes. Read the free Plain Language Summary for this article on the Journal blog.